Electric Ship Control Research Report

Technical Report

Submitted to:The Office of Naval Research

Contract Number: N0014-08-1-0080

September 2012

Approved for public release – distribution unlimited

Any opinions, findings, conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research.

2000 Levy Avenue, Suite 140 | Tallahassee, FL 32310 | www.esrdc.com

MISSION STATEMENTThe Electric Ship Research and Development Consortium brings together in a single entity the combined programs and resources of leading electric power research institutions to advance near- to mid-term electric ship concepts. The consortium is supported through a grant from the United States Office of Naval Research.

2000 Levy Avenue, Suite 140 | Tallahassee, FL 32310 | www.esrdc.com

TABLE OF CONTENTS

1 Executive Summary.................................................................................................................12 Introduction..............................................................................................................................13 Research Work Outside the ESRDC........................................................................................1

3.1 Ship’s System Control......................................................................................................1

3.1.1 Ship’s System Control/Open Control Architecture for Future Electric Ships............1

3.1.2 Optimal Power Distribution Using Expert Systems..................................................1

3.1.3 Self-Healing SPA Strategy.........................................................................................1

4 Research Works of the ESRDC................................................................................................14.1 Low Level Controller Design...........................................................................................1

4.1.1 Coordinated Control of Converters in Ship Systems [6]...........................................1

4.1.2 Positive Feed-Forward Control of Switching Converters Designed for MVDC System [6]...............................................................................................................................1

4.1.3 A DC Fault Protection Test-Bed Implemented on the Multiple PEBB System [6].....1

4.1.4 MVDC Distribution System Circuit Protection [6].....................................................1

4.1.5 Stabilizing Geometric Controller for dc-dc Converters with Instantaneous Constant-power Loads [11]....................................................................................................1

4.1.6 Robust Advanced Controller Design of Bidirectional DC/DC Converters in the MVDC Shipboard Power Systems [12]....................................................................................1

4.1.7 Coordination of Current limiting Controllers for Boost Converters in MVDC [7].....1

4.1.8 Coordination Control of Multiple Active Filters and Active Front-End Units for Power Quality Improvement [14], [15]...................................................................................1

4.1.9 Modeling and Control of MVDC System with Regenerative Motor Drive................1

4.1.10 Modeling and Control of MVDC System with Solid State Transformer....................1

4.2 High Level Controller Design...........................................................................................1

4.2.1 Power System Reconfiguration for Shipboard Applications [5]................................1

4.2.2 Definition of Methods and Algorithms for Robust Control of Power Electronics Systems with Control Loop Including Network Communication [6].......................................1

4.2.3 Development of Sensor Failure Detection Algorithm [6].........................................1

4.2.4 Development of a Methodology for Optimal Compensation of Sensor Dynamics [6]1

4.2.5 Optimal Sensor Querying [21]..................................................................................1

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4.2.6 Stochastic Allocation for Distributed State Estimation [6].......................................1

4.2.7 Wireless Sensing and Data Communication System for Fault Detection [6]............1

4.2.8 Online Monitoring of Switching Converters [6]........................................................1

4.2.9 Multi-agent Control and Optimization of the PEBB Based Power Electronics System [9, [10]........................................................................................................................1

4.2.10 Large-Signal Transient Load Models for Power Electronic Load Aggregation [22]...1

4.2.11 Power Hardware-in-the-loop (PHIL) Activities [6]....................................................1

4.2.12 Multi-Agent System-based Power/Energy Management and Controls for a MVDC System [16], [17].................................................................................................................... 1

4.2.13 Game Theoretic Approach to Distributed Load Management via Aspiration Learning [23]...........................................................................................................................1

4.2.14 Distributed Controls-based Thermal/Fluid Management for Chiller Systems..........1

5 Research Direction for the Shipboard Power System Controls...............................................15.1 Effective Simulation and Optimization Tools for Early Stage Ship Analysis and Design

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5.1.1 Development of an Efficient Distributed Simulation Techniques of Base-line Models 1

5.1.2 Development of a Framework for Early-stage Design Space Exploration................1

5.2 Control-based Power Management for Notional All-Electric Ships................................1

5.2.1 Development of a Performance Specific Decision Support System.........................1

5.2.2 Develop Efficient System-level Control Policies for Managing Disruptive Events... .1

5.2.3 Reconfiguration Design Approach............................................................................1

6 Industrial Practice.....................................................................................................................16.1 Vessel Information and control (VICO) system (marine automation solutions and service).........................................................................................................................................16.2 Integrated platform management system by L3 communication Company.....................16.3 SINAVY Automation- an integrated control system for naval ships from Siemens........16.4 Engineering control system on DDG1000........................................................................16.5 Conclusion........................................................................................................................1

7 Conclusion and Recommendations..........................................................................................18 Acknowledgements..................................................................................................................19 References................................................................................................................................1

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LIST OF FIGURES

Fig. 1: Architecture of the proposed controller system [2]..............................................................1Fig. 2: The Main Components of the Load Controller [2]..............................................................1Fig. 3: (a & b) Current advanced naval power system; (b) proposed regenerative motor drive with ESS..........................................................................................................................................1Fig. 4: Control scheme for power and energy management system................................................1Fig. 5: Proposed power manager architecture.................................................................................1Fig. 6 : Control scheme for power and energy management system...............................................1Fig. 7: SPS state Assessment...........................................................................................................1Fig. 8: VICO Offerings....................................................................................................................1Fig. 9 : SINAVY System Architecture............................................................................................1Fig. 10 : DDG 1000 Control Hierarchy [30]...................................................................................1

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1 EXECUTIVE SUMMARY

Engineers inside and outside of the ESRDC have researched and reported various ideas to foster the high level control of notional all-electric ship systems. This report is a review and summary of the work and conclusions reported by two communities making significant contributions. They are (1) the Electric Ship Research and Development Consortium of universities; and (2) others in the U.S. Navy RDT&E establishment. Contributions include those related to high-level system control, low-level component or subsystem control, and computational methods related to power system modeling and simulation.

In a ship system the overall control is realized via low level zonal controllers, which work according to the policy set by the high level system managers with human machine interface. The high level system includes a core supervisory controller, which works towards the survivability and for quality of service, including metrics such as reliability of service and safety of operation. Survivability has been seen as the prevention of the fault propagation, the uninterrupted supply of electrical energy to vital loads, and the restoration of service under damage conditions. Quality of service ensures that loads receive a reliable source of power under normal operating conditions. The core supervisory controller works in co-ordination with the shipboard simulation manager, the network interface manager, the remote monitoring manager and the maintenance manager to achieve the common goal of complete ship management.

Research relevant to high level control has been cited in the annual reports of the various members of the ESRDC consortium. The University of South Carolina has developed algorithms for robust control of power electronics systems with control loop including network communication; developing a sensor failure detection algorithm; developing optimal compensation of sensor dynamics; stochastic collocation for distributed state estimation; and agent-based power management. Some work relating the monitoring of electric cable health and switching converters has also been reported. To support the use of high-performance computing in system simulation, essential to system-level controls research, the University of Texas Austin has developed a customized parallel computing solver called the “CEM solver” to accelerate computation of solutions to power simulations, although it is in an early stage of design. USC has made an effort to use FPGA based board testing with machines and partitioning simulation schematics to distribute the simulation work across multiple core machines. The Florida State University has developed approaches for reconfiguration and system-level power and energy management control. The power and energy management control architecture, based on PCON concept, will be responsible for the functions like generator start/stop control, load dependent start/stop, energy storage management, blackout monitoring, load shedding, start sequence program, etc in the ship.

Research related to subsystems includes work at Mississippi State University on multi-agent control and optimization of PEBB based power electronics systems This work contributes to the understanding of the coordination of the contributions of different converters to ensure system stability and to improve the power quality. At FSU, sub-system level distributed controls were developed for optimally controlling the cooling process for a distributed chiller configuration and for controlling multiple shunt active filters in a coordinated manner for power quality improvement in the converter circuit. Many other low level control results have been reported by

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the ESRDC. For example, coordinated control of converters in ship systems, positive feed forward control of switching converters designed for MVDC systems, DC fault protection, and artificial neural network theory to detect faults, among many other things.

All such lower level controls research could become embedded in a notional high level power system controller. However, a ship’s power manager is more than the sum of the parts. The conclusion drawn from this review of current research is that the ESRDC should revisit the issue of a ship’s power manager, although it is recognized within the consortium that control of ship’s systems involves other systems that in aggregate represent all critical functions and missions of the ship (e.g., thermal management). We propose to begin by base lining the Navy’s current capability by developing a notional baseline power manager simulation, which focuses more on power and energy management, similar to the existing three notional power system baseline simulations (MVAC, HFAC, and MVDC). This fourth baseline simulation will be a useful tool for evaluating advanced power manager concepts in future research efforts.

