scholarly journals Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ming-Yuan Cho ◽  
Thi Thom Hoang
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Time Domain Reflectometry ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Swarm Optimization

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

scholarly journals Intelligent Bearing Fault Diagnosis using Swarm Decomposition Method and New Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Saeed Nezamivand Chegini ◽  
Pouriya Amini ◽  
Bahman Ahmadi ◽  
Ahmad Bagheri ◽  
Illia Amirmostofian
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Svm Classifier ◽  
Features Selection ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Hybrid Particle Swarm Optimization

Abstract The quality of information extracted from the vibration signals, and the accuracy of the bearing status detection depend on the methods used to process the signal and select the informative features. In this paper, a new hybrid approach is introduced in which the relatively new Swarm Decomposition (SWD) method and the optimized Compensation Distance Evaluation Technique (OCDET) are used to enhance the signal processing stage and to improve the optimal features selection process, respectively. Firstly, the vibration signals are decomposed into their Oscillatory Components (OCs) using the SWD. The feature matrix is constructed by computing the time-domain features for the OCs. The CDET method is consequently utilized to select the most sensitive features corresponding to the bearing status. On the other hand, The CDET approach contains a parameter called threshold which affects the number of the selected features. In this way, the hybrid optimization algorithm, which is a combination of the Particle Swarm Optimization (PSO) algorithm with the Sine-Cosine Algorithm (SCA) and the Levy flight distribution, has been used to select the optimal CDET threshold and improve the Support Vector Machine (SVM) classifier. The proposed technique ability is evaluated by vibration signals corresponding to different bearing defects and various speeds. The results indicate the capability of the proposed fault diagnosis method in identifying the very small-size defects under various bearing conditions. Finally, the presented method shows better performance in comparison with other well-known methods in the most of the case studies.

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