Research for Distribution Systems Loss Minimization by Genetic Algorithm

本研究的目的主要有以下两个方面：(1)通过建立字符串结构和适应度函数等，利用遗传算法建立配电网网损最小化问题的求解算法；(2)对算法进行改进，以获得更准确的大规模问题的解。研究结果总结如下：(1)提出了用遗传算法求解配电网网损最小化问题的算法。(2)对于规模较小的问题，如果串的位数不是很大，通过改进的算法可以得到精确的(接近最优的)解。计算时间比模拟退火法少1/6。对于第二个目标(B)，研究了串结构、适应度函数、交叉率、变异率、交叉点数目、人工交叉、初始串模式等因素对计算结果的影响。研究结果如下：(3)对于每个具体问题，存在最优交叉率和变异率。(4)对于大系统，只有在初始串中存在有效的(有利于损耗最小结构的)模式时，才能得到准确的计算结果。(5)其他人为操作不会对结果产生显著影响。从上面的结果可以看出，目标(A)和(B)都实现了。上述结果发表在下一页所示的论文中。

The objective of this research is the following two subjects.(A) To establish a solution algorithm of the distribution loss minimization problem by the genetic algorithm through creating the string structure and the fitness function, etc.(B) To improve the algorithm so as to get more accurate solutions for large scale problems. The research results are summarized as follows.(1) The solution algorithm of the distribution loss minimization problem by the genetic algorithm is developed.(2) For a small scale problem, if the number of string bits is not so large, an accurate (near optimal) solution can be available through the developed algorithm. The computation time is 1/6 times smaller than that by simulated annealing method. For the second objective, (B), how the string structure, fitness function, crossover rate, mutation rate, number of crossover points, artificial crossovers, initial string patterns, etc. affect the calculation results is investigated. The investigated results are as follows.(3) The optimal crossover rate and mutation rate exist for each specific problem. Ordinarily, they are 0.6-0.8 and one bit for 1 - 2 string(s) respectively.(4) For a large system, an accurate calculation result can be available only when effective (good for loss minimum structure) schema exist in the initial strings.(5) Other artificial operations do not affect the result significantly. From the above result, it can be seen that objectives (A) and (B) are achieved. The above results are published in the papers shown in the next page.