摘要
电网在线故障诊断是保证电网安全稳定运行的必要措施之一。随着区域电网规模的不断扩大,电网出现故障时会有更多不准确的信息传入调度中心,这势必影响原有在线故障诊断方法的速度和准确性。本文考虑自然灾害对故障区域的影响,将T-S模糊模型、粗糙集理论和遗传算法有效融合,充分利用T-S模糊和粗糙集理论对不精确和不完备信息的知识挖掘与处理能力,基于遗传算法的自适应优势改进粗糙集约简,能够更快、更准确地在线诊断区域电网故障。通过故障诊断算例仿真分析,结果表明了所研究的故障诊断算法具有快速性、准确性、有效性。
Online power grid fault diagnosis is one of the necessary measures to guarantee the safety and stability of power grid operation.With the continuously expanded scale of the regional power grid,a larger number of inaccurate alarm information will be sent into the dispatching center if a grid failure occurs.This is bound to affect the speed and accuracy of the original online fault diagnosis algorithm.In this paper,considering the impact of natural disasters on fault areas,the T-S fuzzy model,rough set theory and genetic algorithm are effectively fused.The superiority of T-S fuzzy model and rough set theory is fully utilized to mine and deal with the imprecise and incomplete knowledge,and rough set reduction is improved based on the self-adaptive advantage of the genetic algorithm,such that the improved algorithm can identify fault in the regional power grid more quickly and more accurately.Through simulations and analyses of a numerical example,results show the efficiency,accuracy and effectiveness of the improved fault diagnosis algorithm.
作者
陈哲
江晓燕
岑炳成
安海云
徐野驰
张俊芳
CHEN Zhe;JIANG Xiaoyan;CEN Bingcheng;AN Haiyun;XU Yechi;ZHANG Junfang(Electric Power Research Institute,State Grid Jiangsu Electric Power Co.,Ltd,Nanjing 211103,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《电力系统及其自动化学报》
CSCD
北大核心
2019年第12期10-15,共6页
Proceedings of the CSU-EPSA
基金
国家自然科学基金资助项目(61673213)。
关键词
电网在线故障诊断
T-S模糊模型
粗糙集理论
改进遗传算法
故障诊断决策表
online power grid fault diagnosis
T-S fuzzy model
rough set theory
improved genetic algorithm
fault diagnosis decision table
