Abstract
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.
Highlights
Nowadays, measurement data are the foundation of the power system. e massive collected data especially the quality of electricity such as voltage, current, and load are tightly associated with safe operation and economic dispatch [1]
Load data from the Belgium grid will be conducted as training and test data, and a missing mask generation model is presented to produce generative missing masks under different parameters
In the original load data of the Belgium grid, there are very few missing segments; it could be regarded as ground truth data X. en, we have to manually generate missing masks M for detection and recovery testing
Summary
Measurement data are the foundation of the power system. e massive collected data especially the quality of electricity such as voltage, current, and load are tightly associated with safe operation and economic dispatch [1]. Measurement data are the foundation of the power system. Due to the growing size of the power grid and the massive number of field sensors, the absence and anomalies of data measurements cannot be avoided, which is mainly due to the failures and disabilities of terminal equipment or performance degradation of transmission channels [2, 3]. In the power system, scholars suggest approaches combined with the correlativity of multimeasured data to improve accuracy [11]. These solutions are susceptible to data pollution due to the existence of missing data and lead to unsatisfactory performance
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