Objective: During black-start restoration, grid-forming photovoltaic and battery energy storage systems are subject to weak grid support, frequent operating-condition transitions, and uncertain disturbances, which may lead to frequency deviations, active-power oscillations, and prolonged recovery. To address these challenges, this study develops an adaptive virtual synchronous generator control strategy based on the soft actor-critic algorithm to improve the coordination between transient stability and restoration speed. Method: The online tuning of virtual inertia and damping coefficients is formulated as a continuousaction reinforcement learning problem. Parameter safety bounds, an action-smoothing constraint, and a restoration-time-related reward term are introduced to improve control continuity and balance oscillation suppression with recovery speed. Result: Comparative simulations under load pickup, load shedding, and slave-unit paralleling conditions show that the proposed improved SAC-based strategy achieves smaller frequency deviation, lower active-power overshoot, and shorter recovery time than the fixed-parameter VSG, DDPG, and TD3 methods. Discussion: The results suggest that adaptive tuning of VSG parameters can help balance disturbance suppression and recovery speed in grid-forming photovoltaic and battery energy storage systems during black-start restoration. Since the present validation is based on simulation studies, further investigations considering practical uncertainties, hardware constraints, and more complex operating conditions are needed before engineering application. Conclusion: The simulation results indicate that the proposed strategy can improve the transient regulation and restoration performance of grid-forming photovoltaic and battery energy storage systems during black-start restoration.
Objectives: Traditional quality inspection methods in intelligent manufacturing often suffer from low efficiency, limited accuracy, and poor generalization to real manufacturing environments. This study aims to develop an intelligent quality inspection method with high accuracy and robust generalization capability. Methods: A deep learning model that integrates Convolutional Neural Networks (CNNs) with selfsupervised learning is proposed. CNNs are used to extract key visual features from manufacturing processes. Self-supervised learning is applied for pretraining on unlabeled data to capture latent process representations. In addition, a multi-layer architecture is designed to jointly model spatial and temporal features. An ensemble learning strategy is further introduced to fuse multimodal information and enhance overall detection performance. Results: Experiments were conducted on three quality inspection tasks: printed circuit board solder joints, ceramic substrates, and flexible packaging. The proposed method achieved detection accuracies of 98.1%, 97.3%, and 95.8%, respectively. At a mean average precision (mAP) of 0.5, the convergence speed improved by an average of 33.6%. The inference latency on the same hardware platform was approximately 22 ms. After INT8 quantization, the performance retention reached 98.9%. Discussion: The results indicate that self-supervised pretraining and multi-level feature fusion effectively improve feature representation in complex manufacturing scenarios. At the same time, computational cost is reduced without sacrificing detection accuracy. This enhances the practicality of the method in real industrial environments. Conclusions: The proposed quality inspection method combining CNNs and self-supervised learning outperforms ResNet50 and traditional detection algorithms in terms of accuracy, stability, and computational efficiency. It provides a practical and reliable solution for intelligent manufacturing quality inspection.
Objective: This study aims to quantitatively assess the effectiveness of Vietnam’s energy communication and labeling program for air conditioners by estimating electricity savings, emission reductions, and broader system-level impacts during the 2021-2025 period. Method: The research develops a transparent analytical framework that integrates market data on air conditioner sales and capacity distribution, energy performance characteristics by labeling categories, and seasonal usage patterns. Electricity consumption is calculated under two scenarios: with energy labeling and without energy labeling. The resulting electricity savings are then converted into CO2 emission reductions using national grid emission factors. Results: The analysis indicates that the energy labeling program has delivered consistent and increasing electricity savings over time. During the 2021-2025 period, the average electricity-saving rate is estimated at approximately 15.1%, corresponding to a cumulative saving of about 2.25 billion kWh. These reductions imply a measurable decrease in electricity demand associated with residential air conditioning Discussion: These outcomes suggest that information-based policy instruments can play a meaningful role in influencing energy consumption patterns in the residential sector. By improving transparency regarding appliance performance, energy labeling helps reduce information asymmetry and supports more energy-conscious purchasing behavior. The observed savings are also consistent with broader evidence that demand-side efficiency measures can moderate electricity demand growth and alleviate system stress in rapidly developing economies. Conclusion: Overall, the study provides empirical evidence that energy labeling for air conditioners represents an effective and scalable demand-side policy tool in Vietnam. The findings support the continued use and strengthening of labeling policies, including tighter minimum energy performance standards, periodic revision of efficiency thresholds, and closer integration with complementary demand-side management and public communication strategies.
