ABSTRACT
With the widening application of high-voltage direct-current (HVDC) systems, related issues such as their protection requirements have attracted much attention. Indeed, the various technologies employed in HVDC systems have their particular complexities and protection challenges. Therefore, it is essential to design appropriate fault detection, classification, and location schemes with specific consideration given to each technology. The pattern-recognition and machine-learning techniques can play a highly influential role in designing such schemes for HVDC systems and overcome the complexities and challenges by relying on their inherent capabilities. In this chapter, after briefly stating the advantages and applications of HVDC systems, current-source converter-based HVDC (CSC-HVDC) and voltage-source converter-based HVDC (VSC-HVDC) systems are introduced, and their specific protection challenges are described. Next, the performed studies and achieved advances in fault detection, classification, and location in CSC-HVDC and VSC-HVDC systems are reviewed with specific focus given to the applied pattern-recognition and machine-learning procedures. The related intelligent schemes are discussed considering two main aspects, including the extracted/selected input features and the employed learning algorithms/models. Lastly, some specific challenges in evaluating and practically implementing intelligent fault diagnosis schemes are also briefly discussed, including implementation costs, unseen new cases, high-resistance faults, temporary arc faults, close-to-terminal faults, operation of adjacent circuit breakers, lightning disturbances, measurement noises/errors, inaccurate line parameters, communication delays/disturbances/failures, and time synchronization errors.
