Efficient and robust ANN‐based method for an improved protection of VSC‐HVDC systems

Abstract

This study presents a method based on an artificial neural network (ANN) for protecting voltage source converters (VSC)-high-voltage direct current (HVDC) systems. The proposed method takes advantage of ANNs' excellent capabilities in pattern recognition and classification. The solution presented here is able to detect a fault condition in the whole HVDC system based only on voltage waveforms measured at the rectifier substation, which is an important advantage over other fault detection methods. For any given fault condition, the faulty zone is identified and then the fault is classified concerning the phases or poles involved. The proposed method is shown to be both robust against false operations and very reliable with respect to fault detection and classification. A large number of simulations were performed and results show the effectiveness of the proposed scheme. Different operational conditions and fault cases were considered to evaluate the proposed algorithm, ensuring excellent performance concerning a wide range of possible situations in VSC-HVDC systems.