CAJ | 학술논문

采用改进差分进化算法（ Improved Differential Evolution Algorithm，IDEA）求解配电网无功优化问题。该算法引入基于反学习的种群初始化方法，使算法得到的初始种群具有多样性，能够充分提取搜索空间的信息；引入高斯扰动机制到交叉操作中，提高了在维尺度上的种群多样性；在进化过程中融入人工蜂群搜索思想，引入蜂群加速进化与侦查操作策略，使算法能快速跳出局部最优，避免了早熟问题。建立了配电网无功优化数学模型，并采用IDE算法对IEEE30节点系统求解该模型，并与基本DE算法进行对比，仿真结果证明了所提IDE算法具有更佳的性能，能够有效的求解配电网无功优化的问题。
채용개진차분진화산법（ Improved Differential Evolution Algorithm，IDEA）구해배전망무공우화문제。해산법인입기우반학습적충군초시화방법，사산법득도적초시충군구유다양성，능구충분제취수색공간적신식；인입고사우동궤제도교차조작중，제고료재유척도상적충군다양성；재진화과정중융입인공봉군수색사상，인입봉군가속진화여정사조작책략，사산법능쾌속도출국부최우，피면료조숙문제。건립료배전망무공우화수학모형，병채용IDE산법대IEEE30절점계통구해해모형，병여기본DE산법진행대비，방진결과증명료소제IDE산법구유경가적성능，능구유효적구해배전망무공우화적문제。
Improved differential evolution algorithm ( IDEA) is used to solve the reactive power optimization of distri-bution network problem.An anti-learning population initialization method is introduced to the algorithm which makes the initial population diverse and is able to fully extract the information of the search space.The method introduces Gauss perturbation mechanism to the interlace operation, which improves the diversity of the population in the dimen-sion scale.Meanwhile, the artificial colony search thoughts and the bees accelerated evolution and reconnaissance op-erations strategy are added into the evolution process so that the algorithm can quickly jump out of the local optimum and avoid premature.Based on the above, a distribution network reactive power optimization model is established and solves the model with IEEE30 node system adopting IDE algorithm, and then compares it with the basic DE algorithm. The simulation results prove that the IDE algorithm mentioned in this article has a better performance, which can ef-fectively solve the reactive power optimization problem.