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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.

Abstract:For enhancing the accuracy and rationality of power quality (PQ) evaluation, close value method (CVM) based on the target optimization principle is introduced to symbolize the performance of PQ samples. To tackle with the defect, in calculating close value of PQ samples, that the calculation formula of close value is divided by zero, a modified CVM is correspondingly developed to adapt to the randomness of PQ samples. Meanwhile, for analyzing the impacts of weighting approaches on the evaluation outcome, different weighting approaches are orderly utilized to improve CVM. Case analysis shows that the new calculating method of close value is proved to be more simplified and applicable; the impacts different weighting approaches have on the evaluation outcome differ to some extent.