ABSTRACT Insulating polymer materials are widely utilized in electrical and electronic applications. During their operation, they have the capacity to accumulate electrical charges on their surface or in their volume, which can cause their premature aging and therefore alter their long‐term performance. In the present work, the flow of ions deposited on the surface of polyethylene terephthalate (PET) is analysed using the surface potential decay (SPD) method. Therefore, an experimental study is carried out, followed by modelling of the results obtained. A negative corona discharge, produced in a needle–grid‐plate electrode system, is used to charge the surface of the samples. In order to obtain reproducible results, all SPD measurements are carried out in a commercial climate chamber, where relative humidity (RH) and temperature ( T ) are rigorously controlled. The effects of initial potential, relative humidity, thickness and polarity are also analysed. The results showed that the SPD is strongly conditioned by these factors. It should be noted that temperature and humidity do not contribute equally to the neutralization of deposited charges. Temperature acts as an accelerator of the process, whereas humidity enhances its effect. In addition, the experimental results demonstrate that the SPD depends on the internal structure of the polymer and confirm the influence as well as the role of the electric field in the decay of the potential. The experimental design methodology was used to quantify the effects of different influential factors (i.e., initial potential, humidity and temperature) and to optimize the efficiency of the characterization of the electrical properties of PET. Under identical thermo‐hygrometric conditions ( T = 55°C, RH = 80%), the SPD increases by about 20% as the time increases from 5000 to 80,000 s (i.e., a 16‐fold increase in time). Two mathematical models were developed to estimate these parameters and their interactions under short‐ and long‐term conditions, demonstrating excellent statistical performance, with goodness of fit ( R 2 > 97.5%) and goodness of prediction ( Q 2 > 87%).
ABSTRACT Accurate acoustic localisation of surface discharge on converter valve damping capacitors is essential for the safe operation of high‐voltage direct current (HVDC) systems. However, the stochastic nature of discharge signals, leading to frequency ambiguity, coupled with the shielding effects of near‐field obstacles, poses significant challenges to the accuracy and robustness of traditional sound source localisation algorithms. To address these issues, this study proposes a fuzzy narrowband focusing beamforming method for acoustic source localisation. Initially, the time–frequency characteristics of discharge acoustic signals are thoroughly analysed using continuous wavelet transform (CWT). Subsequently, a generalized bell‐shaped fuzzy function is introduced to focus and fuse the fuzzy narrowband, effectively mitigating problems associated with frequency drift and uneven bandwidth. Furthermore, the sound source localisation task is reformulated as a sparse signal recovery problem, and a compressed beamforming algorithm based on Block Sparse Bayesian Learning (BSBL), combined with the Expectation Maximisation (EM) method, is employed to achieve accurate estimation of the direction of arrival (DOA). Finally, the superiority of the proposed algorithm is validated through simulation experiments and simulated valve hall tests. The results demonstrate that the method achieves excellent localisation accuracy and robustness across varying signal‐to‐noise ratios (SNRs), different characteristic frequencies, and diverse microphone array configurations. In field experiments within a simulated valve hall, the proposed method achieves an average spatial angular error of 0.75 with a standard deviation of 0.92, which is significantly lower than that of conventional algorithms. Overall, this research provides an innovative technical approach for the early detection and high‐precision localisation of concealed defects in converter valves.
