FedCOP: Federated Contrastive Orthonormal Prototype Learning Framework for Multi-Wind Farm Collaborative Fault Detection

Accurate and efficient fault detection for wind turbines is essential for wind farm operation and management. The federated learning supports collaboration across wind farms, concurrently preserving privacy and alleviating data silos. However, existing methods overlook the personalized characteristics of each wind farm and fail to mitigate the deterioration in model performance caused by data heterogeneity. Moreover, transferring parameters at a million-scale results in high communication expenses. To address these challenges, a federated contrastive orthonormal prototype learning (FedCOP) framework designed for collaborative fault detection among multiple wind farms is proposed in this paper. Initially, local normality and fault prototypes are established from latent representations with identical semantics. Following local training, wind farms only upload local prototypes, greatly improving communication speed. Secondly, the server aggregates local prototypes into global prototypes that encapsulate semantic details of various turbine states, and broadcasts to the wind farms for later communication rounds. Thirdly, in local objectives, the regularization term, contrastive loss, and orthonormal loss are integrated to align local prototypes with global ones and enhance the separability and distinguishability. Finally, the server applies momentum updates on the global prototypes to maintain consistency. The FedCOP is validated using data from four wind farms located in Jiangsu, Tianjin, Shanghai, and Hubei, China. Experimental results show that under heterogeneous conditions, FedCOP outperforms existing methods in detection accuracy, latent representation boundaries, and communication efficiency.