Name
Abstract | ||
|---|---|---|
In the presented work, an intelligent model for fault classification of a transmission line is proposed. Ten different types of faults (LAG, LBG, LCG, LABG, LBCG, LCAG, LAB, LBC, LCA and LABC) have been considered along with one healthy condition on a simulated transmission line system. Post fault current signatures have been used for feature extraction for further study. Empirical Mode Decomposition (EMD) method is used to decompose post fault current signals into Intrinsic Mode Functions (IMFs). These IMFs are used as input variables to an artificial neural network (ANN) based intelligent fault classification model. Relief Attribute Evaluator with Ranker search method is used to select the most relevant input variables for fault classification of a three-phase transmission line. Proposed approach is able to select most relevant input variables and gives better result than other combinations. Ours is a first attempt at using EMD for feature selection in fault classification of transmission lines. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/JIFS-169247 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Empirical mode decomposition,artificial neural network,transmission line,fault diagnosis,feature selection | Transmission line,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 4 | 1064-1246 |
Citations | PageRank | References |
5 | 1.06 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasmat Malik | 1 | 5 | 3.42 |
| Rajneesh Sharma | 2 | 61 | 8.63 |