This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.

The most prominent and rapidly increasing source of electrical power generation, wind energy conversion systems (WECS), can significantly improve the situation with regard to remote communities’ power supply. The main constituting elements of a WECS are a wind turbine, a mechanical transmission system, a doubly-fed induction generator (DFIG), a rotor side converter (RSC), a common DC-link capacitor, and a grid-side converter. Vector control is center for RSC and GSC control techniques. Because of direct and quadrature components, the active and reactive power can also be controller precisely. This study tracks the maximum power point (MPP) using a maximum power point tracking (MPPT) controller strategy. The MPPT technique provides a voltage reference to control the maximum power conversion at the turbine end. The performance and efficiency of the suggested control strategy are validated by WECS simulation under fluctuating wind speed. The MATLAB/Simulink environment using simpower system toolbox is used to simulate the proposed control strategy. The results reveal the effectiveness of the proposed control strategy under fluctuating wind speed and provides good dynamic performance. The total harmonic distortions are also within the IEEE 519 standard’s permissible limits which is also an advantage of the proposed control approach.

The present work focus on the comparison between open and closed loop battery charging techniques for lithiumion (Li-ion) battery. Conventional types of open loop charging technologies like pulse charging, constant current-constant voltage (CC-CV) and multi stage charging etc. uses fixed battery parameters to decide the battery charging current. Effect of temperature variation during charging is generally not taken in to account in the case of open loop charging techniques. Temperature must be kept in certain range to enhance the effective battery life time. Charging current in closed loop technique depends on the temperature feedback. Present work compares between open loop CC-CV and close loop constant temperature-constant voltage (CT-CV) battery charging techniques in terms of charging voltage, charging current, battery state of charge (SOC) and battery temperature rise for different initial SOC level. Model is simulated in MATLAB/Simulink environment and study reveals that there is about 24% improvement in charging time as compared to the CC-CV charging technique.

This work presents the role of vehicle to grid (V2G) integration in frequency regulation of renewable based microgrid. The renewable based micro grid is consisting of intermittent nature solar photovoltaic and wind sources; diesel power generator is taken as a backup power source along with residential and industrial load. Complete system is modelled and simulated in MATLAB/Simulink environment. Different types of vehicle profiles are considered for simulation purpose. Simulations are carried out for abnormal operating conditions such as partial shading of solar photovoltaic, discontinuation of wind farm power due to high wind speed and sudden change in load demand. Simulation results shows the effectiveness of V2G integration in power control and grid frequency regulation under abrupt conditions.

This work presents the series voltage regulator (SVR) for DC-microgrid. The SVR consists of two different types of power converters namely, dual active bridge (DAB) and followed by full-bridge dc-dc converter (FBDCC). DC-microgrid of five buses is considered with the nominal grid voltage of 380V. As transmission line length increases the drop in the line is also increases so it becomes necessity for some type of voltage control device that can compensate for this line voltage drop. The main goal of the SVR is to inject varying voltage in series with the microgrid for voltage regulation. This results the voltage level at different buses becomes independent of the load change and remains within the defined grid voltage limit. The system is modeled and simulated in MATLAB/Simulink environment. Simulation results show that the proposed SVR gives good dynamic performance and voltage regulation under different load conditions.