Optimization of Voltage Sag Monitoring Points Considering Fault Uncertainty

This paper addresses the issue of unreasonable voltage sag monitoring point configuration caused by the neglect of fault uncertainty in existing observable area (monitor reach area, MRA) methods. An optimization approach is proposed that incorporates fault uncertainty into the configuration of voltage sag monitoring points. Firstly, a typical fault set is generated using the Monte Carlo method, from which the fault uncertainty MRA matrix and node SAFRI indicator weights are calculated. Next, by constraining both the traditional MRA matrix and the fault uncertainty MRA matrix, while prioritizing node indicator weights, the method minimizes the number of monitoring points and maximizes the weighted priority for voltage sag monitoring point optimization. Finally, the effectiveness of the proposed method is validated through an example using the IEEE 39-bus system. Experimental results demonstrate that the proposed method effectively addresses the issue of monitoring blind spots due to insufficient consideration of fault uncertainty in existing configurations.

Authors

Institutions

Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/eeice65049.2025.11033875
Primary Topic
Power System Reliability and Maintenance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Voltage Sag Monitoring Points Considering Fault Uncertainty

Maoye Wang, Yixin Wang, Pengyu Wu
Power System Reliability and Maintenance
article

Optimization of Voltage Sag Monitoring Points Considering Fault Uncertainty

Maoye Wang, Yixin Wang, Pengyu Wu
article en

Abstract

This paper addresses the issue of unreasonable voltage sag monitoring point configuration caused by the neglect of fault uncertainty in existing observable area (monitor reach area, MRA) methods. An optimization approach is proposed that incorporates fault uncertainty into the configuration of voltage sag monitoring points. Firstly, a typical fault set is generated using the Monte Carlo method, from which the fault uncertainty MRA matrix and node SAFRI indicator weights are calculated. Next, by constraining both the traditional MRA matrix and the fault uncertainty MRA matrix, while prioritizing node indicator weights, the method minimizes the number of monitoring points and maximizes the weighted priority for voltage sag monitoring point optimization. Finally, the effectiveness of the proposed method is validated through an example using the IEEE 39-bus system. Experimental results demonstrate that the proposed method effectively addresses the issue of monitoring blind spots due to insufficient consideration of fault uncertainty in existing configurations.

Shanghai Electric (China) (CN), Shanghai Dianji University (CN)
Climate action
Openalex Percentile: Top 84%
Power System Reliability and Maintenance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.