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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhuang Yang & Qu Zhou & Xiaodong Wu & Zhongyong Zhao & Chao Tang & Weigen Chen, 2019. "Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN," Energies, MDPI, vol. 12(7), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1379-:d:221448
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    References listed on IDEAS

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    1. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

    1. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    2. Ioannis F. Gonos & Issouf Fofana, 2020. "Special Issue “Selected Papers from the 2018 IEEE International Conference on High Voltage Engineering (ICHVE 2018)”," Energies, MDPI, vol. 13(18), pages 1-5, September.
    3. Mingbang Zhu & Shanshan Liu & Ziqing Xia & Guangxing Wang & Yueming Hu & Zhenhua Liu, 2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN," Agriculture, MDPI, vol. 10(8), pages 1-16, August.

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