- Tytuł:
- Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm
- Autorzy:
-
Xia, Xin
Liu, Xiaofeng
Lou, Jichao - Powiązania:
- https://bibliotekanauki.pl/articles/227220.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
smart substation
network fault classification
improved separation interval method (ISIM)
support vector
machine (SVM)
Anti-noise processing (ANP) - Opis:
- Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2019, 65, 4; 657-663
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł