Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter
by
Dongliang Nan 1,2, Weiqing Wang 1, Kaike Wang 2, Rabea Jamil Mahfoud 3
, 
Hassan Haes Alhelou 4,* and Pierluigi Siano 5,*
Dongliang Nan 1,2, Weiqing Wang 1, Kaike Wang 2, Rabea Jamil Mahfoud 3, Hassan Haes Alhelou 4,* and Pierluigi Siano 5,*
1
School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
2
Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830011, China
3
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
4
Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
5
Department of Management & Innovation Systems, University of Salerno, 84084 Salerno, Italy
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5200; https://doi.org/10.3390/app9235200
Received: 13 November 2019 / Revised: 25 November 2019 / Accepted: 26 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Advances and Technologies in High Voltage Power Systems Operation, control, protection and Security)
Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.
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Keywords:
dynamic state estimation (DSE); synchronous machine; ensemble square root filter (EnSRF); Sage–Husa algorithm
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MDPI and ACS Style
Nan, D.; Wang, W.; Wang, K.; Mahfoud, R.J.; Haes Alhelou, H.; Siano, P. Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter. Appl. Sci. 2019, 9, 5200.
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