Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN

Jingmin Fan, Huidong Shao, Yunfei Cao, Lutao Feng, Jianpei Chen, Anbo Meng and Hao Yin ()
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Jingmin Fan: School of Automation, Guangdong University of Technology, Guangzhou 510012, China
Huidong Shao: Guangdong Tianlian Electric Power Design Co., Ltd., Guangzhou 510700, China
Yunfei Cao: School of Automation, Guangdong University of Technology, Guangzhou 510012, China
Lutao Feng: School of Automation, Guangdong University of Technology, Guangzhou 510012, China
Jianpei Chen: School of Automation, Guangdong University of Technology, Guangzhou 510012, China
Anbo Meng: School of Automation, Guangdong University of Technology, Guangzhou 510012, China
Hao Yin: School of Automation, Guangdong University of Technology, Guangzhou 510012, China

Energies, 2022, vol. 15, issue 22, 1-14

Abstract: Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting.

Keywords: online DGA monitor; ensemble learning; CSO-NN; fault diagnosis; faults prediction (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: 2022
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