Introduction: This article presents a Shunt Hybrid Active Filter (SHAF) designed to reduce harmonics and compensate reactive power in nonlinear load environments. The proposed system utilizes a Kalman Filter (KF) for reference current estimation and a Fuzzy Tilt Integral Derivative (TID) controller optimized with Teaching-Learning Optimization (TLO) for improved performance. Materials and Methods: The SHAF system employs a novel control strategy that relies solely on source current sensing, eliminating the need for traditional (id-iq) or (p-q) techniques. The Fuzzy TID controller parameters are tuned using the TLO method, minimizing Integral Time Absolute Error (ITAE). The system's performance is evaluated under nonlinear loads and compared with that of conventional TID and PID controllers. Results: Simulation results demonstrate the proposed system's superior performance, achieving a Total Harmonic Distortion (THD) of 1.16%, well below the IEEE 519 standard of 5%. The Input Power Factor (IPF) is 0.9977, nearly unity. Case studies confirm that the proposed TLO Fuzzy TID Controlled SHAF outperforms other control strategies in harmonic compensation. Discussion: The proposed system's performance is attributed to the effective tuning of Fuzzy TID controller parameters using the TLO method. The Kalman Filter's accurate reference current estimation and the optimized control strategy contribute to the system's robustness. However, the system's performance may degrade under highly dynamic or unpredictable nonlinear load variations. Conclusion: The proposed SHAF system demonstrates excellent harmonic reduction and reactive power compensation capabilities. Future research directions include investigating real-time implementation using advanced control platforms, comparing the performance of TLO with other optimization techniques, and exploring applications in power quality improvement and renewable energy systems.
Introduction: Multi-Energy Virtual Power Plants (MEVPPs) integrating Waste-to-Energy Plants (WEPs), distributed renewables, and Electric Vehicles (EVs) offer low-carbon benefits, yet their uncertainties—fluctuating WEP output, stochastic wind/solar generation, and erratic EV charging— hinder accurate quantification of environmental performance. This study aims to precisely characterize these uncertainties and enhance MEVPP coordination via a combined physical-data modeling approach. Methods: A Distributed Model Predictive Control (DMPC) method based on digital twins is proposed. Specifically, a five-stage physical-digital hybrid digital twin model captures WEP uncertainty; dynamic adaptive spectral clustering and weighted capacity aggregation address spatial heterogeneity of distributed wind/solar; a truncated-gamma-distribution-based ordered charging model handles EV uncertainty. A multi-timescale DMPC framework (day-ahead planning, intraday rolling, real-time adjustment) is established. Results: The hybrid digital twin improved WEP output prediction accuracy by 41% over single-data models, and renewable prediction under sudden weather events by 42.3%. Compared with stochastic MPC, robust MPC, and a consensus-ADMM DMPC baseline, our scheme reduced operating cost by 5.2%–7.1% and computation time by 36.3%–82.5%, while maintaining constraint-violation rate below 0.21%. Discussion: These findings underscore that the hybrid physical-digital framework effectively mitigates multiple uncertainties, enabling more reliable scheduling and significant computational savings. The robustness is further validated across summer, winter, high-renewable, and stressed weather scenarios, alongside sensitivity analyses on EV penetration, wind-to-solar ratio, and carbon pricing, confirming broad applicability under diverse operational conditions. Conclusion: The proposed DMPC with digital-twin enhancement substantially improves scheduling accuracy, operational efficiency, and low-carbon performance of MEVPP, providing a practical pathway for promoting renewable integration and supporting the "dual carbon" goals.
