An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances


Liquan Zhao, Yan Long, Journal of Information Processing Systems Vol. 15, No. 1, pp. 116-126, Feb. 2019  

10.3745/JIPS.04.0102
Keywords: Classification Accuracy, Classification of Power Quality Disturbance, Particle Swarm Optimization, Support Vector Machine
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Abstract

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.


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Cite this article
[APA Style]
Liquan Zhao and Yan Long (2019). An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances. Journal of Information Processing Systems, 15(1), 116-126. DOI: 10.3745/JIPS.04.0102.

[IEEE Style]
L. Zhao and Y. Long, "An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances," Journal of Information Processing Systems, vol. 15, no. 1, pp. 116-126, 2019. DOI: 10.3745/JIPS.04.0102.

[ACM Style]
Liquan Zhao and Yan Long. 2019. An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances. Journal of Information Processing Systems, 15, 1, (2019), 116-126. DOI: 10.3745/JIPS.04.0102.