基于阻抗模型故障特征匹配法的含DG配电网故障测距研究

项目名称： 基于阻抗模型故障特征匹配法的含DG配电网故障测距研究

项目编号： No.61271001

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 康忠健

作者单位： 中国石油大学（华东）

项目金额： 60万元

中文摘要： 电力系统配电网的故障精确测距是一个一直没有得到很好解决的难题，分布式电源(DG)的引入增加了精确测距的难度,其研究对尽快恢复供电及减少停电经济损失具有重要作用。 本研究在现有含DG配电网阻抗故障测距法和故障特征匹配测距法基础上，提出了一种基于改进阻抗模型和差分进化故障特征匹配的含DG配电网测距新算法。该方法考虑量测数据的相关性和接地电阻的非线性建立含DG配电网三相不对称改进阻抗模型，分析并提取含DG配网阻抗模型下的故障特征。以不同仿真故障下与实际故障下的系统故障特征匹配程度为进化适应度指标，利用差分进化算法快速搜索与实际故障特征最为匹配的仿真故障位置作为输出的故障距离。 该方法克服了现有阻抗法测距精度不高和基于遗传算法故障特征匹配法速度慢的缺点，具有测距精度高和计算速度快的优点，可用于含DG复杂配电网的精确故障测距，具有重要的理论和实际应用价值。

中文关键词： 含分布式电源配电网阻抗模型；故障测距；故障特征匹配；差分进化算法；人工蜂群算法

英文摘要： The fault accurate location in distribution network has is a difficult problem that have not been solved well until now. Distribution generators(DGs) make the fault location in distribution network more difficult. The studies on the fault loaction method play a very important role in restoring power supply and decreasing the outage economic loss in the distribution network with DG. Basen on the impendence fault location method and fault characteristics matching method, a new fault location method is proposed in this project to locate the fault position in the distribution network with DG by improved impendence model and differential evolution fault characteristics match algorithm. A three phases asymmetric improved impendence model of the distribution network with DG is built with considering the correlation of the measured data and the nonlinearity of the grounded-fault resistence into the model. Then, the fault characteristics of the distribution network with DG are analyzed and abstracted. Furthermore, the fault charactereistics difference between the simulation fault conditions and the actual fault condition is regarded as the evolution fitness index and the simulation condition with the most similar characteristics is quikly searched with the differential evolution algorithm. The fault position in simula

英文关键词： Impedance Model of Distribution Network with DG；Fault Location；Fault Characteristics Matching；Differential Evolution Algorithm；Artificial Bee Colony Algorithm