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Fault Diagnosis of Transformer Based on Quantum-Behaved Particle Swarm Optimization-Based Least Squares Support Vector Machines

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4 Author(s)
Zhi-biao Shi ; Sch. of Energy Resources & Mech. Eng., Northeast Dianli Univ., Jilin, China ; Yang Li ; Yun-feng Song ; Tao Yu

In order to overcome the deficiencies of artificial neural networks (ANN), such as low convergence rate, local optimal solution, over-fitting and difficult determination of structure, a proposed QPSO-LS-SVMs method is applied to fault diagnosis of power transformer. It takes five characteristic gases dissolved in transformer oil as its inputs and seven transformer states as its outputs, constructs a fault diagnosis model for power transformer based on least squares support vector machines (LSSVMs) and uses QPSO to determine parameters of LS-SVMs. The experimental results indicate that the recognition rate of QPSO-LS-SVMs is 18.8,14.3 and 6.0 percents higher than that of IEC three-ratio method, BPNN and PSO-LS-SVMs, respectively, and that the training speed of QPSO-LS-SVMs is 4.02 times faster than that of PSO-LS-SVMs. So, the correctness and effectiveness of our proposed method are proved, and QPSO-LSSVMs is a proper method for fault diagnosis of power transformer.

Published in:

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Date of Conference:

19-20 Dec. 2009