Edge–Cloud Collaboration Detection Approach for Small-Sample Imbalanced Faults in Power Lines
Wenxia Sima, Han Zhang, Ming Yang
Abstract
Fault detection in power lines is crucial to the reliable operation of power systems. Recent progress in the development of the artificial intelligence-based (AI-based) fault detection for power lines has been receiving an ever-growing attention. Existing AI-based fault detection models mostly rely on the assumption that the power line faults collected in a substation are adequate and balanced. However, obtaining massive and balanced line fault samples is difficult because of the complexity of practical environments and the inherent nature of faults. As a result, the performance of the models gradually deteriorates when the small-sample imbalanced degree of the power line fault set increases. This study proposes a novel edge–cloud collaboration detection method based on transfer and federated learning to address the above issue. Specifically, a fault detection model is established on the basis of a convolutional neural network and pretrained by introducing a new fault set with a sufficient number of labelled samples. The pretrained model is then deployed to each of the substations and fine-tuned in a federated learning manner. By performing fine-tuning in substations and global aggregation in a cloud platform, the proposed method updates the model on the basis of the small-sample imbalanced faults of power lines collected in substations. Case studies verify that after training by using the proposed edge-cloud collaboration detection method, the model can accurately detect faults in power lines even if the fault training set has a small number of samples and is imbalanced.