Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Zhuang Yang, Qu Zhou, Xiaodong Wu, Zhongyong Zhao, Chao Tang and Weigen Chen
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Zhuang Yang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Qu Zhou: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Xiaodong Wu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Zhongyong Zhao: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Chao Tang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Weigen Chen: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China

Energies, 2019, vol. 12, issue 7, 1-12

Abstract: The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10 −5 , with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

Keywords: transformer oil; multi frequency ultrasonic; water content; back propagation neural network; genetic algorithm (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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