Analysis of real time fault data of multi terminal transmission system using python learning tools

Abstract

Transmission line faults are common difficulties in today’s world. Faults mainly depend on types of load and their nature. Transmission lines are divided in different zones and hence cannot predict easily the faults and their types. Several protective devices have incorporated in the past few years to classification of fault but none guaranteed the accuracy. In this paper author proposes an advanced machine learning algorithm to classification of faults and provided some future protection technique to minimize faults and reliability of supply to the consumer. In this python learning tools author compare the two algorithm namely using KNeighbors Classifier and Using Multinomial Logistic Regression. The data for experiments are obtained from BBMB Punjabi Bagh 220 KV substation New Delhi. The real experiments validates the improvements and future forecasting regarding faults in lines and analysis of results with their accuracy are elaborated in this paper. A discussion of real data and their implication in future conservation of energy.