Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

A novel traveling-wave-based method improved by unsupervised learning for fault location of power cables via sheath current monitoring

In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location

Multiple correspondence analysis to study failures in a diverse population of a cable

The study of failure behaviour of a diverse population of cables is challenging. Previous attempts have failed to capture the complexity of cable system failures due to an independent analysis of multiple failure causes or influential factors. In this paper, the Multiple Correspondence Analysis (MCA) is proposed for simultaneous analyses of multiple variables responsible for the cable failures and classification of cables into homogeneous groups in terms of past failure behaviour. The proposed classification method is less subjective as it gives equal consideration to all the cable features. The methodology has been applied to the main cable section and cable joint failure data of a diverse population of cables obtained from a Chinese utility company. The failure data have six categorical variables related to cable features and failure characteristics. The application of MCA provided an enriched view and understanding of failure behaviour by allowing visual exploration of the failure patterns and associations. Based on the past failure, the cable sections and joints were classified into three and four groups, respectively. The failure trend of each classified group is evaluated separately. Results show that failure history and trend of each classified group is different. Thus, they must be analyzed and treated differently in the forecasting or maintenance planning procedures