Abstract

Power cables, the most critical component of the power system, must be extremely reliable in order to avoid revenue losses due to premature failure. The dielectric properties of cable insulation may deteriorate due to ageing phenomena, which may have a negative impact on the polymer materials used for insulation. Therefore, the early detection of such depletion, and the severity of degradation while the equipment is in operation, aids in the avoidance of a total failure. Partial discharge (PD) detection and analysis have been adopted as a predictive test to characterize and assess the state of electric cables in advance. This review provides an in-depth discussion of the reactions that occur in the insulation system of the cables. Moreover, this paper presents a comprehensive review of the state-of-the art of various PD detection techniques regarding sensor types. The drawbacks and challenges of different PD measurement techniques have been elaborated. Following that, the numerous PD localization methods are discussed, as well as the necessity of computational intelligence approaches and their pros and cons. Last but not least, the authors provide a deep insight into the theoretical and practical implications of deep learning in PD localization, as well as recommendations for future research directions. This review will provide valuable insights and act as a starting point for researchers to lead the development of more efficient approaches for diagnosing PD in the cable.