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Being aware of the reliable margin of vital tie-lines, acting on the connection of power exporting area and power importing area, is significant to power systems. However, the high penetration of wind power causes fast variation of boundary limit parameters such as the available amount of power that can be transferred on the tie-lines, namely, total transfer capability (TTC), which may result in the inaccurate security assessment. Unfortunately, the traditional optimal power flow-based TTC model has computation burden for online applications. To address this problem, computational efficiency is improved via a data-driven TTC predictor based on an ensemble learning architecture in this paper. In the first stage, a daily profiles-based method including probabilistic sampling is proposed to simulate plenty of operation scenarios as data samples for ensemble training. Then, a hybrid feature selection approach, which is composed of the maximal information coefficient and nonparametric independence screening, is applied to determine the most correlative features to the objective variable. To enable the TTC predictor with high accuracy and generalization ability, a novel ensemble learning scheme for TTC predictor is constituted through clustering few adaptive hierarchical GA-based neural networks (AHGA-NNs predictor). At last, a modified New England test system is used to validate the proposed methodology. The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin monitoring for the vital tie-lines.

Ensemble Learning for Power Systems TTC Prediction With Wind Farms