A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems

Conference · Fri Oct 04 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/PESGM51994.2024.10688748· OSTI ID:2477191

Zhou, Yuqi; Dong, Yuqing; Yang, Rui

Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2477191
• Report Number(s): NREL/CP-5D00-92022; MainId:93800; UUID:912492fb-915a-4683-8a4e-a51bd7e0740c; MainAdminId:74215
• Country of Publication: United States
• Language: English

Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
explainable artificial intelligence
high-impedance fault
machine learning
piecewise approximation
support vector machine
voltage-current trajectory