| Autor: |
Arup Kumar Goswami, Devaprasad Paul, Shiladitya Dey, Tirunagaru V. Sarathkumar |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Advances in Automation, Signal Processing, Instrumentation, and Control ISBN: 9789811582202 |
| Popis: |
This paper deals with classification of faults based on causes arising in a transmission network. Response of operators to a fault depends upon the type of fault that has occurred itself. Response to a vegetative fault will be much different than that of lightning induced faults. Currently causative fault classification is only possible by physically visiting a place where the fault has occurred. However, if the fault can be classified from the fault data itself, it would massively speed up system response and enhance smoother operation of transmission apparatus. In this paper, identification and classification of various types of electric faults based on their cause are attempted. In this work, the data collected for causative fault analysis in disturbance recorders of North-Eastern grid is being fed to an artificial neural network in order to classify it. The data collected over the last five years is being used to device a predictive system. Causative fault classification has been done in this paper by utilizing artificial neural network (ANN). With the help of ANN, fault classification with an exceedingly high accuracy (99.5% accuracy) has been achieved in this work. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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