In the automation production packing system, appearance defects on the carton packaging films occur frequently. To solve this problem, a deep learning-based method is proposed in this work to identify film defects during the packaging process to ensure the production packaging quality. This method adopts the deformable convolution network (DCN) v2-C3 module to replace the C3 module in the Neck part of the You Only Look Once version 5 (YOLOv5s) network to extract the deep feature information of the defects in the production carton packaging, with the purpose to improve the spatial transformation ability of the detection model and the model generalization ability to different shapes of targets. Field data are used to evaluate the proposed method. The analysis results indicate that the recognition rate of the proposed method is 99.3 % for different carton packaging defects; and compared to the original YOLOv5s method, the detection accuracy of the proposed method increases by 2.7 % and the scrap rate is reduced by 1.3 %. As a result, the proposed method can meet the requirements for defect detection in the production carton packaging in practical applications.
The existing converter relies on a non-isolated transformer and lacks bidirectional support in the case of multiple and regenerative inputs. The proposed work focuses on an isolated multiport bidirectional DC-DC converter topology featuring a battery and a supercapacitor for an electric vehicle (EV) of 2 kW. The proposed topology can provide uninterrupted power to the EV’s electric motor and recover braking energy, allowing bidirectional power flow. As a result, the battery discharge time can increase due to the additional source, such as a supercapacitor and regenerative action. The system integrates two single H-bridge converters at the input and a single H-bridge converter at the output, with an existing switch. The converter enables the reduction of the rated voltages of the battery and supercapacitor packs. Additionally, a power-flow management scheme and its corresponding control scheme are proposed. The performance test has been conducted on traditional and PI control methods under different operating conditions with a switching frequency of 50 kHz, such as switching power from the battery or supercapacitor to the load during driving and from the load back to the source during energy generation. Power flow capabilities and efficiency values validate the viability and effectiveness of the proposed system of 2 kW.
This work introduces a novel intelligent security approach to improve 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 is dependent on reliable measurement data synchronized with strict timing constraints. To address the 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. An experimental evaluation in four scenarios demonstrates that the approach improves security and reliability metrics compared to existing solutions. The results confirm that the proposed method provides an effective pathway for reliable measurement data and resilient smart grid operation.
To protect offshore jacket platforms, it is essential to carry out the structure fault diagnosis. This paper proposes a new approach to identify structural faults in offshore jacket platforms, which is based on the integration of structural vibration data, a sophisticated data fusion process, and an intelligent diagnosis algorithm. In this new approach, firstly, the TCN is adopted to solve the efficiency bottleneck of the 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 artificial lemming algorithm (ALA) is used to optimise the hyperparameters of the TCN and BiGRU to improve the model efficiency and avoid the local optimum during the model training, thereby enhancing the generalisation performance of the proposed model. The validity of the proposed ALA-TCN- BiGRU model is substantiated through simulation and experimental validation. The results indicate that the proposed model can achieve an overall detection accuracy of over 98 % for the jacket structure, which is superior to several popular diagnosis methods.
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 a 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 exhibits motor phase commutation components and electronic speed controller PWM components with propulsion load-dependent sidebands, allowing extraction of time-, frequency-, and amplitude-dependent propulsion-related BLDC motor EM signatures for UAV detection and recognition. The results of the experiment show the capabilities of drone swarm detection and recognition of each flight mode of the propulsion system.
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 a 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 0.4 overlap factor satisfies the theoretical bound, and the rate gains are reported conservatively.
This work addresses the issue of insufficient control stability in the robotic arm feeding system on the packaging production line by proposing an adaptive algorithm based on the fuzzy-optimized proportional-integral-derivative (PID) controller. By establishing the Denavit-Hartenberg (D-H) 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 inputs to dynamically adjust the PID parameters. Hardware integration was achieved based on the Mitsubishi FX2N series programmable logic controller (PLC). Simulation results show that the adjustment time of the fuzzy PID control is 0.45 s, which is reduced by 25 % compared to the traditional PID (i.e., 0.6 s), with the overshoot reduced from 6 % to 0 % and the steady-state error stabilized at 0 mm. Through 10 sets of actual picking experiments, it was verified that the average time error under fuzzy PID control is 0.37 s with a standard deviation of 0.23 s, which is a reduction of 55.4 % and 85.6 % compared to traditional PID and direct start-up, respectively, which significantly improves the stability and efficiency of the robotic arm feeding system. This study proves that 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.
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.
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.
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.
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.
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.
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.
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.
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 twocore 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 twodimensional 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.
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.
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.
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.
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.
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.