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2 INTRODUCTION

The future All Electric Ship (AES) opens new design challenges that were not considered before in the shipbuilding industry. The role of the electric plant, which was minor before the US Navy’s DDG 21 program at the end of the last century and current US Navy electric ship programs, has increased with the development of AES. Today, electric plants are expected to be the backbone of the entire ship system providing for propulsion, weaponry and aircraft launching. New opportunities are being pursued that may lead to exciting possibilities, such as providing energy to pulsed loads including electric guns or aircraft launchers. These challenges can be met only if newer approaches to control and automation are developed.

In order to meet the challenging task of designing new control systems, prior work within ESRDC, by academia and partnering Navy and industry stakeholders, has and must continue to be extended to such a level that innovative control strategies, architectures and algorithms are implemented in ship board systems. This report surveys new control concepts and tasks that are required for the transition from present day control systems to future control systems that are to be integrated with Next Generation Integrated Power Systems (NGIPS). The ESRDC control team is a change agent in the AES program and has the multidisciplinary capacity required and is making significant progress in discovering and making available a broad and necessary set of knowledge, methods and activities that will enable meeting cost and performance goals of future AES.

3 RESEARCH WORK OUTSIDE THE ESRDC

3.1 Ship’s System Control

3.1.1 Ship’s System Control/Open Control Architecture for Future Electric Ships

A proposal for new generation electric ship control system architecture has been introduced in [1], [2]. The proposal aims to realize an open-architecture, flexible multi-layer distributed ship resources manager system in order to provide versatile and reliable platform for monitoring and control of ship power system.

The proposed concept for future shipboard controls would require that, once a set of ship requirements is established, a) identify control actions that yield a specific solution to fulfill the stated requirements, b) establish a well-defined and generic functional decomposition of naval ships, c) develop a ship-wide architecture which is built upon functional modules and coherent interfaces. Requirements for the ship management system [3] include:

Open, Modular Architecture system design: An important objective of an open system is to enable any competent supplier to provide modules or elements conforming to the standards that can be easily and successfully integrated into a working system meeting customer requirements. The Open architecture controller allows independent acquisition of system components, flexibility of developing alternatives resources and solutions.

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Adapting to changing requirements throughout a life cycle: To achieve a robust design, subsystems should be chosen to minimize the propagation of change across multiple subsystems due to changes in requirements or upgrades of equipment in one subsystem. As the ship’s mission or requirements change, performance characteristics can be easily made available as a part of the variation points embedded in the control software design that is independent of the particular configuration of the ship. This focus on cross-platform reuse will cut cost associated with common training, logistics.

Functional decomposition: Equipment on a ship can be classified by relationships to the missions of the ship. Functional decomposition enables system decentralization.

Control hierarchy with command controller at the highest level: Multiple mission controllers (one for each mission) would translate these mission priorities into prioritized requirements for resources from the resource systems. These resource systems in turn, would have resource managers that would either meet all the resource requirements made on it, or allocate the available resources to the highest priority users. Resource managers in the control hierarchy should be able to support a command controller, at the highest level of hierarchy, directly or indirectly. The command module would submit its commands by relaying control instructions to the core controllers and optional mission controllers at various control levels via interfaces.

The proposal for the open control architecture describes two main components: the power manager and the control interface. These are described in details in the remainder of this section.

3.1.1.1 The Power Manager

Components of the proposed power manager system, [1], [3] include a ship-wide network for fault tolerance spanning the ship and crossing zone boundaries, a zonal network that is provided for each ship zone, zonal controls that communicate with each at the direction of a mission system resource manager or distributed system manager, a zonal human-machine interface as well as ship-wide human machine interface, a distribution system manager, a mission system manager, and finally a service manager that is responsible for system monitoring, data logging, and equipment status managements.

The Distributed System Manager [1] gathers operator input from the human interface and implements it by sending configuration commands to zonal controllers and other components on the ship as needed. The Distributed System Managers ensure their systems are configured to meet existing demand, can respond appropriately to anticipated changes in demand, and can successfully identify, isolate, and reconfigure around distributed system equipment failures.

The Mission System ensures the correct mission equipment is online or in standby to meet both current needs and anticipated needs. Mission Systems Managers communicate with Distributed Systems Managers to ensure load shed priorities are appropriate for the current ship operations as well as ensuring distributed systems have sufficient online capacity or “rolling reserve” to handle anticipated increases in load.

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3.1.1.1.1 The Control System Interface

The objective of the work, discussed in [1], is to develop an Open Architecture (OA) standards based approach to controller interface, which enables decoupling the controller design from selection of specific equipment. Due to the distributed nature of the ship board system, the controller would also be implemented in the way that it can be accessed from different locations on the ship. The primary layers of the controller system are: the controller layer, the network layer and the information layers shown in Figure 1.

Information layer: User interface – provides a platform for applications that can process data and provide information to the users.

Network layer: Communication between the control and information layers and between devices within the same layer.

Control Layer: Provides control and monitoring, typically through a programmable logic controller to machinery plant.

Figure 1: Architecture of the proposed controller system [2].

Components within each layer in Figure 1 include:

Load Controller: Embedded controller within certain equipment or zonal subsystem may consist of the following components.

Network Interface: Hardware for connecting the Load Controller to the network.

Network: Methods for routing information, data, and commands transition.

Supervisory Control: For each resource type and mission, a supervisory control provides total ship management, including the Mission Layer Control / Ship Domain Control.

Shipboard Simulation Manager: Provides ship-wide simulation services to the supervisory controllers.

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Remote Monitoring: Provides operators the ability to monitor the condition and information on a given piece of equipment or zonal subsystem, also includes the alarm management and data logging management system.

Condition Based Maintenance Manager: Captures data from multiple equipment to determine the material condition of equipments and to predict future failures.

3.1.1.1.2 Load Controller Architecture Layout

Figure 2 [2] shows a detailed architecture layout of an embedded load controller.

Figure 2: The Main Components of the Load Controller [2].

The embedded load controller consists of the following:

Local Hardware Controller: This controller functions as a low level control for the equipment connected. It gets the configuration data and set points from the Data and Configuration Manager. Operational modes, conditions, and sensor data are provided to the Data and Configuration Manager. This unit gets commands from the Control Server and provides the commands and information needed for system control to the Control Server.

Data and Configuration Manager: Maintains the configuration data for the component and manages all data and configuration information.

Internal Bus: It consists of hardware and software for communicating among load controller elements.

Control Server: It supports a fast, low latency, deterministic communications protocol to work in coordination with system level control. It communicates with the Local Hardware Controller and the Data and Configuration Manager for commands and information needed for control.

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Information Server: It defines more generalized protocol for interfacing with Remote Monitoring and Condition Based Maintenance software. This server can be used to establish various configuration set points.

Web Server: This is similar to the Information Server, but uses HTML / HTTPS to communicate with remote HSI’s via a standard web browser. It communicates with the Shipboard simulation server. It is able to support multiple simulations simultaneously.

Local HSI: Provides local operator access to controller for monitoring and control.

3.1.2 Optimal Power Distribution Using Expert Systems

The patent “Power System Distribution System”1by James Bryan Taliaferro, et al., introduces a general form of a Power Manager for the small micro-grid systems like an electric ship system. The power manager is responsible for commanding the power center configuration and transition between the lineups. Methods based on analytical rule have been proposed in this patent to configure the optimal power distribution, and determine the transition path for optimal power distribution. An expert system is proposed to realize power management under the typical plurality of power sources, loads and switchboards.

The management framework addresses the multi-criteria optimization problem underlying the efficient power distribution by coining the set of rules to define an objective criteria function to be optimized. Optimal power distribution configuration system may be used in case of unexpected drop out of the generating unit and to compensate the scheduled mode of operation or to resist the sudden loss of the load due to attack. An efficient transition path is computed using path optimization techniques which assign the associated cost function to the path corresponding to the various states to be obtained. The cost may depend upon the factors such as time to effect the transition, number of breaker openings required to effect a transition, maximum numbers of breakers allowed at that moment.

A set of 20 rules haves been assigned to achieve the optimal power distribution configuration by allocating possible configurations for the switchboards. Rules define the cost for any power distribution configuration to find the optimum configuration for load distribution. The factors in making a transition are identified and ranked from the most to least to be included in the cost function. Additionally, about 20 rules have been identified to compute the optimum transition path from a present state to the target state.

Due to computational complexity, additional methods have been proposed to determine the need to evaluate and/or change the power distribution configuration. These include the following:

As commanded by human operators.

After intervals of time or distance.

