Download - Predictive Reliability Study
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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
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Outline Objectives
Reliability modeling
Analytical Simulation
Monte Carlo Simulation
Commercial Tools
Challenges
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Objectives To improve the reliability of a specific area or
service territory to meet utilitys 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.
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Reliability modeling Develop a predictive reliability model of a
system.
The model is calibrated to represent currentsystem reliability.
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Reliability Modeling Reliability models are as good as power flow
models
Component reliability parameters:
Failure rates
Repair times
Switching times
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Analytical Simulation Input
System topology
Device locations
Component reliability parameters
Output
Momentary interruptions
Sustained interruptions
Outage duration
Reliability indices
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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
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Analytical Simulation Simulate each contingency
Determine the impact on each component
Weight the impact by its frequency of occurrence.
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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.
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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
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Monte Carlo Simulation
Results of a SAIDI Risk Analysis
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Commercial Tools CymeDist
SynerGEE
WindMil
PSS/Adept DRA
NEPLAN
Power Factory
FeederAll
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Challenges Aging Infrastructure?
Distribution automation?
Distribution energy resources?
Micro grid?
Storm?
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Summary Predictive reliability study can help utilities reduce
cost, improve reliability, and more effectively managerisk.
You dont 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.
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Thank You!