Research On Active Power Regulation Strategy Of Power System With High Penetrationof Wind Power

With the gradual increase of the capacity of the wind farm to the grid, and itsadverse impact to the grid also brings even more obvious, if you rely entirely on theimpact caused by traditional power supply to compensate, it may be needed toincrease spare capacity of traditional power and increase operating costs of thesystem. So, with distributed power capacity increased power into the grid, it needs ahigh penetration of wind farms with a system active strategy research.First, setting the modeling of the DFIG and analyzes its each module to studythe network side of the inverter and the rotor side inverter control strategy. On thisbasis, we design the primary frequency control considering the changes in windspeed and the steady-state frequency error to reduce system frequency deviation.And proposed regulatory strategy based on the DC sensitivity of the power flowbetween the wind farm through this regulatory policy can contribute to theregulation of each change in wind farms to solve the problem occurs when thesystem is active trend line fault. The proposed control strategy is based on thesensitivity of each wind farm system to the flow limit slip value to allocation powerbetween the wind farms. At the same time, the proposed control strategy areverified, the results show that the first frequency modulation control strategy caneffectively improve the system frequency deviation, based on the control strategy ofactive power flow between wind farms and DC sensitivity can eliminate the branchflow limit.Secondly, according to the power system acceptance of wind power reservecapacity is insufficient, considering wind power forecast, wind farm installedcapacity and other factors and based on the proportion of wind power predictionalgorithm, optimal allocation of wind power forecast which are two kinds of limitoptimization strategy between wind farms, we can make the independentparticipation system wind load. And were verified by examples of traditionaloptimization allocation algorithm for wind farm installed capacity and the twooptimization methods, results show that the optimal allocation algorithm for windpower prediction and the proportion of wind power prediction algorithm for thesetwo optimization methods are superior to the optimize the allocation based on windfarm installed capacity of the algorithm. And the optimal allocation algorithm basedon wind power prediction is better than based on the proportion of wind powerprediction algorithm.Finally, presents a wind farm in optimal allocation policy. According to thedifferent operation characteristics of each unit, each unit to calculate characteristic matrix, operation of the fuzzy clustering analysis method to classify the unit, basedon genetic algorithm to unit combination on classification unit, unit operationcombined the selection of the optimum, and through the optimal allocationalgorithm based on wind power prediction to allocation specific numerical power tothe optimal combination of unit operation. And to validate the proposedoptimization strategy by an example, the results show that this method canaccurately optimize and allocate the power between unit in wind power plantaccording to the instruction.