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Operating wind power generation system at optimal power point is essential which is achieved by employing a Maximum Power Point Tracking (MPPT) control strategy. This literature focuses on developing a novel particle swarm optimization algorithm enhanced radial basis function neural network supported TSR based MPPT control strategy for Doubly Fed Induction Generator (DFIG) based wind power generation system. The proposed hybrid MPPT control strategy estimates the effective wind speed and estimates the optimal rotor speed of the wind power generation system to track the maximum power. The proposed controller extremely reduces the speed dissimilarity range of wind power generation system, which leads to rationalizing the pulse width inflection of DFIG rotor side converter. This in turn, increases the system's reliability and delivers an effective power tracking with reduced converter losses. Furthermore, by utilizing the proposed MPPT controller, the converter size can be reduced to 40%. Therefore, the overall cost of the system can be gradually decreased. To validate the performance of the proposed MPPT controller, an extensive simulation study has been carried out under medium and high wind speed conditions in MATLAB/Simulink. The obtained results have been justified using experimental analysis.

High penetration of wind power generation system into grid network has satisfied enormous energy demand and has necessitated the wind power generation system to remain connected with grid network to maintain essential grid stability, even during grid fault conditions. This paper presents a novel hybrid genetic algorithm optimized Elman neural network controller for proton exchange membrane fuel cell supported customized Dynamic Voltage Restorer (DVR) to improve the fault ride-through capability of Doubly Fed Induction Generator (DFIG) based wind power generation systems. The outstanding aspect of the proposed customized DVR is that it has low heat transmission losses, therefore has a better efficiency up to 3% when compared with the conventional energy storage based DVR. The proposed shrewd DVR adequately rides through the fault by improving voltage regulation and prevents wind turbine termination during grid faults. The effectiveness of the customized DVR and proposed control strategy is analyzed for doubly fed induction generator based wind power generation system under symmetrical and unsymmetrical fault conditions. The operation of customized DVR is confirmed using time-domain simulation study carried out in MATLAB/Simulink environment.

This paper aims at modelling an electronically coupled distributed energy resource with an adaptive protection scheme. The electronically coupled distributed energy resource is a microgrid framework formed by coupling the renewable energy source electronically. Further, the proposed adaptive protection scheme provides a suitable protection to the microgrid for various fault conditions irrespective of the operating mode of the microgrid: namely, grid connected mode and islanded mode. The outstanding aspect of the developed adaptive protection scheme is that it monitors the microgrid and instantly updates relay fault current according to the variations that occur in the system. The proposed adaptive protection scheme also employs auto reclosures, through which the proposed adaptive protection scheme recovers faster from the fault and thereby increases the consistency of the microgrid. The effectiveness of the proposed adaptive protection is studied through the time domain simulations carried out in the PSCAD ⧹ EMTDC software environment.