基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估

Power system transient stability assessment based on comprehensive SVM classification model and key sample set

为了提高支持向量机(Support Vector Machine,SVM)的分类性能,提出了根据关键样本集构造的SVM综合分类模型进行电力系统暂态稳定评估的方法。给出了基于不同特征量的SVM综合分类模型的构建方法、关键样本集的产生方法以及基于综合分类模型和关键样本集的SVM分类步骤。采用3机9节点典型算例和某省级电网算例进行分类效果分析。分析结果表明,所提出的基于SVM综合分类模型和关键样本集的方法,相较于传统SVM方法,大幅度减少了将不稳定样本判定为稳定的漏分类数,提高了SVM方法的实用性。所提出的基于关键样本集构造分类模型的思路对于其他数据挖掘方法也有一定的借鉴意义。

A power system transient stability assessment method according to comprehensive Support Vector Machine （SVM） classification model based on key sample set is presented to improve the performance of SVM classification. A construction method of comprehensive SVM classification model based on different features, a producing method of key sample set, and procedures of SVM classification based on comprehensive classification model and key sample set are given. A 3-generator 9-node system and a certain provincial power grid are analyzed to verify the effectiveness of the method. Analysis results show that the presented method largely decreases the false classification number of unstable test samples compared with the traditional SVM method, thus improves the practicability of the method. The thought of using key sample set to form the classification model might be significative to other data mining methods.