As the penetration rate of renewable energy sources increases in new-type power systems,so too does the complexity of the grid structure,leading to more diverse and complex power quality disturbance(PQD).To accu-rately identify power quality,a method for PQD image recognition has been proposed,utilizing a two-dimensional time-frequency spectrograms and an improved YOLOv5.Initially,PQD data is projected onto a two-dimensional time-frequency spectrograms using the S-transform.This approach allows for detail-oriented representation of distur-bances in terms of time,frequency,and amplitude via imagery.Subsequently,a YOLOv5 training network is con-structed that integrates atrous spatial pyramid pooling(ASPP)structure and attention mechanisms.This design broadens the receptive field of the feature map,facilitating a comprehensive extraction of the disturbance image fea-tures,and enables PQD classification recognition through image detection methods.Finally,the accuracy and ro-bustness of the disturbance recognition are validated using simulation data.The results evidence that this method of-fers a high degree of recognition accuracy.Moreover,the integration of the image recognition method enhances the visual representation of the PQD recognition results.
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