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...
| Main Authors: | , , |
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| Format: | Article |
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2003
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2581/ |
| Summary: | 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. |
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