@ARTICLE{Xia_Xin_Smart_2019,
 author={Xia, Xin and Liu, Xiaofeng and Lou, Jichao},
 volume={vol. 65},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={657-663},
 howpublished={online},
 year={2019},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={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.},
 type={Article},
 title={Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm},
 URL={http://www.czasopisma.pan.pl/Content/113329/PDF/87.pdf},
 doi={10.24425/ijet.2019.129825},
 keywords={Smart substation, Network fault classification, improved separation interval method (ISIM), Support vector machine (SVM), Anti-noise processing (ANP)},
}