1. Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization
- Author
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Guo-Qiang Zeng, Huan Wang, Bi Daqiang, Min-Rong Chen, and Yuxing Dai
- Subjects
Extremal optimization ,Control and Optimization ,Computer science ,020209 energy ,Population ,Evolutionary algorithm ,Energy Engineering and Power Technology ,02 engineering and technology ,lcsh:Technology ,extremal optimization ,Approximation error ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,grid-connected inverter ,Electrical and Electronic Engineering ,evolutionary algorithms ,education ,Engineering (miscellaneous) ,education.field_of_study ,Renewable Energy, Sustainability and the Environment ,lcsh:T ,power control ,design optimization ,020208 electrical & electronic engineering ,Particle swarm optimization ,AC power ,Inverter ,Microgrid ,Energy (miscellaneous) ,Power control - Abstract
The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.
- Published
- 2018
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