| Title | Application of Artificial Neural Network for Fault Recognition and Classification in Distribution Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Onaolapo, A.K., Akindeji, K.T. |
| Conference Name | 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) |
| Keywords | artificial neural network, Artificial neural networks, back propagation algorithm, Backpropagation, backpropagation algorithm, cyber physical systems, distribution network, Fault classification, fault recognition, faults, feedforward neural nets, feedforward neural network, MATLAB, policy-based governance, power distribution faults, power engineering computing, power system reliability, power system security, Power systems, power utility, pubcrawl, reliability, Resiliency, Sensors, three phase voltages |
| Abstract | Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB(r) 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method. |
| DOI | 10.1109/RoboMech.2019.8704808 |
| Citation Key | onaolapo_application_2019 |
Application of Artificial Neural Network for Fault Recognition and Classification in Distribution Network