Introduction: Conventional radiated EMI prediction models and electromagnetic simulations are computationally expensive and rarely consider vertically routed cable configurations. A radiated EMI prediction model for flyback converters with long, vertically arranged cables is proposed, aiming to provide theoretical support and engineering guidance for the analysis and suppression of radiated EMI. Method: This paper proposes a theoretical model for predicting radiated emissions from a flyback converter based on port equivalence and dipole antenna theory. Key model parameters are experimentally extracted to predict the maximum radiated electric field strength. The model is validated using a simplified three-dimensional electromagnetic simulation model, and experimental measurements were carried out in a semi-anechoic chamber in accordance with the vertically routed cable requirements of GB/T 9254.1-2021. Result: By comparing the simulation, experimental measurements, and theoretical predictions, the results show that the proposed prediction model is effective and can accurately reflect the radiated emissions at the peak positions, with a prediction error within 15 dB. The error of the simulation model is within 10 dB Discussion: Compared with the traditional horizontal layout, the vertical arrangement of input/output cables saves horizontal space and alleviates wiring congestion. It is suitable for applications with vertically elongated installation spaces or compact and orderly wiring requirements, such as floor emergency lighting and compact power electronic systems with constrained installation space. The radiation calculation prediction model proposed in this paper does not rely on an anechoic chamber environment. The simulation prediction model is simple and features low computational cost. Conclusion: Compared with existing radiated emission prediction methods for switching power supplies, the proposed radiation prediction model based on port equivalence is applicable to application scenarios where the power supply cables are vertically arranged. It provides a practical approach for the analysis and design optimization of radiated interference in switching power supplies.
Introduction: Traditional partial discharge detection methods often rely on manual judgment, which suffers from high subjectivity and low efficiency. To improve the automatic recognition capability of ultraviolet partial discharge images during converter station inspection, this paper proposes an improved ResNet18 recognition method deployed on a quadruped robot platform. Methods: The ultraviolet imaging principle of partial discharge is first analyzed, and the collected ultraviolet images are preprocessed, and then model training is carried out. ResNet18 is adopted as the backbone network, with ImageNet pre-trained weights used for transfer learning to improve feature extraction under limited data conditions. Mixup data augmentation is applied to expand the effective sample distribution, and a Region of Interest (ROI)-guided spatial attention mechanism is integrated into the network to strengthen its focus on small discharge-related regions. Results: The proposed model was trained and evaluated on a self-built dataset containing 1200 ultraviolet images. The experimental results show that the improved model achieved an accuracy of 96.67% on the test set. To further verify its practical applicability, the trained model was deployed on a quadruped robot inspection platform and tested for two months at a converter station in Ningxia, China. The field recognition accuracy reached 94.23% in the converter transformer yard and 90.61% in the Direct Current (DC) area. The average inference time was approximately 5.3 ms per image, meeting the real-time requirements of on-site inspection. Discussion: Although the proposed model achieved good recognition performance, strong reflection and complex background interference may still affect field results. Further dataset expansion and robustness improvement are needed. conclusion: The framework demonstrates high recognition performance and robustness, and has been integrated into a quadruped-robot inspection system to support real-time partial discharge identification in practical field environments. The results confirm its potential for intelligent, non-contact condition monitoring of high-voltage power equipment. Conclusion: This paper proposes an improved ResNet18 model. Compared with traditional methods, the proposed model shows advantages in recognition accuracy, environmental adaptability, and processing speed. Combined with field tests, it is suitable for intelligent partial discharge inspection tasks in converter station scenarios.
