Optimum Closing Time of Circuit Breakers Evaluation during Transformer Energization Using Radial Basis Function Neural Network
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| Title | Optimum Closing Time of Circuit Breakers Evaluation during Transformer Energization Using Radial Basis Function Neural Network |
| Authors | |
| Abstract | In this paper, controlled energization transformers is done using Artificial Intelligence (AI) technique. Radial Basis Function Neural Network (RBFNN) is selected as AI tool. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that it’s minimum value is corresponding to the best case switching time. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated. |
| Publisher | Advances in Computational Mathematics and its Applications |
| Date | 2012-01-26 |
| Source | 2167-6356 |
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