In response to change in the Operational Scenario Definition (OSD), e.g., entering into alert or battle or normal mode.

1 James Bryan Taliaferro, U.S Patent #7,369,921, “Power distribution expert system,” Feb. 10, 2006

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Any major changes in the power generation or load.

The status and the availability of data.

Any other known phenomena.

In order to achieve the optimum configuration for power distribution various state rules are defined along with the scoring functions and relative weight factor. All possible configurations are evaluated to determine the lowest cost as the optimum configuration for power distribution and become the target lineup. The same methodology follows to determine the optimum power distribution transition path. The patent suggested using any of the well-known optimization search techniques.

Implementing the optimal transition path to achieve an optimal configuration is automatic. The automatically generated configuration along with the path may be presented to the human operator for approval.

3.1.3 Self-Healing SPA Strategy

Along with the coordination of the ship wide network sensors and the associated component control, predictive, preventive, and restorative methodologies can be implemented. A preventive control approach will be effective either with any probabilistic approach or any other model-based predictive approach. However, such controls depend upon the situational awareness of the whole ship mission and proper component coordination. The limited-look-ahead approach integrated with Kalman filtering is one potential method.

Due to high computational demand and instant control requirement, the preventive approach is one alternative. The pre-computed configuration can be launched immediately without delay for any contingency condition. Once the preventive or predictive control is in action, the control configuration might not be globally optimal. The restorative approach takes account of any discrepancies from the optimal condition and launches the control action. This approach takes account of load priority, power flow, load shedding and other possible constraints using any available automation system.

4 RESEARCH WORKS OF THE ESRDC

4.1 Low Level Controller Design

The class of controllers that are more tightly coupled to the equipment and that can be utilized and/or directed by system level control is categorized as a low level controller in this section. This classification may include controllers that can be tuned to make them work in coordination with high level controllers. The relevant research works from [6], [7], and [8] conducted in ESRDC universities are discussed in this section. Low level controls will be deployed by the system-wide controller to manage the corresponding hardware resources and to monitor local system stability and performance. Various kinds of low level control design dedicated to optimize different power system components have been developed by the ESRDC. Listed are a set of control schemes that might contribute to the proposed Power Manager design.

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4.1.1 Coordinated Control of Converters in Ship Systems [6]

The power sources for electric ship networks must be coordinated for both a DC grid and an AC grid. One distributed and coordinated control method, called “droop control” of power sources in an islanded mode AC micro-grid, has been explored and simulated for converter coordination. The proposed control method avoided high bandwidth communication in signal transmitting and reduced the vulnerability of the larger system to single points of failure. A secondary control layer is also developed with droop to provide frequent adjustment of the outputs of power sources to minimize cost of or meet other objectives. This so-called “energy management system” can adjust output power of generation in a micro-grid to minimize certain objectives like fuel consumption.

4.1.2 Positive Feed-Forward Control of Switching Converters Designed for MVDC System [6]

MVDC bus stability issues are regarded as a significant potential problem in the design and development of the AES. This is due to the fact that the MVDC power distribution system comprises several load-interface feedback-controlled switching converters that exhibit Constant Power Load (CPL) characteristics at their input terminals, causing potential instability at the MVDC bus level. Moreover, the presence of other demanding highly dynamic loads, such as pulsed loads (e. g. radars, rail guns) may aggravate the stability problem. The Positive Feed Forward (PFF) control is an active control approach applied to load-interface switching converters connected to a DC power distribution to improve MVDC bus stability. In particular, the approach solves the source subsystem interaction problem – the system stability degradation which is observed when a switching converter is connected to a DC voltage source subsystem which presents finite Thévenin impedance. The strategy combines the PFF control with the conventional Negative Feedback (NFB) control. The goal of the PFF control is to stabilize the MVDC bus voltage by providing input impedance with positive real part to the converter in the frequency region where the source interaction occurs, while leaving the FB control loop gain dominating at low frequencies to provide tight output voltage control. The PFF control design can be performed using a recently proposed stability criterion called Passivity-Based Stability Criterion (PBSC). The method is based on the passivity of the overall bus impedance rather than on the impedance ratio known as minor loop gain. The proposed criterion gives new sufficient conditions for the stability of two (or more) interacting subsystems being part of a larger DC power distribution system. The criterion has several advantages compared with previously proposed stability criteria.

4.1.3 A DC Fault Protection Test-Bed Implemented on the Multiple PEBB System [6]

The DC bus fault protection test bed is based on three-phase, IGBT-based PEBB modules manufactured by American Superconductor. Each converter is locally controlled via a custom control board. Supervisory Controller software monitors the DC bus and each of the four loads using deterministic state-machine logic, and controls each of the four load contactors in addition to the fault contactor. The controller runs on a PC-based Lab VIEW RT PC and interfaces with the sensors and contactors via an NI-PCI6259 DAQ card. When a fault is detected the Supervisor goes into “Fault” state and can respond with one of the two different approaches described below: 1) Sequencing the power flow to protect against line faults by shutting down the PEBB,

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reconfiguring the load contactors to isolate the faulted load, and re-energizing the DC bus. 2) Relying on the DC/DC converter output current limit, reconfiguring the load contactors to isolate the faulted load, and re-energizing the DC bus. After the fault is extinguished, the DC bus then completely recovers and all loads, excluding the faulted load, are reconnected to the bus via the contactors. The Supervisory Controller is now in “Normal after Fault” state and continues to monitor the system as in the “Normal Operation” state.

4.1.4 MVDC Distribution System Circuit Protection [6]

A controlled power sequence is proposed to coordinate the control of power converters that are feeding the dc bus and mechanical contacts to limit fault currents and to isolate the faulted part of the system. Network reconfiguration is also performed to assure that traditional circuit protection elements are avoided. After the fault is detected, the bus is de-energized by turning off the main converter (the duty cycle is brought to zero), one or more contactors reconfigured, and then the DC bus is reenergized. In this way, the magnitude of the fault current and the time to eliminate the fault are reduced compared to the employment of traditional circuit breakers.

4.1.5 Stabilizing Geometric Controller for dc-dc Converters with Instantaneous Constant-power Loads [11]

Analysis have been performed by UT-Austin to demonstrate that linear boundary controllers with a negative slope can achieve stable regulated operation points for non-minimum phase dc-dc converters subject to constant-power loads, specifically boost and buck-boost converters.

4.1.6 Robust Advanced Controller Design of Bidirectional DC/DC Converters in the MVDC Shipboard Power Systems [12]

Controls for a bidirectional DC-DC converter were developed. The control objectives of the bi-directional converter are to regulate the output voltage in buck mode and the output current (or power) in boost mode. To optimize the control parameters a new double-layer PSO algorithm was developed and implemented. The developed bi-directional dc/dc converter model was implemented and its performance was validated in a large-scale real-time simulation model of a notional MVDC shipboard power system. Small-signal average models were used as a useful tool for evaluating the control performance and closed loop stability of the converter systems.

4.1.7 Coordination of Current limiting Controllers for Boost Converters in MVDC [7]

In this project, a current limiting control scheme for bidirectional converters was developed for the shipboard power systems. This scheme is a modified version of the Hiccup Mode current limiting for power supplies. The proposed current limiting control turns the converter off and forces the converter into a sleep mode as soon as an over current condition is detected. At the end of sleep time, the converter is soft started. If the fault condition persists, then this process is repeated. The sleep time reduces the average load current and allows the converter to cool down. Current limiting control performance of the proposed controller were evaluated for faults occurring on HV and LV sides of a dc-dc converter (PCM1) and a dc-ac converter (PCM2), using a large-scale simulation of a notional MVDC system on a Real-Time Digital Simulator (RTDS). These results validate the performance of the developed fault limiting controls. It was

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observed that the hiccup mode current limiting reduces the average load current during an overload condition. It also reduces the voltage oscillations in the faulted terminals of the converter at the port side thereby bringing the voltages to their respective steady state values fast. This in turn reduces the power dissipation in the power train and lowers the thermal stress on the components. This property of the proposed current limiting scheme helps in avoiding the derating of components for elevated temperature.

