Abstract
Abstract The accurate identification of power quality disturbances (PQDs) is crucial for maintaining the stability and reliability of power systems. In this paper, an automatic identification method for PQDs based on CNN-BiGRU-Attention Network (CAB-Net) is proposed. To handle the temporal characteristics and complexity of composite PQDs signals, the proposed model leverages the bidirectional information transmission of the BiGRU model to efficiently extract temporal features. Furthermore, to boost recognition accuracy, an attention mechanism is incorporated, the key information can be intelligently locked in the feature extraction stage, and the information can be efficiently screened and utilized, thereby enhancing its ability to identify power quality disturbance signals. To confirm the validity of the method, we use the simulation data for experimental proof. Experimental results demonstrate that compared to traditional power quality disturbance identification methods, the method has excellent recognition ability and stronger robustness.
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