Risk based security assessment of power system using generalized regression neural network with feature extraction
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle compo...
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my.uniten.dspace-301272023-12-29T15:44:45Z Risk based security assessment of power system using generalized regression neural network with feature extraction Marsadek M. Mohamed A. 26423183000 57195440511 generalized regression neural network line overload low voltage principle component analysis risk index voltage collapse Electric loads Feature extraction Meteorology Neural networks Principal component analysis Generalized regression neural networks line overload Low voltages Principle component analysis Risk indices Voltage collapse Risk assessment A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy. � 2013 Central South University Press and Springer-Verlag Berlin Heidelberg. Final 2023-12-29T07:44:45Z 2023-12-29T07:44:45Z 2013 Article 10.1007/s11771-013-1508-9 2-s2.0-84890537980 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890537980&doi=10.1007%2fs11771-013-1508-9&partnerID=40&md5=7e00fb5d7a3b422fd5284cfec58173b1 https://irepository.uniten.edu.my/handle/123456789/30127 20 2 466 479 Scopus |
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generalized regression neural network line overload low voltage principle component analysis risk index voltage collapse Electric loads Feature extraction Meteorology Neural networks Principal component analysis Generalized regression neural networks line overload Low voltages Principle component analysis Risk indices Voltage collapse Risk assessment |
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generalized regression neural network line overload low voltage principle component analysis risk index voltage collapse Electric loads Feature extraction Meteorology Neural networks Principal component analysis Generalized regression neural networks line overload Low voltages Principle component analysis Risk indices Voltage collapse Risk assessment Marsadek M. Mohamed A. Risk based security assessment of power system using generalized regression neural network with feature extraction |
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A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy. � 2013 Central South University Press and Springer-Verlag Berlin Heidelberg. |
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26423183000 |
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26423183000 Marsadek M. Mohamed A. |
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Article |
| author |
Marsadek M. Mohamed A. |
| author_sort |
Marsadek M. |
| title |
Risk based security assessment of power system using generalized regression neural network with feature extraction |
| title_short |
Risk based security assessment of power system using generalized regression neural network with feature extraction |
| title_full |
Risk based security assessment of power system using generalized regression neural network with feature extraction |
| title_fullStr |
Risk based security assessment of power system using generalized regression neural network with feature extraction |
| title_full_unstemmed |
Risk based security assessment of power system using generalized regression neural network with feature extraction |
| title_sort |
risk based security assessment of power system using generalized regression neural network with feature extraction |
| publishDate |
2023 |
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1806426117160042496 |
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13.222552 |