Intelligent coordinated control for improved voltage and frequency regulation with smooth switchover operation in LV microgrid

Abstract

Abstract During demand changes and incidence of grid faults, the microgrid system is subjected to an imbalance between the generation and demand. The maintenance of power balance under such dynamic conditions is a major concern for the proper functioning of the microgrid network. During those dynamic periods, achieving voltage and frequency regulation, in particular, during islanded/standalone mode of operation is a challenging task. For reliable operation, in addition to power balance, voltage and frequency regulation, reduced switchover transients and fast switchover operations are necessary. Conventional coordinated controllers comprising of droop, voltage and current control loops, aim at achieving stability of the microgrid network but fails to ensure a quick and smooth response. This work proposes an Adaptive Neuro-Fuzzy Inference System based intelligent coordinated control strategy by combining control techniques namely, inverse droop control, virtual impedance control and current control. The proposed coordinated control scheme provides improved voltage and frequency regulation, reduced switching transients and quick adaptation of the system during demand changes and fault events. The proposed system uses an inverse droop control technique to achieve power decoupling. Additionally, a virtual impedance-based voltage control loop is implemented which ensures voltage regulation of the microgrid and a feed-forward current control loop is developed to minimize the transients during load switching and switchover operations. The reverse droop based PLL strategy is implemented for grid synchronization and smooth switchover operations. The proposed system is simulated in a 20-kVA grid-tied microgrid system in MATLAB/SIMULINK R2018b and tested in real-time of 5-kVA grid-tied solar PV system which demonstrated that the proposed approach is effective under varying irradiance, changing demand conditions and switchover operations. The experimental results prove that the proposed scheme is performing better under different system conditions with fast switching response when compared with other control algorithms.