Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network - Li, Jinsong; Liu, Hao; Wang, Dengke; Bi, Tianshu (Frontiers in Energy Research, 2021)
Realizing the accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the safety of users. Traditional power quality disturbance signal classification will be affected by many factors, such as noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, the time-frequency matrix with perturbation characteristics is obtained by using S-transform to extract the perturbation signal. As an extension of wavelet transform and Fourier transform, S-transform can avoid the defects of window function selection and fixed window width. At the same time, the feature quantity extracted by S-transform also has better noise resistance. Then, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce the data dimension and obtain the main features of the disturbance signal, and the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.