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Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Author

Listed:
  • Fabio Henrique Pereira

    (Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo 01504-000, Brazil
    Industrial Engineering Graduate Program, Universidade Nove de Julho, São Paulo 01504-000, Brazil
    Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Francisco Elânio Bezerra

    (Industrial Engineering Graduate Program, Universidade Nove de Julho, São Paulo 01504-000, Brazil)

  • Shigueru Junior

    (Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Josemir Santos

    (Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Ivan Chabu

    (Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Gilberto Francisco Martha de Souza

    (Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

  • Fábio Micerino

    (EDP Energias do Brasil, São Paulo 4547006, Brazil)

  • Silvio Ikuyo Nabeta

    (Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil)

Abstract

Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

Suggested Citation

  • Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1691-:d:155004
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    References listed on IDEAS

    as
    1. Jun Lin & Gehao Sheng & Yingjie Yan & Jiejie Dai & Xiuchen Jiang, 2018. "Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model," Energies, MDPI, vol. 11(1), pages 1-13, January.
    2. Xiaobo Wang & Chao Tang & Bo Huang & Jian Hao & George Chen, 2018. "Review of Research Progress on the Electrical Properties and Modification of Mineral Insulating Oils Used in Power Transformers," Energies, MDPI, vol. 11(3), pages 1-31, February.
    3. Xiao Wang & Jun Xu & Yunfei Zhao, 2018. "Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries," Energies, MDPI, vol. 11(5), pages 1-13, May.
    4. Chenmeng Xiang & Quan Zhou & Jian Li & Qingdan Huang & Haoyong Song & Zhaotao Zhang, 2016. "Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults," Energies, MDPI, vol. 9(5), pages 1-22, April.
    5. Lei Peng & Qiang Fu & Yaohong Zhao & Yihua Qian & Tiansheng Chen & Shengping Fan, 2018. "A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil," Energies, MDPI, vol. 11(4), pages 1-9, March.
    6. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    7. Weigen Chen & Xi Chen & Shangyi Peng & Jian Li, 2012. "Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation," Energies, MDPI, vol. 5(4), pages 1-17, April.
    8. Hongkun Li & Rui Yang & Chaoge Wang & Changbo He, 2018. "Investigation on Planetary Bearing Weak Fault Diagnosis Based on a Fault Model and Improved Wavelet Ridge," Energies, MDPI, vol. 11(5), pages 1-23, May.
    9. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
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    Cited by:

    1. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2020. "An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil," Energies, MDPI, vol. 13(7), pages 1-28, April.
    2. Roland Bolboacă & Piroska Haller, 2023. "Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data," Mathematics, MDPI, vol. 11(6), pages 1-35, March.
    3. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    4. Xiaojun Tang & Wenjing Wang & Xuliang Zhang & Erzhen Wang & Xuanjiannan Li, 2018. "On-Line Analysis of Oil-Dissolved Gas in Power Transformers Using Fourier Transform Infrared Spectrometry," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Bing Zeng & Jiang Guo & Fangqing Zhang & Wenqiang Zhu & Zhihuai Xiao & Sixu Huang & Peng Fan, 2020. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition," Energies, MDPI, vol. 13(2), pages 1-20, January.

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