TY - GEN
T1 - Dynamic state prediction based on Auto-Regressive (AR) model using PMU data
AU - Gao, Fenghua
AU - Thorp, James S.
AU - Pal, Anamitra
AU - Gao, Shibin
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Auto-Regressive (AR) Model
KW - Dynamic State Prediction
KW - Phasor Measurement Units (PMUs)
KW - State Estimation
UR - http://www.scopus.com/inward/record.url?scp=84860892132&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860892132&partnerID=8YFLogxK
U2 - 10.1109/PECI.2012.6184586
DO - 10.1109/PECI.2012.6184586
M3 - Conference contribution
AN - SCOPUS:84860892132
SN - 9781457716836
T3 - 2012 IEEE Power and Energy Conference at Illinois, PECI 2012
BT - 2012 IEEE Power and Energy Conference at Illinois, PECI 2012
T2 - 2012 IEEE Power and Energy Conference at Illinois, PECI 2012
Y2 - 24 February 2012 through 25 February 2012
ER -