牵引负荷接入电力系统的随机潮流计算  

Probabilistic Power Flow of the Traction Load Accessing to the Power System

作　　者：乔垚 高锋阳[1] 杜强[1] 黄可[1] 强国栋 QIAO Yao;GAO Fengyang;DU Qiang;HUANG Ke;QIANG Guodong(School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Chin)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《郑州大学学报（理学版）》2017年第4期104-111,共8页Journal of Zhengzhou University:Natural Science Edition

基　　金：甘肃省科技支撑计划项目(1204GKCA038)

摘　　要：基于随机潮流计算对含牵引负荷的电网潮流不确定性进行描述,提出使用群体感应机制的粒子群算法对牵引负荷概率模型进行参数辨识.采用基于Nataf变换的拉丁超立方采样技术控制随机潮流输入变量的相关性.结合算例仿真,分析在不同负荷空间相关性的情况下,牵引负荷的接入对电网电压和支路潮流概率分布的影响.结果表明,使用群体感应机制的粒子群算法参数辨识精度更高,且避免了基本粒子群算法易陷入局部最优解的缺点;考虑牵引负荷随机性的支路功率和电压概率分布因不同的负荷空间相关性变化明显.为新建高铁线路接入电网提供了参考.Particle swarm optimization based on population induction was proposed to identify the traction load probabilistic model. The correlation of input variables could be controlled through the latin hypercube sam-pling based on Nataf transformation. Combined with numerical simulation and in the case of the different space correlation of the loads, analyzing the influence of traction load access on the voltage and branch power flow probability distribution was analyzed. The result showed that particle swarm optimization algorithm based on population induction was more accurate, which could overcome the disadvantage that the ordinary particle swarm algorithm was easy to fall into local optimum. The probability distribution of branch power and voltage considering the randomness of traction load was obviously different due to different loads spatial correlation.This study provided a reference for the new high-speed rail line accessing to the power system.

关 键 词：牵引负荷 粒子群算法 拉丁超立方采样 随机潮流计算 

分 类 号：TM92[电气工程—电力电子与电力传动]