基于协同微粒子群算法的暂态稳定约束最优潮流  

Application of cooperative micro-particle swarm algorithm for transient stability constrained optimal power flow

作　　者：叶琳[1] 肖谭南 吕晓祥[2] 王建全[2] 王超[1] 杨逸[3] 周丽华[3] 

机构地区：[1]浙江省电力公司,浙江杭州310027 [2]浙江大学电气工程学院,浙江杭州310027 [3]浙江省湖州电力局,浙江湖州313000

出　　处：《机电工程》2015年第1期101-106,共6页Journal of Mechanical & Electrical Engineering

摘　　要：针对电力系统预防控制的实现问题,提出了一种基于协同微粒子群算法的暂态稳定约束最优潮流计算方法。暂态稳定约束最优潮流问题是在最优潮流问题上加入暂态稳定功角约束,形成的高维非线性动态优化问题。微粒子群算法能够通过较少的粒子数搜索到全局最优解,有效地求解高维复杂优化问题。利用该方法,对存在失稳预想事故的新英格兰10机39节点系统进行了分析计算,并与多种智能算法的调度结果进行了比较。研究结果表明,使用协同微粒子群进化算法求解暂态稳定约束最优潮流是有效与准确的,其计算量小于已报道算法,优化结果更优,能够应用到电力系统的暂态稳定分析中,为电力系统预防控制提供了一种思路。Aiming at solving transient stability constrained optimal power flow,a new and effective approach which is based on the Cooperative micro-particle swarm is proposed. The technique can be used as a preventive control scheme. The formulas of transient stability constrained optimal power flow were derived through the addition of rotor angle inequality constraints into optimal power flow relationships,which is a high-dimensional nonlinear dynamic optimization problem. Micro-evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. It was proved to be useful in evolutionary computation due to the ability to solve high-dimensional complex problem. The optimal schedule for the New England ten-generator,39-bus system,which has unstable contingencies,was obtained through this method. The method was proved to be effective and accurate by comparing the schedules solved by COMPSO and other reported intelligent algorithms. The results indicate that the proposed method can achieve better optimization through less calculation and be applied to the analysis of transient stability and rescheduling of power system.

关 键 词：微粒子群算法 电力系统 预防控制 暂态稳定 最优潮流 

分 类 号：TM71[电气工程—电力系统及自动化]