ANN based Fault Classification in a DG Integrated Power System

Anshuman Bhuyan, Basanta K. Panigrahi, S. Pati and Nakul Sahu

The penetration of Distributed Generation (DG) increased with time to meet the increased load demand. The DG's integration into the grid complicates the system and creates numerous power system protection issues. The effect of the DG on the grid makes it more difficult to analyze the fault. This paper classifies fault accuracy using the Artificial Neural Network (ANN) technique in a distributed generator integrated power system. The classification technique should be accurate and intelligent so that it can clear the fault quickly. The voltage signal is extracted at the point of common coupling and the extracted signal is given to the ANN as input. The voltage signal is undergoing the training and validation process. Under various circumstances, extensive results analysis is given to present the contribution of the work. Different DGs are connected to make the test system closed to any real-time microgrid. In this work, the proposed model describes using MATLAB / SIMULINK. Results show that the used method is able to classify the faults more accurately.

Volume 11 | Issue 9

Pages: 71-76

DOI: 10.5373/JARDCS/V11I9/20192916