High-frequency current transformer (HFCT) sensors are widely used for partial discharge detection due to their versatility, high sensitivity, and wide bandwidth. This paper reports on a finite-element analysis (FEA) methodology that can be employed to optimize HFCT performance. The FEA model consists of accurate 3D representations of the sensor components. Two different FEA software modules were used in order to cover the wide operating frequency range of the sensor. The simulation computes the frequency response of the sensor in the range 0.3-50 MHz for various HFCT geometric and material parameters, specifically the number of winding turns, spacer thickness, aperture size, and core material. A prototype HFCT was constructed and the measured response compared with that of the simulation. The shapes of the responses were similar, with the simulated sensitivity being higher than the measured sensitivity by 1 dB on average. The measured low-frequency cutoff of the sensor was found to be only 0.05 MHz lower than that of the simulation.

Rotating electrical machines are critical assets in industrial systems, where unexpected failures can lead to costly downtime and safety risks. This review presents a comprehensive and up-to-date analysis of artificial intelligence (AI) techniques for fault diagnosis in electric machines. It categorizes and evaluates supervised, unsupervised, deep learning, and hybrid/ensemble approaches in terms of diagnostic accuracy, adaptability, and implementation complexity. A comparative analysis highlights the strengths and limitations of each method, while emerging trends such as explainable AI, self-supervised learning, and digital twin integration are discussed as enablers of next-generation diagnostic systems. To support practical deployment, the article proposes a modular implementation framework and offers actionable recommendations for practitioners. This work serves as both a reference and a guide for researchers and engineers aiming to develop scalable, interpretable, and robust AI-driven fault diagnosis solutions for rotating electrical machines.

This paper reports on the development and testing of a novel barrier sensor for UHF Partial Discharge (PD) detection in gas-insulated equipment. The sensor features a unique dual-slot planar antenna backed by an air-filled cavity. The dual-slot arrangement allows different parts of the antenna to resonate at different frequency ranges in the UHF spectrum. As a result, the sensor exhibits broader bandwidth and higher sensitivity than other barrier sensors. Finite Element Analysis simulations have been used to optimize the sensor design. Furthermore, testing using a specially made PD test rig, GTEM cell testing and testing on a gas-insulated line section in the high voltage laboratory, have validated the simulation results and the capabilities of the sensor. The Dual-Slot Barrier (DSB) sensor exhibits a bandwidth of 0.3 -2.0 GHz with a mean effective height of 13 mm, and an effective height above 2 mm for 90% of the frequency range. The sensor can be used with both wideband instruments, such as oscilloscopes, and narrow band instruments such as frequency downconverters. Additionally, its optimized dimensions and unique replaceable sealing attachment ensure maximum compatibility for retrofitting on a wide range of equipment.

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

Insulating cross-arms (ICAs) allow compaction or upgrading of transmission lines. The process of designing and verifying the performance of electric-field grading devices is reported for rigid cross-arms on a 132 kV lattice tower. For the grounded end, traditional grading devices resembling rings which follow the general shape of the insulators were designed. For the high-voltage end, an iterative process yielded a novel grading device which is a unibody piece of cast aluminium that manages the field on all four ICA members. Finite-element analysis simulations show that the electric-field magnitude at the triple junctions of the insulating members meet the design criteria of 3.5 kV/cm. Also, the field magnitude on the metallic end-fittings and electric-field grading devices is maintained below 18 kV/cm. The corona extinction test was performed on ICA assemblies showing that the grading devices can effectively control the electric field at voltages up to 132 kV since the average corona extinction voltage was 173.7 kV, well above the required value. The complete ICA assemblies were installed on an existing line in Scotland in August 2013. This paper provides a set of recommendations for use of FEA in the design of complex insulation geometries.