This article presents a data-efficient learning approach for the complex-conjugate control of a wave energy point absorber. Particularly, the Bayesian Optimization algorithm is adopted for maximizing the extracted energy from sea waves subject to physical constraints. The algorithm learns the optimal coefficients of the causal controller. The simulation model of a Wavestar Wave Energy Converter (WEC) is selected to validate the control strategy for both the regular and irregular waves. The results indicate the efficiency and feasibility of the proposed control system. Less than 20 function evaluations are required to converge towards the optimal performance of each sea state. Additionally, this model-free controller can adapt to variations in the real sea state and be insensitive and robust to the WEC modeling bias.
Offshore wind turbines suffer from asymmetrical loading (blades, tower, etc), leading to enhanced structural fatigue. As well as asymmetrical loading different faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance. Individual pitch control (IPC) can achieve rotor asymmetric loads mitigation, but this is accompanied by an enhancement of pitch movements leading to the increased possibility of pitch system faults, which exerts negative effects on the IPC performance. The combined effects of asymmetrical blade and tower bending together with pitch sensor faults are considered as a ''co-design'' problem to minimize performance deterioration and enhance wind turbine sustainability. The essential concept is to attempt to account for all the ''fault effects'' in the rotor and tower systems, which can weaken the load reduction performance through IPC. Pitch sensor faults are compensated by the proposed fault-tolerant control (FTC) strategy to attenuate the fault effects acting in the control system. The work thus constitutes a combination of IPC-based load mitigation and FTC acting at the pitch system level. A linear quadratic regulator (LQR)-based IPC strategy for simultaneous blade and tower loading mitigation is proposed in which the robust fault estimation is achieved using an unknown input observer (UIO), considering four different pitch sensor faults. The analysis of the combined UIO-based FTC scheme with the LQR-based IPC is shown to verify the robustness and effectiveness of these two systems acting together and separately. KEYWORDSfault-tolerant control, individual pitch control, pitch sensor faults, wind turbine asymmetrical load reduction INTRODUCTIONAs a sustainable energy source, wind energy is taking an increasing share of the energy market to meet the growing demand for marine renewable energy. Wind energy has a significant potential for overcoming environmental pollution-related problems by reducing dependence on declining fossil fuel reserves. Wind turbines (WTs) can operate on land or offshore. Offshore WTs have large rotor diameters and high towers for high energy capture. The significant development of offshore wind power in recent years has led to a significant decrease in the levelized cost of energy (LCoE) of this form of renewable energy. However, there are two major challenges that the offshore WTs industry must face for Region 3 operation (above rated wind speed):1. Unexpected malfunction and failures of WT components will result in expensive repairs and typically months of machine unavailability, thus increasing the operation and maintenance (O&M) costs and threatening to increase the LCoE. However, offshore WT O&M are also challenged by the fact that wind farms are sometimes located 100 km offshore.
The decoupling policies enforced by the Trump Administration aim to break the US economic relationship with China. Those policies, however, are escalating strategic costs for the US in at least three unanticipated ways: the decoupling policies are losing the endorsement of US multinational corporations, undermining the solidarity of the US and its allies, and making supply chains more likely to disengage from the US than to disengage from China. We argue that the ongoing decoupling policies are costing more than the US can bear and will end in vain. If the Trump Administration enforces further decoupling policies without considering those implicit costs, it will only set the US up for a more expensive failure.
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