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Traditional load margin estimation approaches, which require a retraining process under dynamic operation variation with increasing uncertainties and consuming excessive time, are insufficient to guarantee the smart grid operates in a stable region. To address this challenge, this article proposes an incremental learning-enhanced LightGBM (IL-LightGBM) method for online load margin estimation utilizing synchronized measurement data. As the key to achieving accurate load margin estimation using the LightGBM algorithm under operation variations, the weight parameters of the pre-trained model are updated online by IL technique through efficiently digesting synchrophasor measurements. The proposed method makes full use of the capability of LightGBM to handle massive measurements, while effectively improving the adaptability to large-scale operational variability. Case studies to evaluate the proposed approach are presented through the numerical simulations of the IEEE 39-bus test system and a larger IEEE 145-bus power system, demonstrating the effectiveness and robustness of the proposed method.
Cai et al. (Mon,) studied this question.