In existing ultrasonic localization methods for transformer partial discharge, three-dimensional sensor array deployment requires a large number of sensors, resulting in a high cost and unfavourable spatial topology. In contrast, two-dimensional array deployment exhibits low sensitivity to the vertical position of partial discharge sources and is prone to multiple solutions and local optimal solutions under ambiguous boundary conditions. To address these limitations, this paper integrates the advantages of both three-dimensional and two-dimensional array deployments while avoiding their respective drawbacks. Proposing a transformer partial discharge localization method based on optical and electric collaborative acoustic sensing technology. Firstly, a collaborative deployment scheme for distributed optical fibre sensing and electronic acoustic sensing is investigated. Subsequently, a hierarchical fusion localization strategy is designed, in which spatial localization is achieved using optical signals followed by precise localization using electrical signals. Finally, a partial discharge experimental platform is established and both types of sensors are deployed. Partial discharge localization is performed based on the acquired optical and electrical signals, and comparative experiments are conducted with existing localization methods. The results demonstrate that the proposed method achieves a localization error of less than 7.12 cm with a localization time of 0.95 s, without the occurrence of local optimal solutions or multiple solutions. Compared with existing partial discharge localization techniques, the proposed method exhibits higher accuracy and good feasibility. By constraining the solution space spatially, it effectively avoids local optimal solutions during nonlinear equation solving, thereby fully validating its effectiveness in transformer partial discharge localization.
ABSTRACT Stator inter‐turn and ground faults in medium‐ and high‐voltage (MV/HV) motors often require full stator rewinding, causing long outages and high costs. This paper proposes a symmetry‐restoration‐based temporary reconstruction approach that allows continued operation with damaged stator coils. Instead of isolating only the faulty coil, which creates electromagnetic imbalance, the proposed pole‐pair‐based approach simultaneously disconnects the defective coil and its geometrically corresponding coils in other phases and pole pairs, preserving three‐phase current symmetry and a balanced rotating magnetic field. Finite‐element analysis (FEA) evaluates stator currents, rotor end‐ring currents, torque, and induced voltages in isolated coils. Results show that the current increase caused by coil removal can be compensated by a slight speed increase and controlled load reduction, restoring currents close to healthy conditions with moderate power derating. Induced voltages in disconnected coils remain well below insulation limits. The approach is validated through field application on two 1100 kW, 6.6 kV motors in a thermal power plant, which operated stably for over seven months under supervised reduced‐load conditions. Both simulations and on‐site measurements confirm acceptable electrical, thermal, and vibration behavior. Overall, the proposed pole‐pair symmetry restoration technique offers a practical, time‐efficient, and cost‐effective interim solution to maintain production continuity before permanent stator rewinding.
This paper presents a physics-aware, spec-driven machine-learning (ML) optimization framework for synthesizing printable ultra-high frequency (UHF) radio-frequency identification (RFID) meandered dipoles targeting the heavily regulated 902-928 MHz operational corridor. Evaluated under a strictly capped 300-solve computational budget within a full-wave 3D EM environment, the proposed methodology-guided by a PCA-hybrid surrogate and dynamic out-of-distribution (OOD) penalties-is comprehensively benchmarked against three solver-integrated baselines: trust-region frameworks (TRF), covariance matrix adaptation evolution strategy (CMA-ES) and Classic Powell (CP). High-fidelity free-space results reveal a fundamental topological bifurcation. Unconstrained stochastic heuristics (CMA-ES and CP) deceptively achieve approximate to 100% in-band coverage through aggressive geometric over-coupling, inducing massive out-of-band spectral leakage (>= 22.71 MHz overflow) that violates regulatory emission masks. Crucially, when subjected to the dielectric shock of standard corrugated cardboard packaging (epsilon(r) = 2.0), these unconstrained topologies catastrophically overfit, suffering downward resonant drifts of up to 216.8 MHz into unregulated cellular bands. Conversely, the proposed Surrogate-TR utilizes its OOD penalty to strictly enforce a zero-leakage emission mask. By intentionally halting free-space bandwidth expansion at an optimal Pareto boundary (approximate to 70% coverage), the algorithm engineers a deliberate spectral 'safety buffer'. Under cardboard dielectric loading, this buffer acts as a physical shock absorber, elegantly absorbing a 72.0 MHz detuning shift while maintaining a pristine -18.02 dB impedance match without structural deformation. Furthermore, the surrogate-optimized geometry delivers an exceptional free-space directivity of 1.86 dBi and approximate to 92.0% radiation efficiency, yielding a massive theoretical forward-link read range of approximate to 26.1 m. To guarantee byte-for-byte reproducibility, all converged 11-dimensional geometric vectors and spectra are explicitly released, providing a robust, zero-leakage baseline for supply-chain antenna engineering.
