Under the background of Energy Internet, the ever-growing scale of the electric power system has brought new challenges and opportunities. Numerous categories of measurement data, as the cornerstone of communication, play a crucial role in the security and stability of the system. However, the present sampling and transmission equipment inevitably suffers from data missing, which seriously degrades the stable operation and state estimation. Therefore, in this paper, we consider the load data as an example and first develop a missing detection algorithm in terms of the absolute difference sequence (ADS) and linear correlation to detect any potential missing data. Then, based on the detected results, we put forward a missing recovery model named cascaded convolutional autoencoders (CCAE), to recover those missing data. Innovatively, a special preprocessing method has been adopted to reshape the one-dimensional load data as a two-dimensional matrix, and hence, the image inpainting technologies can be conducted to address the problem. Also, CCAE is designed to reconstruct the missing data grade by grade due to its priority strategy, which enhances the robustness upon extreme missing situations. The numerical results on the load data of the Belgium grid validate the promising performance and effectiveness of the proposed solutions.

中文翻译：

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基于级联卷积自编码器的电力负荷数据缺失智能检测与恢复

在能源互联网背景下，电力系统规模的不断扩大带来了新的挑战和机遇。众多类别的测量数据作为通信的基石，对系统的安全性和稳定性起着至关重要的作用。然而，目前的采样传输设备不可避免地存在数据丢失的问题，严重影响了稳定运行和状态估计。因此，在本文中，我们以负载数据为例，首先根据绝对差值序列（ADS）和线性相关性开发一种缺失检测算法，以检测任何潜在的缺失数据。然后，基于检测结果，我们提出了一种名为级联卷积自动编码器（CCAE）的丢失恢复模型，以恢复那些丢失的数据。创新地，采用特殊的预处理方法将一维载荷数据重塑为二维矩阵，从而可以通过图像修复技术来解决该问题。此外，CCAE由于其优先级策略而被设计为逐级重建缺失数据，这增强了极端缺失情况下的鲁棒性。比利时电网负载数据的数值结果验证了所提出解决方案的有希望的性能和有效性。