ELEKTRONIKA IR ELEKTROTECHNIKA (ISSN 1392-1215) is a peer-reviewed open access bimonthly research journal of Kaunas University of Technology. The research journal ELEKTRONIKA IR ELEKTROTECHNIKA publishes original research papers on featuring practical developments that might have a significant impact in the field of electronics and electrical engineering, and focuses on automation, robotics & control; automotive electronics; electric vehicles; electrical engineering; electronic measurements; electronics; high frequency technologies, microwaves; micro & nano-electronics; power electronics; renewable energy; signal technologies; telecommunications engineering. It is aimed not only to researchers of certain field , but also to the wider public. WoS: Impact Factor 1.3 (2022); Scopus: SCImago Journal Rank 0.32 (2022)
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed. By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.
Copyright (c) 2026 Elektronika ir Elektrotechnika
Improving Control Stability for A Robot Arm Packaging System using Fuzzy Optimization
This work addresses the issue of insufficient control stability in the robotic arm feeding system in the packaging production line by proposing an adaptive algorithm based on the fuzzy-optimized PID (Proportional-Integral-Derivative) controller. By establishing the D-H (Denavit-Hartenberg) kinematic model of the robotic arm and combining the MATLAB Robot Toolbox with the SIMULINK simulation platform, a fuzzy controller was designed with amplitude deviation and deviation rate as the inputs to dynamically adjust the PID parameters. Hardware integration was achieved based on the Mitsubishi FX2N series PLC (Programmable Logic Controller). Simulation results show that the adjustment time of the fuzzy PID control is 0.45s, which is reduced by 25% compared to the traditional PID (i.e., 0.6s), with the overshoot reduced from 6% to 0% and the steady-state error stabilized at 0mm. Through 10 sets of actual picking experiments, it was verified that the average time error under the fuzzy PID control is 0.37s with a standard deviation of 0.23s, which is a reduction of 55.4% and 85.6% compared to the traditional PID and direct start-up, respectively, which significantly improves the stability and efficiency of the robotic arm feeding system. This study proves that the fuzzy PID, through dynamic parameter optimization and disturbance compensation, can effectively address the control response delay and error accumulation of the robotic arm under non-ideal operating conditions, providing a high-precision and high-robustness solution for industrial packaging automation.
Copyright (c) 2025 Zhimin Lu, Shouquan Gao, Wei Wu, Hongli Deng, Wencan Li, Weiwei Zhang, Mengling Huang
Signal Processing and Fault Diagnosis on Structure Vibration Measurement Using a New Composed Deep Learning Model
This paper proposes a new approach for identifying the structure faults in offshore jacket platforms. The proposed method is based on the integration of the structural vibration data, a sophisticated data fusion process, and an intelligent diagnosis algorithm. In this new approach, firstly, the temporal convolutional network (TCN) overcomes the efficiency bottleneck of traditional recurrent neural networks in long time series signals, and significantly enhances the ability to capture long-distance dependent features in the structural response signals through the introduction of a dilated convolutional structure. Secondly, the bidirectional gated recurrent unit (BiGRU) network fuses forward and reverse gated recurrent units, which can effectively capture the forward and backward correlated timing features in the structure vibration data. The Attention mechanism then further weights and optimises the BiGRU timing outputs so that the model can automatically focus on the signal pattern that is most discriminative for the fault identification. Furthermore, the global optimisation of the hyperparameters of the TCN and BiGRU with the Artificial Lemming Algorithm (ALA) significantly improves the convergence speed of the entire model and avoid falling into the local optimum, thereby enhancing the prediction accuracy and generalisation performance of the combined model. The validity of the proposed ALA-TCN- BiGRU model is substantiated through a combination of the simulation and experimental validation, thereby substantiating the feasibility of the proposed methodology. The proposed model can achieve an overall detection accuracy of over 98% for the jacket structure, which is superior to several popular diagnosis methods.
