TY - GEN
T1 - Advanced Kalman Filter for Current Estimation in AC Microgrids
AU - Vafamand, Navid
AU - Arefi, Mohammad Mehdi
AU - Javadi, Mohammad Sadegh
AU - Anvari-Moghaddam, Amjad
AU - Catalão, João P.S.
PY - 2020
Y1 - 2020
N2 - The stability and monitoring of AC microgrids (AC MG) are greatly influenced by gathering sufficient and precise information. Since installing several sensors on AC MGs is costly and increases AC MG ripple, integrating a minimum number of cost-effective sensors is preferred. In this paper, a joint-estimating augmented-Kalman filter (KF) to estimate the current of the AC MG and unknown time-varying loads from the noisy measurement of the AC bus voltage is developed. The proposed approach also provides smooth and noise-less information from the measured voltage. The method presented in this paper has less complexity to handle and as a robust approach, it would be capable of dealing with uncertainties due to the load, which can be linear, nonlinear, or unbalanced. The joint-estimating augmented-KF outputs can be then utilized in the monitoring, fault detection, and control design purposes. The developed framework is tested on an AC MG supplying a one time-varying load and numerical results verify the applicability and accuracy of the developed technique to estimate the load and filter currents.
AB - The stability and monitoring of AC microgrids (AC MG) are greatly influenced by gathering sufficient and precise information. Since installing several sensors on AC MGs is costly and increases AC MG ripple, integrating a minimum number of cost-effective sensors is preferred. In this paper, a joint-estimating augmented-Kalman filter (KF) to estimate the current of the AC MG and unknown time-varying loads from the noisy measurement of the AC bus voltage is developed. The proposed approach also provides smooth and noise-less information from the measured voltage. The method presented in this paper has less complexity to handle and as a robust approach, it would be capable of dealing with uncertainties due to the load, which can be linear, nonlinear, or unbalanced. The joint-estimating augmented-KF outputs can be then utilized in the monitoring, fault detection, and control design purposes. The developed framework is tested on an AC MG supplying a one time-varying load and numerical results verify the applicability and accuracy of the developed technique to estimate the load and filter currents.
KW - AC microgrid
KW - Joint estimation
KW - Kalman filter
KW - Unknown time-varying load
U2 - 10.1109/EEEIC/ICPSEurope49358.2020.9160612
DO - 10.1109/EEEIC/ICPSEurope49358.2020.9160612
M3 - Article in proceeding
SN - 978-1-7281-7456-3
T3 - Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
SP - 1
EP - 6
BT - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
PB - IEEE Press
T2 - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
Y2 - 9 June 2020 through 12 June 2020
ER -