只利用断路器信息诊断电力系统故障的高级遗传算法

A Refined Genetic Algorithm for Fault Section Estimation Using Information from Circuit Breakers

本文研究了基于断路器跳闸时序信息的电力系统故障诊断方法。主要包括三个方面的工作：①首次构造了只利用在各类保护（如主保护、第一后备保护和第二后备保护等等）动作时段内断路器跳闸的分段时序信息识别故障元件的０－１规划模型。②提出了应用无源信息识别故障区域的方法，有效地把故障诊断问题局限于某（些）个小的局部网络之中，大大加快了故障诊断的速度。③探讨了高级遗传算法（ＲｅｆｉｎｅｄＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍ──ＲＧＡ）在电力系统故障诊断中的应用，这种方法可以有效地求得多个全局最优解，很适用于解决电力系统复杂故障情况下的诊断问题。经算例计算表明，所发展的故障诊断的数学模型是正确的，采用的基于高级遗传算法的故障诊断方法有在线应用的潜力。

A new method of fault section estimation in power systems by using the time series information of tripped circuit breakers is presented. The main contributions of this Paper include the following three aspects: ① the mathematical model of fault section estimation problem by using the time series information of the tripped circuit breakers at the actuating time zone of any protective relays is proposed for the first time;② an efficient method to form the fault subnetworks is developed by using the information from circuit breakers, and by this way the fault diagnosis can be fulfilled in a very short time for large scale power systems and can be implement6d on-line;③ a new method based upon Refined Genetic Algorithm (RGA) is used to solve the fault section estimation problem, and the simulation results have shown that the RGA method can find multiple optimal solutions efficiently in a single run.