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

ABSTRACT: 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 extra

SUBMITTER: Mohammadian KhalafAnsar H 

PROVIDER: S-EPMC12677533 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature