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

Comparison of different regression models to estimate fault location on hybrid power systems

“…WT is a tool that separates data into functions or different frequency components that are used to evaluate each component at a resolution appropriate to its scale [32,33]. DWT is a special case of WT that provides a dense representation that can be efficiently computed for time and frequency signals [28].…”

“…In addition, unlike the aforementioned traditional transmission systems, there have also been similar studies on high-voltage direct current transmission lines, which are often preferred for offshore renewable power plants installed out at sea, and have a lower loss rate when considering the longer distances involved [28]. To review additional studies about detailed protection algorithms for hybrid power systems integrated with distribution networks, readers should refer to the references in [29,30].…”

HVDC Transmission Line Fault Identification: A Learning Based UAV Control Strategy

“…Haleem et al [13] proposed robustness technique of low to high resistance fault detection schemes at different grid and operating configurations. Fault estimation has been done in [14] using discrete wavelet transform and extreme learning machine in an HVDC transmission lines. The discrete wavelet transform-coefficients have been used to find the energy of the signal and Shannon's Entropy in [15].…”

Primary and backup fault detection techniques for multi‐terminal HVdc systems: a review

“…The problem with this model is the extensive training of data failure to retrieve complete information utilises too much memory. Extreme machine learning techniques have been used to find a location of the fault [99]. In [100], the AI method has been employed for fault detection in the MMC based MTdc system and needs higher than a sampling rate of 10 kHz.…”