<span lang="EN-US">A multi-point model predictive control (MPMPC) is widely used for many applications, including wind energy system (WES), notably enhanced power characteristics and oscillation regulation. In this work, MPMPC is adapted to condense the fatigue load of the WES and improve the lifetime of the turbine assembly. The lifetime examination is carried out by considering the three chief parameters: basic lifetime until failure, short-time damage equivalent loads (DELs), and lifetime DELs. The simulation study is performed for two cases: blade root bending moments and tower top bending. Further, fatigue load examination is demonstrated to analyze the effectiveness of the proposed controller. The observed results show that the lifetime analysis of the wind turbine system displayed more excellent characteristics, i.e., 49.50% greater than MPC. Also, the fatigue load mitigation showed greater magnitude due to the control action of the proposed controller, about 37.38% grander than MPC. Therefore, the attained outcomes exhibit outstanding performance compared with conventional controllers.</span>

Growth in the need for electric energy and fossil fuel scarcity endorses renewable energy generation sources. The generation cost of electric power utilizing wind turbines is cost-effective and straightforward compared to other renewable energy sources (RES). Recently, hasty research and developments have been presented in wind turbines (WT) by researchers globally. Although wind-based energy production is more content, planting the WT is challenging. Maintaining the WT from fault incidence is highly crucial. The fault in the WT distresses the power quality of the produced energy. This condensed power quality affects the transmission systems, substations, and loading end of the renewable source. Also, gear malfunctioning is the primary reason for most of the downtime in wind turbines. This work successfully proposed and implemented a deoxyribonucleic acid (DNA) sequencing-based control technique to reduce the drive train vibration. Therefore, fault detection and monitoring in WTs play an active part in power production and quality maintenance. In this work, a vibration-grounded WT gearbox fault observing scheme is proposed to increase the power quality. Precisely, a wavelet is executed to chart the vibration gesture. Also, the current sensor gesture is implemented to discover the power quality variances associated with the WT's vibration magnitude.

No abstract

No abstract