Fault Classification in Transmission Lines using K-Nearest Neighbour Approach
In the generation of pursuing to industrial revolution 4.0, there are major changes in most of the industrial sectors relating with technology and engineering to adapt the revolution. With these changes made, the utilities companies are being affected due to the increase in demand of electrical power. With the importance of electrical power, the reliability of the grid system is to be expected as ideal with minimal fault. In any case of fault occurrence, the grid system should be able to classify a fault efficiently to progress into protection coordination to minimize the effect of fault occurred. The issue is being studied and a machine learning model had been developed in respond to the issue. The model produces an accuracy of 93.9% in fault classification using k-Nearest Neighbor approach.