Power Quality Disturbance Classification Strategy Based on Fast S-Transform and an Improved CNN-LSTM Hybrid Model

With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convolutional neural network–long short-term memory (CNN-LSTM) model to achieve accurate classification and identification of various power quality disturbances. Firstly, the FST is employed to process the power quality disturbance signals, enabling efficient analysis and feature extraction while effectively preserving the time–frequency characteristics of the signals and significantly reducing the computational burden. Secondly, to address the limitations of traditional CNN models in power quality disturbance classification, this paper introduces an improved CNN-LSTM hybrid classification model that integrates mechanism fusion. This model improves the classification performance and generalization ability for power quality disturbances by incorporating an enhanced sparrow search algorithm and learning mechanisms. Finally, the proposed strategy is experimentally validated using a large dataset of power quality disturbances. After analysis and comparison, the method proposed in this paper maintains an identification accuracy of over 97% even in strong noise environments when subjected to a single type of disturbance. Under complex conditions involving mixed disturbances of multiple types, the identification accuracy remains above 95%. Compared to existing methods, the proposed method achieves an improvement in identification accuracy by up to 3.2%. Additionally, its identification accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results demonstrate that the proposed strategy can accurately classify and identify various power quality disturbances, outperforming traditional methods in terms of classification accuracy and robustness.