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.

高阻抗故障的准确快速识别对于配电系统的可靠运行至关重要。本文提出了一种数据驱动的方法，通过优化问题近似高阻抗故障电压电流轨迹的分段函数特征，并利用支持向量机方法在不同位置高效地识别高阻抗故障。对 IEEE 123 节点测试馈线的数值研究证明了该方法在实时高阻抗故障识别方面的有效性和准确性。