基于自适应粒子群算法的电力系统无功优化

Sun Shuqin, Zhang Bingren, Wang Jun, Yang Nan, Meng Qingyun
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引用次数: 11

摘要

由于无功优化的控制变量是离散的,且标准粒子群优化算法中的一些参数需要通过试验预先确定,限制了算法的实用性。为此,提出了一种自适应粒子群优化算法(APSO)。引入自适应调谐策略和边界约束条件,可以找到全局最优解并求解离散变量。标准ieee -30总线电力系统的无功优化结果表明,APSO比标准PSO效率更高。与粒子群算法相比,该算法的全局收敛精度和收敛稳定性明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power System Reactive Power Optimization Based on Adaptive Particle Swarm Optimization Algorithm
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. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. 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 is obviously improved compared with that of PSO.
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