# Recognition of Output-Side Series Arc Fault in Frequency Converter-Controlled Three-Phase Motor Circuit

## TL;DR

This paper introduces a new method to detect series arc faults in three-phase motor circuits controlled by frequency converters, using current signal analysis and machine learning.

## Contribution

A novel approach for identifying output-side series arc faults using empirical wavelet transform and optimized support vector machine.

## Key findings

- Output-side SAFs were effectively recognized using autoregressive model parameters and energy ratios of modal components.
- The proposed method achieved runtime shorter than 1.4 ms and outperformed similar methods in tests.
- The method is suitable for industrial three-phase SAF detection devices.

## Abstract

Timely identification of series arc faults (SAFs) is of vital importance for preventing electrical fires. How to identify SAFs at the output side of a frequency converter (i.e., output-side SAF) is still not clear. A new approach of identifying output-side SAFs by analyzing the output current signals from frequency converters was proposed. First, output-side SAF experiments were performed with harmonic power supplies. Second, the output current signals were decomposed into eight modal components by empirical wavelet transform and an autoregressive model was established. The autoregressive model parameters and the energy ratios of the first three modal components were adopted as the fault features. Finally, an optimized support vector machine was designed and employed to identify SAFs. Comparison tests with similar methods were performed and performance tests under different noise levels and operation conditions were conducted. The test results indicated that the proposed scheme can effectively recognize output-side SAFs. Its runtime is shorter than 1.4 ms. This method provides a reference for the development of industrial three-phase SAF detection devices.