@inproceedings{fb88eff424234c9db1543d060006ad38,
title = "Identification of Noise Covariances for Voltage Dynamics Estimation in Microgrids",
abstract = "For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.",
keywords = "Voltage dynamics, measurement noise, noise covariance, noise iden-tification, process noise",
author = "Niranjan Bhujel and Astha Rai and Ujjwol Tamrakar and Hansen, \{Timothy M.\} and Reinaldo Tonkoski",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Power and Energy Society General Meeting, PESGM 2022 ; Conference date: 17-07-2022 Through 21-07-2022",
year = "2022",
doi = "10.1109/PESGM48719.2022.9916663",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2022 IEEE Power and Energy Society General Meeting, PESGM 2022",
}
