Research on Model Construction and Algorithm of Intelligent Analysis and Fault Diagnosis of Power Equipment

In this paper, a hierarchical intelligent analysis and fault diagnosis model of power equipment is proposed, which includes data acquisition layer, feature extraction layer, fault diagnosis layer and result output layer. Key parameters such as current and voltage are collected in real time by sensors, and fault-related features are extracted by signal processing technology and machine learning algorithm. In the feature extraction layer, wavelet transform (WT) and principal component analysis (PCA) are used to reduce the dimension of features, so as to fully capture the effective information in fault signals. In the fault diagnosis layer, the deep learning (DL) model is adopted, and a large number of historical fault data are trained to realize high-precision fault identification. Aiming at the shortcomings of existing algorithms, this paper proposes a hybrid fault diagnosis algorithm combining DL and physical model. The algorithm improves the diagnosis accuracy and real-time performance by combining the feature learning ability of DL model and the prior knowledge of physical model. Specifically, a dual-stream network structure is designed. One stream processes the original data, and the other stream processes the data converted by the physical model. Finally, the features of the two streams are weighted and fused at the high level of the network for classification and diagnosis. The experimental results show that the hybrid fault diagnosis algorithm proposed in this paper is superior to other algorithms in accuracy, precision, recall and F1 score, which shows its effectiveness and superiority in dealing with complex fault diagnosis problems. In the experiment with 14,000 training data, 4,000 verification data and 2,000 test data, the accuracy of the algorithm is 0.957, the precision is 0.943, the recall rate is 0.962 and the F1 score is 0.952.