Predictive Reliability Study

Le Xu, Ph.D., PEQuanta Technology

Predictive Reliability Task ForceWorking Group on Distribution Reliability

2013 IEEE PES Joint Technical Committee MeetingJanuary 15, 2013 • Memphis, TN, USA

Outline Objectives

Reliability modeling

Analytical Simulation

Monte Carlo Simulation

Commercial Tools

Challenges

Objectives To improve the reliability of a specific area or

service territory to meet utility’s goals and tocomply with regulatory requirements.

To improve the reliability of an area in the mostcost-effective way

Identify projects that give more “bang for the buck”.

Priority projects within certain budget.

To take advantage of existing utility tools, toincrease efficiency, quality, and productivity.

Reliability modeling Develop a predictive reliability model of a

system.

The model is calibrated to represent currentsystem reliability.

Reliability Modeling Reliability models are as good as power flow

models

Component reliability parameters:

Failure rates

Repair times

Switching times

Analytical Simulation Input

System topology

Device locations

Component reliability parameters

Output

Momentary interruptions

Sustained interruptions

Outage duration

Reliability indices

Analytical Simulation All Faults

Fault occurs

Inrush current causes voltage sag

Reclosing attempts to clear fault

Sustained Faults Only

Protection device trips and locks out

Automated switching occurs

Manual switching occurs

Fault is repaired

Analytical Simulation Simulate each contingency

Determine the impact on each component

Weight the impact by its frequency of occurrence.

Decision Making Analysis of historical outage data to identify the

main causes of outages and the most efficientalternatives for improving reliability.

Evaluate the impact of a comprehensive set ofprojects and select the most cost-effectivealternatives for improving the reliability of system.

Estimate the expected reliability of the system dueto the progressive implementation of the optimalmix of projects.

Risk Analytical simulation for expected value analysis

Reliability varies naturally each year

Faults may occur more or less often, in different locations

Protection and switching may vary in effectiveness

Repair times may be shorter or longer

Storms may be more or less prevalent

Changes in data error rate

Monte Carlo simulation for risk analysis

Monte Carlo Simulation

Results of a SAIDI Risk Analysis

Commercial Tools CymeDist

SynerGEE

WindMil

PSS/Adept DRA

NEPLAN

Power Factory

FeederAll

Challenges Aging Infrastructure?

Distribution automation?

Distribution energy resources?

Micro grid?

Storm?

Summary Predictive reliability study can help utilities reduce

cost, improve reliability, and more effectively managerisk.

You don’t need good data to get started, but better datayields more confidence in results.

Predictive reliability study methodologies need toadapt to the new trending in distribution systems.

Thank You!