4.1.8 Coordination Control of Multiple Active Filters and Active Front-End Units for Power Quality Improvement [14], [15]

Advanced control algorithms were developed to coordinate multiple voltage source inverter based active filters and active front-end units to improve power quality of shipboard power systems. The parallel operation of multiple active compensators seems to be an effective solution for compensating the harmonics generated by large non-linear loads. The coordination of parallel active filters and active front-ends requires sharing the compensation efforts between them, which can be accomplished by centralized and/or decentralized control structures. A new algorithm, Multiple Adaptive Feed-forward Cancellation (MAFC), was developed to measure the total harmonic distortion (THD) without calculating individual harmonic components. In this approach the harmonics are computed by taking the integration of the squared harmonic signal and then multiply it with a constant. This feature can greatly reduce the computational burden on many harmonic detection methods when it comes to power quality evaluation. In addition, the proposed algorithm can also facilitate power quality compensation such as shunt active filter control or active front-end coordination. Therefore, the proposed harmonic distortion measurement algorithm can provide a flexible and economical solution for many power quality problems. The simulation results show that in steady-state, the THD computed by p-q and d-q methods are almost the same as the one obtained by the proposed MAFC method. During the transient, the MAFC reaches new steady-state smoothly while there is some over-shoot for p-q and d-q methods. This phenomenon is mainly caused by the integration associated with the proposed algorithm, which can only provide harmonic distortion measurement at the end of each integration cycle. Simulations were also carried out to show that the proposed harmonic measurement algorithm can interface with the traditional p-q or d-q method and realize shunt active filter power control.

4.1.9 Modeling and Control of MVDC System with Regenerative Motor Drive

The current advanced naval power system is shown in Figure 3(a). However, the electric motor drive cannot achieve the bidirectional power flow. Therefore, one bidirectional regenerative motor drive with Energy Storage Systems (ESS) is proposed to replace the traditional one as shown in Figure 3(b). In order to further investigate the performance of advanced naval power systems integrating multiple power sources and energy storages in electric ship, a MVDC system model integrating the regenerative Motor Drive with ESS was firstly developed. In the MVDC system model, the corresponding control methodology for a regenerative motor drive with different power management strategies was also proposed. The proposed power management strategies are helpful to distribute different energy from multiple power sources and energy storages, and the size of these sources and energy storages can be optimized, which reduced the system cost and weight. Moreover, the uniqueness of this approach lies in its implementation of bidirectional power flow control, improved power quality, enhanced energy saving capability

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and improved MVDC system efficiency. In addition, the developed MVDC system model with regenerative motor drive was tested and validated in different scenarios on a hardware prototype at 10kW Permanent Magnet Synchronous Machine (PMSM) in the laboratory. The above unique functions of the MVDC system have been verified in this test bed. The experimental results showed the feasibility of the developed MVDC system.

Figure 3: (a & b) Current advanced naval power system; (b) proposed regenerative motor drive with ESS.

4.1.10 Modeling and Control of MVDC System with Solid State Transformer

In the MVDC system model, the solid state transformer based on high-frequency dc-dc converter is also vital to improve the system efficiency, reduce cost and size, and enhance the system reliability. Hence, the MVDC system model including solid state transformer was developed for the shipboard power systems. The mathematic model of the MVDC system was explored to achieve the control system design. The control system includes individual voltage regulation and coordinated power distribution, which facilitates the solid state transformers to plug and play in this MVDC system. Based on the developed MVDC system model and control system, the actual MVDC system in the future shipboard power systems may achieve bidirectional power flow, high power density, high power efficiency, and flexible capacity expansion. Furthermore, the simplified MVDC model with multiple proposed solid state transformers has been also developed and verified in simulation. The results have shown that it can facilitate to meet multiple load requirements, enhance dc bus stability and improve MVDC system reliability, especially for fault conditions which is critical for continuity of power supply in MVDC system in shipboard power systems.

With all the low level control strategies available for designing a high-level system controller, it is expected that the power manager could gather more detailed information about each individual zonal subsystem, allocate resources among them and coordinate the global ship power system in a more efficient way.

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4.2 High Level Controller Design

The High level controller handles the total ship management using the available resources and by either directing or coordinating with the low level controllers, network communication and human interaction interface. The high level system control assigns the policies to be imposed that looks after overall power and energy management, distribution system automation, remote monitoring, fault recovery, data collection for further evaluation of different cases, etc. Component level modules will handle the autonomous control of module functions, respond to changing load conditions, and work for power flow management by managing the high-level system goal. USC is conducting most of the research work mentioned in this section.

4.2.1 Power System Reconfiguration for Shipboard Applications [5]

In this survey, the authors looked into the various objective formulations of the reconfiguration problem and based on the difference between land-based large-scale conventional power system and the tightly coupled, self-contained shipboard power system, a unique design approach that suits specifically for onboard power system reconfiguration management has been discussed and analyzed. Stability, reliability, and restoration capacity under severe conditions are considered key factors in evaluating the reconfiguration design. A demonstration of a theoretical approach to solve the onboard reconfiguration management system has been proposed.

4.2.2 Definition of Methods and Algorithms for Robust Control of Power Electronics Systems with Control Loop Including Network Communication [6]

The objective of this experiment is to provide a fully-flexible network-based approach for control of distributed power electronics building block (PEBB) systems. In the experiment setup, the low-dynamic loop controller and all higher-level control algorithms run remotely through a network. This approach permits fully flexible reconfiguration of controls for electric ship power systems with minimal changes to hardware connection, setup, and software structure.

4.2.3 Development of Sensor Failure Detection Algorithm [6]

This algorithm could detect sensor failure and distinguish a sensor failure from a fault in the system. The test used a real-time hardware in the loop simulation. One sensor was inserted into each power zone and sensor failure was realized by physically disconnecting the measurement channels from the input to each sensor.

4.2.4 Development of a Methodology for Optimal Compensation of Sensor Dynamics [6]

This work addresses the evaluation of dynamic uncertainty in sensors with dynamic behavior. The results of this analysis can be used in the filter design to enforce certain constraints on the uncertainty of the compensated signal.

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4.2.5 Optimal Sensor Querying [21]

This work addresses large-scale sensor networks where simultaneous data collection from all sensors is prohibitive. The underlying model is a discrete-time control system for which the observations available to the controller are not fixed, but there are a number of options to choose from. Optimal selection of the sensors to query at each time instant is accomplished via dynamic programming.

4.2.6 Stochastic Allocation for Distributed State Estimation [6]

A procedure was proposed to automatically identify how and what state information to exchange for reconstructing the state starting from the partial knowledge acquired in each sub-area in which a network is partitioned. An optimization algorithm based on dynamic programming has been developed to determine the optimal set of strongly coupled variables necessary for a sufficiently accurate estimation. The distributed state estimation creates areas where fast local control action can be taken based on local state estimation. Also, the state estimation in one area can still be done even in case of total failure of other areas, thus improving survivability. The use of dynamic models allows for relaxation of the redundancy requirement on the number of measured data as compared to the classical state estimation. Furthermore it increases the reliability of the estimation against failure of some measuring units and/or data communication channels. Finally, elaboration time seems to be reduced by this approach. In the case study tested in simulation, a reduction by about 65% was achieved.

4.2.7 Wireless Sensing and Data Communication System for Fault Detection [6]

This work includes simulation and hardware measurement to show the efficiency of the sensing system and the precision of detected fault location versus actual fault location.

4.2.8 Online Monitoring of Switching Converters [6]

The monitoring system uses system identification techniques to monitor the state of a switching converter and its surrounding power distribution system in real time. A small-amplitude wide-bandwidth perturbation is introduced in the switching converter digital control to elicit a system response, which can be processed to measure transfer functions and impedances of interest. These quantities can be used for system monitoring and for control adaptation. For example, an adaptive control (system identification based adaptive control) can be synthesized to maintain high control performance despite time-changing system parameters.

4.2.9 Multi-agent Control and Optimization of the PEBB Based Power Electronics System [9, [10]

The objective of this research is to develop a coordination control strategy based on a multi-agent system to improve the power quality of the electrical power distribution while guaranteeing other services such as load feeding. The coordination of the contribution of each converter towards ensuring the stability and the target power quality is the main challenge the multi-agent system is handling in this research. The converters that the Agents control, though,

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have different capacities both at the functional level and the operational level. Each Agent is capable of a Broker behavior, a Monitoring behavior, and a Compensator behavior. When the Agent acts as a Broker, it manages the sharing process by receiving information from the other agents and allocating the compensation among them. When the Agent implements the Monitoring behavior, it analyzes the operating conditions to detect violations of power quality requirements. When the agent has Compensator behavior, it acts by feeding the compensation parameters used for converter control. The compensation sharing among the converters is done in direct proportion to the maximum capacity of each converter and in inverse proportion to the current loading condition. This approach emphasizes a balanced sharing of the compensation burden among the converters and it guarantees that the share to be implemented to achieve the overall compensation does not exceed the limits of the converters.

The control problem consists of operating a distributed system of converters so that the mission objectives are achieved. Mission objective, in this case, consists of the reliable feeding of the loads while guaranteeing power quality of the AC distribution. For this purpose, each converter in the distributed system must smoothly and continuously adjust.

