基于并行动态无功优化蚁群算法的应用  

Dynamic Reactive Power Optimization on the Basis of Dual-Ant Colony algorithm

作　　者：周鑫[1] 刘柱揆[1] 许守东[1] 

机构地区：[1]云南电网公司电力研究院,昆明650217

出　　处：《云南电力技术》2014年第1期98-102,共5页Yunnan Electric Power

摘　　要：为考虑负荷变动下动态无功优化控制变量全天动作次数的限制,针对其多目标、强时空耦合的特点,以全天电能损耗最小、变压器分接头和电容器投切次数最少为目标函数,通过改进调节变量动作的时间约束,提出一种更加实用的新模型。利用并行算法计算不同目标函数,并通过多种信息素交换方可得到多组的较优解,增加了算法的灵活性和实用性。本文蚁群算法在寻优过程中不仅计及整个网络电能损耗的减少,而且改进了蚁群间信息素交换规则,因此能够较快地找到对电能损耗影响较大的节点,提高搜索速度。通过IEEE14、IEEE30系统仿真计算验证了该模型及算法的有效性和可行性。结果表明该文模型及算法能够有效的调节及分配控制变量的动作次数,对调节时机的选择也更为准确。To consider the control variables limits in a day of the dynamic reactive power optimization which load changes, according to the characteristic of the a space - time close coupled a new model for dynamic reactive power optimization is proposed, which the objective functions is minimum power loss throughout the day and the minimum switching operations, by limits of regulation time. In order to get different target groups of the better solutions, many kinds of way are used to change the pheromone. The colony optimiza- tion search strategy of ACOA is improved, they can find compensation buses that have a greater impact on network on network losses and improve the search speed. Test on IEEE 14bus, IEEE 30bus systems demonstrate the efficiency of the proposed model and algo- rithm. The results show that the model and algorithm can effectively control the regulation and distribution of the number of variables.

关 键 词：动态无功优化 并行蚁群算法 多目标 

分 类 号：TM714.3[电气工程—电力系统及自动化]