ABSTRACT The Finite Element Method (FEM) is a powerful tool for simulating non‐linear magnetic devices, but its high computational cost becomes a significant limitation when multiple simulations are required, such as in design optimisation or real‐time control. To address this challenge, this paper proposes a non‐intrusive surrogate modelling framework designed to significantly reduce computation time with minimal accuracy loss. The approach is based on a low‐dimensional parameterisation of the solution space using Proper Orthogonal Decomposition (POD), combined with machine learning‐based interpolation in the reduced space. The approach is validated by creating a surrogate model of a nonlinear inductor near an iron piece, reconstructing the field distribution as a function of current, frequency, and relative position between the two. Two surrogate modelling techniques, Gaussian Process Regression (GPR) and Feedforward Neural Networks (FNN), are investigated and compared, with particular attention to their performance under data‐scarce conditions, common in engineering workflows due to the high cost of generating training data. Numerical results demonstrate that GPR provides more accurate approximations than FNN, especially when few FEM simulations are available. The findings highlight the potential of POD and GPR as efficient and reliable tools for accelerating the simulation and optimisation of magnetic devices.
ABSTRACT This paper presents a new approach for the indirect measurement of the unwanted air gap in dual‐part electromagnetic cores, using designed experiments and simulations. Different possible scenarios for the unwanted air gap in a dual‐part electromagnetic core are investigated. From the experimental and simulation results, a lookup table is derived that allows the unwanted air gap to be determined indirectly. Furthermore, this lookup table is used in a variable inductor of a resonant converter, as a typical application, to demonstrate the importance of accounting for the unwanted air gap in the control of such converters.
Wireless sensor networks (WSNs) in smart grids (SGs) face issues like energy depletion and coverage gaps, with nodes near sink draining faster due to higher communication, causing hotspots. In this paper, a reinforcement learning(RL)-based mobility optimization framework for WSNs is developed to balance two conflicting objectives: maximizing network lifetime and ensuring high throughput. Four network strategies (static, multiple, single mobile, and RL-based mobile sinks) are comparatively evaluated under grid and spiral network topologies. A proposed pareto-optimal reward strategy directed by the RL agent, with a meta-heuristic search simultaneously enhancing its convergence and exploration capabilities in complex environments. The model integrates energy consumption as key parameters within a reward-driven learning process. Simulation results demonstrate that the RL-based mobile sink outperforms static and deterministic multiple-sink strategies, achieving extended network lifetime, higher throughput. Regarding network performance, grid topologies outperform spiral structures in maximizing network lifetime, while spiral structures demonstrate an advantage in achieving greater data throughput. The proposed RL-based approach extends the overall network lifetime from 33.5 months to 36.15 months and maintains high data throughput, outperforming conventional static and multiple-sink methods.
ABSTRACT Finite element formulations using the magnetic vector potential are numerically inefficient for modelling 3‐D magnetic hysteresis, as they rely on computationally expensive inverse material models. This article presents a robust and efficient ‐formulation that overcomes this limitation by incorporating the direct form of the energy‐based hysteresis model. A detailed implementation that resolves key numerical challenges, ensuring a well‐conditioned system and a stable nonlinear solver via a quasi‐Newton scheme is provided. Through a comparative analysis in both magnetostatic and magnetoquasistatic cases, the results show that the ‐formulation achieves a significant performance gain. While maintaining the same level of accuracy as the ‐formulation, it reduces the required computation time by a factor of over five when energy‐based hysteresis models are used. This work establishes the ‐formulation as a viable alternative to accurately and efficiently perform 3‐D simulations where hysteretic effects are non‐negligible.
