针对分布式光伏发电系统广泛接入配电网,导致电力系统潮流计算速度和精度较低的问题,提出一种基于混沌海鸥优化算法的含光伏发电系统负荷模型参数辨识模型。首先,在综合负荷模型的虚拟母线上接入等效光伏发电系统的负荷模型,从而建立配电网广义负荷模型;之后,提出一种将混沌优化与海鸥优化相结合的优化算法,基于该算法完成配电网的等值,并在此基础上进行含光伏发电的综合负荷模型参数辨识。最后,通过仿真表明该文提出的算法,相比于传统的粒子群算法和单一海鸥优化算法,在计算精度和收敛速度等方面具有优越性,并可应用于负荷模型的参数优化。
Abstract
Aiming at the problem that distributed photovoltaic power generation systems are widely connected to the distribution network, resulting in low power flow calculation speed and accuracy, this paper proposes a parameter identification model of photovoltaic power generation system load models based on chaotic seagull optimization algorithm. First, the load model of the equivalent photovoltaic power generation system is connected to the virtual bus of the comprehensive load model to establish a generalized load model of the distribution network; then, an optimization algorithm combining chaos optimization and seagull optimization is proposed, based on this algorithm Complete the equivalence of the distribution network, and on this basis, carry out the parameter identification of the comprehensive load model including photovoltaic power generation. Finally, simulations show that the proposed algorithm is superior to traditional particle swarm optimization and single seagull optimization in terms of calculation accuracy and convergence speed, and can be applied to load model parameter optimization.
关键词
光伏发电 /
负荷模型 /
参数辨识 /
海鸥优化算法
Key words
photovoltaic power generation /
load model /
parameter identification /
seagull optimization algorithm
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基金
国家重点研发计划（2017YFB0902100）
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