An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances
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초록
In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple powerquality disturbances. To solve the problem of parameter selection, an improved particle swarm optimizationalgorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertiaweight expression, the particle swarm optimization algorithm can effectively conduct a global search at theoutset and effectively search locally later in a study, which improves the overall classification accuracy. Theexperimental results show that the improved particle swarm optimization method is more accurate than a gridsearch algorithm optimization and other improved particle swarm optimizations with regard to its classificationof multiple power quality disturbances. Furthermore, the number of support vectors is reduced.
Abstract
In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple powerquality disturbances. To solve the problem of parameter selection, an improved particle swarm optimizationalgorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertiaweight expression, the particle swarm optimization algorithm can effectively conduct a global search at theoutset and effectively search locally later in a study, which improves the overall classification accuracy. Theexperimental results show that the improved particle swarm optimization method is more accurate than a gridsearch algorithm optimization and other improved particle swarm optimizations with regard to its classificationof multiple power quality disturbances. Furthermore, the number of support vectors is reduced.
참고문헌 (21)
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