Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line
Radial basis function neural networks (RBFNN) and fuzzy neural networks (FNN) are finding increasing attention as AI techniques. Power systems protection is a complex task in which AI techniques are successfully employed. Minimal RBFNN (MRBFNN) is a newer version of neural network that provides a mi...
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| Format: | Article |
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2003
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| Online Access: | http://shdl.mmu.edu.my/2581/ |
| _version_ | 1848790093909721088 |
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| author | Dash, PK Pradhan, AK Panda, G |
| author_facet | Dash, PK Pradhan, AK Panda, G |
| author_sort | Dash, PK |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Radial basis function neural networks (RBFNN) and fuzzy neural networks (FNN) are finding increasing attention as AI techniques. Power systems protection is a complex task in which AI techniques are successfully employed. Minimal RBFNN (MRBFNN) is a newer version of neural network that provides a minimum number of neurons using the sequential learning and pruning strategy. On the other hand, the fuzzy neural network, using a pruning strategy, yields fewer fuzzy rules. These new techniques are employed for the Protection of a power network having a thyristor-controlled series capacitor (TCSC) which introduces further complexity into the protection problem. A comparison of the two new schemes is also outlined. |
| first_indexed | 2025-11-14T18:07:08Z |
| format | Article |
| id | mmu-2581 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:07:08Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-25812011-08-23T07:19:01Z http://shdl.mmu.edu.my/2581/ Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line Dash, PK Pradhan, AK Panda, G TA Engineering (General). Civil engineering (General) Radial basis function neural networks (RBFNN) and fuzzy neural networks (FNN) are finding increasing attention as AI techniques. Power systems protection is a complex task in which AI techniques are successfully employed. Minimal RBFNN (MRBFNN) is a newer version of neural network that provides a minimum number of neurons using the sequential learning and pruning strategy. On the other hand, the fuzzy neural network, using a pruning strategy, yields fewer fuzzy rules. These new techniques are employed for the Protection of a power network having a thyristor-controlled series capacitor (TCSC) which introduces further complexity into the protection problem. A comparison of the two new schemes is also outlined. 2003-03 Article NonPeerReviewed Dash, PK and Pradhan, AK and Panda, G (2003) Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line. Electric Power Components and Systems, 31 (3). pp. 241-260. ISSN 1532-5008 http://dx.doi.org/10.1080/15325000390112170 doi:10.1080/15325000390112170 doi:10.1080/15325000390112170 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Dash, PK Pradhan, AK Panda, G Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title | Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title_full | Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title_fullStr | Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title_full_unstemmed | Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title_short | Application of Artificial Intelligence Techniques for Classification and Location of Faults on Thyristor-Controlled Series-Compensated Line |
| title_sort | application of artificial intelligence techniques for classification and location of faults on thyristor-controlled series-compensated line |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://shdl.mmu.edu.my/2581/ http://shdl.mmu.edu.my/2581/ http://shdl.mmu.edu.my/2581/ |