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Optimal Reactive Power Dispatch Using Self-adaptive Real Coded Genetic Algorithm

机译：自适应实编码遗传算法的最优无功调度

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摘要

In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is introduced by applying the simulated binary crossover (SBX) operator. The binary tournament selection and polynomial mutation are also introduced in real coded genetic algorithm. The problem formulation involves continuous (generator voltages), discrete (transformer tap ratios) and binary (var sources) decision variables. The stochastic based SARCA approach can handle all types of decision variables and produce near optimal solutions. The IEEE 14- and 30-bus systems were used as test systems to demonstrate the applicability and efficiency of the proposed method. The performance of the proposed method is compared with evolutionary programming (EP) and previous approaches reported in the literature. The results show that SARGA solves the ORPD problem efficiently.
机译：本文采用自适应实数编码遗传算法（SARGA）作为解决最优无功调度（ORPD）问题的技术之一。通过应用模拟二进制交叉（SBX）运算符介绍了实编码遗传算法（RGA）中的自适应。在实数编码遗传算法中还引入了二进制锦标赛选择和多项式变异。问题表述涉及连续的（发电机电压），离散的（变压器抽头比率）和二进制（无源）决策变量。基于随机的SARCA方法可以处理所有类型的决策变量，并产生接近最优的解决方案。 IEEE 14总线和30总线系统被用作测试系统，以证明所提出方法的适用性和效率。将该方法的性能与进化规划（EP）和文献中报道的先前方法进行了比较。结果表明，SARGA有效地解决了ORPD问题。

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