电网技术2016，Vol.40Issue(8)：2389-2394,6.DOI:10.13335/j.1000-3673.pst.2016.08.021
基于神经网络的法向阻抗模裕度快速计算方法
Fast Estimation Method for Normal Impedance Modulus Margin Based on Neural Network
摘要
Abstract
In order to evaluate power system voltage stability on-line rapidly,a BP neural network model optimized with genetic algorithm was proposed.Based on general principle of extreme value analysis of nonlinear equation,criterion of system static voltage stability was deduced;and normal impedance modulus margin index (NIMMI) suitable for power system voltage stability evaluation was proposed.Compared with Thevenin impedance modulus margin index,NIMMI linearity is better and more suitable for neural network prediction.Nonlinear mapping relationship of node active power (reactive power) and node NIMMI was established under system synchronous power disturbance.Genetic algorithm was used to optimize weights and threshold of BP neural network and improve prediction accuracy.Matlab simulation results show that NIMMI computation speed is greatly accelerated compared with traditional power flow calculation,so it is more favorable to implement on-line voltage stability evaluation.关键词
电力系统/电压稳定/法向动态等值阻抗/法向阻抗模裕度/遗传算法/BP神经网络Key words
power system/voltage stability/normal dynamic equivalent impedance/normal impedance modulus margin index/genetic algorithm/BP neural network分类
信息技术与安全科学引用本文复制引用
颜廷鑫,刘光晔,谢冬冬..基于神经网络的法向阻抗模裕度快速计算方法[J].电网技术,2016,40(8):2389-2394,6.基金项目
国家自然科学基金资助项目(51577053).Project Supported by National Natural Science Foundation of China (51577053). （51577053）
