Feature Selection-Based Low-Voltage AC Arc Fault Diagnosis Method

The performance of the machine learning-based arc fault diagnosis method is significantly influenced by the effectiveness of features. This article proposes the use of the feature selection (FS) method to optimize the feature quality and classification result for arc fault diagnosis. An experimental system is constructed to generate arc fault current signals under various conditions. The limitations of the predetermined threshold-based methods in time, frequency, and time–frequency domains are outlined, and the dependence/redundancy issues present in the feature dataset are analyzed. To mitigate the impact of these issues, a light gradient boosting machine (LightGBM)-based FS method, which establishes the feature importance evaluation criterion for the selected optimal feature subset based on permutation importance (PI), is proposed in this article. The fitness proportionate sharing-based feature clustering (FPS-FC) method searches for potential feature clusters and selects a feature subset with low redundancy to accommodate unlabeled data. Finally, both the proposed FS methods are compared with six popular supervised/unsupervised methods using two classifiers across seven datasets. The results validate the effectiveness of the LightGBM-based and FPS-FC-based FS methods in enhancing the performance of arc fault diagnosis. In addition, a designed case on Raspberry Pi verifies the feasibility of the proposed methods in real-world applications.