ABSTRACT We introduce and validate an innovative generator topology of a flux‐switching machine (FSM) utilising high‐temperature superconducting (HTS) stator‐side field coils (HTS‐FSM). This is particularly suitable for an application in offshore wind energy systems. First, we establish the geo‐strategic and techno‐commercial relevance of this innovation. Second, we report the electromagnetic performance analysis in comparison to the classical FSM of excitation provided by permanent magnets (PM‐FSM). We describe the numerical models of electromagnetic phenomena employing the finite element method. The results demonstrate that the electromagnetic performance of the HTS‐FSM offers a viable, sustainable and geopolitically secure foundation for the development of next‐generation, high‐power‐class offshore wind turbines.
ABSTRACT This contribution presents a practical approach for determining the parameters of a topological lumped‐element (LE) three‐phase transformer model in a shell‐type configuration from a two‐dimensional finite‐element (FE) simulation. Based on the observations of the magnetic flux density from the FE simulation around the coils, we propose a manual fitting of the LE primary current signals to those from the FE simulation by adjusting the leakage parameters of the LE model. This straightforward fitting is possible because the LE leakage parameters influence specific time sections of the primary current signal shape almost independently of each other. The no‐load and short‐circuit cases are considered for the practical fitting procedure, implying that load cases are also captured, in contrast to merely using a no‐load fitting technique. The applicability and the limitations are discussed, and the results are finally concluded.
Local overheating and insulation aging caused by high-temperature environments are the primary factors reducing the electrical lifespan of transformer valve-side bushings. Structural optimization or external shielding devices have partially improved the internal physical field distribution. However, their cooling efficiency and cost under extreme operating conditions remain questionable. This study investigates the electro-thermal-fluid coupling characteristics and temperature rise of transformer valve-side bushings under extreme conditions. A novel cross-shaped quarter pipe with micro-oil-cooling structure is proposed. A 3D fully coupled electro-thermal-fluid model with temperature-dependent material parameters is established to validate the solution. Simulation results demonstrate that the proposed structure reduces the hot-spot temperature at the high-voltage end by 25.4%. The axial average temperature decreases by 23.7%, while the radial temperature gradient drops by approximately 67.9%. This effectively suppresses the "dual-peak" temperature rise phenomenon observed in conventional designs. Multi-condition validation under ambient temperatures of 25-45 degrees C and oil temperatures of 30-90 degrees C confirm the optimized design significantly enhances heat dissipation in extreme conditions. The study provides theoretical foundations and technical solutions for thermal management in high-voltage equipment.
The AC voltage is induced along a pipeline when an overhead power line is aligned with the pipeline. The AC induction needs to be analysed and determined even with cathodic protection systems (CPS) protecting pipelines, storage tanks, and many other metal surfaces in various industries to prevent electrochemical corrosion. Hence, the design and accurate operation of a CPS needs a proper model to monitor and assess the condition of pipelines aligned with overhead power lines. Conventional pipeline models, including lumped resistances introducing cables and connections, anode beds, and metal structures, do not represent the stated AC induction. This paper suggests a non-uniform distributed model for pipelines aligned with overhead power lines driven by the CPS to determine the voltage profile across the long metal surfaces such as pipelines. The case study is an 11.5 km pipeline for gas transmission from a gas field located west of Iran, aligned with a three-phase 20 kV overhead power line. First, the parameters of this gas pipeline model are analysed and obtained, which include resistances, non-uniform inductances, and capacitances. Then, simulations are introduced for the developed distributed model. Finally, the voltage profile of the pipeline is obtained experimentally by measuring 23 points along the pipeline to confirm the model and simulations; also, waveforms of electrical potentials related to both ends and the midpoint are recorded by a portable oscilloscope for further verifications. Experimental results desirably validate the analytical distributed model and simulations.
