Power Transformer Operating State Prediction Method Based on an LSTM Network

Hui Song, Jiejie Dai, Lingen Luo, Gehao Sheng and Xiuchen Jiang
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Hui Song: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Jiejie Dai: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Lingen Luo: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Gehao Sheng: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xiuchen Jiang: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2018, vol. 11, issue 4, 1-15

Abstract: The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

Keywords: power transformer; state prediction; data-driven method; long short-term memory network; state panoramic information (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 complete reference list from CitEc
Citations: View citations in EconPapers (5)

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