New Research on Identification and Classification of Grid Power Quality Disturbances

Ning Xia Yang; Mao Fa Gong; Xiao Fei Wang; Hui Ting Ge; Yu Qing Lin; Bing Qian Liu; Feng Qiu

doi:10.4028/www.scientific.net/AMR.986-987.1431

Paper Titles

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New Research on Identification and Classification of Grid Power Quality Disturbances
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 986-987New Research on Identification and Classification...

New Research on Identification and Classification of Grid Power Quality Disturbances

Abstract:

To improve accuracy and speed of recognising and classifying grid power quality disturbances, this paper presents a new method which combines complex wavelet transform and particle swarm optimization (PSO) neural network to identify and classify the disturbance . This method extract both amplitude-frequency and phase frequency information of the interference signal to make up for the lack of traditional wavelet transform which only extract the amplitude-frequency information. And on this basis, using particle swarm optimization, we seek the optimal solution of neural network weights and thresholds for the identification and classification of power quality. The MATLAB simulation result has verified the accuracy and rapidity of this method compared with the traditional method .

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Periodical:

Advanced Materials Research (Volumes 986-987)

Pages:

1431-1434

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.986-987.1431

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Online since:

July 2014

Authors:

Ning Xia Yang*, Mao Fa Gong, Xiao Fei Wang, Hui Ting Ge, Yu Qing Lin, Bing Qian Liu, Feng Qiu

Keywords:

Complex Wavele Ttransform, Neural Network (NN), Power Quality, PSO

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References

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