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首页> 外文会议>2000 International Conference on Power System Technology >Combination of Rough Set Theory and Artificial Neural Networks for Transient Stability Assessment
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Combination of Rough Set Theory and Artificial Neural Networks for Transient Stability Assessment

机译：粗糙集理论与人工神经网络相结合的暂态稳定性评估

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Power system transient-stability assessment (TSA) based on pattern recognition techniques can usually be treated as a two-pattern classification problem separating the stable class from the unstable class. Two underlying problems are (1) selecting a group of effective features (attributes), and (2) building a pattern classifier with high classification accuracy. This paper proposes to combine the rough set theory (RST) with a back-propagation neural network (BPNN) for TSA, including feature extraction and classifier construction. First, through discretization of the initial input attributes, the inductive learning algorithm based on RST is employed to reduce the input attribute set. Then, a BPNN using a semi-supervised learning algorithm is used as a 'rough classifier' to classify the system stability into three classes―stable class, unstable class and indeterminate class (boundary region). The introduction of the indeterminate class provides a feasible way to reduce misclassifications, and the reliability of the classification results can hence be greatly improved. The validity of the proposed approach for both feature extraction and removing misclassifications of BPNN-based TSA is verified by the 10-unit New England power system.
机译：基于模式识别技术的电力系统暂态稳定评估（TSA）通常可以视为将稳定类别与不稳定类别分开的两模式分类问题。两个基本问题是（1）选择一组有效特征（属性），以及（2）构建具有高分类精度的模式分类器。本文提出将粗糙集理论（RST）与用于TSA的反向传播神经网络（BPNN）相结合，包括特征提取和分类器构造。首先，通过离散化初始输入属性，采用基于RST的归纳学习算法来减少输入属性集。然后，使用半监督学习算法的BPNN作为“粗糙分类器”，将系统稳定性分为稳定稳定类，不稳定不稳定类和不确定不确定性（边界区域）三类。不确定类的引入为减少错误分类提供了一种可行的方法，因此可以大大提高分类结果的可靠性。由10个单元组成的新英格兰电力系统验证了所提方法对基于BPNN的TSA的特征提取和消除误分类的有效性。

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