Copyright (c) 2025 Ruixiang Zhang, Yuanchao Qiu, Yuelei Zhang, Taibing Nie
Electromagnetic Signatures for Drone Detection and Recognition
Modern unmanned aerial vehicle (UAV) platforms predominantly rely on electric propulsion for controlled flight. Most current UAV propulsion systems employ brushless DC (BLDC) thrust motors. This paper demonstrates the passive detection of UAV electric propulsion systems using a dynamically changing alternating electromagnetic (EM) field created by BLDC thrust motors and power lines in the very low frequency and low frequency range. A high-performance low-noise amplifier and a commercial Airspy HF+ SDR paired with compact ferrite-core magnetic antenna, are used to capture near-field magnetic emissions from four different drones. The tested platforms include the mass-produced DJI Mini 2, DJI Mavic 2 Enterprise, DJI Air 3S, and the Eachine Tyro 109. The measured EM spectrum consistently exhibit motor phase-commutation components and electronic speed controller PWM components with propulsion load-dependent sidebands, enabling extraction of time, frequency, and amplitude-dependent propulsion-related BLDC motor EM signatures for UAV detection and recognition. The experiment results show the capabilities of drone swarm detection and recognition of each propulsion system flight mode.
Unmanned aerial vehicles (UAVs); brushless DC (BLDC) motors; thrust-motor electromagnetic (EM) signature
Copyright (c) 2025 Tomas Jačionis, Šarūnas Mikučionis, Vytautas Urbanavičius
A Trustworthy Phasor Measurement Framework Using Artificial Intelligence to Prevent False Data Manipulation
This work introduces a novel intelligent security approach for enhancing the reliability of smart grid operations by protecting measurement data produced by the Phasor Measuring Unit. The increasing demand for power distribution requires continuous and accurate monitoring, which depends on trusted measurement data synchronized with strict timing constraints. To address risks related to inaccurate measurements, unauthorized modification, and malicious disruption, this study develops a self adaptive security analysis process using an artificial intelligence based learning strategy. The system evaluates multiple operational factors and converts them into dynamic multivariate states to support accurate decision making. The proposed method integrates a learning mechanism based on nearest neighbor classification to detect abnormal measurement behavior and prevent false data events before operational failure occurs. Experimental evaluation under four scenarios demonstrates that the approach improves security and reliability metrics when compared to existing solutions. The results confirm that the proposed method provides an effective pathway for trustworthy measurement data and resilient smart grid operation.
Copyright (c) 2025 Hariprasath Manoharan, Sasi Kumar K.
Recent Developments in Inverter Topologies for Direct Torque Controlled Induction Motors
Direct Torque Control, an industrial standard for applications requiring high dynamic performance in induction motors, has reached its performance limits due to the high torque ripple and acoustic noise generated by traditional two-level inverters. This comprehensive review study examines the technological evolution of power converter topologies developed to extend the performance limits of DTC, from classic multi-level structures such as Neutral Point Clamping and Cascaded H-Bridge to the recently prominent Reduced Switching Count and asymmetric hybrid topologies. The study also details the integration of Matrix Converters, which increase power density, and Open-Ended Winding (OEW) architectures, which enable voltage optimization, with DTC. The article compares existing topologies in terms of cost, efficiency, and harmonic performance, and provides critical projections on how SiC/GaN-based wide bandgap semiconductors and fault-tolerant designs will transform future electric drive systems, thereby offering a strategic roadmap for researchers and design engineers.
Copyright (c) 2025 Fatih Korkmaz, Mustafa Karhan
Four Times Extended Reed Solomon Codes, their Decoding and Application in EII Codes for Data Repair
Data recovery in distributed and general storage systems requires a broad range of regenerating codes with different properties such as locality, availability, scalability, etc. The main goal of this paper is to enrich this palette with four times extended Reed Solomon (RS) codes. Recently, it was shown that RS codes can be extended five times, when constructed over finite fields where and is an odd integer. These codes are almost maximum distance separable (AMDS), and they are reaching upper bounds on code distance in known tables on the best linear block codes. However, such codes have limitations for practical applications since has to be an odd integer. To overcome this limitation, four times extended RS codes are presented in this paper. These AMDS codes can be constructed over finite field where the integer is arbitrary. They can be used in numerous different constructions of product codes, Extended Product codes, Integrated Interleaved codes, Extended Integrated Interleaved codes, Staircase codes, and others, which are suitable for data recovery in storage systems.
