Deep Reinforcement Learning for Control of Wave Energy Converters Integrated on Floating Offshore Wind Turbines

Projektbeschreibung

KI-gestütztes Steuerungssystem für hybride Wind- und Wellenenergiesysteme

Offshore-Hybridsysteme für Wind- und Wellenenergie können die Kosten für erneuerbare Energien senken. Die komplexe Dynamik bei der Integration von schwimmenden Windkraftanlagen und Wellenenergiekonvertern erschwert jedoch deren Steuerung und beeinträchtigt die Gesamtleistung. Das durch die Marie-Skłodowska-Curie-Maßnahmen unterstützte Projekt DRIFT plant, mithilfe von KI ein Steuerungssystem für hybride Wind- und Wellenenergiesysteme zu entwickeln. Dazu wird es ein detailliertes numerisches Modell erarbeiten und eine Steuerungseinheit für Deep Reinforcement Learning trainieren, um die Stabilität und die Energieausbeute zu verbessern. Eine modellprädiktive Steuerungsebene wird die Sicherheit und die Einhaltung der betrieblichen Beschränkungen gewährleisten und darauf abzielen, die Plattformbewegung um 30 % zu reduzieren und die Energieausbeute um 25 % zu steigern. Das Projekt wird das Modell durch Hardware-in-the-Loop-Experimente validieren.

Ziel

Offshore hybrid wind-wave energy systems that integrate floating offshore wind turbines (FOWTs) and wave energy converters (WECs) hold significant potential for cost reduction of renewable energy, crucial for energy security and climate neutrality of the EU. However, control of the integrated WECs on these hybrid systems is extremely challenging, due to their complex, multi-physics dynamics under variable ocean conditions. Existing control approaches all face a fundamental dilemma between model accuracy and control optimality: empirical control can be tuned against high-fidelity models but has few adjustable parameters, leading to suboptimality, while model-based optimal control can only employ a simplified model with significant inaccuracies. Consequently, system performance has been strictly limited. DRIFT aims to embed AI to develop a definitive control framework for hybrid wind-wave energy systems, to completely solve the dilemma. First, a high-fidelity numerical model with full aero-hydro-structural-mooring dynamics will be developed. Then, a deep reinforcement learning (DRL) controller will be trained directly on that model to improve platform stability and wave energy generation. Further, a robust model predictive control (MPC) layer will be designed to act as a safety shield to enforce operational constraints. This control strategy will simultaneously ensure effectiveness (reducing 30% platform motion and increasing 25% wave energy capture), safety (guaranteed constraint satisfaction), and computational efficiency (>100 Hz control frequency on industrial embedded controllers), and will be validated in hardware-in-the-loop experiments. The project builds on the applicant and the main supervisor’s strong research foundation in control of offshore renewable energy systems and will be supported by a dedicated mentor in offshore dynamics. The dedicated training, effective work plan, and risk management strategy will ensure successful project delivery.