Paper Info
Title
A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions.
Abstract
Since metal-oxide surge arresters are the important over-voltage protection equipments used in power systems, their operating conditions must be monitored on a timely basis to give an alarm as soon as possible in order to increase the reliability of a power system. The paper proposes a novel differential particle swarm optimization-based (DPSO-based) support vector machine (SVM) classifier for the purpose of monitoring the surge arrester conditions. A DPSO-based technique is investigated to give better results, which optimizes the parameters of SVM classifiers. Three features are extracted as input vectors for evaluating five arrester conditions, including normal (N), pre-fault (A), tracking (T), abnormal (U) and degradation (D). Meanwhile, a comparative study of fault diagnosis is carried out by using a DPSO-based ANN classifier. The results obtained using the proposed method are compared to those obtained using genetic algorithm (GA) and particle swarm optimization (PSO). The experiments using an actual dataset will expectably show the superiority of the proposed approach in improving the performance of the classifiers.
Year
DOI
Venue
2018
10.1016/j.swevo.2017.07.006
Swarm and Evolutionary Computation
Keywords
Field
DocType
Arrester,Condition diagnosis,Particle swarm optimization,Power systems,Support vector machine
Particle swarm optimization,Control theory,Computer science,Support vector machine,Electric power system,Multi-swarm optimization,Artificial intelligence,Classifier (linguistics),Surge arrester,Lightning arrester,Genetic algorithm,Machine learning
Journal
Volume
ISSN
Citations 
38
2210-6502
2
PageRank 
References 
Authors
0.38
4
4
Name
Order
Citations
PageRank
Thi Thom Hoang141.74
Ming Yuan Cho2144.29
Mahamad Nabab Alam392.32
Quoc Tuan Vu420.38