A Fault Detection Algorithm Based on Artificial Neural Network Threshold Selection in Multi-Terminal DC Grids

The DC fault is one of the critical challenges in the High Voltage Multi-Terminal Direct Current (HV-MTDC) grid. The fixed value threshold used in some implementations may be affected by different grid conditions and thus causes errors in the fault detection procedure. This article offers a two-stage fault detection algorithm based on the integrated Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT) theory. The main innovations of the proposed method are: 1) Increasing the reliability of the fault detection operation by solving the fixed value threshold problems; 2) Offering high robustness against the wide range of fault resistances; 3) Providing bus protection capability and 4) Setting relays locally, and avoiding the requirement to high-speed communications links. Also, it offers DC fault detection in a short period of time. The performance of the proposed protection scheme has been ascertained via simulations in the MATLAB/Simulink environment. The results prove the robustness of the proposed method against noise disturbance, high resistance fault, and grid parameters variations.