基于细菌菌落算法的电力系统无功优化

Reactive Power Optimization based on Bacterial Colony Optimization

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。

In allusion to such features as nonlinearity, multi control variables, multi constraints and coexistence of continuous varia- bles and discrete variables in power system reactive power optimization. According to the drawbacks of the existing algorithm which is easy to fall into local optimal and slow convergence speed , this paper first apply bacterial colony optimization algorithm （BCO） in reac- tive power optimization. BCO considers the solution space of the problem as a certain culture medium. A single bacterium or a few bac- teria are used to simulate the evolution process of the bacterial colony. And the BCO itself has a certain evolutionary mechanism and give a new termination criterion. Because the algorithm make use of the idea of breeding individuals of high fitness and death the indi- viduals of low fitness, it could acquire better fitness as quickly as possible , avoid the algorithm fall into local optima and accelerate the convergence. The algorithm was implemented on the IEEE - 14 bus system . The results show that BCO has stronger global optimal searching ability, faster convergence rate and better robustness compared with other optimization algorithms, Therefore, It can solve problems of power system reactive power optimization as a new approach.