基于遗传算法和模拟退火算法的电力系统的故障诊断

根据元件故障与保护动作和断路器跳闸之间的逻辑关系，首先把电力系统的故障诊断问题表示为０－１整数规划问题。在此基础上，采用了两种方法求解，第一种方法是遗传算法，第二种方法是模拟退火算法。这两种方法都能以较大的概率收敛到全局最优解，并能够求得多个最优解。提出的方法理论严密，与当前比较流行的以启发式知识为基础的专家系统方法不同。本文的方法可以处理任意复杂的故障，也可以处理有保护和断路器误动作的情况，且计算时间与故障的复杂程度基本无关。此外，提出的方法原则上适合于并行处理。

Fault Section Estimation in Power Systems Using Genetic Algorithm and Simulated Annealing

By use of Genetic Algorithm(GA)and Simulated Annealing(SA),two new methods to fault section estimation in power systems using are presented. The fault section estimation problem is formulated as a 0-1 integer programming problem,and the proposed methods can find multiple optimal solutions directly in a single run,which is very suitable for complex fsult diagnosis problem especially for the situations with protective relays and/or circuit breakers malfunction,because different combinationsof fault sections and malfunction Of proteCtive relays and/or circuit breakers may cause same alarms. In addition,the proposed and SA methods are ideal for parallel processing computer systems or hardwares.