基于改进HPSO算法的风电场内部无功优化研究

双馈风电机组(DFIG)是当前风电场的主流机型之一,具有有功功率和无功功率可解耦控制的优点。将风电场中每一台DIFG机组作为单独的连续无功源,以每台DIFG机组无功出力为控制变量,把双馈风电场内部有功网损设为目标函数建立无功优化模型。为了减少风电场内部有功网损并稳定节点电压,在基本粒子群(PSO)算法的基础上引入了自适应权重和遗传算法中的杂交概念,提出了一种混合PSO算法,并将该方法应用于风电场内部无功优化模型求解。以华北地区某风电场为例,在MATLAB软件中采用改进HPSO算法对所建立的无功优化模型进行了求解,求解结果与基本PSO算法和线性递减权重的PSO算法相比,改进HPSO算法收敛速度更快且结果更优,验证了文中模型和算法的正确性。

Reactive power optimization in internal wind farm based on improved HPSO algorithm

Doubly-fed wind turbine (DFIG) is one of the mainstream models of current wind farms, which has the advantages of decoupling control of active power and reactive power. In this paper, each DIFG unit in the wind farm is regarded as a separate continuous reactive power source. The reactive power output of each DIFG unit is taken as the control variable, and the active power loss inside the doubly-fed wind farm is set as the objective function to establish the reactive power optimization model. In order to reduce the active power loss and stabilize the node voltage in the wind farms, the hybrid concept of adaptive weight and genetic algorithm is introduced on the basis of basic particle swarm optimization (PSO) algorithm. A hybrid PSO algorithm is proposed and applied to solving reactive power optimization model in wind farm. Finally, taking a wind farm in North China as an example, the improved HPSO algorithm is used to solve the established reactive power optimization model in MATLAB software.Compared with the basic PSO algorithm and the linear decreasing weight PSO algorithm, the improved HPSO algorithm has faster convergence and better results, which verifies the correctness of the model and algorithm.