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

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.

Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection.

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.