ABSTRACT This paper proposes a topology optimisation method for permanent magnet motors that hybridises the Boolean geometry projection (BGP) method and the ON‐OFF method based on Gaussian basis functions. This method represents the topology of a permanent magnet based on the BGP method in addition to generating that of a magnetic core, including flux barriers, using the Gaussian basis functions to obtain a novel but manufacturable machine structure. In contrast to existing optimisation methods such as parameter optimisation, the pre‐setting of the search region for geometric parameters is not required. This leads to the design of machine structures that is completely independent of the designer's experience and knowledge. Optimisation results demonstrate that the proposed method generates diverse and realistic rotor structures, such as U‐, V‐ and double V‐shaped configurations, and achieves higher average torque performance compared with existing parametric and hybrid optimisation methods. The findings confirm that the proposed BGP‐based hybrid framework provides a practical and effective design methodology for PM motors, and it can be further extended to a full motor design that simultaneously considers both mechanical and electromagnetic properties in future work.
Surface dielectric barrier discharge (SDBD) ozone generators are a promising technology for industrial water treatment as well as air purification; however, they are under-studied. In this experimental study, three types of AC voltage waveforms (triangular, rectangular and sinusoidal) are compared to ozone generation and energy performance in the SDBD reactor. The optimal waveform was triangular, with an ozone concentration of 12 mg/L and an efficiency of 120 g/kWh. The second was the sinusoidal waveform (120 mg/L and 95 g/kWh, respectively). The most typical was the square waveform, with the rectangular one less effective. The optimum operating conditions are as follows (by response surface methodology (RSM)): V = 7.84 kV, f = 202.86 Hz (eta = 114.76 g/kWh) and V = 8.99 kV, f = 849.7 Hz (OC = 132.163 mg/L). This study highlights the importance of optimal waveform selection and systematic parameter tuning to improve ozone generation performance while minimising energy consumption for practical industrial SDBD applications.
Lithium-ion batteries, crucial for electric vehicles and energy storage systems, encounter capacity degradation and safety risks over time, necessitating precise state of health (SOH) estimation for reliable operation and risk management. Traditional methods often rely on single-view incremental capacity (IC) curves, which inadequately leverage the full range of available data. A multi-view SOH estimation method based on variational slow features (VSFs) is presented in this paper. Using cubic spline interpolation and Kalman filtering for preprocessing, multi-view IC curves are calculated and generated. Five health indicators (HIs) are then extracted from these multi-view IC curves, and a detailed correlation analysis highlights the HI extracted from the enhanced view IC curves with the highest correlation. To further optimise the use of these HIs, a novel technique employing VSF is proposed. Robust features are extracted from the multi-view HIs using a Siamese-Variational Autoencoder, enhancing the precision of SOH assessment. A two-stage training process captures the dynamic nature of capacity degradation. The method's effectiveness is demonstrated through experiments on the CALCE open dataset, achieving high accuracy and robustness with a root mean square error of 1.019e-2 and an R 2 value of 0.987, confirming its suitability for battery health monitoring.
The magnetically non-linear switched reluctance (SR) machine energy conversion optimisation is a challenging computational task due to the non-linearly varying current and torque and the presence of numerous design and operating parameters. Zero-volt loop (ZVL) control of the SR machine is an effective mode of operation that optimises torque production and minimises the magnetic and the switching losses. This paper presents a mathematically simple and computationally rapid optimisation method that enables accurate search of energy conversion for the ZVL control mode of a given SR machine design.
