TY - GEN
T1 - Steady-state inertia estimation using a neural network approach with modal information
AU - Schmitt, Andreas
AU - Lee, Byongjun
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1514705. Any opinions, findings, and conclusions or recommendations experssed in material ar those of the aothor(s) and do not necessarily reflect the views of the National Science Foundation
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/29
Y1 - 2018/1/29
N2 - The inertia of a power grid plays a significant role in maintaining the stability of a system. If the inertia is large enough, stable operating conditions can be maintained during small scale events. As the percentage of power supplied by renewable energy sources increases, the value of inertia in a system will decrease. Therefore, it has become necessary to accurately estimate the inertia in the system. Traditional methods of estimating the inertia make use of fault conditions to allow for the dynamics in the system to be accurately observable. However, this is not optimal as fault conditions are infrequent and undesirable. The method detailed makes use of modal information which can be obtained via synchrophasor measurements to estimate the inertia during steady-state conditions. The results show that while the estimation is not accurate for individual buses, the values calculated for regional and system inertias are more accurate.
AB - The inertia of a power grid plays a significant role in maintaining the stability of a system. If the inertia is large enough, stable operating conditions can be maintained during small scale events. As the percentage of power supplied by renewable energy sources increases, the value of inertia in a system will decrease. Therefore, it has become necessary to accurately estimate the inertia in the system. Traditional methods of estimating the inertia make use of fault conditions to allow for the dynamics in the system to be accurately observable. However, this is not optimal as fault conditions are infrequent and undesirable. The method detailed makes use of modal information which can be obtained via synchrophasor measurements to estimate the inertia during steady-state conditions. The results show that while the estimation is not accurate for individual buses, the values calculated for regional and system inertias are more accurate.
KW - Modal Information
KW - Neural Networks
KW - Parameter Estimation
KW - Wide Area Measurements
UR - http://www.scopus.com/inward/record.url?scp=85046343918&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2017.8274640
DO - 10.1109/PESGM.2017.8274640
M3 - Conference contribution
AN - SCOPUS:85046343918
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -
