Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods.
Virtual inertia control is considered as an important part of microgrids with high renewable penetration. Virtual inertia emulation based on the derivative of frequency is one of the effective methods for improving system inertia and maintaining frequency stability. However, in this method, the ability to provide virtual damping is usually neglected in its design, and hence, its performance might be insufficient in the system with low damping. Confronted with this issue, this paper proposes a novel design and analysis of virtual inertia control to imitate damping and inertia properties simultaneously to the microgrid, enhancing frequency performance and stability. The proposed virtual inertia control uses the derivative technique to calculate the derivative of frequency for virtual inertia emulation. Trajectory sensitivities have been performed to analyze the dynamic impacts of the virtual inertia and virtual damping variables over the system performance. Time-domain simulations are also presented to evaluate the efficiency of the virtual damping and virtual inertia in enhancing system frequency stability. Finally, the efficiency and robustness of the proposed control technique are compared with the conventional inertia control under a wide range of system operation, including the decrease in system damping and inertia and high integrations of load variation and renewable energy.INDEX TERMS Frequency stability, isolated microgrid, virtual inertia regulation, virtual synchronous machine.
Maintaining frequency stability of low inertia microgrids with high penetration of renewable energy sources (RESs) is a critical challenge. Solving this challenge, the inertia of microgrids would be enhanced by virtual inertia control-based energy storage systems. However, in such systems, the virtual inertia constant is fixed and selection of its value will significantly affect frequency stability of microgrids under different penetration levels of RESs. Higher frequency oscillations may occur due to the fixed virtual inertia constant or unsuitable selection of its value. To overcome such a problem and provide adaptive inertia control, this paper proposes a self-adaptive virtual inertia control system using fuzzy logic for ensuring stable frequency stabilization, which is required for successful microgrid operation in the presence of high RESs penetration. In this concept, the virtual inertia constant is automatically adjusted based on input signals of real power injection of RESs and system frequency deviations, avoiding unsuitable selection and delivering rapid inertia response. To verify the efficiency of the proposed control method, the contrastive simulation results are compared with the conventional method for serious load disturbances and various rates of RESs penetration. The proposed control method shows remarkable performance in transient response improvement and fast damping of oscillations, preserving robustness of operation. INDEX TERMS Frequency control, fuzzy logic, intelligent control, islanded microgrid, virtual inertia control, virtual synchronous generator
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