Enhancing Arc Fault Diagnosis Method Using Feature Selection Strategy Based on Feature Clustering and Maximal Information Coefficient

The accuracy of machine learning-based arc fault diagnosis methods relies on the quality of the selected features. First, an experimental system is constructed for collecting realistic arc fault current signals, and different multi-domain feature extraction methods are described. Then, the feature relationships are investigated using various correlation methods to reveal the negative impact of weakly relevant or redundant features on the machine learning model. Based on the combination of feature clustering (FC) and maximal information coefficient (MIC), this paper proposes a feature selection (FS) strategy. The strategy searches for cluster centers through FC and merging processes to obtain non-redundant features with the highest representational abilities. The elimination of weakly relevant clusters and the selection of the most relevant features within clusters are performed based on the MIC scores. Subsequently, a valid feature subset with the highest representativeness and relevance is selected to improve the accuracy of arc fault diagnosis. Finally, the proposed method is evaluated against several popular FS strategies using three typical classifiers on different arc fault feature datasets, and the results show that the strategy effectively improves the accuracy of fault diagnosis. In addition, the cross-validation and hardware tests verify the stability, interference immunity, and feasibility of the proposed method for practical applications.