ANN-Based Protection Scheme for Bipolar CSC-Based HVDC Transmission Line

The proposed work presents accurate fault detection and classification algorithm for bipolar Current Source Converter (CSC) based High Voltage DC transmission line established on the application of Artificial Neural Network (ANN) with combination of wavelet transform as feature extraction tool. The proposed algorithm uses the extracted features of AC sinusoidal voltage, DC voltage and current signals measured on both poles at the rectifier terminal of the line as input to the Artificial Neural Network model. The input signals are sampled at 1 kHz which is less in comparison with the other existing techniques. The performance of the proposed method has been evaluated under different categories of fault conditions, various fault instants, and at different fault locations. The proposed ANN-based fault detector and classification algorithm are 100% accurate for all the tested fault samples with fault detection periods are less than half cycle.