real time event detection and dynamic model identification
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© Copyr i gh t 2014 O SIs o f t , LLC .
Presented by
Real time event
detection and dynamic
model identification
using PMU data
Raymond A. de Callafon, UCSD
Charles H. Wells, OSIsoft LLC
© Copyr i gh t 2014 O SIs o f t , LLC .
R.A. de Callafon
• Prof. in Mechanical and Aerospace Engineering
at the University of California, San Diego
• Research/background in dynamic systems & control
• Expertise in signal processing and parameter estimation
• Some applications:
– motion and adaptive control for servo systems
– state and parameter estimation in mechanical/electrical systems
– dynamic modeling of mechanical/electrical systems
2
© Copyr i gh t 2014 O SIs o f t , LLC .
C. Wells
• Resident visiting scholar at UCSD since 2012 as an
employee of OSIsoft, LLC
• Installed PMUs at OSIsoft headquarters in 2001 and
directed the development of the IEEE 1344 interface
• Design of the IEEE C37.118 software interface and Fast
Fourier Transform interface that performs moving window
FFTs on phasor data
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© Copyr i gh t 2014 O SIs o f t , LLC .
Outline
Using PI-SDK program to detect events and quantify the
dynamics of an electricity grid (in real-time)…
• UCSD Microgrid and PMUs
• Our contributions to microgrid analysis
• Illustration of event detection and dynamic analysis
• Summary
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© Copyr i gh t 2014 O SIs o f t , LLC .
The UCSD microgrid
• Daily population of 45000
• 2 times energy density of commercial
• 12 million sq. ft. of buildings, $200M/yr building growth
• Self generate 92% of annual demand
– 30 MW natural gas Cogen plant
– 2.8 MW of Fuel Cells installed
– 3 MW of Solar PV installed
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© Copyr i gh t 2014 O SIs o f t , LLC .
Keeping track of the UCSD microgrid
• Data from Phasor
Measurement
Units (PMUs)
• 60Hz sampling
• Data stored in
OSIsoft PI
server(s)
• Data available
for UCSD
research
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© Copyr i gh t 2014 O SIs o f t , LLC .
PMU data is growing…
– Measurements reported at standardized rates (typically 60 Hz), minimum of 14 signals per PMU.
– 1000*14*60=840K/s
– Time synchronizationis essential.
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© Copyr i gh t 2014 O SIs o f t , LLC .
Why (micro)grid monitoring & analysis?
Improved Reliability
• Self-sustaining islanding to reduce cascading system failure
• Overall system less vulnerable to massive (natural) events
• Resolve variability of renewable energy on a local level
Improved Efficiency and Reduced Carbon Footprint
• Implementation of CHP with renewables on a localized level
• Reduce carbon footprint by maximizing efficiency of energy production and consumption on a local level
• Encourages third party investment in the local grid
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© Copyr i gh t 2014 O SIs o f t , LLC .
Our contributions to microgrid analysis
Answers to the questions:
• How do we detect
individual events?
• How can we quantify
these events dynamically?
• What do these events tell
us about our the dynamics
of our (micro)grid?
9
© Copyr i gh t 2014 O SIs o f t , LLC .
Real-time event detection
• Detection of Events via Filtered Rate of Change
• Approach:– Auto Regressive Moving
Average (ARMA) filter
– Definition of FRoC signal for Event Detection
• Detection and classificationof 14 events over 9 hours
• Direct link to event frames
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© Copyr i gh t 2014 O SIs o f t , LLC .
Real-time event detection
• Detection of Events via Filtered Rate of Change
• Approach:– Auto Regressive Moving
Average (ARMA) filter
– Definition of FRoC signal for Event Detection
• Detection and classificationof 14 events over 9 hours
• Direct link to event frames
11
© Copyr i gh t 2014 O SIs o f t , LLC .
Real-time event detection
• It works much better than
ROCOF signal defined
in the IEEE standard.
• Less false alarms for
event detection.
• Smaller threshold values
12
© Copyr i gh t 2014 O SIs o f t , LLC .
Classification of events
13
)()(
)()()1(
tCxtF
tBdtAxtx
detect beginning of event
ring down model
© Copyr i gh t 2014 O SIs o f t , LLC .
Classification of events
• Assume observed event in frequency F(t) is due to a deterministic system
where (unknown) input d(t) can be `impulse’ or `step’ or `known shape’
• Store a finite number of data points of F(t) in a special data matrix H
• Inspect rank of (null projection on) H: determines # modes
• Compute matrices A, B and C via Realization Algorithm.
• Extension of Ho-Kalman, Kung algorithm. Miller, de Callafon (2010)
• Applicable to multiple time-synchronized measurements! (multiple PMUs)
14
)()(
)()()1(
kCxkF
kBdkAxkx
© Copyr i gh t 2014 O SIs o f t , LLC .
Classification of events
• PI server receiving
multiple time-synchronized
PMU data
• Classification of one
MO Ring Down model
capturing grid dynamics
Clear advantage of centralized
data storage/processing
15
© Copyr i gh t 2014 O SIs o f t , LLC .
Real-time detection and classification of events
Main Features:
• Automatic detection of disturbance/transient event
• Automatic estimation of Frequency, Damping and Dynamic Model.
Challenges:
• Distributed computation for centralized dynamics and control of grid dynamics.
• Data management and visualization of results to end-user.
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© Copyr i gh t 2014 O SIs o f t , LLC .
PI System tools used in this research
• PI Server 2012 (PMU data at 30 and 60 Hz)
• ProcessBook (ad hoc queries of the data)
• DataLink (extensive use for extracting data and
importing to MatLab)
• PI-AF (ease of finding data of interest)
– Coresight for viewing AF objects
• Interfaces:
– C37.118, OPC, Bacnet, Modbus
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© Copyr i gh t 2014 O SIs o f t , LLC .
• Automatic event detection
based on real-time data
streams
• Classification with models
• Models can be used to
simulate and/or control
Solution Results and Benefits
Summary
Business Challenge
• Wealth of real-time PMU
data at high sampling rates
• Automatic analysis of
multiple time-synchronized
data streams
• Get early warnings of events
• Real-time Filtered Rate of
Change (FRoC) signal
• Software for automatic
event classification in a
dynamic model
PMU data provides a wealth of information on the dynamics
of a (micro)grid…
Using a PI datalink server to collect PMU data
allows detection of events and quantify the
dynamics of an electricity grid in real-time…
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© Copyr i gh t 2014 O SIs o f t , LLC . 19
Raymond de Callafon
callafon@ucsd.edu
Professor in MAE, UCSD
Charles Wells
cwells@osisoft.com
Visiting scholar at UCSD
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