With the large-scale integration of new power systems and distributed generators (DGs), cable fault detection and localization face numerous challenges, where artificial intelligence (AI) techniques demonstrate significant advantages. This review first outlines the causes of cable faults and traditional methods for fault detection and localization. Subsequently, it comprehensively analyzes the applications of both conventional machine learning and deep learning approaches in this field, elaborating on their application scenarios, strengths, defects, and successful case studies, providing valuable references for researchers and professionals. Additionally, the paper discusses the strengths and limitations of current AI techniques, along with the impacts introduced by DG integration. Finally, it highlights future development trends and potential research directions for advancing AI-based solutions in cable fault detection and localization.

Accurate assessment of high-voltage cable insulation condition is essential for safe operation in complex tunnel environments. Traditional methods relying on single diagnostic indicators and fixed weighting schemes often suffer from limited accuracy and adaptability. This paper proposes a multi-parameter assessment method integrating game theory with fuzzy comprehensive evaluation. Five types of online monitoring data, namely cable surface temperature, sheath grounding current, partial discharge, tunnel humidity, and ambient temperature, are selected as diagnostic parameters. Subjective and objective weights are first derived using the analytic hierarchy process and the entropy weight method, and then optimally integrated through a game-theoretic framework. Fuzzy membership functions are applied to construct an evaluation matrix, enabling quantitative and graded assessment of insulation condition. A case study on 110 kV tunnel high-voltage land cables in Zhejiang, China, verifies the effectiveness of the approach. Results show that the proposed method more accurately reflects actual operating conditions and provides higher diagnostic precision and robustness compared with single-feature and traditional weighting methods. By combining expert knowledge with real monitoring data, this study develops a scientific and practical framework for insulation condition assessment, offering reliable support to real-time insulation monitoring and predictive maintenance applications of high-voltage power transmission systems.

Lead sealing is one of the key accessories for high-voltage cables and its quality directly affects the security and stability of such cables. In order to reduce the accident rate in power processing caused by lead sealing issues, it is necessary to carry out non-destructive testing and evaluation. Due to the bending surface of lead sealings, it is difficult to detect their conductivity and thickness accurately using existing eddy current (EC) testing methods. In this paper, measurements of conductivity and thickness for curved specimens are analysed and an accurate method is subsequently proposed. Firstly, the measurement methods of conductivity and thickness are established based on the transformer model. It is found that the conductivity and thickness can be detected using phase signature at high and low frequencies, respectively. Secondly, the influence of curvature on the phase signature is analysed and the conductivity and thickness measurements of lead sealing are corrected using curvature correction coefficients. Finally, an EC system is built and experiments are carried out to evaluate the novel methods. The results show that the methods achieve a good level of accuracy in detecting the conductivity and thickness of curved lead sealings.

Many studies have investigated tree-contact arcing ground faults. However, the effects of branch moisture content and wind speed are still not fully understood. Therefore, this paper addresses the wildfire risk caused by tree-contact arc grounding faults in distribution networks. A 10 kV distribution-line tree-contact arcing fault test platform is built. A two-dimensional multi-physics plasma model is also developed based on magnetohydrodynamics. Experiments and simulations are combined. The effects of wind speed, branch moisture content, and conductor type on arc evolution and fault characteristics are systematically studied. The results show that higher wind speed causes stronger arc-column deformation. The fault current contains more high-frequency components and sharp spikes. At 9 m/s and 16 m/s, the fault current shows strong disturbances and much lower stability. Higher moisture content increases the branch conductivity indirectly. It strengthens the carbonized conductive path and helps sustain stable arcing. For the high-moisture sample (64%), the current waveform is smooth, and its amplitude increases monotonically with fault development. For the low-moisture sample (30%), the current amplitude decreases, and spikes become more frequent. The arc tends to extinguish and reignite repeatedly, which indicates an unstable discharge process. The simulations further reveal the coupling between the arc-root temperature field and the airflow field under different wind speeds and conductivities. They also show clear differences in temperature evolution between bare conductors and insulated conductors. These findings provide experimental evidence and simulation support for identifying wildfires initiated by tree-contact arcing faults.