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

Yanzhen Zhou, Junyong Wu, Zhihong Yu, Luyu Ji and Liangliang Hao
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Yanzhen Zhou: School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China
Junyong Wu: School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China
Zhihong Yu: China Electric Power Research Institute, Beijing 100192, China
Luyu Ji: School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China
Liangliang Hao: School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China

Energies, 2016, vol. 9, issue 10, 1-20

Abstract: 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, different ensemble classifiers with different response times are built to construct different layers of the 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.

Keywords: transient stability prediction; support vector machine (SVM); ensemble classifier; machine learning; confidence level; hierarchical method; power systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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