TY - GEN
T1 - Data-Driven Flow and Injection Estimation in PMU-Unobservable Transmission Systems
AU - Sahoo, Satyaprajna
AU - Sifat, Anwarul Islam
AU - Pal, Anamitra
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.
AB - Fast and accurate knowledge of power flows and power injections is needed for a variety of applications in the electric grid. Phasor measurement units (PMUs) can be used to directly compute them at high speeds; however, a large number of PMUs will be needed for computing all the flows and injections. Similarly, if they are calculated from the outputs of a linear state estimator, then their accuracy will deteriorate due to the quadratic relationship between voltage and power. This paper employs machine learning to perform fast and accurate flow and injection estimation in power systems that are sparsely observed by PMUs. We train a deep neural network (DNN) to learn the mapping function between PMU measurements and power flows/injections. The relation between power flows and injections is incorporated into the DNN by adding a linear constraint to its loss function. The results obtained using the IEEE 118-bus system indicate that the proposed approach performs more accurate flow/injection estimation in severely unobservable power systems compared to other data-driven methods.
KW - Flow and Injection estimation
KW - Machine learning (ML)
KW - Phasor measurement unit (PMU)
KW - Unobservability
UR - http://www.scopus.com/inward/record.url?scp=85174732953&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174732953&partnerID=8YFLogxK
U2 - 10.1109/PESGM52003.2023.10252719
DO - 10.1109/PESGM52003.2023.10252719
M3 - Conference contribution
AN - SCOPUS:85174732953
T3 - IEEE Power and Energy Society General Meeting
BT - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023
PB - IEEE Computer Society
T2 - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Y2 - 16 July 2023 through 20 July 2023
ER -