Introduction: Excitation failures can occur in the Doubly Salient Electromagnetic Machine (DSEM) due to overheating and aging during long-term operation. However, such excitation faults pose a severe threat to DSEMs that rely on excitation torque for operation, causing the motor to lose driving capability in motoring mode and power generation capability in braking mode, thereby preventing it from fulfilling the requirements of four-quadrant operation Methods: To meet the four-quadrant operation requirements of DSEM under excitation loss, this paper proposes a four-quadrant fault-tolerant control strategy. First, based on the torque output characteristics of the DSEM during forward operation, a Two-Cycle Twelve-State Control (TCTSC) strategy is proposed for the forward motoring mode in the first quadrant and the reverse braking mode in the second quadrant. Second, based on the torque output characteristics of the DSEM during braking operation, a Two-Cycle Six-State Control (TCSSC) strategy is proposed for the reverse motoring mode in the third quadrant and the forward braking mode in the fourth quadrant. Finally, by combining the polarity of the change rate of reference speed with the direction of the feedback speed, the motoring and braking control are switched Results: This four-quadrant operating system enables four-quadrant operation of the DSEM and reduces torque pulsation during operation. Discussion: The proposed quadrant-switching method enables four-quadrant operation of the DSEM. Under the two proposed control strategies, torque ripple during operation can be reduced, and the output torque can be fully utilized based on the variation characteristics of inductance. The proposed strategies provide an effective method for achieving four-quadrant operation of the DSEM and suppressing torque ripple Conclusion: Simulation results verified that the proposed four-quadrant system can achieve fourquadrant fault-tolerant operation with low torque ripple and smooth operation when switching between different quadrants for the DSEM under excitation loss.
Introduction/Objective: Migrant workers in Gulf countries face elevated risks of depression, anxiety, and stress, while access to culturally sensitive screening remains limited. This study aimed to develop an interpretable AI-enabled framework for identifying probable depressive symptoms among Gulf migrants and to demonstrate a proof-of-concept neuroimaging extension. Methods: A cross-sectional secondary data analysis was conducted using an anonymized public DASS-21 dataset filtered to immigrants residing in Saudi Arabia, the United Arab Emirates, Qatar, and Oman. After applying the eligibility criteria, 124 respondents were analyzed using DASS-21 items, demographic variables, TIPI personality scores, and VCL indicators. A Random Forest classifier was evaluated using stratified validation, ROC-AUC, confusion matrices, and feature- importance analysis. A separate CNN-based MRI pipeline was included only as a technical proof of concept. Results: The Random Forest model achieved 86.8% accuracy, 0.94 ROC-AUC, 96.6% sensitivity, 55.6% specificity, 87.5% precision, and 0.92 F1-score. Higher depressive-symptom patterns were observed among respondents from the UAE and Oman, younger participants, and females. Discussion: The findings indicate that explainable machine learning can support early screening in underserved migrant populations. Conclusion: The framework offers a scalable screening-support tool, but requires larger, balanced, longitudinal validation.
Introduction/Objective: The integration of technology into dentistry is rapidly expanding, improving various aspects of dental care, including treatments and services. This study introduces a novel system to address challenges in managing prosthetics from dental labs and scheduling patient appointments in dental clinics. The system has been granted an Indian patent. Methods: The system integrates hardware and software components, including a Raspberry Pi, barcode scanner, output devices (speaker/LCD), input devices (keyboard/microphone), a database management system, and email/SMS API services for notifications. It was implemented in a busy dental clinic in Delhi, India. Validation involved surveys of dentists and patients, along with measurement of objective performance metrics such as appointment turnaround delay, order mismatch/ error rates, staff time saved, and notification latency. Results: Implementation of the automated system reduced average appointment turnaround delay, decreased order mismatch/error rates, saved staff time per day, and minimized notification latency. Discussion: These findings demonstrate that the system effectively streamlines appointment scheduling and prosthetic management. The integration of automated tracking and notifications provides operational efficiency and improves workflow for clinic staff, visiting dentists, and patient experience. Conclusion: The study highlights the practical benefits of a unified automated system in dental clinics, showing measurable improvements in efficiency, accuracy, and overall service delivery.
