Detection Method of Power Quality Disturbances Based on Double Resolution S Transform and Learning Vector Quantization Neural Network
Li Jianmin1, Lin Haijun1, Liang Chengbin2, Teng Zhaosheng2, Cheng Da3
1. College of Engineering and Design Hunan Normal University Changsha 410081 China; 2. College of Electrical and Information Engineering Hunan University Changsha 410082 China; 3. China Electric Power Research Institute Beijing 100192 China
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.
李建闽, 林海军, 梁成斌, 滕召胜, 成达. 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[J]. 电工技术学报, 2019, 34(16): 3453-3463.
Li Jianmin, Lin Haijun, Liang Chengbin, Teng Zhaosheng, Cheng Da. Detection Method of Power Quality Disturbances Based on Double Resolution S Transform and Learning Vector Quantization Neural Network. Transactions of China Electrotechnical Society, 2019, 34(16): 3453-3463.
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2019, Vol. 34 
