基于自然选择粒子群算法的含DG接入的配电网无功优化

Reactive Power Optimization in Distribution Network with DG Based on Natural Selection Particle Swarm Optimization

以分布式电源接入配电网运行时产生的有功网损最小并能改善电压质量为目标,提出将自然选择机理与粒子群算法相结合的配电网无功优化方法。将DG向系统注入的无功功率作为配电网无功优化的控制变量,建立了包括目标函数、潮流方程等式约束和不等式约束的配电网无功优化数学模型。基于自然选择的粒子群算法其核心思想为每次迭代过程中将整个粒子群按适应值排序,用群体中最好的一半的粒子的速度和位置替换最差的一半的速度和位置,同时保留原来每个个体所记忆的历史最优值。通过对改进后的IEEE33节点配电系统进行仿真分析,结果表明所提出的算法具有很强的全局收敛性和稳定性,并能以最快的收敛速度搜索到系统最小网损值。

To reach the goal of minimum active power loss and improved node voltage quality, an improved particle swarm optimization algorithm combined with natural selection mechanism is presented for distribution network reactive power optimization, the reactive power of distributed generation （DG） generated to the system is considered as control variables for reactive power optimization in distribution network, and then the mathematical model of the distribution network including the objective function, the equality and inequality constraint of the power flow equations is estab- lished. Main idea of the intelligent algorithm is that the whole particle swarm is first sorted according to its fitness val- ue, and then the velocity and position of the worst half are replaced by the best half of the whole particle swarm, while the historical optimal value of each individual is conserved. Simulation analysis on distribution network of a mended IEEE33-node system verifies that the algorithm is globally convergent, stable, and can search the minimum active power loss with the fastest rate of convergence.