Wavelet multiscale principal components and traveling waves enabled machine learning technique for protection of MT-HVDC systems

The interconnection of renewable energy sources to conventional power grids is widely recognized and promoted by the deployment of high voltage direct current (HVDC) technology. One of the biggest challenges associated with the HVDC systems is the rapid rate of rising DC fault currents, resulting in the immediate collapse of the converter stations. Conventional schemes of AC protection and low-speed DC protection are not worthy and mature because of the abrupt rising characteristics of DC currents. Therefore, protection techniques are being developed for reliable bulk power transfer and interconnection of unsynchronized grids via DC links. Timely identification, classification, and determination of location are the most fundamental characteristics of any protection scheme. This research paper proposes a machine learning-based protection method that establishes a rapid and reliable solution addressing the drawbacks of existing protection schemes. A multi-terminal high voltage direct current (MT-HVDC) test system is designed in Matlab/Simulink. Pole-to-ground and pole-to-pole faults are applied at different fault locations to assess the validity of the proposed algorithm. For this, DC voltages and currents are measured and analyzed. Power spectral analysis of traveling waves and Wavelet multiscale principal components analysis are employed for the extraction of featured data to classify and locate the DC faults. This reduced dimensional featured data is utilized for training and testing the support vector machine learning algorithm to analyze the accuracy of the proposed protection technique. Simulation results confirm the accuracy of rapid identification, classification, and location of DC faults.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).