融合量测重构的RGAN-UKF智能电网状态估计模型RGAN-UKF Smart Grid State Estimation Model Incorporating Reconstructed Measurements
李海英,裴康鑫
摘要(Abstract):
针对电力系统量测数据不完整导致状态估计失效进而危害智能电网安全经济运行的问题,本文提出一种融合量测重构的残差生成对抗网络-无迹卡尔曼滤波状态估计方法,以进行电力系统状态估计。首先,分析模型重构量测的基本工作原理,以重构不完整量测数据;然后,结合Holt’s双参数平滑法与无迹卡尔曼滤波方法,构造了融合重构量测的动态估计方法;最后,在IEEE 30节点系统进行仿真,验证了所提方法的可行性和有效性。
关键词(KeyWords): 智能电网;量测重构;状态估计;Holt's双参数平滑法;无迹卡尔曼滤波
基金项目(Foundation): 国家自然科学基金资助项目(51777126);; 山西省重点研发计划高新领域重点项目(202003011008)
作者(Author): 李海英,裴康鑫
DOI: 10.19635/j.cnki.csu-epsa.000863
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