Artificial Neural Network based Fault Detection System for 11 kV Transmission Line

• Published in: 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) pp. 1 - 4
• Main Authors: Pandey, Aditya, Gadekar, Paritosh S., Khadse, Chetan B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.02.2021
• Subjects: Artificial neural networks; backpropagation; Mathematical model; Matlab; Monitoring; neural network; online monitoring; Power system protection; Power transmission lines; Testing; Training
• DOI: 10.1109/ICAECT49130.2021.9392433

In this paper, a fault identification system based on the artificial neural network is proposed. An 11 KV transmission line model is developed in MATLAB simulink model. The faults under considerations are line to ground, line to line, double line to ground, triple line and triple line to ground. These faults are created with the help of fault creation block. The duration as well as magnitude of faults are changed for producing the datasets for training the neural network. The scaled conjugate gradient descent backpropagation algorithm is used as a learning algorithm. The inputs to the neural network are the current dataset under normal as well as fault conditions. The target matrix is prepared by considering the time duration of fault in a considered current signal. The pattern recognition tool in MATLAB is used as a training platform for neural network. A trained model is generated after training. This model is used in transmission line model for testing the fault conditions with the different magnitude and duration than training fault conditions. In this way the monitoring of faults is done in online mode. The obtained results of fault testings are presented in the paper