Adaptive constrained population extremal optimisation‐based robust proportional‐integral‐derivation frequency control method for an islanded microgrid

• Published in: IET cyber-systems and robotics Vol. 3; no. 3; pp. 210 - 227
• Main Authors: Lu, Kang‐Di, Zeng, Guo‐Qiang, Zhou, Wuneng
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 01.09.2021; Wiley
• Subjects: Adaptive algorithms; Adaptive control; constrained evolutionary algorithm; Constraints; Control methods; control system synthesis; Controllers; Derivation; Design; Distributed generation; distributed power generation; Evolutionary algorithms; evolutionary computation; Frequency control; Genetic algorithms; islanded microgrid; Literature reviews; Load; Load fluctuation; optimisation; Optimization; Performance evaluation; Performance indices; population extremal optimisation; power generation control; Proportional integral derivative; Robust control; robust PID controller; Tracking control; Tracking errors; Uncertainty
• ISSN: 2631-6315; 2631-6315
• DOI: 10.1049/csy2.12028

The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties, which have highly influence the reliability and complexities of frequency control. To alleviate the influence caused by load fluctuations and inherent variability of renewable sources, this article proposes an optimised robust proportional‐integral‐derivation (PID) frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID controller. During the process of iterated optimisation, a weighted objective function is used to balance the tracking error performance, robust stability and disturbance attenuation performance. Then, the robust PID frequency (RPIDF) controller is determined by an adaptive constrained population extremal optimisation algorithm based on self‐adaptive penalty constraint‐handling technique. The proposed control method is examined on a typical islanded microgrid, and the control performance is evaluated under various disturbances and parametric uncertainties. Finally, the simulation results indicate that the fitness value of the proposed method is 1.7872, which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms‐based RPIDF controllers. Moreover, the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.