Estimation of transmission line sequence impedances using real PMU data. Mansani, P. K., Pal, A., Rhodes, M., & Keel, B. In IEEE North American Power Symposium (NAPS), pages 1–6, Fargo, ND, September, 2018. ![Estimation of transmission line sequence impedances using real PMU data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Accurate knowledge of transmission line parameters in general, and sequence impedances, in particular, plays an important role in state estimation, fault detection, and adjustment of relay settings. Line parameter estimation using online methods has attracted considerable interest with the widespread installation of phasor measurement units (PMUs). Although various methods have been proposed in the literature for line parameter estimation, most of them have been tested on purely synthetic datasets. A synthetic dataset does not capture the nuances of real data, such as measurement invariance and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. In this paper, a three-stage test procedure is developed to compare the performance of two algorithms, namely, moving-window total least squares (MWTLS) recursive Kalman filter (RKF), on real PMU data. The results prove that RKF is better than MWTLS. This paper also proposes using ASPEN data as an initial estimate to RKF for further improving its performance. Finally, to circumvent the problems faced due to data dropouts, an auto regressive integrated moving average (ARIMA) model is implemented to predict the variations in sequence impedances.
@inproceedings{mansani_estimation_2018,
address = {Fargo, ND},
title = {Estimation of transmission line sequence impedances using real PMU data},
url = {https://ieeexplore.ieee.org/abstract/document/8600605},
abstract = {Accurate knowledge of transmission line parameters in general, and sequence impedances, in particular, plays an important role in state estimation, fault detection, and adjustment of relay settings. Line parameter estimation using online methods has attracted considerable interest with the widespread installation of phasor measurement units (PMUs). Although various methods have been proposed in the literature for line parameter estimation, most of them have been tested on purely synthetic datasets. A synthetic dataset does not capture the nuances of real data, such as measurement invariance and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. In this paper, a three-stage test procedure is developed to compare the performance of two algorithms, namely, moving-window total least squares (MWTLS) recursive Kalman filter (RKF), on real PMU data. The results prove that RKF is better than MWTLS. This paper also proposes using ASPEN data as an initial estimate to RKF for further improving its performance. Finally, to circumvent the problems faced due to data dropouts, an auto regressive integrated moving average (ARIMA) model is implemented to predict the variations in sequence impedances.},
booktitle = {IEEE North American Power Symposium (NAPS)},
author = {Mansani, Prashanth Kumar and Pal, Anamitra and Rhodes, Matthew and Keel, Brian},
month = sep,
year = {2018},
keywords = {ARIMA, ASPEN, Current measurement, Kalman filters, parameter estimation, phasor measurement unit (PMU), Phasor measurement units, Power transmission lines, recursive Kalman filter (RKF), total least squares (TLS), Transmission line matrix methods, Transmission line measurements, Voltage measurement},
pages = {1--6},
}
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A synthetic dataset does not capture the nuances of real data, such as measurement invariance and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. In this paper, a three-stage test procedure is developed to compare the performance of two algorithms, namely, moving-window total least squares (MWTLS) recursive Kalman filter (RKF), on real PMU data. The results prove that RKF is better than MWTLS. This paper also proposes using ASPEN data as an initial estimate to RKF for further improving its performance. 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