A novel dual-attention optimization model for points classification of power quality disturbances

The rapid development of the power grid makes power quality disturbances (PQDs) more complex. Accurately classifying PQDs and measuring the duration of each disturbance element are both crucial for studying the causes of PQDs and subsequent countermeasures. To satisfy the requirements, a novel dual-attention optimization model (DAOM) is presented in this paper for points classification. First, the local feature attention mechanism (LFAM) based on Hilbert transform (HT) is proposed in the input layer to enhance the local features of PQDs. Subsequently, on the basis of convolutional neural network (CNN), a channel attention mechanism (CAM) based on squeeze-and-excitation network (SENet) is introduced to each convolutional layer to achieve the purpose of automatically learning the importance of each channel feature. Finally, each sampling point is classified in the form of multiclass-multioutput. The proposed model is built through the Keras framework, and a synthetic database containing 49 types of PQDs is established to test the model. The proposal achieves an average classification accuracy of 98.95% in a 30 dB white noise environment, which is more precise and robust than other deep learning-based models. At the same time, the proposed model consistently performs best when evaluated through imbalanced data classification metrics and nonparametric test results. Unlike traditional methods, the proposal can accurately identify the occurrence and end time of each element in complex PQDs. Moreover, through a hardware experiment based on the AC source, the proposed model achieves an average accuracy of 98.30%, which is ahead of other comparison models as well.

Automated summary

This paper proposes a novel dual-attention optimization model (DAOM) for the classification of power quality disturbances (PQDs). The model utilizes local feature attention mechanism (LFAM) and channel attention mechanism (CAM) to classify the PQDs and sampling points. The proposed model achieves an average classification accuracy of 98.95% in a 30 dB white noise environment, outperforming other deep learning-based models.