A Power Quality Monitoring Framework Based on HWST Time-Frequency Analysis and DNNs
With the rapid development of power grids and the increased use of electrical equipment,monitoring power quality disturbances has become critical for reliable and stable grid operation.A deep neural network(DNN)model based on stacked autoencoders(SAE)is proposed to extract time-frequency features of both single and combined power quality disturbances.First,the hyperparameters are tuned using a random search optimization technique,and the hyperbolic windowed Stockwell transform(HWST)is employed to analyze the time-frequency characteristics of the power quality signals.Then,the HWST time-frequency matrix is input into a 3-layer SAE network to automatically learn 50-dimensional deep features.Finally,the extracted deep features are fed into various machine learning classifiers for identification.Experimental results show that the XGBoost classifier achieves a recognition accuracy of 99.86% for 18 types of single and combined power quality events.The framework also demonstrates robustness in noisy environments and under frequency variations,and has been successfully applied to real power grid data.
power quality monitoringhyperbolic window stockwell transformdeep neural networkstacked autoencoderclassification
万方数据
维普
