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Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

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  • Hazlee Azil Illias
  • Xin Rui Chai
  • Ab Halim Abu Bakar
  • Hazlie Mokhlis

Abstract

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Suggested Citation

  • Hazlee Azil Illias & Xin Rui Chai & Ab Halim Abu Bakar & Hazlie Mokhlis, 2015. "Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
  • Handle: RePEc:plo:pone00:0129363
    DOI: 10.1371/journal.pone.0129363
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    Citations

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    Cited by:

    1. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    2. Hazlee Azil Illias & Wee Zhao Liang, 2018. "Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
    3. Oussama Laayati & Hicham El Hadraoui & Adila El Magharaoui & Nabil El-Bazi & Mostafa Bouzi & Ahmed Chebak & Josep M. Guerrero, 2022. "An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems," Energies, MDPI, vol. 15(19), pages 1-28, October.
    4. Xiaoqin Zhang & Hongbin Zhu & Bo Li & Ruihan Wu & Jun Jiang, 2022. "Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function," Energies, MDPI, vol. 15(12), pages 1-14, June.
    5. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    6. Lingjie Sun & Yingyi Liu & Boyang Zhang & Yuwei Shang & Haiwen Yuan & Zhao Ma, 2016. "An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers," Energies, MDPI, vol. 9(9), pages 1-22, August.

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