Copyright (c) 2025 Peter Farkas, Martin Rakús, Katarína Farkasova, Frederik Pavelka, Ladislav Divinec
Tomato Ripeness Detection and Harvesting Decision Making in a Greenhouse Using BIIE-YOLOv10n
Accurate detection of tomato ripeness is crucial to improving harvesting efficiency and supporting precise picking decisions. However, occlusion and overlap of fruits, along with complex background interference, can lead to loss of local information, unstable color distribution, and increased difficulty in ripeness discrimination, thereby undermining the stability and reliability of detection. To address these challenges, this study proposes a real-time tomato ripeness detection model based on an improved YOLOv10n framework, named BIIE-YOLOv10n. The model employs an improved bidirectional feature pyramid network (IBiFPN) to achieve adaptive multi-scale feature fusion and enhanced contextual information exchange, integrates an improved iterative channel-spatial attentional feature fusion (ICSAFF) mechanism into the C2f module for effective global-local feature aggregation, and introduces the Inner-EIoU loss function to balance positive and negative samples, thereby improving bounding box regression accuracy under complex environments. Experimental results on a self-constructed tomato ripeness dataset show that the proposed model achieves an accuracy of 82.6 %, a recall of 80.5 %, an F1-score of 82.0 %, and an mAP50 of 85.4 %, representing improvements of 1.3 %, 5.2 %, 4.0 %, and 5.0 % over the baseline model, respectively. Based on the detection results, a visual-driven strategy is developed for the assessment of cluster-level ripeness and picking decision-making, providing support for automated harvesting systems in greenhouse environments. In summary, BIIE-YOLOv10n significantly enhances tomato ripeness detection performance and provides reliable decision-making support for automated harvesting and intelligent grading in greenhouse settings.
Copyright (c) 2025 Fanjia Meng, Ming Lu, Lihong Huang, Xin Wang
Machine Learning Driven Design and Optimisation of a Dual-Band SRR Metamaterial Antenna for Emerging IoT Platforms
This research introduces a machine learning (ML) technique derived from the dual-band split-ring resonator (SRR) metamaterial antenna development and optimisation. The patch antenna with an SRR design is intended for use in 5G and 6G IoT applications. The method utilizes data driven prediction models to speed up design and optimisation instead of a normal trial and error process. The antenna responses were anticipated using different regression methods, including artificial neural network (ANN), K nearest neighbour (KNN), gradient boosting (GB), and eXtreme gradient boosting (XGBoost). When it comes to the techniques utilized, the eXtreme gradient boosting model was the one that reached the highest accuracy of 87.4 % in estimating return loss and thus it was the one that stood out the most. To support the claim of the effectiveness of the method, a dual-band split-ring resonator loaded microstrip antenna was designed and produced to operate in the ranges of 2.3 GHz to 2.55 GHz and 3.95 GHz to 4.15 GHz, respectively. The close agreement between predictions, simulations, and measurements further confirms the reliability of the machine learning-based design strategy. The proposed approach not only enables fast prototyping of compact antennas suitable for IoT devices, but also for industrial automation, smart home, and smart city applications.
