Modified Genetic Algorithm In Reactive Power Optimization And Comparison Of Wind Farms

Wind power is one of the most important modern new energy resources, and itstechnology has been very mature, and therefore subject to a wide range of worldwideconcern, experts and scholars for research of wind power generation is also increasing.However, with the continuous expansion of wind power, the grid uncertainties areincreasing, there are major problems, drop voltage quality, stability and reliability ofpower supply voltage is reduced, and so on. Therefore, the study of wind power, windfarms must be considered reactive power optimization problem, especially after theoptimization and network, to improve performance and quality of wind power hasvery important significance.In response to these problems, this paper studies the wind farm reactive poweroptimization problem, a wind farm model, reactive power optimization model,making the numerical results closer to the actual situation. And the introduction of anew trend calculations, improved genetic algorithm, the algorithm uses the solutioncharacteristics to ensure that the flow calculation to get global optimal solution, easyto fall into local optimal circumstances, the traditional model of reactive poweroptimization made on the basis of taking into account the minimum power loss andreactive power compensation smallest multi-objective optimization model of reactivepower, to complete the wind farm system reactive power optimization. Finally, basedon a simulation of an example, the target proposed for reactive power optimization,and compare their advantages and disadvantages, the results show that the improvedgenetic algorithm can effectively control the network loss and reactive Power, windfarms connected to the grid can be reduced unstable situation occurs.