基于自适应微粒群算法的电力系统无功优化

Power System Reactive Power Optimization Based on Adaptive Particle Swarm Algorithm

针对当前电力系统进行无功优化采用传统的优化方法,存在诸多不足的问题,提出了一种自适应微粒群优化(APSO)算法,用以解决无功优化时控制变量一般为离散变量和标准微粒群优化(PSO)算法中参数需经多次试验确定而影响实用性的问题。APSO算法采用自适应参数策略和边界约束条件,能够取得问题的全局优化解。通过建立基于APSO算法的无功优化模型,成功解决了变量的离散问题。在Visual Studio 2008环境下,采用C#语言编写,应用在IEEE 30节点系统的无功优化程序计算结果表明,APSO算法较标准PSO算法有效地提高了收敛精度及稳定性,具有较好的自适应性和有效性,而且全局寻优能力更强。

Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization （PSO） algorithm need to be predefined by test, so the algorithm＇s practicability is restricted. For these reasons, an adaptive particle swarm optimization （APSO） algorithm is proposed by the authors. In the algorithm, the self-adaptive tuning strategy and boundary constraint conditions are introduced and the global optimal solution is easily found. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability are obviously improved compared with that of PSO.