Copyright (c) 2026 Hemalatha Balakrishnan, Chitra Sivathanu, William Johnson, Susan Christina Xavier
Hybrid Dual-Loop Control of PV DC-DC Boost Converters Using Type-2 Fuzzy Logic and Model Predictive Control
This paper presents a hybrid dual-loop control strategy for a standalone photovoltaic energy system interfaced with a boost converter. Photovoltaic systems exhibit strong non-linear behavior and high sensitivity to irradiance, temperature, and load variations, which makes voltage regulation and dynamic performance challenging, in especially with a single-loop control configuration. To address these issues, a cascaded control architecture is proposed, where an outer voltage regulation loop based on Type-2 fuzzy logic generates a robust reference current, while an inner loop based on model predictive control ensures fast and accurate inductor current tracking. A comparative study with a Type-1 fuzzy logic-based predictive control strategy is conducted using dynamic performance indicators. Simulation results show that both strategies achieve overshoot free voltage regulation, while the proposed Type-2 fuzzy logic-based approach reduces the response and settling times by approximately twenty-one percent. In addition, the proposed fuzzy logic and predictive control architecture demonstrate a strong disturbance rejection capability under varying environmental and load conditions, confirming its effectiveness for high performance photovoltaic power conversion applications.
Copyright (c) 2025 Nesrine Cherigui, Abdelkarim Chemidi, Ahmed Tahour
Rotor Ground Capacitance Coupled Harmonic Fault Detection Boundary in Large Synchronous Generators
Focusing on the impact of rotor-to-ground capacitance on passive ground fault detection in large-scale synchronous generators, this paper proposes an error analysis method using an amplitude deviation factor. Based on the classical equivalent circuit model of the excitation system, the proposed method explicitly incorporates the rotor-to-ground capacitance parameters. It systematically reveals the non-linear mechanism by which ground capacitance affects the harmonic voltage amplitudes of faults on both the DC and the AC sides. This study addresses the limitations of existing research, which is primarily confined to small and medium capacity machines and often neglects the ground capacitance. Verified by computer simulations and field data from large-scale units, the results demonstrate that the proposed method effectively clarifies the detection boundaries and improves fault detection accuracy in high-capacitance scenarios.
Copyright (c) 2025 Yongbin Du, Wenhao Chao, Qian Zhang, Bingbing Zhao
Copyright (c) 2025 Elektronika ir Elektrotechnika
Prosthetic Arm Controller Based on the sEMG Signal and Deep Learning Network
Prosthetic hands help patients gain greater confidence in daily life, and enabling automatic control of prosthetic hands using real biosignals is an essential task. This paper proposes a prosthetic hand control system based on surface electromyography (sEMG) signals. In particular, the sEMG signals are collected in real time corresponding to each hand gesture and pre-processed to eliminate noise components. A deep learning network is then employed to recognize hand gestures from the pre-processed sEMG signals. Recognized gestures are subsequently used to control the prosthetic hand to perform the corresponding movements. The proposed deep learning network achieves a recognition accuracy of 98.15 % for hand gestures from sEMG signals. Furthermore, the experimental results demonstrate that the proposed system can control the prosthetic hand with an accuracy of up to 96.97 % and a variance of 3.2194 across multiple subjects. These results suggest that the proposed system holds great potential for real-time prosthetic hand control based on sEMG signals, thereby supporting patients in gaining confidence in social interactions.
Copyright (c) 2026 Dinh-Hung Le, Thanh-Nghia Nguyen
Wavelet-Based Channel Modeling for OFDM-Integrated Pinching Antenna Systems
This paper studies wavelet-based channel modeling for orthogonal frequency-division multiplexing (OFDM) links with pinching antenna systems (PASS). A Daubechies-4 decomposition of the PASS channel response gives 4.76 percent sparsity in the transform domain, used for compressed-sensing-based channel estimation. The level-three approximation coefficients carry about 92 percent of the signal energy and mainly correspond to waveguide dispersion. The level-two and level-three detail coefficients are linked to pinching-antenna coupling, while the level-one detail coefficients capture free-space fading and noise. A relative change of up to 20 percent in the phase-matching parameters changes the normalized mean-square error (NMSE) by less than 0.25 dB, so the method does not depend on exact phase matching. The wavelet representation reduces cyclic-prefix overhead from 10.2 percent to 6.1 percent, while orthogonal matching pursuit (OMP) based recovery gives about forty times lower complexity than minimum mean-square error (MMSE) estimation. Together with the 15 percent sparse-denoising gain, the net throughput improvement is about 20 percent. The overlap factor of 0.4 satisfies the theoretical bound, and the rate gains are reported conservatively.
