In a smart grid, many transformers are equipped for both power transmission and conversion. Because a stable operation of transformers is essential to maintain grid security, studying the fault diagnosis method of transformers can improve both fault detection and fault prevention. In this paper, a data-driven method, which uses a combination of Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) to enable a better fault diagnosis of transformers, is proposed and investigated. PCA is used to reduce the dimension of transformer fault state data, and an improved PSO algorithm is used to obtain the optimal parameters for the SVM model. SVM, which is optimized using PSO, is used for the transformer-fault diagnosis. The diagnostic-results of the actual transformers confirm that the new method is effective. We also verified the importance of data richness with respect to the accuracy of the transformer-fault diagnosis.
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