  The detection and classification of power quality disturbances (PQDs) carries
significant importance for power systems. In response to this imperative,
numerous intelligent diagnostic methods have been developed. However, existing
identification methods usually concentrate on single-type signals or on complex
signals with two types, rendering them susceptible to noisy labels and
environmental effects. This study proposes a novel method for the
classification of PQDs, termed ST-GSResNet, which utilizes the S-Transform and
an improved residual neural network (ResNet) with a channel attention
mechanism. The ST-GSResNet approach initially uses the S-Transform to transform
a time-series signal into a 2D time-frequency image for feature enhancement.
Then, an improved ResNet model is introduced, which employs grouped convolution
instead of the traditional convolution operation. This improvement aims to
facilitate learning with a block-diagonal structured sparsity on the channel
dimension, the highly-correlated filters are learned in a more structured way
in the networks with filter groups. By reducing the number of parameters in the
network in this significant manner, the model becomes less prone to
overfitting. Furthermore, the SE module concentrates on primary components,
which enhances the model's robustness in recognition and immunity to noise.
Experimental results demonstrate that, compared to existing deep learning
models, our approach has advantages in computational efficiency and
classification accuracy.
