Capsule-based federated reinforcement learning adaptive sliding mode for anomaly detection and control of floating wind turbines

Floating wind turbines (FWTs) are now recognized as one of the most effective and affordable renewable energy sources. However, their performance is strongly influenced by dynamic environmental conditions, particularly sea waves under significant oscillatory conditions. Ocean wave and wind disturbance affect turbine positioning, underscoring the critical essential for adaptive and robust control mechanisms to manage the unpredictable external inputs. In this context, we present an innovative method based on federated deep learning for training capsule networks to detect disturbances and enable adaptive robust control of FWTs among the environmental uncertainty. Through the proposed technique, a unique mixture of sliding mode control and deep reinforcement learning (DRL) yields in the extraction of wide features and modeling of spatial relationships between sensor data in the capsule networks framework. Furthermore, by employing federated learning, the capsule-net model is trained in a distributed manner across multiple wind turbines. Therefore, enhanced accuracy and effectiveness of disturbance detection are guaranteed. Simulation results reveal effective identification of disturbances which in turn improves the performance and stability of FWTs under the coarse environmental situation. The global Lyapunov stability analysis proves the FWTs' closed-loop stability. Performance of the superior DRL is evaluated in comparison with a radial basis function neural network (RBFNN) estimation. The innovative DRL method represents a significant advancement in the control of FWTs as a high potential of development for intelligent management of similar systems. As a final aim, this research work finds out the reliability and efficiency of FWTs in variable weather conditions (short-term) and erratic ocean environments (long-term). Moreover, the control system makes a substantial impact on the sustainable development of the wind and renewable energy sector.