基于细菌菌落算法的电力系统无功优化

发布时间：2018-11-25 21:19

【摘要】：电力系统无功优化具有非线性,多控制变量,多约束条件,连续变量和离散变量混杂的特点,针对现有算法或容易陷入局部最优解或收敛速度慢的缺点,提出了一种细菌菌落(bacterial colony optimization,BCO)优化算法,将BCO优化算法首次应用于电力系统无功优化问题。BCO算法将问题的解空间视为细菌培养液,在其中放置单个或少量细菌个体,模拟细菌菌落的生长进化过程,该算法本身具有进化机制,并且提出了一种新的结束准则。BCO算法通过繁殖适应度高的个体,死亡适应度低的个体,可以尽快的获得适应度更优的个体,从而可以避免算法陷入局部最优解,同时也加快了收敛速度。用BCO算法对IEEE14节点标准测试系统进行无功优化计算,实验结果表明,细菌菌落(BCO)优化算法较其他算法具有较强的全局寻优能力,且收敛速度快,鲁棒性好,可以作为求解电力系统无功优化问题的一种新途径。
[Abstract]:Reactive power optimization in power system has the characteristics of nonlinearity, multi-control variables, multi-constraints, continuous variables and discrete variables. A bacterial colony (bacterial colony optimization,BCO) optimization algorithm is proposed. The BCO optimization algorithm is first applied to the reactive power optimization problem in power system. BCO algorithm treats the solution space of the problem as a bacterial culture medium in which a single or a small number of bacterial individuals are placed. In order to simulate the growth and evolution of bacterial colonies, the algorithm has the evolutionary mechanism and proposes a new closure criterion. BCO algorithm can reproduce individuals with high fitness and low death fitness. Individuals with better fitness can be obtained as soon as possible so that the algorithm can avoid falling into the local optimal solution and speed up the convergence. The BCO algorithm is used to calculate the reactive power optimization of the IEEE14 node standard test system. The experimental results show that the bacterial colony (BCO) optimization algorithm has better global optimization ability, faster convergence speed and better robustness than other algorithms. It can be used as a new way to solve reactive power optimization problem in power system.
【作者单位】：上海理工大学电气工程系;
【基金】：国家自然科学基金(41075019) 上海市研究生创新基金项目(JWCXSL1302)
【分类号】：TM714.3

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