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Conference proceeding
Machine Learning Approach to Solving the Transient Stability Assessment Problem
2018 IEEE TEXAS POWER AND ENERGY CONFERENCE (TPEC), Vol.2018
IEEE Texas Power and Energy Conference (TPEC) (College Station, TX, USA, 02/08/2018–02/09/2018)
01/01/2018
Web of Science ID: WOS:000432258100046
Abstract
In this paper, transient stability assessment is performed on a power system using a classification approach and data mining algorithms. As a first step, offline training data was collected by conducting load flow studies under normal operating conditions and faulty operating conditions at buses, at three different locations at lines and at different load levels. Twenty-three features were chosen to represent the training data for each load flow simulation. A support vector machine model was built and trained using the training data as well as a Naive Bayes model and Decision Tree model. Then an online testing model was developed and real-time data was used to test the validity of the model developed. The results indicate a higher accuracy and less time consumed by the core vector machine model compared to previous models available in literature. The IEEE 14 bus system was used for training data and for verifying the speed and accuracy of the proposed data mining algorithm.
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Details
- Title
- Machine Learning Approach to Solving the Transient Stability Assessment Problem
- Publication Details
- 2018 IEEE TEXAS POWER AND ENERGY CONFERENCE (TPEC), Vol.2018
- Resource Type
- Conference proceeding
- Conference
- IEEE Texas Power and Energy Conference (TPEC) (College Station, TX, USA, 02/08/2018–02/09/2018)
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- University of West Florida
- Copyright
- © 2018, IEEE
- Identifiers
- WOS:000432258100046; 99380178334306600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Dr. Muhammad Harunur Rashid Department of Electrical and Computer Engineering; Cybersecurity and Information Technology
- Language
- English