During the last decade, wind energy gained much importance as an energy source in power systems. DFIG energy is one of the most widely accepted types of renewable energy generation because of its several benefits. This paper presents a comparative study on the performance of different control strategies for DFIG wind turbines: proportional–integral (PI), Artificial Neural Networks (ANN), H∞, and adaptive Fuzzy PI controller. Simulation results show that DFIG’s performance, dynamic response, and robustness against machine parameter variations are improved with H∞ control technique.

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Purpose. In recent years, the photovoltaic systems (PV) become popular due to several advantages among the renewable energy. Tracking maximum power point in PV systems is an important task and represents a challenging issue to increase their efficiency. Many different maximum power point tracking (MPPT) control methods have been proposed to adjust the peak power output and improve the generating efficiency of the PV system connected to the grid. Methods. This paper presents a Beta technique based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of this algorithm based MPPT is supplemented by a comparative study with incremental conductance (INC), particle swarm optimization (PSO), and fuzzy logic control (FLC). Results Faster MPPT, lower computational burden, and higher efficiency are the key contributions of the Beta based MPPT technique than the other three techniques.

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