基于变尺度混沌理论与免疫遗传算法的电力系统无功优化

Reactive Power Optimization Based on Mutative Scale Chaos Theory and Immune Genetic Algorithm

提出一种基于变尺度混沌理论与免疫遗传算法的混合智能无功优化算法。该算法利用混沌变量的遍历性、随机性和规律性特点,将混沌运动自身的遍历区域变换成优化变量的取值范围,通过对整个解空间进行考察实现了可行域内的全局优化搜索。同时通过变尺度方法不断缩小优化变量的搜索范围来实现局部细化搜索,从而增强混沌局部搜索能力,加快解的收敛速度,提高解的精度。文章最后以某地区实际电网为例,分别采用免疫遗传算法和混合智能算法对其进行无功优化计算,结果表明本文提出的混合智能算法在计算速度和全局收敛性方面有很大的提高。

A hybrid intelligent reactive power optimization algorithm in which mutative scale chaos theory is combined with immune genetic algorithm is proposed. Using the features of chaotic variable such as ergodicity, randomness and regularity, in the proposed algorithm the ergodic regions of chaotic motion its own are transformed into the value region of optimization variables, then by means of investigating whole solution space the global optimal search in the feasible region is implemented. Through mutative scale method, the search region of optimal variables is unceasingly reduced to implement local refined search to enhance local chaotic search ability and speed up the convergence of solution as well as to improve the accuracy of the solution. Taking a certain practical power network as the case, the immune genetic algorithm and the proposed hybrid intelligent algorithm are applied to reactive power optimization of the case study, calculation results show that the proposed hybrid algorithm is more better than immune genetic algorithm in computing speed and global convergence.