TY - GEN
T1 - Convolutional Deep Leaning-Based Distribution System Topology Identification with Renewables
AU - Wu, Huayi
AU - Xu, Zhao
AU - Wang, Minghao
N1 - Funding Information:
The work of H. Wu is partially supported by Teaching Postgraduate Studentship (TPS) Scheme of Hong Kong Polytechnic University. The work of M. Wang is partially supported in part by National Natural Science Foundation of China Under Grant No. 62101473 and PolyU Start-up Fund for RAPs under the Strategic Hiring Scheme P0035553.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - Obtaining the distribution system topology states timely is critical for system monitoring while challenged by correlations brought by high penetrated renewable energy sources (RES). To address this issue, a deep learning model is proposed for distribution system topology identification considering the underlying complex correlations of renewables. Specifically, to remove the dependence of the power system model parameters like line impedance, the input of the model only consists of the voltage magnitudes. Then, this is fed into the proposed Convolutional deep learning model (CDLM), which can fully capture the data features and thus classify the topology of the grid to hedge against the correlations of the RES and thus enhance the identification accuracy. The simulation results demonstrate the accuracy and efficiency of the proposed model in the IEEE 33-node distribution system.
AB - Obtaining the distribution system topology states timely is critical for system monitoring while challenged by correlations brought by high penetrated renewable energy sources (RES). To address this issue, a deep learning model is proposed for distribution system topology identification considering the underlying complex correlations of renewables. Specifically, to remove the dependence of the power system model parameters like line impedance, the input of the model only consists of the voltage magnitudes. Then, this is fed into the proposed Convolutional deep learning model (CDLM), which can fully capture the data features and thus classify the topology of the grid to hedge against the correlations of the RES and thus enhance the identification accuracy. The simulation results demonstrate the accuracy and efficiency of the proposed model in the IEEE 33-node distribution system.
KW - correlation
KW - deep learning
KW - Distribution system topology identification
KW - renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85125078954&partnerID=8YFLogxK
U2 - 10.1109/CIYCEE53554.2021.9676913
DO - 10.1109/CIYCEE53554.2021.9676913
M3 - Conference article published in proceeding or book
AN - SCOPUS:85125078954
T3 - 2021 IEEE 2nd China International Youth Conference on Electrical Engineering, CIYCEE 2021
BT - 2021 IEEE 2nd China International Youth Conference on Electrical Engineering, CIYCEE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE China International Youth Conference on Electrical Engineering, CIYCEE 2021
Y2 - 15 December 2021 through 17 December 2021
ER -
