Application Of Sliding Mode Extreme Seeking Control Based On Chaotic Firefly Algorithm In Wind Power MPPT

With the deterioration of the natural environment and the reduction of fossil energy,the development of renewable energy becomes more urgent.In order to maximize the use of wind energy,Maximum Power Point Tracking(MPPT)for Wind Energy Conversion System(WECS)came into being.However,the existing technologies still have some shortcomings,such as: speed sensor,the contradiction between speed and accuracy and steady-state oscillation.In this paper,a small Permanent Magnet Synchronous Generator(PMSG)WECS is taken as a research platform,and the MPPT of WECS is achieved through the sliding mode extreme seeking control(SMESC).SMESC has the advantages such as: without the mathematical model and adjustable tracking speed.However,improper parameters may cause problems such as oscillation,poor accuracy or failed tracking.On the other hand,it’s to avoid the intelligent algorithm premature during the search due to individual stasis.In order to improve the dynamic tracking speed of the SMESC and eliminate the steady-state oscillation,this paper optimizes the control structure of WECS,and the traditional PI control is replaced by power sliding mode controller.The speed sensor is replaced by the way of measuring the voltage to improve the control precision.In the aspect of parameter optimization,this paper analyzes the influence of parameters on the tracking effect and proposes a new objective function.Based on this,a framework for parameter optimization is proposed.The intelligent algorithms could be used to optimize control parameters of SMESC in this frameworkOn the other hand,in order to avoid the intelligent algorithm in the search due to individual stasis caused the algorithm premature.Based on the Kent map,this paper proposes a new Chaos Firefly Algorithm(CFA).CFA takes advantage of the global search capability of the FA.In addition,it also increases individual dispensability and enhances local search capability through the chaotic process.CFA applies the individual elimination mechanism and space contraction mechanism to improve search efficiency.Finally,the simulation results prove the effectiveness of the proposed method.