An ANN-based protection technique for MTDC systems with multiple configurations

Abstract

A unified protection technique for different configurations of Multi-Terminal high voltage Direct Current (MTDC) systems is presented in this paper. The technique is based on using positive and negative poles’ currents of one end for each line. The spectral energy of two distinct frequency bands captured from fault currents, together with the fault current initial slope, are used to supply a feed forward Artificial Neural Network (ANN) for identifying fault zone and classifying fault type. Different configurations of MTDC systems are used to train and test the proposed ANN including radial system, meshed system without Current Limiting Reactors (CLRs), and meshed system with CLRs. Different fault types, fault locations, fault inception times, and fault resistances are used to train and test the proposed ANN. The proposed algorithm showed an excellent performance in the identification of the fault zone and the classification of the fault type for various MTDC configurations. Moreover, the algorithm does not respond to non- DC fault abnormal conditions.

Keywords

• HVDC
• MTDC
• Neural networks
• Protection
• Wind farm