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.
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
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
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
Mathematical Modelling of Transient Processes in a Section of an Electricity Network with Varying Configurations of its Components
This article presents mathematical models of power grid segments. Three load configurations representing real power systems were analysed. The mathematical models were developed using a field-circuit approach. A long line element was considered as a system with distributed parameters. A second-order partial differential equation was used to describe the line. The spatial derivative was discretised using the method of lines. Boundary conditions for the line equation were sought on the basis of circuit approaches. First-order boundary conditions were used. The resulting systems of differential equations were integrated using the implicit Euler method. Using the developed mathematical models, simulations of the behaviour of the systems were carried out. Transient processes during voltage switching were analysed. The results are presented in the form of waveforms of current and voltage at various points on the line. Spatial waveforms of the voltage and current in the line were also prepared.
Copyright (c) 2025 Andriy Chaban, Aleksander Dydycz, Vitaliy Levoniuk, Yuriy Biletskyi, Ivan Drobot
Performance Analysis of a Secondary-Side Resonance 1 MHz GaN-based Active-Clamp Flyback DC-DC Converter in CCM
Traditional active-clamp flyback (ACF) DC-DC converters operate in discontinuous current mode (DCM) and continuous current mode (CCM). In high-frequency traditional CCM-ACF, zero-voltage switching (ZVS) of main switch is unrealised particularly under light load, necessitating complex control strategies to transition between CCM and DCM depending on the load. In contrast, the secondary-side resonance CCM-ACF topology alters the primary current waveform, affecting the energy stored in the leakage inductance when the active-clamp switch is off, simultaneously reducing the primary rms current, thereby lowering conduction losses and improving overall efficiency. This study presents a comparative performance analysis between high-frequency traditional and secondary-side resonance CCM-ACF. A 60 W 1 MHz gallium nitride (GaN)-based model of both topologies is simulated and analysed using MATLAB Simscape. The results indicate that the secondary-side resonance CCM-ACF achieves ZVS of main switch even under light load with a low output voltage ripple and a better transient response at low output voltage. Finally, a peak efficiency of 93.99 %, which marks a 0.56 % improvement compared to traditional topology, is achieved.
Copyright (c) 2026 Mohd Izhar Ali, Muhammad Ammirrul Atiqi Mohd Zainuri, Ahmad Asrul Ibrahim, Ahmad Hafiz Mohd Hashim, Mohd Shamsul Ali
Predictive Current Control with Selection Vector Table Scheme for 3L-NPC Inverter
The predictive current control (PCC) strategy has been widely applied to three-level neutral-point-clamped (3L-NPC) inverters to regulate the load current and balance the capacitor voltage. However, evaluating all 27 switching states in each sampling period imposes a significant computational burden. At the same time, current quality is sensitive to selection of weighting factors when multiple control objectives are simultaneously considered. To overcome these limitations, this paper proposes two methods, namely predictive current control with vector selection Table I (PCCT1) and vector selection Table II (PCCT2), in which only a subset of pre-selected switching states is employed for prediction and optimisation, thereby eliminating the need for weighting factor tuning in the cost function. As a result, computational complexity is significantly reduced and system stability is enhanced. The proposed PCCT methods demonstrate superior performance compared to conventional PCC under various operating conditions, ensuring accurate current tracking and consistent capacitor voltage balancing.
Copyright (c) 2025 Viet Nguyen Hoang, Duong Pham Hong, Dai Pham Duc
Robust Passivity-based Control Integrated with UDE Strategy for Grid-Tied Inverter
Uncertainties caused by parametric variations, external disturbances, and modelling errors of grid-tied inverters (GTI) make it challenging to select a proper gain for the desired controller. The performance of conventional passivity-based controller (PBC), which depends on a strictly accurate mathematical model of the system, is seriously deteriorated due to having poor robustness against parametric uncertainties and disturbances. To this end, this paper presents an improved passivity-based controller scheme assisted with uncertainty and disturbance estimator (UDE) for GTI. The UDE that has been adopted into the proposed UDE-PBC loops is used to simultaneously estimate uncertainties, which can achieve robust control and provide zero steady-state error. Moreover, the proposed UDE-PBC enables a two-degree-of-freedom (2DOF) control structure; one dedicated for disturbance rejection and the other for closed-loop tracking response of GTI system. The effectiveness of the proposed control scheme has been verified in an experimental prototype of 2 kW and compared with the base controllers in terms of robustness and transient response.
