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.

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.

In the condition of the 3 mm gap, experiments for 360 Hz intermediate-frequency vacuum arc are carried out in interrupters with the diameters being 41 mm and with the contact materials being CuCr50 and Cu-W-WC alloy respectively. The results indicate that the contacts material is closely related to the breaking capacity of the vacuum interrupters and characteristics of an intermediate-frequency vacuum arc. For contacts with the same diameter, the breaking capacity of CuCr50 is better than that of Cu-W-WC. When the current fails to be interrupted, the arcs overflow the gap and present irregular performances in the first half wave. Consequently a voltage spike appears. More macroscopic metal droplets can be seen in the arc column between CuCr50 contacts because of the lower melting point. It is observed that the droplet emission is much more severe during arc reignition than that in the first half wave. It is much more conspicuous that the high frequency arc voltage noises appear in Cu-W-WC contacts when the vacuum arcs reignite, for higher temperature and stronger electronic emission ability of Cu-W-WC contacts.

DC microgrid systems that integrate energy distribution, energy storage, and load units can be viewed as examples of reliable and efficient power systems. However, the isolated operation of DC microgrids, in the case of a power-grid failure, is a key factor limiting their development. In this paper, we analyze the six typical operation modes of an off-grid DC microgrid based on a photovoltaic energy storage system (PV-ESS), as well as the operational characteristics of the different units that comprise the microgrid, from the perspective of power balance. We also analyze the key distributed control techniques for mode transformation, based on the demands of the different modes of operation. Possible reasons for the failure of PV systems under the control of a voltage stabilizer are also explored, according to the characteristics of the PV output. Based on this information, we propose a novel control scheme for the seamless transition of the PV generation units between the maximum PV power tracking and steady voltage control processes, to avoid power and voltage oscillations. Adaptive drooping and stabilization control of the state of charge of the energy storage units are also considered, for the protection of the ESS and for reducing the possibilities of overcharging and/or over-discharging. Finally, various operation conditions are simulated using MATLAB/Simulink, to validate the performance of the proposed control strategy.

Intermediate frequency (IF) electric power supply system has been widely applied in airplanes. Reignition after interruption of 360-850 Hz vacuum arc in axial magnetic field (AMF) is studied in this paper. According to the experimental results, the current interruption ability decreases from 22.4 kA at 360 Hz to 14.0 kA at 850 Hz. With the increase of frequency, the rate of change of current, di/dt, at current zero (CZ) reaches the breaking threshold at a smaller current value. Based on the least squares solution of the Mayr model, the time of decay of arc energy is prolonged when interruption fails, which is disadvantageous to the breaking process. On the other hand, the eddy effect is enhanced by higher frequency, which increases the residual magnetic induction intensity and the hysteresis phase of AMF after CZ. As a result, the diffusion of plasma is blocked. In the experiments, we find that the position of arc reignition always appears at the margin of contacts. It is shown by calculation that the electric field at the marginal area is twice as strong as that inside the contact. The current density is high, where the electric field is strong, leading to the higher probability of field emission. Arc images are analyzed by frame differential method. The sputtering velocity of macroscopic particles is estimated to be 10-20 m/s. The dissipation time of the macroscopic particles is longer than the arc time of IF vacuum arc, and the presence of macroscopic particles leads to the arc reignition. The findings of this paper would be beneficial to the parameter design of IF vacuum circuit breakers, such as the magnetic flux density of AMF, the chamfering of contact margin, and contact materials.