Dynamic state prediction based on Auto-Regressive (AR) model using PMU data. Gao, F., Thorp, J. S., Pal, A., & Gao, S. In IEEE Power and Energy Conference at Illinois (PECI), pages 1–5, Champaign, IL, February, 2012. ![Dynamic state prediction based on Auto-Regressive (AR) model using PMU data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This paper presents a dynamic state prediction method based on an Auto-Regressive Model (AR model) using PMU data. In recent years, state prediction has played a key role in improving power system performance and reliability. When load is increased linearly at a constant power factor, it is proved in this paper that the bus voltages are quadratic and the AR model for predicting the next voltage is based on three prior estimates. This logic is then tested on the IEEE-118 bus system. The test results demonstrate that under morning load pick-up, economic dispatch, line opening and generator oscillations, the proposed method is correct and gives valid predictions. Furthermore, based on the error in quadratic fit, it is advocated that this method could be applied to detect abnormal conditions in the transmission systems. Theoretical analysis and results show that the proposed method based on AR model has great potential in predicting power system states.
@inproceedings{gao_dynamic_2012,
address = {Champaign, IL},
title = {Dynamic state prediction based on Auto-Regressive (AR) model using PMU data},
url = {https://ieeexplore.ieee.org/abstract/document/6184586},
abstract = {This paper presents a dynamic state prediction method based on an Auto-Regressive Model (AR model) using PMU data. In recent years, state prediction has played a key role in improving power system performance and reliability. When load is increased linearly at a constant power factor, it is proved in this paper that the bus voltages are quadratic and the AR model for predicting the next voltage is based on three prior estimates. This logic is then tested on the IEEE-118 bus system. The test results demonstrate that under morning load pick-up, economic dispatch, line opening and generator oscillations, the proposed method is correct and gives valid predictions. Furthermore, based on the error in quadratic fit, it is advocated that this method could be applied to detect abnormal conditions in the transmission systems. Theoretical analysis and results show that the proposed method based on AR model has great potential in predicting power system states.},
booktitle = {{IEEE} {Power} and {Energy} {Conference} at {Illinois} (PECI)},
author = {Gao, Fenghua and Thorp, James S. and Pal, Anamitra and Gao, Shibin},
month = feb,
year = {2012},
keywords = {Auto-Regressive (AR) Model, Circuit faults, Dynamic State Prediction, Educational institutions, Load modeling, Mathematical model, Phasor measurement units, Phasor Measurement Units (PMUs), Power system dynamics, Predictive models, State Estimation},
pages = {1--5},
}
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The test results demonstrate that under morning load pick-up, economic dispatch, line opening and generator oscillations, the proposed method is correct and gives valid predictions. Furthermore, based on the error in quadratic fit, it is advocated that this method could be applied to detect abnormal conditions in the transmission systems. 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In recent years, state prediction has played a key role in improving power system performance and reliability. When load is increased linearly at a constant power factor, it is proved in this paper that the bus voltages are quadratic and the AR model for predicting the next voltage is based on three prior estimates. This logic is then tested on the IEEE-118 bus system. The test results demonstrate that under morning load pick-up, economic dispatch, line opening and generator oscillations, the proposed method is correct and gives valid predictions. Furthermore, based on the error in quadratic fit, it is advocated that this method could be applied to detect abnormal conditions in the transmission systems. 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