电网自动电压控制精细规则自动发现技术研究

Research on Automatic Discovery Technology of Fine Rules for Automatic Voltage Control in Power Grid

自动电压控制(AVC)已经在各级电网调度中心广泛使用。传统上AVC中的电压限值、功率因数限值等控制参数由人工经验制定。当电网结构发送变化或局部电网区域的负荷特性变化时,经常出现多级电压限值不能适配,影响AVC控制效能。文章提出一种基于大数据分析自动提取的AVC精细规则来优化AVC控制限值参数的方法。首先提出基于控制仿真在环的滚动潮流计算方法来模拟全天的电网自动电压控制过程,并设计了并行快速仿真计算平台,为后续AVC控制规则提取提供海量仿真数据;然后基于海量数据采用人工智能机器学习方法自动学习AVC控制精细规则,自动发现AVC限值参数与电网运行状态指标的内在规律;最后,基于提取的AVC精细规则构建优化模型来自动决策AVC中的母线电压限值、功率因数限值等控制参数,从而实现AVC控制参数的自动优化和辅助决策。

Automatic voltage control(AVC) has been widely used in all levels of power grid dispatching center.Traditionally, the control parameters of AVC, such as voltage limit and power factor limit, are determined by human experience. This paper proposes a method to optimize AVC control limit parameters based on AVC fine rules automatically extracted from big data analysis. Firstly, the rolling power flow calculation method based on Control Simulation in the loop is proposed to simulate the whole day AVC process of power grid, and a parallel fast simulation platform is designed to provide massive simulation data;Then, based on massive data,the AVC control fine rules are automatically learned by using artificial intelligent machine learning method, which reflect the relationship betwenn AVC limit parameters and power grid;Finally, based on the extracted AVC fine rules, an optimization model is constructed to automatically determine the bus voltage limit, power factor limit and other control parameters in AVC, so as to realize the automatic optimization and auxiliary decision-making of AVC control parameters.