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首页> 外文期刊>Science, Measurement & Technology, IET >Surrogate-splits ensembles for real-time voltage stability assessment in the presence of missing synchrophasor measurements
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Surrogate-splits ensembles for real-time voltage stability assessment in the presence of missing synchrophasor measurements

机译：在缺少同步相量测量的情况下，替代分裂合体用于实时电压稳定性评估

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摘要

This paper proposes a new machine learning approach in the presence of missing measurements using surrogate-splits ensembles for real-time voltage stability assessment comprising of the classification of the operating state of the power system, and the prediction of the power system's margin to voltage collapse. The proposed approach applied the Boosting and Bagging machine learning methods in the design of the ensemble models required for the classification and regression tasks, respectively, based on the feature attributes obtained from synchrophasor measurements. The performance of the trained classifiers and regressors was evaluated for the case of missing phasor measurement unit (PMU) measurements. Robust classifier and regressor models immune to missing PMU measurements were afterwards developed using the surrogate-splits technique. The validity of the proposed approach was tested using the New England 39-bus test system. The experimental results obtained validate the effectiveness of the proposed method for various operating scenarios and contingencies even in the presence of missing PMU measurements.
机译：本文提出了一种新的机器学习方法，该方法在存在缺失测量的情况下使用替代分割组合进行实时电压稳定性评估，包括电力系统工作状态的分类以及电力系统对电压崩溃的裕度的预测。所提出的方法基于从同步相量测量获得的特征属性，分别将Boosting和Bagging机器学习方法应用于分类和回归任务所需的整体模型的设计中。对于缺少相量测量单元（PMU）测量的情况，评估了受过训练的分类器和回归器的性能。随后使用替代分割技术开发了针对丢失PMU测量值的鲁棒分类器和回归模型。使用新英格兰39总线测试系统测试了该方法的有效性。获得的实验结果验证了所提出方法在各种操作场景和突发事件中的有效性，即使在缺少PMU测量的情况下也是如此。

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