The converters must autonomously decide to what extent to operate in each capacity. This decision must be achieved avoiding single points of failure (thus avoiding centralized control and enabling the system to continue to operate in case of loss of one or more of the converters.

The proposed control architecture is based on a Multi-Agent System. Each Agent is capable of a Broker behavior, a Monitoring behavior and Compensation behavior. The Broker behavior is started upon the detection of a power quality violation; in particular, the agent that has detected the violation assigns to itself the role of Broker. Each converter is controlled by one Agent. The Agent is in charge of enforcing the control strategy onto the device level control. Each agent decides the control strategy in agreement with the other agents. This control approach allows for decentralized and maximally flexible response to variations of loads, power availability, power demand and operating conditions. Dynamic behavior is limited by the agent interactions.

4.2.10 Large-Signal Transient Load Models for Power Electronic Load Aggregation [22]

In this work we develop a new large-signal transient load model to represent the composite power electronic load at a network bus. Traditional load models do not account for the transientresponses of power electronic loads which occur at the onset and clearing of voltage sags. The new model is supported with actual fault response data and laboratory controlled tests. This work shows that power electronic loads have a unique impact on generator rotor angle swings compared to those using conventional load models.

4.2.11 Power Hardware-in-the-loop (PHIL) Activities [6]

A Power-Hardware-In-the-Loop (PHIL) test-bed has been developed at USC and tested using a standardized Power Electronic Building Block (PEBB) controlled by a VTB simulation running on a Labview real-time platform. Captured data of the PHIL experiment demonstrates that the PHIL simulation successfully emulates the load profile generated by the VTB schematic.

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4.2.12 Multi-Agent System-based Power/Energy Management and Controls for a MVDC System [16], [17]

A high level control scheme for a power and energy management system (PEMS) was designed as a two layer hierarchical architecture, viz. system level and zonal level, as shown in Figure 4. This PEMS architecture utilizes the Power CONtrol Module (PCON) and QOS concepts as defined in [18] and [19]. The primary function of PCON module is power and energy management in the ship under normal and faulty conditions which takes into account of the QOS and Survivability requirements for the war ships. Within this architecture of PEMS, several modules with different functionalities were used. For example, a Power Generation Module (PGM) performs the function of a generator and a Power Conversion Module (PCM) performs the functions of a DC/DC or DC/AC conversion. Some other modules are Power Propulsion Module (PPM), Energy Storage Module (ESM), Load Shedding Module (LSHED), and PCON. Both the ESM and LSHED modules are designed to be distributed in every zone ship-wide, in order to assure QOS and improve the survivability of the ship when a fault occurs in a zone. The size of ESM in each zone is decided by power needs of the vital loads in that zone.

Figure 4: Control scheme for power and energy management system.

In zonal level control, a zonal PCON monitors and manages the zone. For example, when a fault occurs in the DC/DC converter in a zone, the zonal PCON receives failure signal from the DC/DC converter and sends a control signal to activate the ESM to power the uninterruptible loads and LSHED will also be activated to shed the long term interruptible loads. The DC/DC converter will be reconfigured during this period. In system level control, the human operator can set the ship mode based on the ship mission through the HMI. PEMS will initiate the

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sequence program and the user request will be delivered to the system level PCON. System PCON communicates with zonal level PCON to get the zonal working status and make decision based on the whole system level. The system level PCON monitors the voltage through the port and starboard buses to make sure the system working properly. This PEMS was implemented within a multiagent framework and was tested in real-time simulations as well as through CHIL experiments.

4.2.13 Game Theoretic Approach to Distributed Load Management via Aspiration Learning [23]

This work addresses the problem efficient distributed optimization and control of shipboard power systems. Distributed coordination is of particular interest in many engineering systems, since agents need to utilize their resources efficiently so that a global objective is achieved. In these scenarios, expected utility maximization (or best reply) by each agent is not often feasible in terms of information or formulation of beliefs about other agents' actions. Subsequently, the solution of such an optimization problem might be extremely demanding. Therefore, attention has been drawn recently towards non-cooperative game formulations and distributed learning processes where each agent learns which action to play based only on its own previous experience of the game (actions played and utilities received). We propose a framework for distributed learning and control in a multi-agent environment. We view each agent as a player in a coordination game and address the issue of distributed convergence to efficient outcomes through payoff-based learning dynamics, namely aspiration learning. The proposed learning scheme assumes that players reinforce well performed actions, by successively playing these actions, otherwise they randomize among alternative actions. Though this work is of more abstract nature, it should be valuable in generating algorithms for distributed load management in an electric ship.

4.2.14 Distributed Controls-based Thermal/Fluid Management for Chiller Systems

A strategy has been developed for the optimally controlling the cooling process for a distributed chiller configuration. When the temperature of the equipment is beyond a critical temperature, the water pump will work to cool down the system until it is below a pre-defined low temperature. The water pump does not work during the process when the equipment temperature increases from the pre-defined low temperature to the critical temperature. Therefore, the water pump is not working all the time, which saves the electric energy and lengthen the life time of the pump. A weight-based multi-objective optimization model, based on evolutionary programming, has been developed to optimize the flow rates of the simplest enclosed cooing system in Zone 1 of the ship. The cooling system needs to response to the high temperate in a short time with as lesser energy consumption. These objectives are considered in the optimization model. The traditional cooling control utilizes the open and close of the valve to control the cooling. Over this course, the motor of the pump is working all the time and the valve has only two states (ON and OFF). Also, flow rate control can be done through throttling of the regulator. Unnecessary energy is consumed in the above two ways. The developed algorithm can generate needed flow rate for the pump water in a reasonable computation time. The results show

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a large amount of electricity saving compared to the traditional cooling strategy and responsiveness in terms of cooing time.

Different high level control strategies could offer more functional options to put into the Power Manager designs, also it would be desirable to compare the proposed power management performance with existing high level controller systems like the multi-agent system. Some techniques mentioned in this section would be important references to set standardized protocols for future design.

5 RESEARCH DIRECTION FOR THE SHIPBOARD POWER SYSTEM CONTROLS

All the control challenges in managing the shipboard power system need to be addressed from system wide prospective. Such collective approach takes account of all functional components and their interaction to the system status. Due to involvement of more and more components and complex system interaction, research approach with sound mathematical formalization is of utmost importance. A supervisory framework that works in coordination with multiple lower level controllers can be well envisioned through model based control approach. Open architecture to include multiple functional modules in addressing the control issues will be an added benefit of such an approach. The proposed research is expected to support the sustainability and reliability of SPS with timely tuning of proper controllable parameters.

Figure 5 demonstrates the functional decomposition of the proposed power management system. Different modules work in coordination with each other towards the common system goal. Sensors check faults in IPS line or other components, voltage, currents, switch status, and transformer tap settings, which are distributed throughout the ship that provides the information to the observer. Key elements within the proposed system include:

1. Environment Module: The environmental variables such as system load profile and operating conditions are not deterministic. To control the quasi-instantaneous behavior, the future knowledge of such variables is necessary. The environment module contains the predictive filter which takes the input of current environmental measurement data gathered by distributed environment monitors and generates the predicted workload and operation condition forecast estimations for system module. Predictions are made based on the prediction & estimation library and environment model to provide real-time decision support for optimized operations.

2. System Module: System Module consists of the abstract model of the system. The constructed system model of the ship-board power system takes the forecasted environmental variables along with the current system states and computes the expected system state, resource level, and service capacity based on the real-time data measurement and existing information stored in system databases. These expected data will then be sent to the management module which optimizes certain parameters for the specifications provided.

3. Management Module: The specification determination is the main concern to solve the optimal control command in order to associate all the optimization parameters to optimize the utility function while satisfying the constraints. Fault management, stability

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management, power quality management, power balance or any other optimal reconfiguration strategies are specified through performance specifications.

Figure 5: Proposed power manager architecture.

4. Decision support: This module plays vital role on specification determination. Different functional modules are integrated as a part of decision support which provides the management modules with proper utility functions and constraints. Modules including system assessment, generator dispatch control, unit commitment, and state estimation could be a part of decision support. All possible system conditions are visualized through these blocks. Environment module of the power manager architecture works closely with decision support depending upon the application.

5. Human Interface: Human interface provides the required specifications according to the mission status, and it is tightly coupled with the power management system through SLA module, where all the performance specification parameters are continuously monitored to further define the system objectives. Human machine interaction (HMI) also is given preference in the design with a proper HMI interface. The HMI block reads from the system states and expected system states. An operator can always override the optimizer action as per the operator’s knowledge of the system. Better interaction between the autonomic computation of the control variables, human intervention and HMI components are needed.

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The performance of the proposed power manager will be evaluated and analyzed based on Matlab simulations and RTDS tests. With different testing scenarios including three phase fault, loss of generators, loss of loads, the power manager will be implemented in multiple environment. The performance analysis of each functional modules and the monitoring system shall be conducted both from the individual perspective and system wide view.