Introduction/Objective: With the rapid global energy transition and advancement of clean energy technologies, integrated energy systems (IES) must progressively evolve towards economic, low-carbon, and high-efficiency operation. Methods: To achieve this, this paper proposes a multi-objective coupled optimization strategy for IES, targeting economy, low-carbon footprint, and exergy efficiency (EE), based on deep reinforcement learning (DRL). The strategy incorporates an electricity-cooling-gas demand response (DR) model to optimize the load profile and introduces a stepped carbon trading (SCT) mechanism. Furthermore, a model for comprehensive EE is constructed using the EE coefficient method. Finally, a multi-objective optimization framework for IES is developed, integrating system economic cost, low-carbon operation, and comprehensive EE. Results: Case studies demonstrate that the proposed optimal scheduling scheme enhances the system's operational economy while simultaneously balancing low-carbon and high-efficiency performance. Discussion: The proposed DRL-based multi-objective coupled optimization model significantly reduces total operating costs (TOC), effectively cuts overall carbon emissions (CE), and improves comprehensive energy utilization efficiency, thereby achieving low-carbon and economic operation while ensuring high operational efficiency. By incorporating electricity-cooling-gas demand response models for load optimization and introducing a stepped carbon pricing mechanism, along with establishing an integrated EE model using the EE coefficient method, the study enables synergistic supply-demand optimization and substantially enhances EE. Conclusion: The adopted Deep Deterministic Policy Gradient (DDPG) algorithm effectively addresses the highly nonlinear, multi-constrained, and strongly coupled complexities of IES, successfully identifying balanced optimal solutions among the three interdependent objectives—economic cost, CE, and system efficiency—demonstrating its strong potential and effectiveness in solving such multi-objective collaborative optimization problems.
Introduction: The growing global concern for the high prevalence of diabetes mellitus has resulted in an increased focus on measures for its early and accurate diagnosis and strategies for its control. Various machine learning and deep learning models can be utilized for accurate diabetes diagnosis. This study focuses on designing a comparative framework for Diabetes Mellitus detection using the deep learning model of Artificial Neural Networks (ANN). The study examines the behaviour of the altering hyperparameters, learning rate, epochs, and the number of hidden layers in an ANN, on the accuracy of diabetes mellitus prediction. Materials and Methods: This study developed ANN models on a novel diabetes dataset for diabetes mellitus prediction, and compared them on key performance metrics. Results: The results demonstrate that an ANN model with two and three hidden layers and learning rates of 0.005 and 0.0005, respectively, achieved 88.0% accuracy and an AUC score above 0.8. Discussion: Interpreting the ROC Curve, the study found that small or very large epochs showed lower accuracy in the dataset. Learning rates of 0.005 and 0.0005 demonstrated a higher performance than the learning rate of 0.05. Conclusion: A holistic plan for diabetes management requires integrating deep learning-based ANN models for prediction, coupling them into clinical practice. Future studies would apply the hybrid and ensemble algorithms to the novel dataset and compare the performance of the prediction. Our research would contribute to the global effort in diabetes mellitus management, thus reducing the overall cost of this life-long disease.
Introduction As global renewable energy capacity continues to expand, the development of large-scale, flexibly dispatchable integrated energy systems has become increasingly important. Hydro- Photovoltaic-Storage (HPS) systems in high-altitude plateau regions provide abundant hydropower and solar resources. However, persistently low temperatures in these areas severely impair the efficiency of lithium iron phosphate (LFP) battery storage systems (BESS), thereby reducing performance and limiting the overall economic benefits and operational flexibility of the integrated system.Methods This study proposes an optimized dispatch strategy for integrated HPS systems operating in low-temperature environments. A joint modeling framework coupling the HPS system with Heating, Ventilation, and Air Conditioning (HVAC) thermal regulation is established to mitigate battery efficiency degradation under extreme cold conditions. The proposed strategy enables coordinated operation across hydropower, photovoltaic generation, and temperature-compensated battery storage, ultimately achieving globally optimal scheduling of the overall system.Results A case study in Southeastern Tibet demonstrates that active HVAC heating improves battery efficiency under low temperatures, reducing system operating costs by 11.1% compared to conventional operation without thermal support. The proposed approach enhances both storage utilization and system resilience.Discussion The temperature-coupled joint model effectively improves energy storage efficiency in low-temperature environments. Spatial correlation modeling and HVAC compensation enhance system resilience in extreme climates.Conclusion The optimized operating mode of HPS in cold-plateau environments can effectively reduce costs and make the operation of large-scale new energy bases more economical.
