Power Quality Disturbance Identification Based on Optimized Generalized S⁃transform and Hybrid Input Neural Network
LIU Haitao1,2, WU Xiang1, ZHANG Shuqing1, LIU Dapeng3, LIU Yong3, MU Yong3
1.Institute of Electrical Engineering， Yanshan University， Qinhuangdao， Hebei 066004， China
2.College of Electrical Engineering， North China University of Science and Technology， Tangshan， Hebei 063210， China
3.Tangshan Power Supply Company， North Hebei Electric Power Co. Ltd， Tangshan， Hebei 063000， China
Abstract：Based on the correlation between the time maximum value curve and frequency maximum value curve of generalized S-transform time-frequency matrix and the amplitude and spectrum envelope of power quality signal， a method of optimizing generalized S-transform is proposed to select the parameters of Gaussian window function adaptively， which fully preserves the amplitude and frequency characteristics of power quality disturbance. Then， a hybrid input neural network framework is proposed to automatically extract the features of the original time series and the time-frequency matrix obtained from the optimized generalized S-transform. Finally， the features extracted from the two inputs are combined and the disturbance types are identified by the fully connected layer. Through the training and verification of the simulation data set containing 26 types of power quality disturbance， the results show that the disturbance recognition accuracy of the proposed method is 99.77%， and then the two kinds of actual grid disturbance signals are tested， and the disturbance recognition accuracy can still reach 92.5%， which is higher than the traditional single input neural network.
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2025, Vol. 46 
