A Federated Learning-Based Fault Detection Algorithm for Power Terminals

Abstract

Power terminal is an important part of the power grid, and fault detection of power terminals is essential for the safety of the power grid. Existing fault detection of power terminals is usually based on artificial intelligent or deep learning models in the cloud or edge servers to achieve high accuracy and low latency. However, these methods cannot protect the privacy of the terminals and update the detection model incrementally. A terminal-edge-server collaborative fault detection model based on federated learning is proposed in this study to improve the accuracy of fault detection, reduce the data transmission and protect the privacy of the terminals. The fault detection model is initially trained in the server using historical data and updated using the parameters of local models from edge servers according to different updating strategies, then the parameters will be sent to each edge server and further to all terminals. Each edge server updates the local model via the compressed system log from terminals in its coverage region, and each terminal uses the model to detect fault according to the system behavior in the log. Experiment results show that this fault detection algorithm has high accuracy and low latency, and the accuracy increases with more model updating.