Transmission Line Fault Location and Classification Using Machine Learning Technique

The fault types and location in a power transmission line are detected based on the voltage and current wave forms. Fault types and location varies slightly depending on the location of the accident, which is not easy to grasp with the human eye. Therefore, many sensors are needed to diagnose faults, and it is very difficult for an administrator to determine the type and location of fault using voltage and current waveforms. The traditional systems adopted for fault classification result in complexity, lack of economy, nonhomogeneity and sometimes even cause unacceptable classification errors. The detection of fault in transmission line is done by using magnetic field sensors. The k-Nearest-Neighbour (kNN) algorithm and linear regression Machine Learning Technique can be used to locate and classify fault. This will reduce the computation techniques and will help in improving the power transmission system protection. It will also reduce the time necessary to clear the faults, especially for a long transmission line.