Cooperative Frequency Regulation for Thermal Power and Energy Storage Clusters via Hierarchical SOC Control
Distributed energy storage is crucial for frequency stability in low-inertia grids. However, conventional lumped models mask unit heterogeneity, causing premature depletion of low-state of charge (SOC) units known as the “barrel principle”. To address this, a hierarchical cooperative control strategy for thermal energy storage systems (thermal power and energy storage) prioritizing SOC consistency is proposed. A discrete-time state-space model is constructed to capture heterogeneous dynamics. The proposed two-layer architecture features an upper layer discrete filter for spectral-based power allocation and a lower layer adaptive consistency algorithm. A non-linear power-exponent weighting mechanism dynamically recalibrates output weights based on real-time SOC deviations. The simulation results demonstrate that the strategy effectively suppresses frequency fluctuations and ensures rapid SOC convergence. By preventing overcharge or overdischarge of the individual unit, the approach significantly enhances the effective capacity and operational robustness of the joint system.
An Automated Prediction of Plant Diseases Using Leaf Images Based on Efficient Deep Learning and Image Processing
In all nations' economies, the agricultural sector can play a vital role through crop production. The discovery of plant diseases could be one of the most significant factors in the preservation of an agriculturally productive country. Crop farmers lose a considerable sum annually since numerous diseases have affected their plants. Various plant parts could have been contaminated by fungi, viruses, and microorganisms, but in this work, the focus is on the discovery and categorization of banana leaf disease. Improved food production quality and reduced economic losses will be achieved with an accurate early prediction of plant leaf disease. Nowadays, deep learning (DL) and artificial intelligence (AI) have been widely applied for the construction of automated systems to discover and categorize banana leaf diseases. In this paper, an automated banana leaf disease prediction system is presented by proposing an enhanced deep learning model. In this work, captured banana leaf images can be pre-processed through combined filters, in which image smoothing and noise reduction are done by a Gaussian filter, followed by a de-noising process that is refined additionally using the Wiener filter scheme. Subsequently, a DL model convolutional neural network (CNN) can be exploited to extract pertinent features. In this paper, an attention and fuzzy logic-based recurrent neural network (AFLRNN) model is proposed and trained with extracted features for efficient prediction and categorization of plant leaf disease. These systems utilize image processing and computer vision techniques to analyze images of plant leaves and identify potential diseases. The proposed DL system is able to efficiently detect leaf disease in plants at an early stage. The presented scheme can learn and extract features from captured plant images to facilitate precise leaf disease discovery and categorization. This kind of early detection of leaf disease is used to provide suitable treatment and reduce crop waste. The experimental results have exposed that the proposed system has attained a above 98 % higher disease prediction accuracy that has been higher to state-of-the-art schemes.
Copyright (c) 2026 Gayathri Palanisamy, Vadivel Ponnusamy
A Portable Sensor Device for Real-Time Viscosity Measurement of Lubricating Oil
The viscosity of lubricating oil is a critical indicator for assessing both its performance and degradation, and it plays a vital role in the monitoring of equipment condition. To meet the need for portable and automated viscosity measurement of lubricant oil in engineering applications, this paper presents a novel device based on Newton’s law of viscosity and strain-based sensing principles. The device detects the minute deflection of a cantilever beam under fluid impact and converts the induced strain into an electrical signal via bonded strain gauges to measure viscosity. Then, a finite element simulation was performed to optimise the structure of the cantilever beam and determine the placement of the strain gauges. Experimental validation using glycerol-water mixtures and 4050 lubricating oil demonstrated that the device exhibited a strong linear correlation between its output and reference viscosity values, and that it maintained high measurement accuracy and strong applicability under critical temperature conditions, such as the ISO standard temperature of 40 °C, as well as in high-viscosity regimes. Finally, the comparison between the proposed device and the representative commercial products indicated that the device is well suited for on-site industrial viscosity measurement requiring portability, automation, and cost-effectiveness. This work provides a technical reference and practical foundation for viscosity measurement in engineering applications.
