A Forecasting Aided State Estimation for Distribution Network Based on Weighted Average Interpolation and Cubature Kalman Filter

doi:10.12204/j.issn.1000-7229.2021.08.001

Abstract

Abstract:

The micro synchronous phasor measurement unit (μPMU) and remote terminal unit (RTU) provide high precision measurement data for distribution network. This paper presents a forecasting aided state estimation (FASE) method based on hybrid measurement data, which uses data imputation and the cubature Kalman filter to improve the synchronous and filtering profile of μPMU and RTU. Firstly, to improve the synchronous profile of RTU with the shorter updating period, the historical data and linear interpolation are considered comprehensively to realize the data imputation by weighted average interpolation. Then, the cubature Kalman filter is used to construct a state prediction equation, measurement prediction equation, and filter correction equation. A FASE for distribution network with mixed data is proposed. Finally, the validity of the proposed method is verified in the IEEE 37-node system.

Key words: state estimation, micro synchronous phasor measurement unit (μPMU), remote terminal unit (RTU), cubature Kalman filter, data imputation

CLC Number:

TM76

CHAI Linjie, CAI Yinong, GAO Ming, HAO Yun, CHEN Jikai, LI Jiang. A Forecasting Aided State Estimation for Distribution Network Based on Weighted Average Interpolation and Cubature Kalman Filter[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(8): 1-9.

Figures/Tables 8

Fig.1 Update cycle relationship of the data from RTU and μPMU

Fig.2 Diagram of mixed data pre-processing

Fig.3 Flowchart of the proposed FASE

Fig.4 IEEE 37-node system

Table 1 Three-phase smoothing parameter

Fig.5 State estimation of branch current

Fig.6 Phasor estimation errors under different filters

Table 2 Calculation time using EKF, UKF and CKF

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