A fault location technique for HVDC transmission lines using extreme learning machines

In this study, a new approach is proposed for fault estimation in high voltage direct current transmission lines using discrete wavelet transform and extreme learning machine. Recently, signal processing and intelligent systems have gained importance to ease very different tasks such as fault location and estimation, load estimations, reactive power compensation, the risk of blackouts. Therefore, a fast, accurate and reliable protection algorithms have a major interest in the extended usage of high voltage direct current systems for many areas. In this study, single phase-ground faults on DC lines examined and a new machine learning approach also discussed. The virtual faults obtained from Matlab simulation is utilized in the course of feature extraction of the wavelet transform. Furthermore, for identifying steady state and faulted condition, Shannon entropy and signal's energy values have been calculated by using coefficients of the wavelet transform. After that, the coefficients normalized between [−1, 1]. Finally, the extreme learning machine used to fault estimation and location process.