基于人工智能算法的主动配电网分解协调多目标优化研究

缪辰宇1; 刘  尧1; 杨叶昕1; 张  勇1; 陈建钿1; 丘冠新1; 谢  虎2

基于人工智能算法的主动配电网分解协调多目标优化研究

引用本文：缪辰宇1,刘尧1,杨叶昕1,张勇1,陈建钿1,丘冠新1,谢虎2.基于人工智能算法的主动配电网分解协调多目标优化研究[J].计算技术与自动化,2022,(2):71-76

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作者	单位
缪辰宇1,刘尧1,杨叶昕1,张勇1,陈建钿1,丘冠新1,谢虎2	（1.广东电网有限责任公司珠海供电局，广东珠海 519075；2.南方电网数字电网研究院有限公司，广东广州 510663）

中文摘要:为了更好地促进中国电力行业的健康、平稳发展，对主动配电网分解协调进行了多目标的优化研究。采用了电源管理单元(power management unit, PMU)拓扑分析研究法，根据全局和分区的思想，对IEEE-30的数据进行了研究。结果表明：分区优化与全局优化相比能够明显降低网络损耗、运行成本和更新次数，分区的求解思路在拓扑分析模型中更加有效。然后将IEEE-30、IEEE-51和IEEE-112测试系统中得出的时间与免疫遗传算法的时间进行比较，发现拓扑约束分析能够使电源管理单元得到良好的优化效果。结论证实了本文所选取模型的有效性以及这种研究方法在企业管理的适用性。

中文关键词:人工智能主动配电网分解协调拓扑分析模型

Design of Nonlinear Observer for Dynamic Positioning Based on Particle Swarm Optimization Algorithm

Abstract:In order to better promote the healthy and stable development of China’s power industry, this paper makes a multi-objective optimization research on the decomposition and coordination of active distribution network. This paper mainly adopts the power management unit (PMU) topology analysis research method, and studies the IEEE-30 data according to the idea of global and partition. The results show that partition optimization can significantly reduce the network loss, operation cost and update times compared with global optimization, and the partition solution idea is more effective in the topology analysis model. Then, the time obtained in IEEE-30, IEEE-51 and IEEE-112 test systems is compared with the time of immune genetic algorithm. It is found that topology constraint analysis can make the power management unit get a good optimization effect. Therefore, this confirms the effectiveness of the model selected and the applicability of this research method in enterprise management.

keywords:artificial intelligence active distribution network decomposition and coordination topology analysis model

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