Decision tree for static security assessment classification
This paper addresses the on going work of the application of Machine Learning on Static Security Assessment of Power Systems. Several techniques, which have been applied for static Security Assessment .A Decision Tree types comparison for the purpose of static security assessment classification is d...
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| Format: | Book Section |
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IEEE Computer Soc
2009
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| _version_ | 1796855221309145088 |
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| author | Saeh, Ibrahim Khairuddin, Azhar |
| author_facet | Saeh, Ibrahim Khairuddin, Azhar |
| author_sort | Saeh, Ibrahim |
| collection | ePrints |
| description | This paper addresses the on going work of the application of Machine Learning on Static Security Assessment of Power Systems. Several techniques, which have been applied for static Security Assessment .A Decision Tree types comparison for the purpose of static security assessment classification is discussed and the comparison results from these methods on operating point are presented. Decision Tree examines whether the power system is secured under steadystate operating conditions.DT gauges the bus voltages and the line flow conditions. Using minimum number of cases from the available large number of contingencies in terms of their impact on the system security is the methodology that has been developed. Newton Raphson load flow analysis method is used for training and test data. The input variables to the network are loadings of the lines and the voltage magnitude of the load buses. The algorithms are initially tested on the 5 IEEE bus systems. The results obtained indicate that DT method is comparable in accuracy and computational time to the Newton Raphson load flow method.
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| first_indexed | 2024-03-05T18:25:28Z |
| format | Book Section |
| id | utm.eprints-13304 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:25:28Z |
| publishDate | 2009 |
| publisher | IEEE Computer Soc |
| record_format | dspace |
| spelling | utm.eprints-133042017-10-05T05:54:36Z http://eprints.utm.my/13304/ Decision tree for static security assessment classification Saeh, Ibrahim Khairuddin, Azhar QA75 Electronic computers. Computer science This paper addresses the on going work of the application of Machine Learning on Static Security Assessment of Power Systems. Several techniques, which have been applied for static Security Assessment .A Decision Tree types comparison for the purpose of static security assessment classification is discussed and the comparison results from these methods on operating point are presented. Decision Tree examines whether the power system is secured under steadystate operating conditions.DT gauges the bus voltages and the line flow conditions. Using minimum number of cases from the available large number of contingencies in terms of their impact on the system security is the methodology that has been developed. Newton Raphson load flow analysis method is used for training and test data. The input variables to the network are loadings of the lines and the voltage magnitude of the load buses. The algorithms are initially tested on the 5 IEEE bus systems. The results obtained indicate that DT method is comparable in accuracy and computational time to the Newton Raphson load flow method. IEEE Computer Soc 2009 Book Section PeerReviewed Saeh, Ibrahim and Khairuddin, Azhar (2009) Decision tree for static security assessment classification. In: 2009 International Conference on Future Computer and Communication. IEEE Computer Soc, pp. 681-684. ISBN 978-076953591-3 http://dx.doi.org/10.1109/ICFCC.2009.64 doi:10.1109/ICFCC.2009.64 |
| spellingShingle | QA75 Electronic computers. Computer science Saeh, Ibrahim Khairuddin, Azhar Decision tree for static security assessment classification |
| title | Decision tree for static security assessment classification |
| title_full | Decision tree for static security assessment classification |
| title_fullStr | Decision tree for static security assessment classification |
| title_full_unstemmed | Decision tree for static security assessment classification |
| title_short | Decision tree for static security assessment classification |
| title_sort | decision tree for static security assessment classification |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT saehibrahim decisiontreeforstaticsecurityassessmentclassification AT khairuddinazhar decisiontreeforstaticsecurityassessmentclassification |