基于双分辨率S变换和学习向量量化神经网络的电能质量扰动检测方法

doi:10.19595/j.cnki.1000-6753.tces.180900

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Abstract As the nonlinear loads and impact loads in power grid increase, the power quality problems are becoming more and more serious. Accurate and fast detection of power quality disturbance signals has great significance for finding the cause of power quality problems and improving the power quality. Therefore, an algorithm for recognizing power quality disturbance signals is proposed in this paper based on double resolution S-transform and learning vector quantization (LVQ) neural network. Firstly, double resolution S-transform is used to extract the feature vectors of disturbance signals accurately and quickly. Then, the obtained feature vectors of disturbance signals are normalized and the trained LVQ neural network is used to classify and identify the disturbance signals. The simulation and actual test results show that the proposed algorithm based on double resolution S-transform and LVQ neural network has fast training speed, high classification accuracy and is suitable for embedded implementation.

Key words： Power quality disturbance classification S transform learning vector quantization (LVQ) neural network time-frequency analysis

Received: 28 May 2018 Published: 02 September 2019

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TM935

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	Li Jianmin
	Lin Haijun
	Liang Chengbin
	Teng Zhaosheng
	Cheng Da

Cite this article:

Li Jianmin,Lin Haijun,Liang Chengbin等. Detection Method of Power Quality Disturbances Based on Double Resolution S Transform and Learning Vector Quantization Neural Network[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3453-3463.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180900 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I16/3453

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