Ensemble learning based linear power flow
This is an incremental improvement for power system optimization, offering faster and more accurate linear models for optimal power flow calculations.
The paper tackles the problem of linearizing power flow equations by developing an ensemble learning-based approach, which outperforms traditional methods like DC power flow and SDP relaxation in computational accuracy and speed, as shown in IEEE case simulations.
This paper develops an ensemble learning-based linearization approach for power flow, which differs from the network-parameter based direct current (DC) power flow or other extended versions of linearization. As a novel data-driven linearization through data mining, it firstly applies the polynomial regression (PR) as a basic learner to capture the linear relationships between the bus voltage as the independent variable 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 fitted linear power flow model is also relaxed to compute the optimal power flow (OPF). The simulating results of standard IEEE cases indicate that (1) ensemble learning methods outperform PR and GB works better than bagging; (2) as for solving OPF, the data-driven model excels the DC model and the SDP relaxation in the computational accuracy, and works faster than ACOPF and SDPOPF.