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Fault Diagnosis of Power Transformers using Kernel based Extreme Learning Machine with Particle Swarm Optimization |
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PP: 1003-1010 |
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Author(s) |
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Liwei Zhang,
Jinsha Yuan,
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Abstract |
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| To improve the fault diagnosis accuracy for power transformers, this paper presents a kernel based extreme learning machine
(KELM) with particle swarm optimization (PSO). The parameters of KELM are optimized by using PSO, and then the optimized
KELM is implemented for fault classification of power transformers. To verify its effectiveness, the proposed method was tested on
nine benchmark classification data sets compared with KELM optimized by Grid algorithm. Fault diagnosis of power transformers
based on KELM with PSO were compared with the other two ELMs, back-propagation neural network (BPNN) and support vector
machines (SVM) on dissolved gas analysis (DGA) samples. Experimental results show that the proposed method is more stable, could
achieve better generalization performance, and runs at much faster learning speed. |
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