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Title: Ensemble Learning based Linear Power Flow
This paper develops an ensemble learning-based linearization approach for power flow with reactive power modeled, where the polynomial regression (PR) is first used as a basic learner to capture the linear relationships between the bus voltages as the independent variables and the active or reactive power as the dependent variable in rectangular coordinates. Then, gradient boosting (GB) and bagging as ensemble learning methods are introduced to combine all basic learners to boost the model performance. The inferred linear power flow model is applied to solve the well-known optimal power flow (OPF) problem. The simulation results on IEEE standard power systems indicate that (1) ensemble learning methods can significantly improve the efficiency of PR, and GB works better than bagging; (2) as for solving OPF, the data-driven model outperforms the DC model and the SDP relaxation in both accuracy, and computational efficiency.  more » « less
Award ID(s):
1808988
PAR ID:
10299582
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2020 IEEE Power & Energy Society General Meeting (PESGM)
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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