Abstract The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

中文翻译：

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用于提高智能电网状态估计中数据完整性的超分辨率感知

摘要 智能电网是一种不断发展的关键基础设施，它结合了可再生能源和最先进的信息和通信技术，以提供更经济、更安全的供电服务。为了应对不断增长的可再生能源的间歇性和确保智能电网的安全性，状态估计是了解智能电网真实状态的基本工具，应该高频进行。需要更完整的系统状态数据来支持高频状态估计。因此，本文研究了智能电网状态估计的数据完整性问题。通过从低频数据中恢复高频数据来提高数据完整性的问题在本文中被表述为超分辨率感知（SRP）问题。此后提出了一种新颖的基于机器学习的 SRP 方法。所提出的方法，即状态估计的超分辨率感知网络（SRPNSE），包括三个步骤：特征提取、信息完成和数据重建。案例研究证明了所提出的 SRPNSE 方法在从低频数据中恢复高频数据以进行状态估计的有效性和价值。