Extreme learning machine-based fault location approach for terminal-hybrid LCC-VSC-HVDC transmission lines

This paper proposes an Extreme Learning Machine-based fault location method to evaluate the DC transmission line faults of Terminal-hybrid LCC-VSC-HVDC systems. For this purpose, the voltage and current fault signals are firstly measured from the terminals of the hybrid HVDC system. Then, the high-frequency components of the input signals will be extracted and decomposed employing wavelet transform. In the next step, the energy of these extracted signals is calculated and provided to the extreme learning machine as intake train and test data. Finally, the exact location of the fault point will be determined using ELM. The proposed strategy is also applied to the same hybrid system using single-layer and double-layer neural networks, and the results have been thoroughly compared with ELM. The outcomes unequivocally indicate that by increasing the amount of DC filters in the transmission line, the average percentage error of fault location will be remarkably diminished. A +/- 100 kV Terminal-hybrid LCC-VSC-HVDC system has been implemented and simulated via MATLAB software to assess the proposed method. It can be perceived that the ELM-based method not only works properly under various fault resistances, fault locations, and types of faults but also has significant superiority over the ANN-based approaches.