Interconnection and damping assignment (IDA)
Uncertainty and disturbance estimator (UDE)
Copyright (c) 2025 Umit Akin Uslu, Mehmet Timur Aydemir
DynaEdge-Net: Dynamic Feature Gating and Edge Enhancement for Precise Road Crack Segmentation
Automatic detection of road cracks is essential for the long-term safety maintenance of roads, bridges, and other infrastructure. Although existing deep learning-based crack segmentation methods have improved detection accuracy, challenges remain in terms of high computational complexity and inadequate capture of fine cracks and edge details. To address these issues, this study proposes an enhanced UNet-based architecture, termed DynaEdge-Net. In the encoder and decoder stages, a Residual Detail Enhancement Block (RDEB) and a Cascaded Group Attention (CGA) module are incorporated to strengthen edge feature representation and focus on critical regions, respectively. In the skip connections, a Group-wise Dynamic Gating (GDG) module is introduced to adaptively suppress background noise and optimize feature transmission. During decoding, a Dynamic Upsampling (DySample) strategy replaces conventional interpolation, enabling high-fidelity reconstruction of crack structures. Experimental results show that DynaEdge-Net achieves IoU, F1-score, boundary F1-score, and recall rates of 82.34%, 90.83%, 83.27%, and 90.12%, respectively, outperforming several state-of-the-art road segmentation algorithms. The proposed method not only improves the continuity and accuracy of crack extraction but also demonstrates strong robustness and generalization capability, providing a reliable solution for intelligent inspection and maintenance of transportation infrastructure.
Copyright (c) 2025 Lingjie Cao, Hailing Sun, Sunwen Du
Optical Characterization of Silicon Nitride-based Bilayer Thin Films Using Spectroscopic Ellipsometry and the Maxwell-Garnett Model
This study investigates the optical and geometrical properties of silicon oxynitride (SiOxNy) thin films, which were prepared using a low-pressure chemical vapor deposition (LPCVD) process at 850 °C from SiH2Cl2, N2O, and NH3 as precursors. The opto-geometrical parameters of these were determined through spectroscopic ellipsometry measurements, employing the Maxwell-Garnett model for data analysis. This model posits silicon oxynitride as a heterogeneous medium composed of silicon oxide (SiO2) and silicon nitride (Si3N4). The refractive index of SiOxNy was computed as a function of both wavelength and gas flow ratio NH3/N2O, and was further evaluated based on the volume fraction of its constituents and film thickness. Subsequently, the effect of deposition time on film thickness and refractive index was examined. It was observed that the refractive index decreases with increasing wavelength, yet increases with both the gas flow ratio and film thickness, ranging from 1.457 to 1.618. The results also indicate that the SiO2 volume fraction decreases as the NH3/N2O ratio increases, while the Si3N4 volume fraction concurrently increases. They equally indicate that prolonged deposition time leads to a significant increase in film thickness and a higher refractive index. This study provides a fundamental basis for selecting deposition parameters that are tailored to specific applications.
Copyright (c) 2026 Abdelaziz Beddiaf, Malika Medjaldi, Djemouai Djamai, Abderrahim Lanani
Autonomous Driving Support System with Image Processing Techniques
In this study, an autonomous driving support system is developed using image processing and machine learning techniques. The aim of the study is to create a driving support system that can perform basic autonomous driving functions on low-cost hardware. Within the scope of the project, important driving functions, such as lane following, red light detection, sign recognition, and obstacle recognition, are addressed. While the lane following algorithm guides the vehicle by keeping it in the lane within the camera angle, the red light detection algorithm, sign recognition and obstacle recognition algorithms aim to identify the situations that need to be performed and perform the necessary activities autonomously. Within the scope of the study, the images taken through a camera were processed using the C++ module of the OpenCV library and the specified tasks were performed using the necessary decision-making algorithms. The machine learning algorithms developed were also tested on a Raspberry Pi computer in real time and their performance was evaluated. In this way, the feasibility of autonomous driving technologies on a low-cost platform was examined, and successful results were obtained.
Copyright (c) 2026 Onur Alaca, Fahriye Gemci
Multi-Objective Design Improvement of a Double-Stator Single-Rotor Axial Flux Brushless DC Motor with a Focus on Efficiency
This study comprehensively analyzes the sizing and performance optimization of a double-stator single-rotor axial flux permanent magnet brushless DC (DSSR AFPM BLDC) motor. Key design parameters, such as outer and inner diameters, slot dimensions, number of turns per phase, and phase current, were analytically determined based on the required output power, voltage, and speed. Electrical and magnetic loadings were carefully evaluated, and total losses were calculated to estimate motor efficiency. Critical parameters, including magnet thickness, air gap length, magnetic flux densities in the stator yoke and teeth, and electric loading, were optimized using genetic algorithm (GA) and ant colony optimization (ACO) techniques to maximize efficiency. These optimization processes were carried out in MATLAB. Furthermore, the motor torque and efficiency characteristics were analyzed using the finite element method (FEM). From an application perspective, this study presents novel design improvements achieved through ACO. The initial motor efficiency of 91.01 % was improved to 91.57 % using the GA method and to 91.80 % using the ACO method.
