This paper proposes a maximum power point tracking (MPPT) technique based on the tip speed ratio control for small scale wind turbines (WTs). In this paper, artificial neural network based particle swarm optimization has been trained offline to learn the characteristic of the turbine power as a function of wind and machine speeds. Afterwards, it has been realized online to estimate the varying wind speed. It is essential to design a controller that can track the maximum peak of energy regardless of wind speed changes. Therefore, this work provides a novel robust direct adaptive fuzzy–Proportional-Integral (PI) controller during the MPPT process through tuning duty cycle of the boost converter for permanent magnet synchronous generator driven by a WT. The proposed method has successfully decreased the ripples of coefficient of power (Cp) which is the index of MPPT mode, under variations of the wind speed in comparison with conventional controller. Finally, a systematic analysis is presented which is in good agreement with simulation results, confirming the effectiveness of the proposed strategy.

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

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