Lissajous Locus-based Efficient Identification Framework for Power Quality Disturbances Based on Edge-Cloud Collaboration

The on-site real-time diagnosis mode of edge computing can effectively solve the strong information delay problem existing in the traditional cloud-based identification mode of power quality disturbances. Faced with the problems of poor universality of identification algorithms, the large size of the training model, and accuracy defects caused by sending it to the edge in the current identification method for power quality disturbance based on edge-cloud collaboration, this paper proposes a Lissajous locus-based identification framework for power quality disturbances based on edge-cloud collaboration. Firstly, leveraging the latest advancements in the image processing field, a visual conversion method based on the double-phase Lissajous locus is proposed to convert disturbance signals into locus images with unique shapes. Secondly, to enhance the feature capture ability while reducing the computational complexity, a lightweight cyclic squeeze convolutional neural network is developed to perform primary identification tasks. By sharing weight parameters of edge-cloud, the proposed framework can achieve the real-time disturbance identification. To continuously optimize the model performance, a deeper network is designed at the cloud to assist in model updating. Finally, the effectiveness of the proposed framework is verified based on the IEEE standard simulation dataset and the real-time measured disturbance dataset from substations. The results show that this framework achieves excellent disturbance identification generalization performance while realizing the simultaneous lightweight of cloud-edge identification models, and avoids performance losses caused by the distribution of the training model through edge-cloud weight interaction. © 2024 Automation of Electric Power Systems Press. All rights reserved.