TY - GEN
T1 - Data-Driven Risk Assessment Early-Warning Model for Power System Transmission Congestions
AU - Zhang, Qiang
AU - Li, Xinwei
AU - Liu, Xiaoming
AU - Zhao, Chenhao
AU - Shi, Renwei
AU - Jiao, Zaibin
AU - Liu, Jun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the continuous development of renewable energy in modern power systems, the balance between the power supply and demand side become more volatile, which may cause potential power system transmission congestions. Traditional risk assessment in the power system static security analysis area always uses the power flow model-based method, which cannot address all the possible operation scenarios. Therefore, a novel machine learning (ML) based data-driven risk assessment model for early-warning of power system transmission congestion is proposed in this paper. The proposed model can make full use of the power system historical operation data as well as the measurement of the current time step, which can be used in real-time for early-warning of the power system transmission congestion in advance. A feature selection method called Max-Relevance and Min-Redundancy (mRMR), is adopted to reduce the calculation burden of the ML model. Numerical tests are performed on a regional power grid of France. The proposed data-driven risk assessment model can accurately predict the risk conditions under normal operation, single and multiple component outage scenarios, over 93.3%. The result validates that our model can be used for real-time early-warning of power system transmission congestions.
AB - With the continuous development of renewable energy in modern power systems, the balance between the power supply and demand side become more volatile, which may cause potential power system transmission congestions. Traditional risk assessment in the power system static security analysis area always uses the power flow model-based method, which cannot address all the possible operation scenarios. Therefore, a novel machine learning (ML) based data-driven risk assessment model for early-warning of power system transmission congestion is proposed in this paper. The proposed model can make full use of the power system historical operation data as well as the measurement of the current time step, which can be used in real-time for early-warning of the power system transmission congestion in advance. A feature selection method called Max-Relevance and Min-Redundancy (mRMR), is adopted to reduce the calculation burden of the ML model. Numerical tests are performed on a regional power grid of France. The proposed data-driven risk assessment model can accurately predict the risk conditions under normal operation, single and multiple component outage scenarios, over 93.3%. The result validates that our model can be used for real-time early-warning of power system transmission congestions.
KW - Early-warning
KW - data-driven
KW - machine learning
KW - power system transmission congestion
KW - risk assessment
UR - https://www.scopus.com/pages/publications/85127947960
U2 - 10.1109/CPEEE54404.2022.9738719
DO - 10.1109/CPEEE54404.2022.9738719
M3 - 会议稿件
AN - SCOPUS:85127947960
T3 - Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
SP - 201
EP - 206
BT - Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
Y2 - 25 February 2022 through 27 February 2022
ER -
