Application of Power Data Mining Analysis in Fault Diagnosis and Preventive Maintenance

In order to ensure the stable transmission of electric power, it is an effective way to diagnose and maintain the operating status of electric power equipment from the operation data of electric power equipment. This paper uses a stacked sparse autoencoder to design a training model to realize the data function operation function in the fault detection model. After collecting and classifying the power system data, the line current is standardized and transformed. Then, the processed data is input into the stacked sparse autoencoder, and the model is trained layer by layer. On this basis, the long-term memory network model is introduced to establish a fault diagnosis model. To solve the double-sample situation of power data, the maximum mean difference method must be used. A preventive maintenance strategy is constructed based on failure prediction and remaining life to optimize the implementation path. Evaluate the model’s value in terms of its performance, reliability, and economic benefits of preventive O&M methods. However, judging from the fuzzy fault degree, the electrical components with a high probability of failure are T1, T3, L3, L9, and B1, and the fuzzy fault degrees are 0.3154, 0.2789, 0.0648, 0.2657, and 0.0678, respectively—fusion of multidimensional evidence. The components most likely to fail are T1, T3, and L9 . From the perspective of operation and maintenance costs, when the MMC maintenance time of Dublin Fang Electric Farm is 17 times, the lowest operation and maintenance cost is 22.8615 million yuan. © 2024 Dan Jiang, Yue He, Yuzhen Wang, Xi Chen and Tao Li, published by Sciendo.