Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier

Abstract

As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods.

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

“…High voltage circuit breakers (HVCBs) are widely used in the power system and have complex mechanical operation structures. Their operation state is directly related to the power supply reliability of the power grid [ 1 ]. At present, the main diagnosis basis of high voltage circuit breaker fault diagnosis research includes vibration signal, sound signal, image signal, travel time of moving contact, and current signal of the opening (closing) coil [ 2 , 3 , 4 , 5 ].…”

A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

“…In Reference [22], a hybrid fault feature extraction method using VMD combined with multipoint kurtosis was proposed. In Reference [23], focusing on high voltage circuit breakers, a fault diagnosis method using VMD and multi-layer classifier was proposed to improve the accuracy of fault diagnosis. In Reference [24], a new detection method for atrial fibrillation using electrocardiogram signal was proposed, sample entropy and center frequency were extracted from IMFs by VMD, which can effectively distinguish the normal sinus rhythm and atrial fibrillation.…”

A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing

“…High-voltage circuit breakers (HVCBs) are the most important control and primary protection equipment in electric power systems [1,2]. The operation states of HVCBs are directly related to the stability and reliability of the power system.…”