Ensemble learning based linear power flow

TL;DR

This paper introduces an ensemble learning-based linearization method for power flow analysis, leveraging polynomial regression and ensemble techniques to improve accuracy and computational efficiency over traditional models.

Contribution

It presents a novel data-driven linearization approach using ensemble learning, outperforming existing methods in accuracy and speed for power flow and optimal power flow calculations.

Findings

Ensemble learning methods outperform polynomial regression.

Gradient boosting performs better than bagging.

Data-driven model surpasses DC and SDP relaxation in accuracy and speed.

Abstract

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.