Forecasting the Stability of A 4-node Architecture Smart Grid Using Machine Learning
Forecasting the Stability of A 4-node Architecture Smart Grid Using Machine Learning
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使用机器学习预测 4 节点架构智能电网的稳定性
DOI：
10.1109/icsmartgrid55722.2022.9848635
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
M. Beken
中科院分区：
文献类型：
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作者：
Batuhan Hangun;O. Eyecioglu;M. Beken
智能电网的稳定性是衡量智能电网体系结构可用性的重要指标之一，因此测试和预测各种情况下的稳定性具有重要意义。由于住宅和工业结构的增加，以及可再生能源与智能电网的集成，需要一些智能解决方案来预测稳定性，以防止未来智能电网架构中不必要的不稳定性。在这项研究中，我们使用了各种机器学习方法来预测智能电网的稳定性。我们将这个问题作为一个分类问题来处理，我们使用了一个4节点架构的智能电网数据集，并应用一些著名的分类方法将数据集分为两类，这是“稳定”和“不稳定”。对于分类部分，我们使用了k-最近邻（kNN），神经网络（NN），支持向量机（SVM）和决策树。所有四种方法都在不同的超参数下进行了测试。最后，报告了具有最佳结果的那些。
Smart grid stability is one of the most important factors that can be used as a criterion for assessing the usability of smart grid architecture, so testing and predicting stability under various circumstances hold great importance. As a result of the increase in residential and industrial structures, and the integration of renewable energy into the smart grids, some intelligent solutions to predict stability to prevent unwanted instabilities in a future smart grid architecture is needed. In this study, we used various machine learning methods to predict smart grid stability. We approached the problem as a classification problem, we used a 4-node architecture smart grid dataset, and applied some well-known classification methods to classify the dataset into two classes which are “stable” and “unstable”. For the classification part, we used k-Nearest Neighbour (kNN), neural networks (NN), a support vector machine (SVM), and a decision tree. All four methods were tested under different hyper parameters. Finally, the ones with the best results were reported.