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Energies 2019, 12(7), 1379; https://doi.org/10.3390/en12071379

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

1
College of Engineering and Technology, Southwest University, Chongqing 400715, China
2
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Athens, Greece, 10–13 September 2018.
Received: 11 March 2019 / Revised: 31 March 2019 / Accepted: 4 April 2019 / Published: 10 April 2019
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PDF [2675 KB, uploaded 10 April 2019]
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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. View Full-Text
Keywords: transformer oil; multi frequency ultrasonic; water content; back propagation neural network; genetic algorithm transformer oil; multi frequency ultrasonic; water content; back propagation neural network; genetic algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, Z.; Zhou, Q.; Wu, X.; Zhao, Z.; Tang, C.; Chen, W. Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN. Energies 2019, 12, 1379.

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