Most of the current fault diagnosis based on vibration analysis method has fixed feature vectors and is prone to diagnostic errors. To identify the transformer structural faults more effectively, a mechanical structure fault diagnosis method for power transformers based on integrated feature extraction and SABO-PNN is proposed. Firstly, double feature extraction is performed on the original vibration signals based on wavelet packet transform and optimized variational modal decomposition based on the Subtraction-Average-Based Optimizer (SABO). Then, weight coefficients are assigned to the vibration feature vectors based on the fuzzy analytical hierarchy process, and the combined eigenvalues are calculated from the feature vectors and fuzzy weights. The integrated eigenvalues are used as input vectors to improve the probabilistic neural network using the SABO algorithm and train the vibration signals for diagnosis. Based on digital twin technology, a vibration signal monitoring platform is constructed, which can display vibration signals and diagnostic analysis results in real time. Based on the experimental prototype of 400 kVA transformer, the vibration signals from multiple measurement points of the tank under different fault conditions were collected and diagnostic analyses were performed. The test results show that the method can accurately diagnose the mechanical structure faults of the transformer.

As the “blood vessels” and “nerves” of the power grid, cables are important carriers for transmitting electric energy, and their failures will affect the safe and stable operation of the power grid. In this paper, a digital twin model of thermal characteristics is constructed for 10 kV YJV×400 cluster cable and its joint model. Based on the multi-physical field coupling analysis method, the fluid field, temperature field and current-carrying capacity of the cluster cable are analyzed. The temperature rise and airflow distribution characteristics of the joint and its external insulation when the defect of joint false connection occurs are analyzed. Based on the fuzzy analytic hierarchy process (FAHP), weights are assigned to the locations of multiple measurement points of the cable external insulation, and the temperature distribution of each measurement point is combined with the digital twin technology to calculate the hot spot temperature of the cable, so as to assess the defects of the joints of the cables. The digital twin platform is used for real-time monitoring of cable status and assessment of defects. The temperature rise experiments of cables under different carrying capacity and joint defects show that this method can accurately and efficiently calculate hot spot temperatures and assess defects of joints in cluster cables.