Summary In this paper, a new synchronous reference frame theory–based power angle control (PAC) method for unified power quality conditioner with distributed generation (UPQCDG) is proposed for effective grid integration of solar PV. In UPQCDG, shunt active power filter (APF) feeds power from distributed generator to load, apart from supplying reactive power demand, which leads to increase in VA burden, hence its rating. Power angle control method aims at effective utilization of series and shunt APFs through sharing of reactive power to reduce VA burden on shunt APF. Proposed PAC method is based on instantaneous three‐phase power estimation technique, which is simple and robust and utilizes already available measurements of UPQCDG. Performance of proposed system is tested in the presence of nonlinear and reactive loads with solar PV generation system. Dynamic performance of system is studied during grid disturbances such as voltage sag and swell, solar irradiation variation, and change in load. Effectiveness of proposed method is validated using real‐time simulation performed in Opal‐RT, in which electrical circuit of UPQCDG is simulated on FPGA computation engine with submicrosecond time step to emulate real hardware closely.

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Summary This paper addresses the method of forecasting the wind and solar power and its application to an islanded microgrid (MG) model for load frequency control. Due to high penetration of renewable energy sources, the islanded MG suffers from lower equivalent inertia. The islanded MG faces several challenges in order to ensure the stable operation by maintaining the frequency and voltage at nominal value. The supply and demand power mismatch is mainly due to continuously changing solar irradiance, fluctuating wind speed, variable inertia, and load fluctuations. The intermittent nature of RESs can significantly affect the system stability; hence, the challenge lies in accurate forecasting of power from the renewable energy sources (RESs) so that a proactive arrangement is made available for compensation of active power or frequency variations. The forecasting will determine the correct estimate of power availability so that the power reserves can be activated prior to large variations in active power affecting the stability of the MGs. To address these challenges, a stochastic model of wind and solar has been developed using “Time series modeling” of the data obtained from Charanka Solar Park under Gujarat Energy Development Agency, India. Wind and solar power availability are forecasted using autoregressive integrated moving average (ARIMA) method including the seasonality factor. The proportional and integral (PI) controller is used for regulating the frequency fluctuations caused due to intermittency in the output of RESs and load power. Various load patterns are applied to the MG model to analyze its load frequency behavior along with variations in secondary sources.

The robot manipulator is a highly complex system, which is multi-input, multi-output, nonlinear, and time variant. Controlling such a system is a tedious and challenging task. In this paper, some new hybrid fuzzy control algorithms have been proposed for manipulator control. These hybrid fuzzy controllers consist of two parts: a fuzzy controller and a conventional or adaptive controller. The outputs of these controllers are superimposed to produce the final actuation signal based on current position and velocity errors. Simulation is used to test these controllers for different trajectories and for varying manipulator parameters. Various performance indices like the RMS error, steady state error, and maximum error are used for comparison. It is observed that the hybrid controllers perform better than only fuzzy or only conventional/adaptive controllers.

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