Mechanical fault diagnosis of a circuit breaker can help improve the reliability of power systems. Therefore, a new method based on multiscale entropy (MSE) and the support vector machine (SVM) is proposed to diagnose the fault in high voltage circuit breakers. First, Variational Mode Decomposition (VMD) is used to process the high voltage circuit breaker’s vibration signals, and the reconstructed signal can eliminate the effect of noise. Second, the multiscale entropy of the reconstructed signal is calculated and selected as a feature vector. Finally, based on the feature vector, the fault identification and classification are realized by SVM. The feature vector constructed by multiscale entropy is compared with other feature vectors to illustrate the superiority of the proposed method. Through experimentation on a 35 kV SF₆ circuit breaker, the feasibility and applicability of the proposed method for fault diagnosis are verified.

The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature via the envelope demodulation method. However, the FSK method has some limitations due to its susceptibility to noise and random knocks. To overcome this shortage, a new method is proposed in this paper. Firstly, we use the binary wavelet packet transform (BWPT) instead of the finite impulse response (FIR) filter bank as the frequency band segmentation method. Following this, the Shannon entropy of each frequency band is calculated. The appropriate center frequency and bandwidth are chosen for filtering by using the inverse of the Shannon entropy as the index. Finally, the envelope spectrum of the filtered signal is analyzed and the faulty feature information is obtained from the envelope spectrum. Through simulation and experimental verification, we found that Shannon entropy is—to some extent—better than kurtosis as a frequency-selective index, and that the Shannon entropy of the binary wavelet packet transform method is more accurate for fault feature extraction.

The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature by the envelope demodulation method. However, in practical applications, the fault source may be located in different resonant frequency bands; plus in noise interference, the weak side of the compound fault is not easy to be identified by the FSK. In order to improve the accuracy of fast spectral kurtosis analysis method, a modified method based on maximum correlation kurtosis deconvolution (MCKD) is proposed. According to the possible fault characteristic frequencies, the period of MCKD is calculated, and the appropriate filter length is selected to filter the original compound fault signal. In this way, the compound fault located in different resonance bands is separated. Then, the signal after MCKD filtering is analyzed by FSK. Through the simulation and experimental analysis, the MCKD can separate the compound fault information in different frequency band and eliminate the noise interference; the FSK can accurately identify the resonance frequency and identify the weak fault characteristics of compound fault.

The periodic impacts are regarded as the typical characteristics of local defect of wind turbine bearing. For this reason, it is significant to extract the periodic impacts from original measured vibration signal with background noise interferences during the defect identification process. For the purpose of effectively solving this issue, an innovative diagnostic frame based on Lkurtogram guided adaptive empirical wavelet transform (LGAEWT) and purified instantaneous energy operation (PIEO) is put forward. Within this diagnostic frame, the L-kurtosis indicator guided wavelet equivalent filter band mergence is carried out to confirm the optimal filter boundary, then the resonance frequency band containing rich characteristic information can be extracted and the characteristic sensitive component is able to be separated from original vibration signal. In additional, a novel signal processing strategy called purified instantaneous energy operation (PIEO) is presented to inhibit the background interferences and enhance the periodic impact signatures in the sensitive components, and the purified instantaneous energy spectrum is adopted to replace the traditional envelope spectrum for characteristic frequency spectral lines identification. The feasibility of proposed diagnostic frame has been demonstrated by the experimental signals and the actual engineering case, and the results manifest that it is suitable for bearing fault enhance detection under low signal-to-noise ratio (SNR) environment. Furthermore, the comparison results with the widely used Kurtogram, Autogram and DWT methods indicate this innovative diagnostic strategy has more prominent advantages on background interference suppression and weak characteristic intensification.

To address the severe physical wear of actual circuit breakers, limited logic verification under complex operating conditions, and maintenance scheduling conflicts during operational testing in power systems, this paper presents a portable auxiliary verification device for relay protection based on LoRa spread-spectrum technology. The device adopts a distributed modular architecture consisting of a master unit and multiple slave units, with STM32 MCU and LoRa communication serving as the hardware core. Circuit breaker simulator logic circuits are employed to replace the physical operation of actual high-voltage circuit breakers during testing. A dedicated software platform provides system management, real-time online monitoring, and historical data query and export functions. Experimental results indicate that the proposed device can accurately reproduce the state information of multiple circuit breakers, enabling complex logic verification, including automatic bus transfer (ABT) and busbar protection, without de-energizing primary equipment. Consequently, routine substation maintenance efficiency is significantly improved while operational risks are effectively reduced.