TY - JOUR N2 - 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. L1 - http://www.journals.pan.pl/Content/113329/PDF/87.pdf L2 - http://www.journals.pan.pl/Content/113329 PY - 2019 IS - No 4 EP - 663 DO - 10.24425/ijet.2019.129825 KW - Smart substation KW - Network fault classification KW - improved separation interval method (ISIM) KW - Support vector machine (SVM) KW - Anti-noise processing (ANP) A1 - Xia, Xin A1 - Liu, Xiaofeng A1 - Lou, Jichao PB - Polish Academy of Sciences Committee of Electronics and Telecommunications VL - vol. 65 DA - 2019.11.03 T1 - Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm SP - 657 UR - http://www.journals.pan.pl/dlibra/publication/edition/113329 T2 - International Journal of Electronics and Telecommunications ER -