LAPSE

LAPSE:2019.0208
Published Article
LAPSE:2019.0208
Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method
Luyu Ji, Junyong Wu, Yanzhen Zhou, Liangliang Hao
January 31, 2019
To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter⁻wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS) information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system.
Keywords
feature extraction and selection, support vector machines, trajectory clusters, transient stability prediction
Suggested Citation
Ji L, Wu J, Zhou Y, Hao L. Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method. (2019). LAPSE:2019.0208
Author Affiliations
Ji L: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Wu J: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Zhou Y: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Hao L: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E898
Year
2016
Publication Date
2016-11-01
Published Version
ISSN
1996-1073
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PII: en9110898, Publication Type: Journal Article
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LAPSE:2019.0208
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doi:10.3390/en9110898
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Jan 31, 2019
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Jan 31, 2019
 
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Calvin Tsay
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