基于细菌群体趋药性算法的电力系统无功优化  

Reactive Power Optimization in Power System Based on Bacterial Colony Chemotaxis Algorithm

作　　者：黄伟[1] 张建华[1] 张聪[1] 刘自发[1] 魏志连[1] 潘东立[1] 

机构地区：[1]华北电力大学电力系统保护与动态安全监控教育部重点实验室,北京市102206

出　　处：《电力系统自动化》2007年第7期29-33,共5页Automation of Electric Power Systems

摘　　要：将细菌群体趋药性(BCC——bacterial colcony chemotaxis)优化方法应用在电力系统无功优化中,以网损最小为目标函数,建立了BCC无功优化数学模型。由发电机机端电压、变压器分接头和电容器组3部分控制变量构成初始矩阵,它们在算法中作为细菌位置坐标,表征细菌移动寻优时的空间位置。控制变量即细菌移动遵循在n维空间中的移动寻优规律,每个细菌通过感知周围的信息不断向最优方向移动,从而提高了全局搜索能力。通过优化参数,加快了收敛速度。对IEEE30,IEEE57,IEEE118测试系统进行了测试,与免疫记忆遗传算法和混沌粒子群算法相比较,结果令人满意。Bacterial colony chemotaxis （BCC） optimization algorithm is used in reactive power optimization problem in electric power system in this paper. The modeling of reactive power optimization is established taking the minimum network losses as the objective. Reference axes are three bacterial values： generator voltage, transformer tap and capacitor tank, respectively. All of bacterial values for every space position forms initial matrix in reactive power optimal modeling. Bacterium finds its foraging way with whole search ability by bacteria＇ s searching strategy rule. Convergence speed is improved by optimizing initial parameters. This method is applies to the IEEE 30, IEEE 57, IEEE 118 system and compared with IMGA and CPSO optimal method, the result is acceptable.

关 键 词：无功优化 细菌群体趋药性算法 进化策略 智能优化 电力系统 

分 类 号：TM714[电气工程—电力系统及自动化]