基于广域量测和高斯过程分类器的暂态稳定评估
摘要：为了克服现有基于模式识别的暂态稳定评估(PRTSA)方法的不足,本文考虑广域测量系统可以提供的故障后实测信息,在合理选取一组具有代表性的输入特征基础上,提出了一种基于组合核函数高斯过程的PRTSA方法。相对于支持向量机,该方法具有参数自适应选取、输出具有概率意义等优点,并通过将不同特性的单一协方差函数相加构造组合核函数,进一步提高了评估模型的分类能力。应用于新英格兰39节点系统的仿真结果验证了所提出方法的有效性。
Abstract:To overcome the disadvantages of the existing pattern recognition-based transient stability assessment (PRTSA) methods, a new PRTSA method based on Gaussian process with composite kernel function (CKF-GP) is presented by considering the possible real-time information provided by WAMS from which a group of system-level classification features are selected in this paper. Compared to support vector machine, the presented method has such advantages as automatic parameter selection and prediction with probability interpretation. Furthermore, the classification ability of the proposed method is improved by combining different single covariance functions to construct a composite kernel function. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus system.
标题：基于广域量测和高斯过程分类器的暂态稳定评估
英文标题：Transient Stability Assessment Based on WAMS and Gaussian Process Classifier
作者：高 凯, 闫春生, 李正文, 韩子娇, 田博文, 李 扬, 李国庆,
关键词：暂态稳定, 模式识别, 贝叶斯框架, 组合核函数, 广域量测,
发表日期：2017-04-19
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