Abstract: This paper proposes a fault diagnosis method for power electronic rectifiers based on PCA-neural network. First, the feature vector of the fault signal is extracted using Principal Component Analysis (PCA). Then, the neural network is used for training and testing. Experimental results on thyristor open-circuit fault diagnosis in a three-phase controllable rectifier circuit show that this method simplifies the neural network structure, improves the network training speed, and achieves good diagnostic results. Keywords: Fault diagnosis; Neural network; Principal component analysis Abstract: A fault diagnosis method for power electronics rectifier based on PCA-Neural Network was proposed. First, extract the feature vector from the fault signal with the principal component analysis (PCA) method, and then use neural network training and testing. Experimental results of thyristor open circuit fault diagnosis in power electronics rectifier showed that this method can simplify the structure of the neural network, improve the training speed of the network, and have obtained very good diagnostic effect. Key words: Fault diagnosis; Neural Network; Principle Component Analysis 1 Introduction With the rapid development of power electronics technology, power electronic rectifiers that realize energy conversion have become increasingly widely used due to their high efficiency, flexible and convenient control, and ease of implementation [1]. At the same time, the fault problems of power electronic rectifiers are becoming more and more prominent. Therefore, the application of automatic fault diagnosis technology in power electronic rectifiers has practical and economic significance, and it is particularly important to carry out relevant theoretical and methodological research. This paper proposes a fault diagnosis method for power electronic rectifiers based on a combination of principal component analysis (PCA-NN) and neural networks. This method combines the advantages of both theories, first performing PCA-NN-based principal component transformation on different types of fault signals to extract fault feature vectors, which are then fed into a neural network for classification and decision-making. For details, please click: Fault Diagnosis of Power Electronic Rectifiers Based on PCA-NN