Fault Detection Using Backward Propagating Traveling Waves for Bipolar LCC-HVDC Lines

Abstract

The line commutated converter (LCC)-high voltage direct current (HVDC) transmission lines may have various complementary protection algorithms based on derivatives of line parameters or differential line currents. The DC line fault detection based on distance relaying, traveling wave (TW), artificial neural network, fuzzy logic, and wavelet are a few of the popular schemes. However, the TW-based technique has been reported as one of the fastest techniques to detect and locate faults. The conventional TW techniques face limitations in detecting high resistance DC line faults. Therefore, this paper introduces an improved fault detection, classification, and location algorithm based on backward propagating TWs and fault classifier indices. The proposed algorithm is tested to detect, classify, and locate the faults under different system conditions such as pole-to-ground, pole-to-pole, and rectifier and inverter sides’ external faults. A bipolar LCC-HVDC transmission system of 900 km length is utilized to simulate the various fault conditions. The simulation results indicate fast, accurate, and reliable detection of faults. This scheme uses only single-end data measurement. The simulation study was performed for various fault locations (0–900 km) and transition resistances (0–150 Ω).