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.

As a non-invasive imaging method, electrical impedance tomography (EIT) technology has become a research focus for grounding grid corrosion diagnosis. However, the existing algorithms have not produced ideal image reconstruction results. This article proposes an electrical impedance imaging method based on sparse representation, which can improve the accuracy of reconstructed images obviously. First, the basic principles of EIT are outlined, and the limitations of existing reconstruction methods are analyzed. Then, an EIT reconstruction algorithm based on sparse representation is proposed to address these limitations. It constructs constraints using the sparsity of conductivity distribution under a certain sparse basis and utilizes the accelerated Fast Iterative Shrinkage Threshold Algorithm (FISTA) for iterative solutions, aiming to improve the imaging quality and reconstruction accuracy. Finally, the grounding grid model is established by COMSOL simulation software to obtain voltage data, and the reconstruction effects of the Tikhonov regularization algorithm, the total variation regularization algorithm (TV), the one-step Newton algorithm (NOSER), and the sparse reconstruction algorithm proposed in this article are compared in MATLAB. The voltage relative error is introduced to evaluate the reconstructed image. The results show that the reconstruction algorithm based on sparse representation is superior to other methods in terms of reconstruction error and image quality. The relative error of the grounding grid reconstructed image is reduced by an average of 12.54%.

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.

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.

To address the limitation of the IEC 60287 standard in accurately representing the electrothermal characteristics of cables under different grounding conditions, this study proposes a modified equivalent thermal resistance method, using a YJLW03-Z 64/110 1 × 1200 mm² high-voltage single-core cable as a case study to analyze three typical grounding modes, namely two-end solid bonding, segmented solid bonding, and semiconductive outer sheath. Equivalent circuit models are established to calculate the induced current, voltage, and losses of the metallic sheath and armor. Based on these results, the equivalent thermal resistance model is modified, and correction formulas for cable ampacity considering grounding effects are derived. The proposed model is validated through numerical simulations under typical laying conditions and field tests conducted in Zhoushan, Zhejiang Province. Results show that grounding modes significantly influence the electromagnetic losses and temperature distribution of cables. Segmented solid bonding effectively reduces sheath losses and increases ampacity, while its enhancement tends to stabilize beyond two bonding sections. The semiconductive outer sheath improves electric field distribution and thermal stability with limited ampacity gain. This study provides theoretical guidance and engineering reference for optimizing grounding designs, ampacity evaluation, and digital operation of high-voltage cable systems.

Tree-contact single-phase-to-ground faults (TSF) along transmission and distribution line corridors can ignite trees, trigger fires, and threaten normal line operation. Existing research remains unclear on the mechanism by which tree temperature influences fault evolution. This study therefore investigates fault current characteristics during TSF. First, numerical relationships between fault currents under tree temperature conditions were derived based on conductive medium transmission mechanisms and equivalent circuit models. Second, a fitting model for tree conductivity versus tree temperature was constructed; these numerical relationships were applied to iterative calculations within the fault model. A tree-line fault model based on tree temperature was established and integrated into the fault model for iterative solution, capturing the dynamic changes in the actual tree state. This enabled simulation of fault currents during the TSF process. Finally, experimental data validated the model, demonstrating that the developed equivalent model accurately fits both the magnitude and waveform of TSF currents. These findings hold significant implications for the detection, diagnosis, and prediction of TSF.