Abstract
Transformers play an important role in power systems. Dissolved gases analysis (DGA) has been widely used in the transformer fault diagnosis. A novel fault diagnosis method using support vector machine (SVM) with particle swarm optimization (PSO) algorithm is developed for transformer in this paper. To enhance the ability and performance of SVM, the particle swarm algorithm is used to optimize the parameters of SVM in this study. The proposed model is named PSO-SVM. Then, the optimized model was applied to identify and classify the faults. The diagnostic accuracy of the proposed PSO-SVM model reached 82%. Results demonstrate that the superior performance of the PSO-SVM, compared with the ordinary support vector machine classifier.
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This work was supported by the key technology project of the State Grid Corporation of China under Grant 522821200090.
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Zhou, X., Liu, Z., Shi, Z., Ma, L., Du, H., Han, D. (2023). Fault Diagnosis of the Power Transformer Based on PSO-SVM. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-1260-5_12
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DOI: https://doi.org/10.1007/978-981-99-1260-5_12
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