Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm

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

Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm

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

Soft granular computing based classification using hybrid fuzzy-KNN-SVM

This paper aims at providing the concept of information granulation in Granular computing based pattern classification that is used to deal with incomplete, unreliable, uncertain knowledge from the view of a dataset. Data Discretization provides us the granules which further can be used to classify the instances. We use Equal width and Equal frequency Discretization as unsupervised ones; Fayyad-Irani's Minimum description length and Kononenko's supervised discretization approaches along with Fuzzy logic, neural network, Support vector machine and their hybrids to develop an efficient granular information processing paradigm. The experimental results show the effectiveness of our approach. We use benchmark datasets in UCI Machine Learning Repository in order to verify the performance of granular computing based approach in comparison with other existing approaches. Finally, we perform statistical significance test for confirming validity of the results obtained.Web of Science10212811