Copyright (c) 2026 Chengze Liu, Chenxing Sheng, Xiang Rao, Bo Zhao, Zhijun Chen
Automatic Modulation Recognition Algorithm Based on Multiscale Feature Extraction and Improved Encoder Structure
Automatic modulation recognition (AMR) technology is crucial in modern communication systems. To enhance the recognition performance of wireless communication systems for modulation signals, a method based on multiscale feature extraction and an improved encoder structure is proposed. First, in this proposed method, in the data preprocessing stage, phase transformation (PT) is used to eliminate the influence of phase offset. Second, the multiscale feature extraction (MSFE) module is utilised to capture both local details and global structural features of the signal, thereby reducing the information loss rate. Third, the autoencoder (AE) module preserves temporal information, reconstructs features, and suppresses noise, significantly improving the feature reconstruction and recognition capabilities of modulation signals while minimising reconstruction loss. Simulation results have demonstrated that the proposed algorithm can achieve an average recognition accuracy of 92.5 % in the RML2016.10A, which has an improvement of about 2 % to 11 % in comparison with other mainstream algorithms in average recognition accuracy, can obtain peak recognition accuracies of 93.5 % and 93.9 % in the RML2016.10A and RML2016.10B datasets, respectively, within a signal-to-noise ratio (SNR) range of 0 dB to 18 dB, thus demonstrating its excellent recognition accuracy and robustness.
Copyright (c) 2026 Tianfei Zhang, Yecai Guo, Jiao Ding, Haiyan Long, Lei Zhang
Optimal Control Strategy of a Series-Connected Multimachine System Supplied by a Single Inverter Using Third-Order Sliding-Mode Algorithm
This paper presents a robust control strategy for multimachine systems (MMSs) composed of two five-phase permanent magnet synchronous motors (5Ph-PMSMs) connected in series through their stator windings. A single five-phase inverter can drive both machines while ensuring independent regulation using a phase transposition scheme. Conventional vector control with proportional-integral (PI) regulators is sensitive to parameter variations, whereas classical sliding mode control (SMC) suffers from chattering, transient errors, and reduced robustness. To address these issues, an advanced method based on third-order sliding mode control (TOSMC) is proposed for speed and current regulation. The approach was tested in MATLAB/Simulink, showing clear improvements. For 5Ph-PMSM1, the response time decreased by 45.29 % compared to MMS-SMC and 84.16 % compared to the PI-based control; for 5Ph-PMSM2, the reductions were 32.25 % and 81.73 %, respectively. Torque ripple was also reduced, reaching 9 % and 75.40 % for 5Ph-PMSM1, and 9.3 % and 76.03 % for 5Ph-PMSM2, relative to the MMS-PI and MMS-SMC technique. These results demonstrate the robustness and high efficiency of the proposed MMS-TOSMC method, which makes it suitable for demanding MMS drive applications.
