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

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摘要 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-integralderivation(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.
出处 《IET Cyber-Systems and Robotics》 EI 2021年第3期210-227,共18页 智能系统与机器人(英文)
基金 Key-Area Research and Development Program of Guangdong Province,Grant/Award Number:2020B0101090004 National Natural Science Foundation of China,Grant/Award Number:61972288 Natural Science Foundation of Shanghai,Grant/Award Number:20ZR1402800。
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  • 1da Costa J P, Pinheiro H, Degner T, Arnold G. Ro- bust controller for DFIGs of grid-connected wind turbines. IEEE Transactions on Industrial Electronics, 2011, 58(9): 4023-4038.
  • 2Jabr H M, Lu D Y, Kar N C. Design and implementation of neuro-fuzzy vector control for wind-driven doubly-fed induc- tion generator. IEEE Transactions on Sustainable Energy, 2011, 2(4): 404-413.
  • 3Xu L, Zhi D W, Williams B W. Predictive current control of doubly fed induction generators. IEEE Transactions on Industrial Electronics, 2009, 56(10): 4143-4153.
  • 4Liu X J, Guan P, Chan C W. Nonlinear multivariable power plant coordinate control by constrained predictive scheme. IEEE Transactions on Control Systems Technology, 2010, 18(5): 1116-1125.
  • 5Liu X J, Chan C W. Neuro-fuzzy generalized predictive con- trol of boiler steam temperature. IEEE Transactions on En- ergy Conversion, 2006, 21(4): 900-908.
  • 6刘向杰,刘吉臻.基于模糊神;耋模型的电厂协调预测控制.自动化学报,2006,32(5):785-790.
  • 7Abad G, Rodriguez M A, Poza J. Three-level npc converter- based predictive direct power control of the doubly fed induction machine at low constant switching frequency. IEEE Transactions on Industrial Electronics, 2008, 55(12): 4417-4429.
  • 8Sguarezi Filho A J, de Oliveira Filho M E, Ruppert Filho E. A predictive power control for wind energy. IEEE Transac- tions on Sustainable Energy, 2011, 2(1): 97-105.
  • 9Wang L P. Model Predictive Control System Design and Im- plementation Using MATLAB. New York: Springer, 2009, 22-26.
  • 10史宏宇,冯勇.感应电机高阶终端滑模磁链观测器的研究[J].自动化学报,2012,38(2):288-294. 被引量:37

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