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电力大数据:2019,22(9):-
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粒子群算法在电网无功优化中的应用研究
周林波1, 石平灯2, 田滔2, 王雪2
(1.贵州电网有限责任公司贵安供电局;2.贵州电网公司贵安供电局)
Application of Particle Swarm Optimization on Grid Reactive Power Optimization
ZHOU LinBo1, SHI Pingdeng2, TIAN Tao2, WANG Xue2
(1.Power Supply Bureau, Guian, Guizhou Power Grid Corporation;2.Power Supply Bureau,Guian,Guizhou Power Grid Corporation,Guian,Guizhou)
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投稿时间:2019-04-16    修订日期:2019-06-18
中文摘要: 为解决目前电网系统无功优化潮流计算中存在的问题,如计算量大,计算结果中的各节点电压值可能导致无功电源出力接近极限值,并可能与系统电压安全发生冲突,发电机出力越限等。本文采用带罚函数、学习因子和惯性权重的改进粒子群算法,通过模拟编程,求解了在给定约束条件情况下,两个典型系统(5节点典型系统 和39节点典型系统)的无功优化潮流计算问题。通过计算结果分析比较,总结出了在无功优化计算中,如何对电网中的约束条件进行处理,以及如何设置粒子群算法中的相关参数和范围。并讨论了电网的约束条件对无功优化结果的影响,给出了粒子群算法中罚函数、惯性权重及学习因子等参数的设置原则以及对算法收敛性的影响,并对算法的改进进行了展望。
Abstract:In order to solve the existing problems in reactive power optimization power flow calculation of power grid system, such as the large amount of calculation, the voltage values of each node in the calculation result may lead to the output of reactive power source approaching the limit value, and may conflict with the system voltage safety, and the output of generator exceeding the limit, etc. In this paper, an improved particle swarm optimization algorithm with penalty function, learning factor and inertia weight is used to solve the reactive power flow calculation problem of two typical systems (5-node typical system and 39-node typical system) under given constraints. Through the analysis and comparison of the calculation results, this paper summarizes how to deal with the constraints in the power grid and how to set the parameters and ranges in the particle swarm optimization. The influence of grid constraints on reactive power optimization results is discussed. The setting principles of penalty function, inertia weight and learning factor in particle swarm optimization (PSO) and their effects on the convergence of PSO are given. The improvement of PSO is prospected.
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