Steps to achieve the above framework are as follows:

1. Choosing the representative simulation for E-ship system. 2. Creating of the individual and combined abstract E-ship model depending upon the

specific objective. 3. Developing the Diagnosis support to support the management module. 4. Integrating the diagnosis support with the system model to apply central control

algorithm. 5. Different operational scenario testing on the framework.

In the following sections, Section 5.1 gives a brief introduction about the simulation environment and programmable tool that is being developed for the proposed system design. Section 5.2, on the other hand, shows an instance of the power management approach including functionality design and control technique.

5.1 Effective Simulation and Optimization Tools for Early Stage Ship Analysis and Design

The objective of this work is to provide a fully-flexible network-based approach for control of distributed power management of electric SPS. In the experimental setup, the low-dynamic loop controller and all higher-level control algorithms run remotely through a network. This approach permits fully flexible reconfiguration of controls for electric ship power systems with minimal changes to hardware connection, setup, and software structure.

5.1.1 Development of an Efficient Distributed Simulation Techniques of Base-line Models

Model decomposition techniques can be utilized to support network-oriented simulation and design tools. A system decomposition approach can be developed based on important results from prior work on the distributed control of linear systems and simulation of large scale systems, such as parallel domain decomposition (DD) techniques. The main idea is to break a complex electric power model into several subsystem models so that the computation of the

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program can be parallel distributed over several computation nodes to raise the execution speed of simulations. This effort will also include: natural decoupling methods for network distributed solvers with different step sizes and the integration of network interaction points for real-time data-driven simulations. Discrete event simulation methods will be introduced to support this activity.

5.1.2 Development of a Framework for Early-stage Design Space Exploration

In the early stages of the design of shipboard power systems, it is important to assess and optimize such aspects as size, weight, cost, reliability, and survivability of a design as functions of design considerations such as topology, voltage levels, and system frequency. Additionally, in these early stages, the uncertainty involved may be considerable and should be properly accounted for in any design optimization using robust design techniques.

Techniques to support early-stage design exploration should be investigated that consider technology elements at various level of function, for example material characteristics and building up from materials to components, components to subsystems, subsystems to systems, and finally integrate systems of systems to arrive at a ship design. One aspect of this task is to pursue this goal through the use of layered meta-modeling, with the ultimate goal of having a tool wherein the impact of some new material on the overall ship design could be readily and quickly understood. Such a paradigm would not only be valuable for ship design but also in guiding research investments.

Model integrated computing is a formal system design methodology that has gained momentum in recent years as a sound methodology of applying computer-based modeling and synthesis methods to a variety of problem domains, including cyber-physical systems, in which power systems is an example. The underlying models formally capture relevant properties of the system to be developed and can be used directly to auto generate implementation code and other engineering artifacts, which are tedious and error-prone to produce manually. Model-integrated computing (MIC) techniques can be used to raise the abstraction of control theoretic/optimization methods and make them available to domain engineers. In particular, a carefully designed domain-specific meta-modeling language will allow specification of various technology elements along with the underlying performance and reliability measures in an integrated manner using formal model-integrated computing techniques. Available generic meta-modeling environment tool suites currently used in several DoD sponsored projects, can be used to automatically generate code for models (including simulation models) as well as verification, management, and design support structures from a given set of performance, reliability and fault tolerance requirements.

One opportunity is to leverage the Design Space Exploration Tool (DESERT) that was developed at Vanderbilt University. DESERT is a meta-programmable tool for navigation and pruning of large design spaces using constraints. It provides a generic structured representation of design-spaces based on the concept of alternatives and parameters. DESERT has been used to represent design spaces in a rich variety of problem domains – product-line architectures, hardware-software co-design, and automated model-compilation among others. An expressive constraint language based on a subset of OCL allows expression of compositional, resources, and performance (time, energy, size, weight, cost, etc.) constraints. Internally, DESERT employs a

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powerful and highly scalable symbolic representation based on Ordered Binary Decision Diagrams that allows for rapid and efficient manipulation of very large design spaces with constraints. In order to solve constraints that involve complex mathematical operations, DESERT interfaces with Mozart, a powerful environment for constraint logic programming based on the Oz constraint language. An XML based input and output interface accompanied with a programmatic API, allows easy and semantically correct integration of DESERT with custom Domain-Specific Modeling Languages.

5.2 Control-based Power Management for Notional All-Electric Ships

Figure 6 : Control scheme for power and energy management system

The integrated model based control framework for shipboard power system is shown in figure 6. Measured real time data and dynamic models are the key components for model based framework. Measurements are obtained from the system using current transformers (CT), voltage transformers (VT) or even in advanced form, from phasor measurements unit (PMU). Breaker statuses are also monitored for reconfiguration and protection scheme. Thus obtained measurements and status are used by the state estimator and then by the power flow equations. The model is formed and updated periodically from the power flow data and from direct measurements depending upon the application. Specific assessment of the system condition is performed to detect any abnormalities. This assessment block represents the functions such as online dynamic security assessment. However, the particular nature of the assessment can be In case of occurrence of any disturbance and prediction of the potential violation of pre-defined system constraints, the model based control action is invoked.

The need has come to address the more advanced requirements to address the control of the IPS distribution system in all-electric ship. The IPS control needs to be more intelligent in terms of system efficiency, power quality, generation dispatch, voltage control, and reconfiguration among many, towards achieving the optimal performance. To enhance the survivability and reliability, system control should be distributed among power conversion modules (PCM). This

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can be achieved by exploiting the power system reconfiguration capability of a Zonal Electrical Distribution System (ZEDS).

Basic power management features include the automated Power Dispatch control followed by electric main switchboard control, generator set control, transformer controls or electric propulsion and motor system control in response to changing load demands or other various possible scenarios. A distributed control structure that provides for manual supervisory control to recover from battle damage is a necessity. Autonomous action is required from the ZEDS controller to serve the automatic system reconfiguration and fault localization.

The control structure will be divided into high level system control, where the policies will be imposed to look after overall power management, distribution system automation, remote monitoring, fault recovery, data collection for further evaluation of different cases, etc. Component level modules will handle the autonomous control of module functions, respond to changing load conditions, work for power flow management by implementing the high level system goal, HMI to each module, receive/send health status of the component to the higher level controller, etc.

The ship power system offers a challenge for controls, nearly independent of the control approach used. The power system will be tightly coupled and highly automated. This provides for optimal usage of all resources. It also provides the new challenge in that control system failures can lead to catastrophic failures of the entire system without the conventional warnings. This may require a new approach to system safety. Simulations and laboratory tests are being developed to determine the severity of the problem and mitigation strategies under different scenarios.

5.2.1 Development of a Performance Specific Decision Support System

Figure 7: SPS state Assessment

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Mission type, present system status, and resource availability mainly governs the performance specification along with the available control variables. SPS could be volatile with the outage of generator, cable, or any other equipment. Violations of any constraints including fuel, voltage, and power requirements could drive the ship unstable to sustain the particular mission. Knowledge of the consequences of the present system status to shipboard operator or autonomic shipboard controls helps to take remedial or corrective actions to drive the system back to stable condition. This self-diagnosis function acts as one of the main features of self-healing of the SPS and towards autonomic computing.

Inclusion of the load priority, different load types, power ratings, power pick up time, which are some of the unique features of SPS, helps for better state screening. System assessment for voltage stability, loss of load identification, fuel requirement for mission, and time frame helps to judge the control requirements for operator. As contingencies can violate the operating limits, security assessment must consider such cases. Possibility for assessing multiple contingencies in deterministic criteria for real time decision support is likely to have time constraints. The condition even worsens with more features included in diagnosis. Machine learning techniques for classification is a potential solution to address this issue.

Figure 7 explains the potential features to classify into three classes for possible contingencies. Active power flow, reactive power, generator status, mission type, fuel, etc can be the feature vectors for the state classification. According to the classified state, utility function and constraints will be tuned in the central control framework as shown in figure 5.

All the necessary data are collected from the Matlab, RTDS, VTB, test-bed, or any other commercial simulation software for preprocessing and offline training. This process includes the machine learning techniques to extract the important feature including some signal processing to define the statistical model. The generated model is used to predict the state of the system depending upon the provided current measurements. With wide range of possible scenarios, training data can be made reliable and free from over-fitting problem. The developed system will be tested with various case studies for accuracy, memory requirements, speed and other performance criteria.

Loss of vital/Non vital load identification criteria can be solved by optimal power flow calculation. This identification helps the machine learning to have a knowledge base and to train it. Formulation as of reference [24] can be used to create the knowledge base with some modification to fit the SPS specifications.

