A Novel Integrated Method to Diagnose Faults in Power Transformers

Jing Wu, Kun Li, Jing Sun and Li Xie
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Jing Wu: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Kun Li: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Jing Sun: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Li Xie: China Electric Power Research Institute, Beijing 100192, China

Energies, 2018, vol. 11, issue 11, 1-8

Abstract: 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.

Keywords: smart grid; transformer-fault diagnosis; principal component analysis; particle swarm optimization; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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