Introduction: Traditional flux-weakening control methods struggle to address the issue of high-performance and stable operation of permanent magnet synchronous motors within the high-speed range. This paper designs an adaptive hybrid flux-weakening current observer and proposes a single-regulator control method with dual current compensation, aiming to enhance motor control performance under high-speed conditions. Methods: An adaptive hybrid flux-weakening current observer was designed, along with a dual- current compensation strategy. A speed regulator with anti-windup based on conditional integration was incorporated into the PMSM flux-weakening system. Furthermore, a nonlinear variable-parameter regulator was developed to combine fast PI-like convergence with improved stability. Results: Experiments were conducted using the TI digital signal processor TMS320F28335 and a three-phase voltage-source converter. The proposed method reduces voltage overshoot by approximately 75% during transients. The d-axis current responds rapidly to load-torque changes with minimal overshoot. Discussion: The adaptive observer and dual-current compensation significantly enhance dynamic response and disturbance rejection in high-speed operation. The nonlinear regulator boosts stability while preserving fast response, offering a viable solution for high-performance drives. Conclusion: The proposed method enables high-speed, high-precision PMSM control, overcoming traditional drawbacks in overshoot, response speed, and anti-interference capability, and provides a reliable approach for stable high-performance motor operation.
Background: Flexible interconnection technology is pivotal for medium-voltage distribution areas in new power system setups, enabling power exchange, load transfer, and dynamic capacity enhancement. This study reviews the research status of flexible interconnection systems in medium-voltage distribution areas, aiming to clarify current progress and identify future directions Objective: A comprehensive review approach was adopted. We analyzed existing literature, demonstration projects, and technical reports. Focus areas included topological forms of flexible interconnection devices, control technologies, configuration planning, optimal scheduling, and emerging technologies like multi-layer collaboration and virtual power plants. Data was collected from academic databases, industry publications, and project documentation, then systematically categorized and evaluated. Method: First, the current topologies of flexible interconnection networks were expounded, and medium-voltage flexible interconnection demonstration projects were summarized, demonstrating diverse structural applications. Second, in-depth insights into the control technologies, configuration planning, and optimal scheduling of flexible interconnection systems were obtained, highlighting their technical feasibility and application potential. Third, prospects for multi-layer collaborative and virtual power plant technologies in flexible interconnection systems were outlined, indicating promising integration paths. Result: The diversity of topological forms reflects the adaptability of flexible interconnection technology, but also brings challenges in standardization. Control and scheduling technologies are mature but need to be integrated with actual grid operation scenarios. Multi-layer collaboration and virtual power plants offer new directions, yet require addressing issues such as data interoperability and coordination mechanisms. Overall, the technology shows great promise but faces practical implementation hurdles. Conclusion: Flexible interconnection technology is key for medium-voltage distribution area operation. Current research covers topology, control, and planning, with demonstration projects verifying feasibility. Future development lies in promoting multi-technology collaboration, addressing implementation challenges, and exploring new application models to fully unleash its potential in new power systems.
