摘要
配电网线损管理中面临的主要问题有表计配置不齐备、运行数据不易收集、元件和节点数过多。这些问题导致线损率计算工作十分繁杂。提出了一种基于改进K-Means聚类算法和Levenberg-Marquardt(LM)算法优化的BP神经网络模型快速计算低压台区线损率的方法,并通过编程加以实现。根据样本的电气特征参数,提出了改进K-Means聚类算法,将台区样本分类,解决了台区线损率数值分散的问题。在此基础上,采用LM算法优化的BP神经网络模型对样本数据按类进行训练,利用BP神经网络拟合样本线损率与电气特征参数之间的关系,得到其变化规律。以某地区601个台区样本数据为例进行仿真计算,验证了所提方法的准确性。结果表明,与标准BP神经网络模型相比,LM算法优化的BP神经网络模型具有快速收敛、高精度等优点。
Incomplete meter configuration, the difficulty of collecting operation data and vast numbers of components and nodes in low voltage transformer district lead to rather complicated work of calculation of line loss rate. To solve existing problems, a novel method of line loss rate calculation in transformer district was presented and realized by programming, which was combined improved K-Means clustering algorithm with BP neural network model optimized by Levenberg-Marquardt（LM） algorithm. Samples were classified by improved K-Means clustering algorithm according to electric characteristics. Thus, the numerical dispersion of line loss rate in transformer district was solved. On this basis, each class was trained by BP neural network optimized by LM algorithm. Variation of transformer district line loss rate was obtained by using BP neural network to map relation between line loss rate and electric characteristic parameters. 601 transformer districts in a region as an example, simulation and calculation were performed to verify the accuracy of the proposed method. The results show that the method has the advantages of fast convergence and high accuracy, compared to standard BP neural network.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2016年第17期4543-4551,共9页
Proceedings of the CSEE
基金
国家科技部智能配用电大数据应用关键技术(2015AA050203)~~
关键词
低压台区
电气特征参数
线损率
改进K-Means聚类算法
LM算法优化的BP神经网络
low voltage transformer district
electrical characteristic parameters
line loss rate
improved K-Means clustering algorithm
BP neural network optimized by LM algorithm
