Research on high voltage circuit breaker multi-sensor signal feature selection method based on GA-Kmeans algorithm

Abstract Currently, in the research on fault information of high-voltage circuit breakers (HVCBs) based on multi-sensor data, issues such as shallow feature assessment and unclear feature selection rules exist, leading to a decrease in fault identification accuracy due to redundant characteristics. To address this, this paper proposes a novel feature selection method based on GA-Kmeans. This method encodes the original feature space into binary format and employs the clustering accuracy of the Kmeans model as the fitness function to obtain a high-quality feature subset that maximally represents and distinguishes faults. By combining different feature selection methods with fault diagnosis models, six typical application scenarios are constructed. Results indicate that compared to traditional methods such as Relief-F, KPCA, GA-SVM for feature selection, and SVM for fault diagnosis, the proposed GA-Kmeans method reduces the dimensionality of the original feature space and employs the Kmeans clustering algorithm as the diagnostic model, achieving a final diagnostic accuracy of 95.29%. This method outperforms others, with a 37.24% higher diagnostic accuracy than SVM under the original feature space, and a decrease of 47.90% in standard deviation. This validates the necessity of feature selection and the superiority of the proposed method, providing a reliable and stable diagnostic basis for subsequent mechanical fault diagnosis of HVCBs.