Copyright (c) 2025 Faycal Mehedi, Abdelkader Yousfi, Ismail Bouyakoub, Zakaria Reguieg
Innovative Narwhal MPPT with Feed forward Decoupling for Enhanced Solar Grid Integration
This paper presents a novel hybrid maximum power point tracking (MPPT) strategy that integrates the Narwhal optimisation algorithm (NWO) with a feed forward decoupling control (FFDC) scheme for a grid-connected single-ended primary-inductor converter (SEPIC) inverter-based photovoltaic (PV) system. Conventional and metaheuristic MPPT methods often exhibit a trade-off between convergence speed, tracking accuracy, and robustness under partial shading and dynamic irradiance. The proposed NWO-FFDC approach addresses these limitations by leveraging the efficient global search capabilities for rapid MPP localisation and the precise control of the FFDC for stable grid integration. Simulation results demonstrate that the NWO-FFDC achieves a tracking efficiency of 99.2 %, reduces steady-state oscillations to 0.5 W, and achieves convergence within 42 ms. Furthermore, inverter performance analysis confirms that the proposed controller maintains DC link voltage stability at 700 V with minimal droop and delivers AC power to the grid with a total harmonic distortion (THD) of only 2 % under all conditions, ensuring full compliance with IEEE 1547 standards. These results establish NWO-FFDC as a superior solution, offering improved tracking precision, faster dynamic response, and improved grid compatibility for next-generation PV systems.
Copyright (c) 2025 Ravi Shankar Pranavasundaram, Gokul Chandrasekaran
Effect of Connected and Automated Vehicles on Disturbance Suppression in Mixed Traffic Flow: Spatial Distribution and Stability
With the progressive integration of connected and automated vehicles (CAVs) into existing transportation systems, the characteristics of mixed traffic flow comprising CAVs and human‐driven vehicles (HDVs) undergo a profound transformation. However, the combined effects of the CAV penetration rate and spatial distribution on mixed‐flow performance have not been thoroughly investigated. To address this gap, this study develops a generalised modelling framework for mixed traffic, in which separate car‐following models are specified for HDVs and CAVs, and microscopic traffic simulations are carried out under traffic oscillation scenarios. The results demonstrate that increasing the penetration rate of the CAV markedly enhances the operational performance of the mixed flow. On this basis, the impact of CAV spatial distribution strategies is examined. Numerical experiments reveal that platoon‐based arrangements of CAVs are not globally optimal; notably, when HDV stability is poor, the performance gains of platooning are minimal, while a uniform distribution of CAVs yields superior suppression of traffic disturbances. These findings indicate that, in realistic mixed traffic environments, CAV deployment strategies should go beyond traditional platooning and explore more diversified spatial distribution patterns to optimise traffic flow performance.
Copyright (c) 2025 Yuelei Zhang, Feng Yue, Rui Zhang
Analysis and Rapid Calculation of Magnetic Thermal Coupling Characteristics of Large Power Transformers
Conventional single-phase nuclear main transformers are susceptible to certain risks such as an increase in high local temperature in the windings, high leakage fields, and high short-circuit forces. Moreover, these transformers are too costly to use. In this paper, simulation and test studies are carried out for a 500 kV single-phase two-core column nuclear power main transformer. First, using the principle of fluid-thermal coupling simulation, a three-dimensional simulation of a single-phase two-core column nuclear power main transformer is performed to obtain its temperature field distribution, and the accuracy of the established 3D model is validated through experimental tests. Second, because the 3D simulation calculation time is long, the computer occupies a large amount of memory, but the 3D winding temperature distribution in the allowable error range in line with the axisymmetric distribution can be replaced by the 2D axisymmetric model. Therefore, a two-dimensional axisymmetric simulation is conducted for a single-phase two-core column nuclear power main transformer. Simultaneously, the h-type adaptive mesh refinement method is used to optimise the two-dimensional mesh distribution based on the coupled fluid-thermal field. The optimised simulation results are compared with those from the three-dimensional model and experimental tests, confirming the accuracy of the two-dimensional approach. Finally, the winding temperature distribution under various loading conditions is computed via a two-dimensional optimised simulation and validated against the three-dimensional temperature profile, thereby verifying the precision of the two-dimensional method. The temperature data obtained under different operational loads can serve as a critical dataset to construct a digital twin model of the transformer.
Copyright (c) 2025 Hong Liu, Guodong Li, Xuan Wang, Hua Yu, Bin Li, Tong Song, Changjiang Na, Haibo Wang