A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier
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利用基于 SVM 的集成分类器的置信度进行电力系统暂态稳定性预测的分层方法

DOI：
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发表时间：
2016
期刊：
energies
影响因子：
3.2
通讯作者：
Liangliang Hao
Liangliang Hao
中科院分区：
工程技术4区
文献类型：
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作者：
Yanzhen Zhou;Junyong Wu;Zhihong Yu;Luyu Ji;Liangliang Hao

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机器学习技术在电力系统暂态稳定预测中得到了广泛应用。在使用故障后动态响应时，很难得出DefifiNite结论，即为了平衡精度和速度，使用的响应数据的持续时间应该有多长。此外，以往的研究还存在对fi水平考虑不足的问题。针对这些问题，提出了一种基于多支持向量机(fi)集成支持向量机(Classifier)一致性的递阶暂态稳定预测方法。首先通过Bootstrap抽样生成多个数据集，然后随机抽取特征对数据集进行压缩。其次，对生成的数据集进行fi检验，并建立多个支持向量机。fi-Ned是一种支持向量机。通过综合多个支持向量机的概率输出，可以得到集成的Classifier的预测结果和置信度。最后，differentensembleclassifierswithdifferentresponsetimesarebuilttoconstructdifferentlayersofthe提出了分层方案。仿真结果表明，该方法能够在暂态稳定预测的准确性和快速性之间取得平衡。此外，分层方法可以减少不稳定情况下的误判，并与时域仿真相配合，以确保电力系统的安全稳定。
Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, differentensembleclassifierswithdifferentresponsetimesarebuilttoconstructdifferentlayersofthe proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems..