{"id":2999,"date":"2026-04-09T22:52:10","date_gmt":"2026-04-09T14:52:10","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2999"},"modified":"2026-06-08T17:29:59","modified_gmt":"2026-06-08T09:29:59","slug":"power-transformer-fault-diagnosis-based-on-hybrid-intelligent-algorithm","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=power-transformer-fault-diagnosis-based-on-hybrid-intelligent-algorithm","title":{"rendered":"Power Transformer fault Diagnosis based on Hybrid Intelligent Algorithm"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 1<\/a><\/p>\n<\/div>\n\n\n\n

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Yong Xu and Xiaojuan Lu<\/i><\/a><\/p>\n\n\n\n

School of Automation & Electrical Engineering of Lanzhou Jiaotong University Lanzhou, 730070, P.R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0June 28, 2022
Accepted:\u00a0March 4, 2023
Publication Date:\u00a0April 9, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Diagnosis results for GWO1-PNN<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202401_27(1).0002<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The gas content in the oil is used as the fault input characteristic for the power transformer. Still, the accuracy of the diagnosis results is not ideal, and such a model is unstable. This research proposes a hybrid intelligent fault diagnosis method based on the improved grey wolf algorithm and an optimized probabilistic neural network. Firstly, a strategy of three nonlinear control factors is introduced to fit the grey wolves\u2019 search process. The weighted distance was modified to update the position information of grey wolf elements to avoid the algorithm falling into the local optimum. Secondly, the performance of the improved grey wolf algorithm was tested through six commonly used functions. The results show that the improved grey wolf algorithm has high convergence accuracy and stability in both multimodal and unimodal functions. Finally, the improved grey wolf algorithm and the probabilistic neural network were combined to diagnose the oil-immersed power transformer through hybrid intelligent algorithms. As a result, the fault diagnosis model proved valid for transformer fault diagnosis.<\/p>\n\n\n\n

Keywords:\u00a0transformer; fault diagnosis; control factor; weighted distance; grey wolf algorithm; probabilistic neural network<\/em><\/p>\n\n\n\n

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  1. [1] R. Liepniece, S. Vitolina, and J. Marks, (2017) \u201cStudy of approaches to incipient fault detection in power transformer by using dissolved gas analysis” Energetika 63(2): DOI: 10.6001\/energetika.v63i2.3521.<\/li>\n
  2. [2] S. A. Wani, A. S. Rana, S. Sohail, O. Rahman, S. Parveen, and S. A. Khan, (2021) \u201cAdvances in DGA based condition monitoring of transformers: A review” Renewable and Sustainable Energy Reviews 149: 111347. DOI: 10.1016\/j.rser.2021.111347.<\/li>\n
  3. [3] R. Rogers, (1978) \u201cIEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis” IEEE transactions on electrical insulation (5): 349\u2013354. DOI: 10.1109\/TEI.1978.298141.<\/li>\n
  4. [4] \u201cIEEE guide for the interpretation of gases generated in oil-immersed Transformers(S)\u201d. In: DGA Guide Working Group. IEEE Std C57.104-2008. 2008.<\/li>\n
  5. [5] A. Mollmann and B. Pahlavanpour, (1999) \u201cNew guidelines for interpretation of dissolved gas analysis in oil-filled transformers” Electra 186: 31\u201351.<\/li>\n
  6. [6] M. Duval and A. DePabla, (2001) \u201cInterpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases” IEEE Electrical Insulation Magazine 17(2): 31\u201341. DOI: 10.1109\/57.917529.<\/li>\n
  7. [7] A. R. Abbasi and M. R. Mahmoudi, (2021) \u201cApplication of statistical control charts to discriminate transformer winding defects” Electric Power Systems Research 191: 106890. DOI: 10.1016\/j.epsr.2020.106890.<\/li>\n
  8. [8] A. R. Abbasi, M. R. Mahmoudi, and Z. Avazzadeh, (2018) \u201cDiagnosis and clustering of power transformer winding fault types by cross-correlation and clustering analysis of FRA results” IET Generation, Transmission & Distribution 12(19): 4301\u20134309. DOI: 10.1049\/iet-gtd.2018.5812.<\/li>\n
  9. [9] A. R. Abbasi, M. R. Mahmoudi, and M. M. Arefi, (2021) \u201cTransformer winding faults detection based on time series analysis” IEEE Transactions on Instrumentation and Measurement 70: 1\u201310. DOI: 10.1109\/TIM.2021.3076835.<\/li>\n
  10. [10] H. Zheng, R. Liao, S. Grzybowski, and L. Yang, (2011) \u201cFault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation” IET Electric Power Applications 5(9): 691\u2013696.<\/li>\n
  11. [11] Q. Su, C. Mi, L. Lai, and P. Austin, (2000) \u201cA fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer” IEEE Transactions on Power Systems 15(2): 593\u2013598. DOI: 10.1109\/59.867146.<\/li>\n
  12. [12] G. Jun and H. Junjia, (2010) \u201cApplication of quantum genetic ANNs in transformer dissolved gas-in-oil analysis” Proceedings of the CSEE 30(30): 121\u2013127.<\/li>\n
  13. [13] C.-H. Lin, C.-H. Wu, and P.-Z. Huang, (2009) \u201cGrey clustering analysis for incipient fault diagnosis in oilimmersed transformers” Expert Systems with Applications 36(2): 1371\u20131379. DOI: 10.1016\/j.eswa.2007.11.019.<\/li>\n
  14. [14] A. R. Abbasi, (2022) \u201cFault detection and diagnosis in power transformers: A comprehensive review and classification of publications and methods” Electric Power Systems Research 209: 107990.<\/li>\n
  15. [15] S. Mirjalili, S. M. Mirjalili, and A. Lewis, (2014) \u201cGrey wolf optimizer” Advances in engineering software 69: 46\u201361.<\/li>\n
  16. [16] L. K. Wang Yagang Zhang Tao, (2022) \u201cEdge Computing Task Scheduling Method Based on an Improved Grey Wolf Optimization Algorithm” Information and control 51(4): 489\u2013497, 512.<\/li>\n
  17. [17] X. Yu, W. Xu, and C. Li, (2021) \u201cOpposition-based learning grey wolf optimizer for global optimization” Knowledge-Based Systems 226: 107139. DOI: 10.1016\/j.knosys.2021.107139.<\/li>\n
  18. [18] D. H. F. \u201cTransformer fault diagnosis based on genetic algorithm optimi zation of BP neural network”. (phdthesis). Beijing Jiaotong University, China, 2008.<\/li>\n
  19. [19] Y. J. L. \u201cResearch on fault diagnosis method of oilimmersed power transformer based on correlation vector machine”. (phdthesis). North China Electric Power University, China., 2013.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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