TY - GEN
T1 - Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise
AU - Azimian, B.
AU - Biswas, R. Sen
AU - Pal, A.
AU - Tong, Lang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to perform DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.
AB - Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to perform DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.
KW - Deep neural network (DNN)
KW - Gaussian mixture model (GMM)
KW - State estimation
KW - Synchrophasor measurements
UR - http://www.scopus.com/inward/record.url?scp=85106383539&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106383539&partnerID=8YFLogxK
U2 - 10.1109/PESGM46819.2021.9637858
DO - 10.1109/PESGM46819.2021.9637858
M3 - Conference contribution
AN - SCOPUS:85106383539
T3 - IEEE Power and Energy Society General Meeting
BT - 2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PB - IEEE Computer Society
T2 - 2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Y2 - 26 July 2021 through 29 July 2021
ER -