With the wide use of nonlinear loads, power grids’ power quality problems are becoming more prominent, threatening the stability of power systems seriously. To address these problems, this paper proposes a power quality disturbances (PQD)classification method based on the Kaiser window S-transform (KST) and deep neural networks. Firstly, the Kaiser window S-transform is applied to the processing of PQD signals. Secondly, the Kaiser window control function is modified to adjust the shape of the window, and the window function parameters are automatically optimized according to the maximum energy density to achieve a better time-frequency resolution. Then, deep neural networks are utilized to perform deep feature extraction on the feature vectors of the Kaiser window S-transform. Lastly, a Softmax layer is applied to the extracted features. The results show that the method has excellent classification accuracy and noise resistance: the 99.1% accuracy achieved under 20 dB noise.
Xiong et al. (Mon,) studied this question.