This paper proposes a modified grey wolf optimization (MGWO) which has improved rate of convergence and avoids local optima stagnation. This is achieved by improving the exploration-exploitation balance in conventional grey wolf optimization (GWO). Moreover, the use of weighted distance strategy allows quick identification of the global optima in the proposed MGWO. This optimization is then applied for the control of a three-phase, eleven-level hybrid cascaded multilevel inverter (HC-MLI). Selective harmonic elimination pulse width modulation (SHE-PWM) technique is implemented through MGWO which generates optimal switching angles for the HC-MLI so as to eliminate lower order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In MGWO optimized HC-MLI, balancing of capacitor voltage is made possible at higher modulation index by making use of the available redundant switching states. The performance of the proposed work is validated through simulation and experimentation on a 1.5 kW prototype. The results obtained through simulation show that the MGWO algorithm is more efficient and accurate than other reported algorithms such as GWO, genetic algorithm (GA), and particle swarm optimization (PSO) in terms of performance, harmonic reduction, and convergence rate.

Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity ($H_{\infty }$), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the $H_{\infty }$ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.

Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.

This study presents a full operational envelope controller for a full nonlinear 5 MW Supergen exemplar wind turbine based on multivariable model predictive control (MPC). Below the rated wind speed, the primary objective of the controller is to maximize the energy capture. Above the rated wind speed, the controller maintains the rated power. The controller switches between control modes according to the prevailing wind speed to maintain control over the full envelope of operating regions. A linearized model is derived from a model of an exemplar 5 MW Supergen wind turbine in state-space form. The linearized model is then used to realize a feedback MPC (FB-MPC) and feedforward MPC (FF-MPC) to consider the lack and incorporation, respectively, of wind speed information. The switching strategy is tracked on the torque–speed plane. Simulations are performed at multiple wind speeds to evaluate the robustness and switching performances of the designed controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved tracking is achieved over the full envelope with FF-MPC rather than FB-MPC. Simulation results are presented in the time and frequency domain in addition to the torque–speed plane demonstrating that the control performance is improved without an increase in the control activity (i.e., pitch action) of the turbine; that is, the controller’s gain crossover frequency remains within the acceptable range, around 1 rad/s. Note that most work presented in the literature focus on specific wind speeds only; that is, either only below rated wind speed or above rated wind speed. In contrast, the controllers reported here cover the full envelope of operation regions, making this work more practical and novel.

This article presents a modified whale optimization (MWO) for harmonic mitigation in a three‐phase 11‐level hybrid cascaded multilevel inverter (HC‐MLI). The proposed MWO has improved rate of convergence as compared with conventional whale optimization (WO). This is achieved by improving the exploration‐exploitation feature of the conventional WO. Selective harmonic elimination–pulse width modulation technique is applied through MWO for generating the optimal switching angles of the HC‐MLI so as to eliminate the lower‐order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In the proposed MWO optimized HC‐MLI, capacitor voltage balance is made possible even at higher modulation indices using the available redundant switching states of HC‐MLI. The proposed work is useful where the HC‐MLI has to work at the low switching frequency and the conversion efficiency is of prime importance such as medium‐ and high‐power motor drives, static synchronous compensators, and active power filters. The performance of the proposed work is validated through simulation and experimentation on a 1.5‐kW prototype. The results obtained show that the MWO algorithm is more efficient and accurate than other reported algorithms such as genetic algorithm, particle swarm optimization, and WO in terms of performance, harmonic mitigation, and rate of convergence.