Method of amplitude data recovery in PMU measurements that considers synchronisation errors. Issue 24 (18th September 2020)

• Record Type: Journal Article
• Authors: Xue, Ancheng; Kong, He; Lao, Yongzhao; Xu, Feiyang; Wang, Li; Wei, Guoen; Shi, Bonian; Leng, Shuang; Bi, Tianshu
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 24(2020)
• Page Start: 5746
• Page End: 5755
• Publication Date: 2020-09-18
• Subjects: pattern clustering -- power grids -- power system state estimation -- phasor measurement -- synchronisation -- power system measurement -- power system control; density‐based spatial clustering -- recovery method -- voltage amplitude -- active power -- reactive power -- current amplitude -- historical amplitude data -- related recovery coefficients -- amplitude data recovery -- PMU measurements -- synchronisation errors -- synchronised phasor measurement units -- observability -- controllability -- power grid -- high‐upload rate -- missing amplitude data -- abnormal amplitude data -- historical PMU data -- transmission line parameters -- measured PMU data -- bad data -- historical data; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2020.0785
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The synchronised phasor measurement units (PMUs), serving as 'GridEye', provide data that improve the observability and controllability of the power grid due to its high accuracy and high‐upload rate. However, missing or abnormal data may seriously affect its applications. This study proposes a method to recover the missing or abnormal amplitude data in PMU measurements (i.e. the active power, reactive power, positive sequence current, and voltage amplitude), based on the historical PMU data obtained from both ends of the line, which is independent of the transmission line parameters and the phase angle that may be influenced by synchronisation. First, the issues in the quality of measured PMU data are analysed, and the motivation of the proposed method is stated. Then, a method to screen out the bad data from the historical data is proposed based on the density‐based spatial clustering of applications with noise. Furthermore, the model of data recovery is established, and the recovery method, which recovers the voltage amplitude, active power, reactive power, and current amplitude in sequence, using historical amplitude data to calculate related recovery coefficients is proposed. Finally, the effectiveness and practicability of the proposed method are verified by examples of simulation and measured data.