In recent years, machine learning techniques have been applied to test the fault type in high-voltage circuit breakers (HVCBs). Most related research involves in improving the classification method for higher precision. Nevertheless, as an important part of the diagnosis, the feature information description of the vibration signal of an HVCB has been neglected; in particular, its diversity and significance are rarely considered in many real-world fault-diagnosis applications. Therefore, in this paper, a hybrid feature transformation is proposed to optimize the diagnosis performance for HVCB faults. First, we introduce a nonlinear feature mapping in the wavelet package time- frequency energy rate feature space based on random forest binary coding to extend the feature width. Then, a stacked autoencoder neural network is used for compressing the feature depth. Finally, five typical classifiers are applied in the hybrid feature space based on the experimental dataset. The superiority of the proposed feature optimal approach is verified by comparing the results in the three abovementioned feature spaces.

The output–voltage–power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective.

As a kind of widely used switchgear in power system, the reliability of gas insulated switchgear (GIS) is very important for the safe operation of power system. However, there is a lack of research on intelligent detection technology of mechanical state of GIS at present. A new method is urgently needed to improve the operability, effectiveness and accuracy of fault detection in GIS. Aiming at the abnormal vibration signals generated by GIS faults, this paper presents a fault diagnosis method (GA-DBSCAN) consisting of a feature selection method based on genetic algorithm(GA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and a fault diagnosis method based on DBSCAN. Firstly, this paper analyzes the incentive force of GIS, and discusses the characteristic frequency of response signal combining with the non-linear characteristics of a GIS system. Secondly, GA and DBSCAN are used to screen features for dimension reduction and get the optimized feature space, and DBSCAN-based classification is used to classify faults. Finally, optimized feature space is verified to be superior to the original feature space by typical classification method; the superiority and reliability of DBSCAN-based classification method under optimized feature space is verified by comparing to other classification method. The proposed GA-DBSCAN approach can substantially increase the performance of the fault diagnosis method, which indicates that the method promotes development of intelligent detection technology of mechanical state in GIS.

Mechanical faults of high-voltage circuit breakers (HVCBs) always occur during long-term operation; thus, the recognition of such fault types has become a key issue. Now, most related research involves improvements to the classification method for higher precision based on a single sensor, ignoring the multi-sensor information fusion of the vibration signal for the fault diagnosis of HVCB. Classical evidence theory is a common approach for solving multi-sensor fusion. However, it fails upon encountering conflicting evidence. Therefore, this study proposes an improved multi-sensor evidence combined rule to optimize the evidence gathered from different sensors, especially for the highly conflicting and invalid evidence. A numerical simulation example indicates that the proposed method achieves a better performance. On this basis, the proposed method is applied to diagnose HVCB faults with multi-sensor. Further, the superiority of the multi-sensor fusion is proved via a comparison with the diagnostic results of five typical classifiers under a single sensor; specifically, by comparing it with traditional Dempster’s combination rules, it is verified that the proposed method is better than other multi-sensor fusion methods, which can be used in the field of HVCB fault diagnosis.

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.