Research on Reactive Power Optimization Based on Immunity Genetic Algorithm

被引：1
|
作者：
Liu, Keyan [1 ]
Sheng, Wanxing [2 ]
Li, Yunhua [1 ]
机构：
[1] Beijing Univ Aeronaut & Astronaut, Beijing 100083, Peoples R China
[2] China Electric Power Res Inst, Beijing 100085, Peoples R China
来源：
INTELLIGENT COMPUTING, PART I: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, ICIC 2006, PART I | 2006年 / 4113卷
关键词：
D O I：
10.1007/11816157_72
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE 30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than current known algorithms.
引用
收藏
页码：600 / 611
页数：12
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