Power System State Estimation Based on Fusion of PMU and SCADA Data

Jiaming Zhu, Wengen Gao (), Yunfei Li, Xinxin Guo, Guoqing Zhang and Wanjun Sun
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Jiaming Zhu: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Wengen Gao: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Yunfei Li: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Xinxin Guo: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Guoqing Zhang: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Wanjun Sun: School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China

Energies, 2024, vol. 17, issue 11, 1-19

Abstract: This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

Keywords: power system; extended Kalman filter; data fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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