Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Xiaowen Wu, Ling Li, Nianguang Zhou, Ling Lu, Sheng Hu, Hao Cao and Zhiqiang He
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Xiaowen Wu: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
Ling Li: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Nianguang Zhou: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
Ling Lu: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
Sheng Hu: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
Hao Cao: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China
Zhiqiang He: State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China

Energies, 2018, vol. 11, issue 7, 1-20

Abstract: DC bias is a great threat to the safe operation of power transformers. This paper deals with a new vibration-based technique to diagnose DC bias in power transformers. With this technique, the DC bias status of power transformers can be automatically recognized. The vibration variation process of a 500 kV autotransformer is tested under the influence of DC bias in the monopole trail operation stage of a ±800 kV HVDC transmission system. Comparison of transformer vibration under normal and DC-biased conditions is conducted. Three features are proposed and are validated by sensitivity analysis. The principal component analysis method is employed for feature de-correlation and dimensionality reduction. The least square support vector machine algorithm is used and verified successful in DC bias recognition. A remote on-line monitoring device based on the proposed algorithm is designed and applied in field DC bias diagnosis of power transformers. The suggested diagnostic algorithm and monitoring device could be useful in targeted DC bias control and improving the safe operation level of power transformers.

Keywords: power transformer; DC bias; feature extraction; pattern recognition; vibration (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 (2)

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