Introduction In view of the practical demand for electromagnetic interference shielding in the terahertz (THz) band in actual engineering, a Frequency-Selective Surface (FSS) shielding device suitable for this band is designed.Methods To achieve the dual-bandstop filtering function, this study employs the multi-layer cascading method and designs a Frequency-Selective Surface (FSS) element structure with a square ring structure as the basic resonant unit. The structure selects a low-loss liquid crystal polymer (LCP) rectangular dielectric as the substrate, and the upper and lower surfaces of the substrate are uniformly coated with square ring metal structure units with good consistency, forming a symmetrical layout to enhance the electromagnetic coupling effect.Results The designed FSS features well-defined dual stopbands, covering the frequency ranges of 0.987-1.186 THz and 1.846-2.175 THz. For incident electromagnetic waves within these frequency bands, the FSS exhibits a strong shielding effect, achieving an average attenuation of approximately -48 dB and effectively suppressing unwanted terahertz signals.Discussion The angle stability, polarization stability, and structural parameters of the FSS were analyzed using ANSYS HFSS simulation software. The angle stability of the FSS reached 45 degrees, and it is polarization-insensitive with stable performance under TE/TM polarizations. By adjusting the relevant parameters of the unit structure, its band-stop performance can be tuned.Conclusion The designed FSS has excellent dual-stopband performance. It also has a simple, easy-to-process structure, and with its 45 degrees angular stability, polarization insensitivity, and adjustable band-stop performance, it holds broad application prospects in high-speed THz communication, precision electromagnetic shielding, and security inspection fields.
Introduction Solar energy represents a highly promising renewable energy source in India, supported by favorable climatic conditions and national initiatives such as the National Solar Mission. The objective of this study is to enhance the energy conversion efficiency of CIGS thin-film solar cells by systematically optimizing key absorber layer parameters and developing an environmentally benign, cadmium-free device architecture.Methods A standard CIGS/CdS/ZnO reference cell was first established, followed by the development of a calibrated and correlated numerical model using SCAPS-1D. The effects of absorber bandgap, acceptor doping concentration, and thickness on device performance were investigated over the ranges of 1.040-1.411 eV, 1015-1017 (/cm3), and 0.5-2 & micro;m, respectively. Multiple simulation iterations were performed to identify the optimal absorber parameters. Additionally, toxic CdS was replaced with ZnS, and various buffer layer compositions were evaluated.Results The optimized CIGS/ZnS/ZnO solar cell with absorber bandgap of 1.268eV, acceptor doping concentration of 1015(/cm3), and thickness of 1.50 & micro;m achieved a conversion efficiency of 26.82%, which is 29.12% higher than the reference cell 'A'(20.77%).Discussion The observed efficiency enhancement is attributed to improved optical absorption, carrier transport, and reduced recombination losses achieved through systematic absorber optimization and the use of a wide-bandgap ZnS buffer layer. The cadmium-free configuration also reduces parasitic absorption at short wavelengths, leading to improved current density.Conclusion This study demonstrates that systematic optimization of absorber layer parameters combined with the successful implementation of a cadmium-free ZnS buffer can significantly enhance the performance of CIGS thin-film solar cells. The proposed CIGS/ZnS/ZnO architecture offers a high-efficiency, environmentally friendly alternative for next-generation photovoltaic devices.
