Illias Hazlee Azil, Chai Xin Rui, Abu Bakar Ab Halim, Mokhlis Hazlie
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D UM, University of Malaya, Jalan Pantai Baharu, 59990 Kuala Lumpur, Malaysia.
PLoS One. 2015 Jun 23;10(6):e0129363. doi: 10.1371/journal.pone.0129363. eCollection 2015.
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.
准确预测变压器油中的早期故障非常重要,这样才能正确地进行变压器油的维护,降低维护成本并将误差降至最低。溶解气体分析(DGA)已被广泛用于预测电力变压器中的早期故障。然而,有时现有的DGA方法对变压器油早期故障的预测并不准确,因为每种方法都只适用于特定条件。许多先前的研究报告了使用智能方法来预测变压器故障。然而,人们认为先前提出的方法的准确性仍可提高。由于人工神经网络(ANN)和粒子群优化(PSO)技术在先前报道的工作中从未被使用过,因此本文提出将ANN与各种PSO技术相结合来预测变压器早期故障。PSO的优点是简单且易于实现。通过与实际故障诊断结果、现有诊断方法以及单独使用ANN的结果进行比较,验证了各种PSO技术与ANN相结合的有效性。还将所提出方法的结果与先前报道的工作进行了比较,以展示所提出方法的改进之处。结果发现,与现有诊断方法和先前报道的工作相比,所提出的ANN - 进化PSO方法对变压器故障类型的正确识别率最高。