A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems
This addresses a critical reliability issue for power grid operators by improving fault localization, though it appears incremental as it builds on existing data-driven and SVM techniques.
The paper tackles the problem of localizing high-impedance faults in distribution systems, which are hard to detect due to low fault currents, by proposing a data-driven method that uses piecewise function approximation and support vector machines, achieving validity and accuracy in numerical tests on the IEEE 123-node feeder.
Accurate and quick identification of high-impedance faults 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.