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Study On The Coordination Strategy Of Reactive Power And Voltage Control On DFIG-based Wind Farms

Posted on:2016-01-19Degree:MasterType:Thesis
Country:ChinaCandidate:L PengFull Text:PDF
GTID:2272330467489918Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
With increasing of wind power capacity, the problem of grid voltage stability isbecoming increasingly serious. The traditional voltage control mode depended onindependent of each wind farm is difficult to meet the voltage requirements of windfarm group with large capacity. Double fed induction generator has been widely usedfor PQ decoupling control while most DFIGs are running in constant power mode.Firstly the mathematical model of double-fed wind turbine is established. Basedon this, the reactive power capability of DFIG is analyzed. The Gamesa G58-850kWdouble-fed wind turbine is analyzed under different wind speed.Currently reactive power compensation strategies of wind farms in a same windpower base are mainly controlled by each farm independently, which lackscoordinated control mechanism. The aim of the proposed work is to present anoptimal multilevel control system which mainly allows the doubly fed synchronousgenerators to participate at reactive power compensation in wind farms. According tothe current active power output, this paper makes a probability assessment on reactivepower capacity range of DFIGs for next time internal.First, the reference values of reactive power are estimated by monitoring thevoltage of central point. Second, the optimal reference values of reactive power foreach wind farm at each PCC are calculated by genetic algorithm. The proposedmulti-level control system recalculates the available reserve of the reactive power ofDFIGs to determine the optimal references. The simulation results show a betterperformance of the proposed model and strategy in respects of making full use thereactive power capacity of DFIGs and stabilizing the static voltage of wind farms.
Keywords/Search Tags:clustered wind farms, reactive power control, coordination strategy, multi-objective optimization, genetic algorithm
PDF Full Text Request
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