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This study explores the Whales Optimization Algorithm (WOA)-based PI controller for regulating the voltage and frequency of an inverter-based autonomous microgrid (MG). The MG comprises two 50 kW DGs (solid oxide fuel cells, SOFCs) interfaced using a power electronics-based voltage source inverter (VSI) with a 120-kV conventional grid. Four PI controller schemes for the MG are implemented: (i) stationary PI controller with fixed gain values (Kp and Ki), (ii) PSO tuned PI controller, (iii) GWO tuned PI controller, and (iv) WOA tuned PI controller. The performance of these controllers is evaluated by monitoring the system voltage and frequency during the transition of MG operation mode and changes in the load. The MATLAB/SIMULINK tool is utilised to design the proposed model of grid-tied MG alongside the MATLAB m-file to apply an optimisation technique. The simulation results show that the WOA-based PI controller which optimises the control parameters, achieve 62.7% and 59% better results for voltage and frequency regulation, respectively. The eigenvalue analysis is also provided to check the stability of the proposed controller. Furthermore, the proposed system also satisfies the limits specified in IEEE-1547-2003 for voltage and frequency.

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Nowadays, new trends in the industry of electricity generation are to enhance the power generation by employing distributed generation (DG) system which is mostly based on renewable generation sources such as wind, solar, etc. However, many power quality problems could arise on existing grid when DG is connected or the operation of distributed energy resources (DER) is not controlled properly. That's why, while integrating DG with the power grid, a seamless attention should be given to power generation and safe running of system. Several methods, having a diverse concept, have been divided into two main sets: linear and nonlinear controllers. The first group comes with PI controller and parameter feedback controller, and control by means of constant frequency with predictive techniques. The second group includes hysteresis current control and on-line optimization for predictive controllers. Additionally, new current control techniques with neural networks and fuzzy based controllers are also discussed. Selected methods associate the arrangement for the sake to demonstrate the described groups of the controller.