基于GA-ADAM优化的BPNN配电网潮流计算

Distribution network power flow calculation based on the BPNN optimized by GA-ADAM

潮流计算是电力系统运行与控制的基础。为解决配电网可再生能源渗透率不断增加带来的负荷点电压波动的不确定性,以及传统电力系统潮流数据收集能力不足导致潮流计算不准确等问题。本文提出了一种基于数据驱动的潮流分析模型,构建了一种基于BPNN结合GA-ADAM优化算法模型来分析随机性下配电网的潮流计算方法。首先,引入潮流初值信息、拓扑结构特征以及功率因数指标构建训练集,通过对回归模型的训练,充分挖掘节点电压与功率之间的映射关系。其次,使用GA-ADAM算法优化模型初值和权重参数。最后,基于IEEE-33节点配电网模型进行验证,本文模型潮流计算的最大误差3.93×10^(-3),平均绝对误差1.46×10^(-3),均方根误差1.81×10^(-3),优化后的BPNN潮流计算电压误差值降低37.66%。实际算例仿真结果表明,与其他方法比较,本文构建的模型各误差指标小、准确度高,提高了潮流计算的效率和准确性。

Power flow calculations are the basis for the operation and control of power systems.In order to solve the problems of uncertainty of voltage fluctuation at the point of load caused by the increasing penetration rate of renewable energy in the distribution network,and the inaccuracy of power flow calculation caused by the insufficient power flow data collection capacity of traditional power system.In this paper,a data-driven power flow analysis model is proposed,and a power flow calculation method based on back propagation neural network combined with genetic algorithm and adaptive moment estimation optimization algorithm is constructed to analyze the power flow calculation method of distribution network under randomness.Firstly,the initial power flow information,topological structure characteristics and power factor indicators are introduced to construct the training set,and the mapping relationship between node voltage and power is fully explored through the training of the regression model.Secondly,the GA-ADAM algorithm is used to optimize the initial value and weight parameters of the model.Finally,based on the IEEE-33 bus distribution network model,the maximum error is 3.93×10^(-3),average absolute error is 1.46×10^(-3),and root mean square error is 1.81×10^(-3) of the model power flow calculation in this article,the optimized BPNN power flow calculation voltage error value is reduced by 37.66%.The simulation results of actual examples show that compared with other methods,the model constructed in this paper has smaller error indicators and higher accuracy,which improves the efficiency and accuracy of power flow calculation.