New Constriction Particle Swarm Optimization for Security-Constrained Optimal Power Flow Solution

Z. Gaing, Xun Liu
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引用次数: 8

Abstract

This paper presents a new version of constriction particle swarm optimization (NCPSO) with mutation mechanism for solving the security-constrained optimal power flow (OPF) with both the steady-state security constraints and the transient stability constraints. The objective of SCOPF in considering the valve-point loading effect of the unit and the operating limits of FACTS devices is defined not only to minimize total generation cost but also to enhance transmission security, reduce transmission loss, and improve the bus voltage profile under normal or post-contingent states. The proposed NCPSO method involves a new cognitive behavior of particle. The particle can remember its previously visited best and worst positions, thus it can explore effectively the search space. The effectiveness of the proposed method is demonstrated for the IEEE 30-bus and 57-bus systems.
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安全约束下的新型收缩粒子群最优潮流解
针对既有稳态安全约束又有暂态稳定约束的安全约束最优潮流问题,提出了一种具有突变机制的新型收缩粒子群优化算法。SCOPF在考虑机组的阀点负荷效应和FACTS设备的运行限制时,其目标不仅是为了使发电总成本最小化,而且还要提高输电安全性,降低输电损耗,改善正常或事后状态下的母线电压分布。提出的NCPSO方法涉及到一种新的粒子认知行为。该粒子能够记住其先前访问过的最佳和最差位置,从而能够有效地探索搜索空间。在IEEE 30总线和57总线系统中验证了该方法的有效性。
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