Control and Optimisation for Offshore Renewable Energy

Floating offshore wind turbines (FOWTs) experience unbalanced loads and platform motion due to the coupling of variable wind and wave loads, which leads to output power fluctuation and increased fatigue loads. This paper introduces a new blade pitch control strategy for FOWTs that combines fuzzy logic with a linear quadratic regulator (LQR) and a proportional-integral (PI) controller. The fuzzy PI controller dynamically adjusts the PI control gains to regulate rotor speed and stabilize output power. Fuzzy LQR is employed for individual pitch control, utilizing fuzzy logic to adaptively update feedback gains to achieve stable power output, suppress platform motion, and reduce fatigue load. Co-simulations conducted with OpenFAST (Fatigue, Aerodynamics, Structures, and Turbulence) and MATLAB/Simulink under diverse conditions demonstrate the superiority of the proposed method over traditional PI control. The results show significant reductions in platform pitch, roll, and heave motion by 17%, 27%, and 48%, respectively; blade out-of-plane, pitch, and flapwise bending moments are reduced by 38%, 44%, and 36%; and the tower base roll and pitch bending moments are reduced by up to 29% and 22%, respectively. The proposed control scheme exhibits exceptional environmental adaptability, enhancing FOWT’s power regulation, platform stability, and reliability in complex marine environments. Full article

A Surface-Riding Wave Energy Converter (SR-WEC) featuring a sliding magnet inside a pitching cylindrical hull is investigated as an easily deployable small power device to support small-scale marine operations. This study extends the earlier development of the system by authors to enhance power performance through the application of end spring and latching control. The inclusion of springs at the tube’s end enhances the magnet release and travel speeds as well as the average power output compared to systems without them. Further improvement of power output can also be achieved by employing optimal latching control. We introduced constant-angle and variable-angle unlatching strategies to determine optimal parameters in combination with passive and reactive power take-off (PTO) controls to assess their effectiveness. The optimized latching control and end spring can increase 60–80% more power output compared with the case without them under certain PTO damping. Additionally, we discussed the effects of limiting peak powers and associated energy leaks with latching. Full article

This research investigates the application of photovoltaic (PV) systems on ship retrofits with the aim of reducing the emission of harmful gases. By using renewable energy resources, this research presents the potential for reducing greenhouse gas (GHG) emissions and improving energy efficiency in maritime operations, specifically within the Split coastal area. Overcoming the space restrictions on ships, an innovative design is presented to maximize the installation area for solar power. The research is conducted for several cases based on the IHOGA simulator, for all ship phases, and it aims to minimize fuel consumption by the diesel generators, thus emphasizing the use of renewable energy resources. A model with two operational modes is designed: Mode 1 allows surplus power to charge batteries or supply the port network, while Mode 2 covers power deficits from alternative sources. The implementation of renewables results in carbon dioxide (CO₂) and nitrogen oxide (NO_X) emission reductions. Furthermore, during the ship hotelling phase, the load is supplied entirely by batteries, resulting in zero emissions at the port. Full article

Green hydrogen is likely to play an important role in meeting the net-zero targets of countries around the globe. One potential option for green hydrogen production is to run electrolysers directly from offshore wind turbines, with no grid connection and hence no expensive cabling to shore. In this work, an innovative proof of concept of a wind farm control methodology designed to reduce variability in wind farm active power output is presented. Smoothing the power supplied by the wind farm to the battery reduces the size and number of battery charge cycles and helps to increase battery lifetime. This work quantifies the impact of the wind farm control method on battery lifetime for wind farms of 1, 4, 9 and 16 wind turbines using suitable wind farm, battery and electrolyser models. The work presented shows that wind farm control for smoothing wind farm power output could play a critical role in reducing the levelised cost of green hydrogen produced from wind farms with no grid connection by reducing the damaging load cycles on batteries in the system. Hence, this work paves the way for the design and testing of a full implementation of the wind farm controller. Full article

Floating structures have become a major part of offshore structure communities as offshore engineering moves from shallow waters to deeper ones. Floating installation ships or platforms are widely used in these engineering operations. Unexpected wave-induced motions affect floating structures, especially in harsh sea conditions. Horizontal motions on the sea surface can be offset by a dynamic positioning system, and heave motions can be controlled by a heave compensation system. Active heave compensation (AHC) systems are applied to control vertical heave motions and improve safety and efficiency. Predictive control based on machine learning prediction algorithms further improves the performance of active heave compensation control systems. This study proposes a predictive control strategy for an active heave compensation system with a machine learning prediction algorithm to minimise the heave motion of crane payload. A predictive active compensation model is presented to verify the proposed predictive control strategy, and proportion–integration–differentiation control with predictive control is adopted. The reliability of back propagation neural network (BPNN) and long short-term memory recurrent neural network (LSTM RNN) prediction algorithms is proven. The influence of the predictive error on compensation performance is analysed by comparing predictive feedforward cases with actual-data feedforward cases. Predictive feedforward control with regular and irregular wave conditions is discussed, and the possible strategies are examined. After implementing the proposed predictive control strategy based on a machine learning algorithm in an active heave compensation system, the heave motion of the payload is reduced considerably. This investigation is expected to contribute to the motion control strategy of floating structures. Full article