Transient Instability Prediction Using Decision Tree Technique
Transient Instability Prediction Using Decision Tree Technique
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DOI：
10.1109/tpwrs.2013.2238684
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发表时间：
2013-08-01
影响因子：
6.6
通讯作者：
Ranjbar, Soheil
中科院分区：
文献类型：
--
作者：
Amraee, Turaj;Ranjbar, Soheil
提出了一种基于决策树的同步发电机失步预测方法。为了区分稳定和失步状态，在各种故障情况下进行了一系列测量，包括操作和拓扑干扰。将输入特征和输出目标类的数据作为决策树归纳和演绎的输入输出对。基于决策树的暂态不稳定检测的优点在于对新的未见样本进行鲁棒分类。在9总线动态网络和1696总线伊朗国家电网两个测试用例上验证了该方法的性能。给出了不同输入特征和学习参数下的仿真结果。
This paper presents a decision tree based method for out-of-step prediction of synchronous generators. For distinguishing between stable and out-of-step conditions, a series of measurements are taken under various fault scenarios including operational and topological disturbances. The data of input features and output target classes are used as the input-output pairs for decision tree induction and deduction. The merit of decision tree based detection of transient instability lies in robust classification of new unseen samples. The performance of the proposed method is verified on two test cases including a 9-bus dynamic network and the practical 1696-bus Iran national grid. The simulation results are presented for various input features and learning parameters.