A novel method of reactive voltage optimisation for photovoltaic systems

W. Zhang, Y. Zhang
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引用次数: 1

Abstract

In this paper, a novel method is proposed for real-time adaptive reactive power optimisation of photovoltaic systems derived from the improved dynamic teaching and learning reactive power optimisation based on moth-flame optimisation algorithm. By constructing the mathematical model for the reactive power voltage control of a photovoltaic power station, learning is optimised continuously in the process of teaching and learning. According to real-time data, the optimal cooperation strategy between the RPVC/AVC control system and intelligent power grid command is formed, and real-time, adaptive and dynamic control of the system is realised. A simulation study is made in this paper, and the simulation results show that the proposed method is reasonable and effective for the 220 kV substation and its feeder system.
一种新的光伏系统无功电压优化方法
本文在改进的基于飞蛾火焰优化算法的动态教与学无功优化的基础上,提出了一种光伏系统实时自适应无功优化方法。通过构建光伏电站无功电压控制数学模型,在教与学的过程中不断优化学习。根据实时数据,形成了RPVC/AVC控制系统与智能电网指令的最优协同策略,实现了系统的实时、自适应和动态控制。本文进行了仿真研究,仿真结果表明该方法对220kv变电站及其馈线系统是合理有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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