基于非平方估计风力发电系统的最优控制的双馈感应电机-翻译

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1、基于非平方估计风力发电系统的最优控制的双馈感应电机・翻译OptimalControlofaWindGeneratorSystemDoubly-FedInductionUsingNon-SquaresEstimators基于非平方估计风力发电系统的最优控制的双馈感应电机FederalUniversityofMaranhao,StateUniversityofMaranh?aoEmbeddedSystemsandIntelligentControlLaboratoryBiologicalInformationProcessingLaboratorySaoLuis-MA,Brazil

2、Email:prof.queiroz.jonathan@gmail.comAbstract—Becausetherandomnessnatureassociatedtoprimaryenergysources,thecontrolofeolicandsolarenergysystemsdemandsmethodsandtechnicsthataresuitablewiththehighdegreeoftheenvironmentuncertainties.Thereinforcementlearning(RL)andapproximatedynamicprogramming(A

3、DP)approachesfurnishthekeyideasandthemathematicalformulationstodevelopoptimalcontrolsystemmethodsandstrategiesforalternativeenergysystems・Weareproposingaonlinedesignmethodtoestablishcontrolstrategiesforthethemainunitofaeolicsystemthatisthedoublyfedinductiongenerator(DFIG).Theproposedmethodol

4、ogyisbasedondevelopmentofonlinealgorithmsforapproximatesolutionsoftheHamilton-JacobiBellman(HJB)equationthroughafamiliyofnon・squaresapproximatorsforcriticadaptivesolutionoftheDiscreteAlgebraicRiccatiEquation(DARE),associatedwiththeproblemofDiscreteLinearQuadraticRegulator(DLQR)・摘要-由于初级能源具有的随

5、机性本质，风力和太阳能所构成的控制系统需求的理论和技术要适用于极端不确定的环境。强化学习(RL)和近似动态规划(ADP)方法提供关键思想和数学公式制定替代能源系统优化控制系统的方法和策略。我们提出一个在线设计方法建立控制策略的风成系统,该系统是双馈感应发电机(DFIG)•该方法是基于对Hamilton-JacobiBellman(HJB)在线的近似解，通过离散代数Riccati方程(DARE)的精确解中的非平方逼近的系列的方程自适应解决在离散的线性二次型调节器问题相关(DLQR)oIndexTerms—HeuristicDynamicProgramming,DynamicPro

6、gramming,Convergenee,DiscreteLinearQuadraticRegulator,DigitalControl,DFIGwindturbines;DoublyFedInductionGenerator.关键词：启发式动态规划，动态规划，收敛，离散线性二次调节，数字控制，双馈风力发电机组;双馈感应发电机。I.INTRODUCTIONI.简介Electricityisfundamentaltothedevelopmentofcountriesandthequalityoflife[1].Asmorecountriesdevelop,itbecomesnec

7、essarytofurtherincreasetheenergyproduct!on[2].Atthesametime,youalsoneedtopreservetheenvironmentbyusingnaturalresourceswithconscienee.Therefore,inadditiontoexpandingthecapacityofpowergenerationbyimprovingtheuseofconventionalsources,itisalsonecessary