《工程(英文)》 >> 2020年 第6卷 第7期 doi: 10.1016/j.eng.2020.06.006
智能电网状态估计中用于提高数据完整性的超分辨率感知技术
a School of Science and Engineering, Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China
b School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
c Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, China
d School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
e School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
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摘要
智能电网是一个结合了可再生能源与先进信息和通信技术的国家关键基础设施,为社会提供经济且安全的电力供应。为了应对可再生能源的间歇性特点并确保智能电网的安全性,应该进行更高频率的状态估计运算,而高频率的状态估计运算需要更加完整的系统状态信息。本文从智能电网状态估计数据完整性角度出发,将如何基于低频数据恢复高频数据的问题视为一个超分辨率感知(super resolution perception, SRP)问题。然后提出了一种新颖的基于机器学习的SRP方法,即超分辨率状态估计网络(super resolution perception net for state estimation, SRPNSE)来提高状态估计的数据完整性。此方法主要包含三部分:特征提取、信息补全和数据重建。算例证明了我们提出的SRPNSE方法在状态估计中从低频数据恢复至高频数据的有效性和实用价值。
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