Ensemble Learning Based Convex Approximation of Three-Phase Power Flow

Journal Article · Wed Sep 01 04:00:00 EDT 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/TPWRS.2021.3055481· OSTI ID:1838933

Hu, Ren; Li, Qifeng; Qiu, Feng

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex approximation for alternating current (AC) power flow equations that differs from the existing convex relaxations. The proposed approach is based on three-phase quadratic power flow equations in rectangular coordinates. To develop this data-driven convex approximation of power flows, the polynomial regression (PR) is first deployed as a basic learner to fit convex relationships between the independent and dependent variables. Then, ensemble learning algorithms such as gradient boosting (GB) and bagging are introduced to combine learners to boost model performance. Based on the learned convex approximation of power flow, optimal power flow (OPF) is formulated as a convex quadratic programming problem. The simulation results on IEEE standard cases of both balanced and unbalanced systems show that, in the context of solving OPF, the proposed data-driven convex approximation outperforms the conventional semi-definite programming (SDP) relaxation in both accuracy and computational efficiency, especially in the cases that the conventional SDP relaxation fails

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1838933
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 5 Vol. 36; ISSN 0885-8950
• Country of Publication: United States
• Language: English

Related Subjects

Convex approximation
data-driven
ensemble learning
power flow