ABSTRACT Dry‐type distribution transformers are widely used in indoor power supply systems on the user side, where their short‐circuit withstand capability is critical for operational reliability. To enhance this capability, this study established field‐circuit coupled and magnetic‐structural coupled simulation models with identical structural dimensions. Taking an SCB‐630 kVA/10 kV transformer as an example, single‐phase‐to‐ground, two‐phase‐to‐ground, and three‐phase short‐circuit faults were simulated to analyse winding current and magnetic field distributions. The Lorentz force under three‐phase fault conditions were calculated, and winding deformation was compared with and without axial constraints at the upper end. The results show that: approximately 85.71% of unqualified transformers failed due to excessive partial discharge during short‐circuit withstand tests; the short‐circuit current increased by about 30 times compared to the rated load, with the magnetic flux density rising by up to 50 times to 0.36 T under all three short‐circuit types; the maximum radial and axial forces on the low‐voltage winding reached 3.64 × 10 6 N/m 3 and 8.20 × 10 5 N/m 3 , respectively; with axial constraints applied, the maximum displacement was 5.49 × 10 − 5 mm. This result provides quantitative data for understanding the damage cause of a dry type transformer under short circuit.A statistical analysis was carried out on 57 sampled 10 kV dry‐type transformers with short‐circuit test data from a testing centre. Taking the SCB‐630 kVA/10 kV model transformer as a simulation type, we established a field‐circuit coupling model and a magnetic‐structure coupling model with the same structural dimensions.
ABSTRACT Nanosensors represent a cornerstone of modern nanomedicine, enabling ultrasensitive, real‐time biosensing that bridges diagnostics and therapeutics towards personalised and proactive healthcare. This comprehensive review systematically examines the core principles of advanced nanosensors, including their classification by transduction mechanisms – electrochemical, optical (with emphasis on surface‐enhanced Raman spectroscopy for single‐molecule‐level detection), piezoelectric, and thermometric – and by biorecognition elements (enzymes, antibodies, aptamers, whole cells, and synthetic receptors). We highlight their transformative applications in early disease detection (e.g., cancer biomarkers, neurodegenerative aggregates, and infectious agents); continuous physiological monitoring (glucose, cardiac troponins, and inflammatory markers); enhanced in vivo imaging (magnetic resonance imaging/PET/fluorescence contrast agents); and intelligent stimuli‐responsive theranostic platforms that integrate targeted drug delivery with real‐time efficacy feedback. Recent breakthroughs (2023–2026) incorporate wearable and implantable devices, AI‐driven data analytics, multimodal sensing strategies, and closed‐loop systems for precision oncology, neurological disorders, and metabolic diseases. Despite remarkable progress, critical challenges persist, including long‐term biocompatibility, shape‐ and size‐dependent nanotoxicity (particularly for gold and silver nanoparticles in suspension for in vivo use), manufacturing scalability, and stringent regulatory pathways. By critically addressing these gaps and outlining promising future directions – such as biodegradable nanomaterials, AI‐optimised designs, and large‐scale clinical validation – this review positions nanosensors as indispensable tools for revolutionising precision medicine, improving global health equity, and enabling predictive, patient‐centric care.
Accurate evaluation of electromagnetic shielding effectiveness (SE) is crucial for protecting modern electronic systems against electromagnetic interference (EMI) and transient disturbances such as electromagnetic pulses (EMP). This study investigates both time‐domain shielding effectiveness (TDSE) and frequency‐domain shielding effectiveness (FDSE) of metallic grid structures on dielectric substrates. TDSE metrics, including Peak SE and Derivative SE for electric and magnetic fields, quantify the attenuation of both field amplitude and its temporal rate of change under transient EM exposure. Full‐wave simulations using the Finite Integration Technique (FIT) in computer simulation technology microwave studio (CST‐MWS) were performed to generate datasets for training multilayer perceptron (MLP) neural networks. The MLP models map five structural and material parameters—aperture width, metal thickness, substrate thickness, relative permittivity, and loss tangent—to TDSE and FDSE responses. For TDSE prediction, the trained network achieves a root mean square error (RMSE) of 0.02215 and R 2 of 0.9846 on test data, demonstrating high predictive accuracy. For FDSE prediction across 1–4 GHz, the network provides close agreement with simulated spectra. Furthermore, a neural network‐based surrogate model is employed for rapid optimisation of metallic grid design under target shielding criteria. Comparisons with the Trust Region Framework in CST show that the surrogate‐based approach maintains high accuracy while significantly reducing computational time and optimisation cost. The proposed methodology enables efficient evaluation, prediction, and optimisation of metallic grid configurations for electromagnetic shielding applications under transient conditions.
