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

arXiv: https://arxiv.org/abs/2311.15168 · HTML: https://mlsanity.com/paper/2311.15168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [A Feature Selection Method for High Impedance Fault Detection](https://mlsanity.com/paper/1808.04454/) (2018)
- [High Impedance Fault Detection and Isolation in Power Distribution Networks using Support Vector Machines](https://mlsanity.com/paper/1909.10583/) (2019)
- [Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders](https://mlsanity.com/paper/2106.13276/) (2021)

## Built on by (most cited first, 1 of 1)

- [Transmission Line Outage Probability Prediction Under Extreme Events Using Peter-Clark Bayesian Structural Learning](https://mlsanity.com/paper/2411.11980/) (2024)

## Abstract (arXiv)

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.