Power Quality Disturbances Identification Method Based on Novel Hybrid Kernel Function


Liquan Zhao, Meijiao Gai, Journal of Information Processing Systems Vol. 15, No. 2, pp. 422-432, Apr. 2019  

https://doi.org/10.3745/JIPS.04.0112
Keywords: Hybrid Kernel Function, Power Quality Disturbance, Support Vector Machine, Wavelet transform
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Abstract

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.


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Cite this article
[APA Style]
Zhao, L. & Gai, M. (2019). Power Quality Disturbances Identification Method Based on Novel Hybrid Kernel Function. Journal of Information Processing Systems, 15(2), 422-432. DOI: 10.3745/JIPS.04.0112.

[IEEE Style]
L. Zhao and M. Gai, "Power Quality Disturbances Identification Method Based on Novel Hybrid Kernel Function," Journal of Information Processing Systems, vol. 15, no. 2, pp. 422-432, 2019. DOI: 10.3745/JIPS.04.0112.

[ACM Style]
Liquan Zhao and Meijiao Gai. 2019. Power Quality Disturbances Identification Method Based on Novel Hybrid Kernel Function. Journal of Information Processing Systems, 15, 2, (2019), 422-432. DOI: 10.3745/JIPS.04.0112.