Multi-vibration information fusion for detection of HVCB faults using CART and D-S evidence theory

Abstract

The condition of a high-voltage circuit breaker (HVCB) may have a major effect on a power system. In the practical application of artificial intelligence, many advanced technologies have been applied to the assessment of the state of health of a HVCB or the identification of a fault. To date, most related research related to the improvement of a feature extraction process or a classification method intended to attain a higher level of precision have been based on a single sensor. However, any method that relies on data from a single sensor cannot exceed a given level of precision. Most studies have neglected to consider whether the information provided by a single vibration signal is sufficient and effective. Therefore, this study proposes a multi-vibration Information joint diagnosis method to improve the diagnosis of HVCB faults. The procedure has two key steps: 1) the basic probability assigns an acquisition using a classification and regression tree (CART); and 2) a combination rule design based on the Gini index in the CART. By comparing the results of eight typical classifiers and three traditional fusion methods in a case of HVCB system, the validity and superiority of the proposed method has been verified.

Keywords: CART; D–S evidence theory; Fault diagnosis; Gini index; HVCB; Multi-vibration information fusion.