Support vector and multilayer perceptron neural networks applied to power systems transient stability analysis with input dimensionality reduction
Support vector and multilayer perceptron neural networks applied to power systems transient stability analysis with input dimensionality reduction
复制标题
支持向量和多层感知器神经网络应用于输入降维电力系统暂态稳定性分析
DOI：
10.1109/pess.2002.1043580
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
R. J. Marks
中科院分区：
文献类型：
--
作者：
L. S. Moulin;A.P.A. da Silva;Mohamed A. El;R. J. Marks
神经网络（NN）方法用于电力系统暂态稳定分析（TSA）已被提出作为一种潜在的在线应用工具，但电力系统的高维性使得必须实现特征提取技术才能使其在实际应用中可行。同时，特征提取可以提供灵敏度信息，帮助识别最适合控制动作的输入特征。本文提出了一种新的基于学习的非线性分类器——支持向量机（svm）神经网络，并证明了它在电力系统TSA中的适用性。由于它具有快速的训练能力，可以与现有的特征提取技术相结合，因此可以看作是处理高维问题的一种不同的方法。从概念上解释了支持向量机的理论动机，并将其应用于ieee50发电机系统的TSA问题。讨论了模型充分性、训练时间和分类精度方面的问题，并与多层感知器（mlp）获得的稳定性分类进行了比较。这两个模型都是用完整和简化的输入特征集训练的。
The neural network (NN) approach to power system transient stability analysis (TSA) has been presented as a potential tool for online applications, but the high dimensionality of the power systems turns it necessary to implement feature extraction techniques to make the application feasible in practice. At the same time, feature extraction can offer sensitivity information to help the identification of input features best suited for control action. This paper presents a new learning-based nonlinear classifier, the support vector machines (SVMs) NNs, showing its suitability for power system TSA. It can be seen as a different approach to cope with the problem of high dimensionality due to its fast training capability, which can be combined with existing feature extraction techniques. SVMs' theoretical motivation is conceptually explained and they are applied to the IEEE 50 generator system TSA problem. Aspects of model adequacy, training time and classification accuracy are discussed and compared to stability classifications obtained by multi-layer perceptrons (MLPs). Both models are trained with complete and reduced Input features sets.