In this article, proportional resonant (PR) controller‐based pulse width modulation (PWM) current control for three‐phase, three‐leg SAPF with the optimized dc‐link controller is implemented to compensate for current harmonics produced by nonlinear loads. The optimization of the dc‐link voltage regulator is implemented using Bat Algorithm (BA). The stability of the current controller with the proposed system with a mathematical model is evaluated in the time and frequency domain. Instantaneous real and reactive power theory (pq‐theory) is applied for reference currents generation. Simulation of the proposed controller is performed in MATLAB/Simulink environment. The controller is implemented in FPGA platform and the results of simulation are verified with the experimental prototype. The optimized dc‐link results in the improved performance of SAPF. The new dimension of the PR controller in a grid‐connected system for harmonic regulation is implemented and validated with experimental results. The objective of reduced current harmonics with a minimum per phase fundamental current and unity power factor at the point of common coupling is achieved.

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An effective and robust controller is designed using Levenberg-Marquardt (LM) algorithm-based Artificial Neural Network (ANN) for the solar Photo-Voltaic (PV) based distributed generation units for stabilizing the grid-connected microgrid (MG) under load changes and irradiance variations. A test system comprising of two PV units and one diesel generator unit connected to the utility grid is modelled and considered for the controller design in MATLAB/Simulink environment. PV generated power is injected into the grid through voltage source converter (VSC) regulated by using the proposed ANN controller. Based on the grid voltage and available PV generation, the ANN controller regulates the inverter current by setting the reference voltage vector to synthesize gating pulses for the inverter. The robustness of the controller design is analysed and validated through time-domain simulations by subjecting it to extreme operating conditions. The controller performance is evaluated by Integral Square Error (ISE) and Integral Time Absolute Error (ITAE) for the test system. The results are compared with conventional PI and PID controllers to prove the superior performing ANN controller.