Using federated learning technology to improve smart grid fault diagnosis efficiency and privacy protection

Power grid line fault diagnosis is a key task to ensure the reliability and stability of the power system. Traditional fault diagnosis methods rely on centralized data collection and processing, which are often constrained by data privacy concerns, security issues, and high transmission costs. This paper proposes a method based on federated learning for power grid line fault diagnosis to address the challenges of data dispersion, privacy protection, and efficient computing. The study designed a federated learning architecture that includes multiple power grid substations. Each substation trains on local data and aggregates model parameters on the central server through a variety of aggregation algorithms (such as FedAvg, FedProx, etc.). The experiment evaluates the performance of five different neural network architectures (MLP, RNN, LSTM, GRU, and CNN) in processing non-independent and identically distributed (non-iid) data. Results show that the federated learning model is comparable to centralized learning in prediction accuracy while significantly reducing computational and communication costs. In addition, the accuracy of fault diagnosis is further improved through appropriate preprocessing techniques such as outlier processing and global normalization. This paper verifies the potential of federated learning in power grid line fault diagnosis and provides new ideas for efficient operation and maintenance of smart grids.