Introduction: As the mainstay of urban transportation, the accurate identification of key stations in the metro–bus two-layer coupled network is essential for ensuring network anti-interference capability, improving operational reliability, and enhancing urban traffic efficiency. Most existing methods focus on a single traffic layer or a single-index approach. These often ignore the inter-layer coupling characteristics between metro and bus systems, leading to low identification accuracy. To address this issue, this study proposes a key station identification method based on an importance evaluation matrix Methods: The Space-L method is adopted to construct a two-layer coupled composite transportation network model. Based on the importance evaluation matrix, intra-layer passenger flow, network efficiency, and average degree are taken as core indicators. A coupling strength factor—defined by internal degree (intra-layer connections) and external degree (inter-layer transfer connections)—is further introduced. On the basis of the importance evaluation matrix and coupling strength modeling, an improved key station identification method is proposed. Results: Simulation results based on Chengdu’s metro–bus composite network show that compared with traditional methods (e.g., Degree Centrality, Betweenness Centrality) that ignore inter-layer coupling or rely on single indicators, the proposed method can effectively identify crosslayer key stations with significantly improved accuracy. Specifically, the optimal internal degree contribution ratio a = 0.3 (within the range (0.1, 0.5)) maximizes the method’s recognition precision, and the failure of stations identified by this method results in more pronounced degradation of the network’s largest connected subgraph. Discussion: The proposed method demonstrates promising applicability in metro–bus composite transportation networks. Compared with traditional approaches that ignore inter-layer coupling or rely on single indicators, it better captures both the structural and functional roles of stations by jointly considering coupling strength, node efficiency, average degree, and passenger flow. This improves the identification of cross-layer key stations and provides a practical basis for network robustness evaluation and reliability-oriented transportation planning. Conclusion: The proposed method provides a scientific basis for the reliability optimization of urban transportation networks, emergency station layout, and transfer resource allocation. Additionally, its computational efficiency meets the requirements of large-scale network analysis, supporting the enhancement of urban transit system resilience.
Introduction The adoption of hybrid cascaded direct current (DC) interconnection for transmission from renewable energy bases is regarded as a key future direction. Most new energy bases are located in remote areas, with extremely weak electrical connections to the main alternating current grid. This makes the voltage security and stability issues of the sending-end system increasingly critical challenges.Methods A model is established to quantitatively characterize the voltage coupling relationship between the modular multilevel converter (MMC) bus and the line-commutated converter (LCC) bus. On the basis of analyzing the reactive power support capability of the MMC, an MMC reactive power adaptive control strategy based on a voltage regulation dead zone and its main parameters is proposed.Results For the hybrid cascaded DC transmission system, this paper proposes an MMC adaptive reactive power control strategy, which enhances the system voltage security and stability while enhancing the power transmission capacity of the hybrid cascaded high-voltage direct current (HVDC) system.Discussion The validity of the bus voltage coupling relationship between the MMC and LCC stations is verified through simulations. In addition, a comparative simulation analysis is conducted between the proposed method and traditional methods. The results show that the proposed method not only suppresses transient voltage fluctuations but also suppresses active power fluctuations.Conclusion This method establishes a quantitative voltage coupling relationship and designs a voltage regulation dead band, thereby effectively solving the problems of insufficient selectivity and adaptability in the MMC-based voltage support strategy. The proposed control strategy can significantly improve the voltage stability of the sending-end system with minimal impact on power transmission, providing a theoretical basis for the safe and stable operation of hybrid cascaded HVDC systems.
Background: Wind power integration challenges and the limited flexibility of combined heat and power (CHP) systems hinder the efficient operation of integrated energy systems. Objective: To enhance system adaptability to wind power uncertainties and achieve economical operation by addressing the above issues. Method: An integrated energy system coordination model centered on CHP units with electric heat storage boilers and thermal storage tanks is established. A multi-time-scale robust optimization strategy is proposed: day-ahead scheduling uses a hybrid reinforcement learning–genetic algorithm approach, with deep Q networks for discrete operation decisions and genetic algorithms for continuous power allocation; intra-day scheduling adopts a 4-hour prediction horizon and a 15- minute time step; real-time scheduling uses a 1-hour dispatch cycle and a 5-minute interval. Results: The model expands the feasible domain of electricity–heat decoupling, avoids local optimization associated with single algorithms, reduces total operating costs by approximately 10%, and significantly improves the renewable energy accommodation rate and system flexibility. Discussion: The proposed multi-time-scale scheduling strategy with electric heat storage effectively balances wind power volatility and CHP operational rigidity, reducing costs and enhancing flexibility through a hybrid algorithm. However, it does not consider multi-energy network constraints and lacks large-scale validation. Conclusion: The proposed approach achieves dynamic complementarity between electricity and heat resources, providing a feasible solution for the economical and efficient operation of integrated energy systems.