Several other criteria can be defined to properly access the system. Voltage stability index, fuel statistics, speed fluctuation, etc can make a strong knowledge base for state classification.

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5.2.2 Develop Efficient System-level Control Policies for Managing Disruptive Events

For effective implementation of the distributed optimization and control technique, system models should effectively reflect both the structural and dynamic aspects of the system. The dynamics of multifunctional heterogeneous units in the ship power system can be arbitrarily complex depending on underlying elementary components, the way they interact, the condition of the operating environment, and the operating platform. However, the dynamics of individual components is usually much simpler. Various levels of abstraction are needed for effective modeling, analysis, and control of such complex systems. A general semantic domain, such as a hybrid system model, can capture the dynamics of common components, identify their system functions, and reflect the corresponding optional impacts on the global system in a system of distributed coordinating units. In this task we aim to develop a generic control-based framework that could address system reconfiguration to balance energy generations and load consumptions under both normal situations like load switching and disruptive events such as failures and attacks, at the same time, this proposed framework would also be able to maintain certain specific system ratings without requiring constant communications with each measurement unit during the operation, in another words, to restrict the system data flow effective control techniques, such as MPC and supervisory control, would allow performance objectives to be represented explicitly as a multi-variable optimization problem that can be applied to complex dynamics including ones that are event-driven as well as for systems with long delays or dead times. The distributed nature of the targeted converter systems suggests a decentralized control structure. To this end, a hierarchical structure will be developed wherein the overall problem is decomposed into smaller sub-problems that individual controllers solve cooperatively. Each controller can manage a single system unit, and groups of controllers report to higher-level coordinators that manage the interaction between the various subsystems.

The performance of the proposed control algorithms will be evaluated empirically and analyzed in terms of their relevant stability and robustness properties. The focus will be on providing accurate estimates of the performance levels and stability properties of the decentralized control strategies. Related work on stability analysis of hybrid systems can be used as a starting point. The aim of this effort is to determine the feasible operating region, defined as a bounded region around the set-point that the controller can drive the system into from any initial state, and then maintain it there.

5.2.3 Reconfiguration Design Approach

The backbone of the proposed Power manager is reconfiguration control strategies which depend upon different operating scenarios. Altering of the topological structure of distributed components and reconfiguration of the device parameters are some of the potential control actions. The reconfiguration depends upon complete layout of the shipboard power system and the possible reduced equivalent topology for future study.

The following is a general design approach preview for the power manager system:

Functional decomposition to simulation and building the corresponding mathematical models of components: The principles of performing function decomposition are

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Each function block provides its own individual ship service.

Each function block has the capacity to perform its own diagnosis/data feedback.

Each function block should have both a full bandwidth model and a reduced order average model.

Function blocks might have different forms in different system topologies.

Perform real-time evaluation/analysis of the system status: This procedure needs the following embedded modules, (a) measurement module that contains all the real-time system data, (b) database module that includes the historical data and default parameters preset for ships under different operation modes, and (c) an assessment module that has the ability to analyze and diagnose scenarios based on embedded specifications. The measurement module can provide real-time measurement data to the power manager system. Those measurements include current, voltage, generator frequency and circuit breaker status. The data base contains the historical and default information of the system setting in different modes, for example, connectivity information, static information of all electric components, working mode profiles. Within the assessment module, the real-time data stream are continuously updated and compared with the historical data, if the differences go beyond the limits that are regulated, an error signal would be triggered automatically. The power manager would then gather information from each error signal, start to analyze, and output the possible diagnosis, e.g. the ship is experiencing a load change, or a fault happened in a certain area within the system.

Determine the reconfiguration objective: After a system exception happens, the power manager needs to determine a list of things to set up the appropriate solution. First is the priority of each component in the system, for example, in battle mode, weapon systems would have the highest priority while in cruise mode, weapon systems are listed as non-critical components. The priority determines how the component is going to be rearranged in the updated topology. Second, the system requirement at the moment, e.g. isolating certain damaged areas after a fault happens or altering device parameters after operational mode switching. The system requirement profiles for different possible scenarios should be defined as defaults in the power manager. The last thing to do is to establish a set of non-linear equations to represent the system status and the reconfiguration objective while satisfying the system requirements. Candidate objectives would be calculated based on real-time data and preset component priorities.

Determine and implement the reconfiguration procedures: After the reconfiguration objective is determined, the constraints of the system should also be determined before solving the equations. Common constraints include equality constraints like active/reactive power balance between load and source; inequality constraints like limits on current/voltage, operating temperatures, and other system parameters. Once all the constraints are set, the power manager would start solving the optimization equations, solutions will be sent to the corresponding components for the implementation. When the update is finished, the system will be re-evaluated to assure stability and performance. The control scheme is saved with all logged data for future use.

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6 INDUSTRIAL PRACTICE

This section surveys both the current DDG1000 engineering control system and common commercial integrated management solutions that have been practically installed and evaluated on vessels. The ultimate goal is to essentially maximize the control system functionality, real-time performance, extendibility for integration of future technologies and the utilization efficiency of resources, assure the stability and reliability of the system under severe conditions, while minimize the cost of maintenance and associate operator training. Typical industrial procurement criteria include the following [25]:

1. System performance: The performance is evaluated based on system bandwidth and controllability, integration of distributed equipments within the global control solution, and the accessibility of human operation.

2. Fault Tolerance: Design with various levels of redundancy and protection schemes to increase the fault tolerance of the overall vessel control system.

3. Safety and availability: Safety is always the first priority of shipboard operations, and availability ensures the optimized usage of limited resources onboard.

4. Lifecycle cost: The total investment required for maintaining and operating over the service period of a ship is considered an important benchmark.

The purpose of this section is to provide an overview of various latest developed and installed control platforms on both military and commercial vessels. Different characteristics and aspects of those platforms are analyzed and compared based on the criteria Illustrated. One thing worth noticing here is that most of the available description of industrial applications merely covers the general descriptions of the system structure, user interface, overall performance and advantages in practical shipboard operations. The respective information in more detail is typically not publically available. So this section will mainly sketch an overview of the current commercial and military shipboard control platforms. All the information collected in this section is available in the form of non-proprietary presentations, technical reports, data sheets and product leaflets of corresponding companies.

6.1 Vessel Information and control (VICO) system (marine automation solutions and service)2

2 All the documents and resources are available online at www.abb.com

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ABB has long been involved in marine automation with several product lines. Since 2011, ABB has put forward the concept of VICO as a common platform for complete vessel control solutions. VICO system can be seen as the accumulation of existing ABB marine electrical, propulsion, and automation and advisory solutions with advanced information technology34. VICO mainly offers two categories of solutions. For the shipboard automation solution series, VICO provides an enhanced integration of SMART marine operation environment based on system 800xA. For the intelligent advisory portfolio, EMMA advisory suite offers real-time generated solutions to decision support for operators in order to achieve optimal fuel and energy management onboard.

Figure 8: VICO Offerings

Platform 800xA is an integrated control environment for vessel process control, safety supervision and monitoring, emergency control, power generation and management5. The priority of 800xA is to provide an tight integration of automation process with distributed devices and applications on board. Via system 800xA, the operation and maintenance cost is expected to be minimized while uncompromised versatility and flexibility. The open architecture at the same time offers support for a wide range of other components like EMMA advisory suite. EMMA advisory suite67 is a vessel energy manager that helps operators to establish the best energy practice. It consists of modules such as: EMMA Onboard Tracker, EMMA Fleet Control, and EMMA Advanced Optimizer and through simple display settings let the operators monitor the current energy usage and automatically decide the expected optimal performance with the corresponding operation procedures [26]. Case study has been made with EMMA advisory Suite and it is believed that typical operational profile can have at least 1.5% savings potential and the dynamic trimming advice could save up to 3% propulsion energy consumption.

3 “VICO Marine Automation Presentation”4 “Diesel Generator monitoring system (DGMS): a reliable safeguard against blackout”.5 “Smart Marine Automation: Enhanced integration of marine systems based on system 800xA and IEC61850”6 Jukka Ignatius, Jan-Eric Rasanen, Kalevi Tervo, “EMMA ship energy manager: Know, Understand and Change”7 Jukka Ignatius, “EMMA Advisory Suite: Power-Efficiency-Savings”

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Integrated Automation System

6.2 Integrated platform management system by L3 Communication Company8

L-3 MAPPS is a solution based on the development of integrated platform management system (IPMS) and has been widely used on military fleets like Canadian Navy’s Halifax Patrol Frigate [27] and German Navy K130 Covettes [28] to provide the integrated monitoring and control of ship propulsion, electrical functions, auxiliaries and damage control machinery and systems.IPMS features distributed architecture real-time control system that utilizes multiple control modules and remote terminal units (RTU)9. RTUs are mainly used to process data acquisition for a specific region, and control console provides the Human Machine Interface (HMI) for operators. The distributed communication network is achieved via redundant fiber optic databus across the shipboard with maximum ship bandwidth and minimized data transferring latency. Open system structure allows the customization capability for a range of vessels systems that have specific requirements based on the mission given. It also allows the IPMS to be extended with other application modules and devices. One typical application component for IPMS is the Battle Damage Control System (BDCS) module which contains functions like real-time damage area plotting, Kill cards, and ship stability calculator.

