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VSC MT-HVDC Fault Identification Based on the VMD-TEO and Artificial Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2023) Farkhani, Jalal S.; Celik, Ozgur; Ma, Kaiqi; Bak, Claus Leth; Chen, Zhe
This paper presents a single-ended protection scheme based on an artificial neural network (ANN) and a variational mode decomposition (VMD) for the voltage source converter (VSC) multiterminal high voltage DC (MT-HVDC) system. The developed technique depends on the utilization of VMD for feature extraction of measured voltage and current signals which are transferred into the intrinsic mode function (IMF). Then the Teager energy operator (TEO) is applied for tracking the energy content of the IMFs. The energy content of the IMFs is fed into the multilayer perceptron neural network (MLP) neural network for the identification of faults in a test system. The effectiveness of the proposed method is verified on a four-terminal HVDC system with several case studies including high impedance faults (HIFs), and disturbances by using the PSCAD/EMTDC software. The developed ANN-based fault identification method provides promising results in terms of accurate and fast fault detection features. Simulation results verify the accuracy of the proposed protection method for VSC MT-HVDC systems. © 2023 IEEE.