Copyright (c) 2026 Ozturk Tosun, Necibe Fusun Oyman Serteller, Ugur Demir, Onur Akar, Vedat Esen
An Approach to CFOA-based Second-Order Universal Filters
In this work, two universal current-mode (CM) filters are proposed. These filters are single-input three-output filter and three-input single-output filter. The proposed single-input CM filter simultaneously provides low-pass filter (LPF), high-pass filter (HPF), and band-pass filter (BPF) responses without requiring passive component matching. The LPF and BPF provide gains. Additionally, a notch filter (NF) response can be obtained by subtracting the output current of the LPF from the output current of the HPF, while an all-pass filter (APF) response can be achieved by subtracting the output current of the BPF from the output current of the NF. The proposed three-input CM filter generates LPF, HPF, BPF, NF, and APF responses at its output when the inputs are appropriately selected. The BPF response of the three-input CM filter has a gain. However, HPF, NF, and APF responses require a passive component matching condition. Both proposed circuits contain two grounded capacitors, which makes them suitable for integrated circuit technology and exhibit low active and passive sensitivities. The performance of the proposed filters is verified through SPICE simulations using 0.18 µm Taiwan Semiconductor Manufacturing Company (TSMC) complementary metal-oxide-semiconductor (CMOS) technology parameters. Additionally, experimental validation is performed using AD844s to demonstrate their practical performance.
Copyright (c) 2021 Mehmet Dogan, Erkan Yuce, Shahram Minaei
A Time-Domain Pilot Protection for AC Transmission Lines Connecting BESSs Based on Sequence Matching Index
Battery energy storage station (BESS) possesses flexible operating states and complex fault characteristics. With the continuous increase in its installed capacity and number, the risk of maloperation of traditional relay protection significantly increases. To mitigate this risk, this paper presents a time-domain pilot protection that accommodates BESS’s operating states and fault characteristics. Firstly, the issue of the traditional current differential protection (CDP) caused by the integration of BESS is analyzed. Subsequently, the fundamental principle of the Jaro distance is introduced, and the criteria for calculating the number of matching points and transpositions are redesigned accordingly. The Jaro distance is utilized to evaluate the matching degree of current sampling sequences between BESS side and grid side, thereby accurately identifying internal and external faults. Additionally, by introducing delayed operating current sequences in the process of calculating the number of matching points, the security issue caused by current transformer (CT) saturation is resolved. Finally, PSCAD/EMTDC simulations and digital-physical co-simulation (DPCS) testing validate the proposed protection’s significant advantage. The results indicate that proposed protection algorithm offers gratifying performance in dependability, security and selectivity, and outperforms several emerging pilot protections.
Copyright (c) 2026 Ping Xiong, Xiaoqian Zhu, Xing Wang, Lie Li, Yifan Zhao, Xiaoyang Yang
Identification of Ground Fissures in Mining Areas from UAV Images Based on RDC-UNet
As coal mining deepens, ground fissures in mining areas pose significant risks to safety and the environment. Traditional geological exploration methods are inefficient and costly, making the precise detection of large-scale fissures difficult. Deep learning methods using unmanned aerial vehicle (UAV) images have become a popular approach for fissure detection, although challenges such as insufficient accuracy and poor adaptability in complex backgrounds remain. This study proposes an residual-depthwise separable convolution UNet (RDC-UNet) model to address these issues, building on U-Net by incorporating residual connections (RC), depthwise separable convolutions (DSC), and the convolutional block attention module (CBAM) attention mechanism. The model was trained on a dataset of 300 UAV images from a DJI Mavic 3e and outperformed the benchmark models, achieving 86.12 % mPrecision, 75.01 % mRecall, 70.72 % mIoU, and 0.7951 mF1. Ablation experiments show that removing any core module leads to a performance drop, with the DSC module reducing mF1 by 3.28 %, CBAM decreasing mIoU by 2.67 %, and RC lowering mRecall by 3.96 %. RDC-UNet is highly efficient, requiring only 7.8 million parameters, with an inference time of 75 milliseconds and a memory footprint of 180 MB, much lower than other models. This makes it well-suited for real-time UAV-based fissure monitoring. The RDC-UNet model offers high accuracy and efficiency, making it an ideal solution for cost-effective real-time monitoring of ground fissures in mining areas.