An adaptive PMU missing data recovery method. (March 2020)

• Record Type: Journal Article
• Authors: Yang, Zhiwei; Liu, Hao; Bi, Tianshu; Li, Zikang; Yang, Qixun
• Is Part Of: International journal of electrical power & energy systems. Volume 116(2020)
• Publication Date: 2020-03
• Subjects: Phasor measurement units -- Data recovery -- Decision tree -- Priority allocation strategy -- Singular value decomposition; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105577
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The proposed method can effectively identify the ambient and disturbance data. For ambient data loss, this method can recover ambient data quickly and accurately. For disturbance data loss, this method can greatly improve the recovery accuracy. This method can achieve data identification and recovery only based on data. Abstract: A high penetration of renewable energies into the modern grid creates randomness and uncertainties which require advanced real-time monitoring and control. Phasor measurement units (PMUs) and wide-area measurement systems (WAMS) show great promise for operation monitoring and stability enhancement due to characteristics of synchronization, rapidity, and accuracy. However, different levels of data loss issues can occur in practical applications as a result of varying conditions, including communication congestion, hardware failure, and transmission delay. Data loss issues can severely restrict such monitoring applications in power systems, and may even threaten the security of the grid. To address this problem, an adaptive PMU missing data recovery method is proposed in this paper. A data recovery method framework is proposed, in which the data is classified as either ambient or disturbance data, and recovered by different methods to achieve good performance efficiently. An approach based on decision tree is developed for identifying ambient and disturbance data. Then, an improved cubic spline interpolation based on the priority allocation strategy is proposed for ambient data loss, which can quickly and accurately recover ambient data. Simultaneously, a disturbance data recovery method based on singular value decomposition is presented. It can achieve disturbance data recovery accurately by a single channel of measurement. Finally, the feasibility and accuracy of the proposed methods are verified through simulation and hardware-based test platform fed by field recorded data. The simulation and testing results show this method can achieve data identification and recovery efficiently solely based on data, and that applying the proposed method to all aspects of the power system can provide superior PMU measurement guarantees.