6.3 SINAVY Automation- an integrated control system for naval ships from Siemens10

SINAVY is an integrated platform management system (IPMS) developed by Siemens for the highest system availability, management efficiency and operation reliability onboard for naval vessels11. Very similar to L3-MAPPS, SINAVY controls and monitors all the components onboard including diesel engines, gas turbines, exhaust systems and distribution networks and keeps operators always in the loop via a uniform and comprehensive human machine interface12. SINAVY is also an open system with possibilities of integration of third party components like battle damage control, cruise-range calculation, central battery, etc. The structure of SINAVY Automation can be represented as a distributed network including consoles with operator stations, data servers, local data acquisition and processing units and a redundant communication bus. In case of fleet damage in a specific area, the decentralized, modular and redundant system ensures the vital functions remain controllable and the parallel supplied processing data remains flowing within the distribution network without loss of data. SINAVY provides the maximum protection against potential damage and destructions during the operations.

8 More detailed information is available online at http://www.l-3mps.com/9 Datasheet: L-3 MAPPS, an integrated platform management system10 Available at Siemens.com11 “SINAVY – Solutions for the NAVY: More stability, more availability, more power”12 “SINAVY Automation: The integrated control system for naval ships”

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Figure 9 : SINAVY System Architecture

6.4 Engineering control system on DDG1000

The DDG 1000 Zumwalt class destroyer is the navy’s next generation multi-mission surface combatant, thus the control system is also the most advanced and intelligent for optimal operation during cruising and battle [29]. The efforts to produce the local machinery control and the corresponding development of software operating environment have been included in the design and construction contracts with Northrop Grumman Ship Systems and General Dynamic’s Bath Iron Works.The hierarchy of automation on DDG1000 can be represented as commands flowing from top level to lower levels within the hierarchy [30]. Command, Control and Intelligence (CCI) defines what a certain ship needs at a mission level via an Integrated Ship Plan (ISP). ISP then regulates the timeline and scheduled events based on the mission requirements and predefined specifications. The overall task is then divided into sets of tasks in different Ship Domain Controllers (SDC) and sent out to the corresponding component onboard. As the domain controllers have better knowledge within their own systems and subsystems, those sets of tasks are further analyzed to check states availability and then broken into lower levels of components that are more detailed and refined to initiate appropriate behaviors. The lowest level control mission can be decomposed into the I/O level unit control within the Engineering Control System (ECS) level. If the control command comes from the Human-Machine Interface, then this unit control is seen as operating in manual mode; but if the control command comes from a higher level domain controller, then it is seen as operating in automatic mode. Embedded controllers also exist under ECS that are designed as integrated subsystems of the device with the ability to perform fast operations.

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Figure 10 : DDG 1000 Control Hierarchy [30]

For the computational burden, DDG control system implements a Total Ship Computing Environment (TSCE) to process data and perform various types of calculations. The TSCE has three tiers: Core, Adaptation and Presentation. The Core tier consists of servers to hold most of DDG1000’s software operations. The Presentation tier provides raw data and processed data displayed on the consoles. The Adaptation tier is the common interface to work with different other applications running on the TSCE infrastructure. For the hardware implementation, the ECS distributes the monitoring and control across the shipboard via networked Distributed Control Units (DCU) and Remote Terminal Units (RTU). DCUs serve as the main processing units interfacing with the TSCE network to take control commands, translate the top level control commands into predefined sequences of hardware operations, execute the operations and monitor the propulsion, auxiliary, electrical and damage control system measurement to validate that the control objective is achieved. In the meantime, RTUs serve as the interface point to embedded controllers and a variety of analog and digital inputs and outputs.Integrated Power Control System (IPS) is an important feature supported by ECS. ECS is integrated with electrical propulsion motors and manages power and loads to support a variety of ship missions. The power management system can coordinate system sequences, perform starts and stops of power equipment, and manage other system activities automatically. The IPS computes power availability from generators and consumptions from load systems to decide the detailed supplement plan to feed loads in an optimal manner. Loads can be connected or disconnected based on their priority levels and available power supply. The IPS can also communicate with other ECS components to coordinate the power usage.

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Requirements for DDG 1000 suggest that after initial system alignment, the level of ECS automation should allow a single operator to coordinate the entire engineering plant during normal operations and to permit all ship-level missions to be performed without human intervention at the machinery level. However during special circumstances like battle damage or system fault, at least one other operator should be involved with control of key systems and the control capability of each ECS component needs to be transferred to manual mode. In this case, the human operator could monitor the running states of all the components and send remote directives to certain components. All the real-time data is available to operators at any time and, based on the specification, to certain applications as well.The DDG 1000 operating system will be the most advanced marine operating system the Navy has ever made. With the highly integrated control and automation system design, it should deliver a more robust, more automated and more flexible control strategy than existing machinery control systems.

6.5 Conclusion

The industry working for marine vessel automation and controls seems to be focusing on such issues as manning, energy cost, run time, fault tolerance, safety and reliability, and life cycle cost. Most of the existing technologies implemented in current marine automation and controls are SCADA oriented systems (i.e., derived from terrestrial electric utility control systems). ModBus has been slowly replaced by the more secure, reliable, fast IEC61850 bus technology. The most recent controls frameworks of ABB, Siemens, and L3 includes the IEC61850 protocol for information flow throughout the automation system. Traditional, proven rule-based systems still seem to dominate major industrial practice. However, the new visionary technologies including VICO of ABB, SINAVY of Siemens and IPMS by L3 communications incorporates open architecture. Implementation of machine learning and Model Predictive Control (MPC) has been cited in the ABB approach. Extendable distributed controls with remote terminal units have been reported for IPMS. Integrated controls has become a part of the new era in marine controls. Such integration provides the leverage to extend automation over the full system and for appropriate resource allocation. Overall performance management will be made robust by the inclusion of factors including situational awareness, fault tolerance, present and predicted future system states, and emergency control support. However, situational awareness has not been addressed to exploit all the relevant data available to make predictions for and about other functional modules. Linking fault management with performance management could play a vital role in managing vessels intended for combat. Industry control practice needs to advance further before reducing manning much further, for example by setting the goal to reduce the impact of human errors. This could add robustness and survivability to naval vessels during warfare.Distributed controls with proper system decomposition will certainly be one vital improvement in future navy vessels. This technology should sustain performance better during failure at a particular location and will keep the remaining system running. For commercial vessels energy efficiency will be the primary concern and the role of controls will be critical. Hybrid propulsion and ballast-free ships will add challenges to the automation algorithms. In order to accelerate the innovations in control and automation, such algorithms should be adaptive to the changes that

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will come to shipboard components over time. Industry practice needs to pick up the pace toward intelligent autonomic shipboard power systems.

7 CONCLUSION AND RECOMMENDATIONS

In recent years, research communities, and in particular the ESRDC, have made a remarkable leap in knowledge about shipboard power systems. Recent research work with focus on shipboard power system controls and computation strategies are discussed in this report. With the increased complexity and capability of an integrated ships power system comes the need for more effort towards control. Potential control frameworks and architectures should be highly automated with support for self healing capacity to increase the survivability of the ship’s power system. Faster simulation tools to support the tremendous power of current computing hardware should also be prioritized for testing and validating power management concepts at an earlier stage of ship design and analysis.

A ship’s power system controller is more than the sum of the parts. The recommendation drawn from this review of current research is that the ESRDC should revisit the issue of ship power system management, although it is recognized within the consortium that control of ship power systems involves other systems that in aggregate represent all critical functions and missions of the ship (e.g., thermal management). We propose to begin by base lining the Navy’s current capability by developing a notional baseline power manager simulation similar to the existing three notional power system baseline simulations (MVAC, HFAC, and MVDC). This fourth baseline simulation will be a useful tool for evaluating advanced power system control concepts in future research efforts.

8 ACKNOWLEDGEMENTS

This report contains significant technical contributions from Α. Arapostathis, D. Cartes, C. Edrington, H. Ginn, A. Ouroua, B. Ramachandran, E. Santi, S. Srivastava, and E. Zivi.

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