Support Capability Research Articles

The Evolution and Economic and Social Effects of the Spatial and Temporal Pattern of Transport Superiority Degree in Southern Xinjiang, China

Transportation significantly influences economically underdeveloped arid regions, impacting economic growth and social progress. Analyzing Transport Superiority Degree (TSD) and its implications in such regions is crucial. A new arid region-specific evaluation framework addresses traditional limitations by considering indicators like route connectivity and desert interference. This article conducts an empirical study using Southern Xinjiang as a research case. It combines comprehensive evaluation methods, spatial autocorrelation methods, spatial Durbin models, and coupling coordination models to depict Transport Support Capability (TSC), Transport Access Capacity (TAC), and Transport Guarantee Capacity (TGC) at different scales in Southern Xinjiang from 2000 to 2020. The study reveals spatial patterns, evolutionary characteristics, economic impacts, and social effects of TSD at various scales. Key findings include: (1) Rapid expansion of transportation infrastructure in Southern Xinjiang. The levels of TSD at different scales have gradually increased, and spatial and temporal pattern differences are evident. At the county level, TSD forms a “core-periphery” spatial pattern centered around the Southern Xinjiang Railway, with “high-high” agglomeration areas centered around Kashgar city and “low-low” agglomeration areas centered around Qiemo County. (2) Prefectural-level TSD improvements have limited impacts on regional development, while county-level TSC, TAC, TGC, and TSD positively affect economic and social growth but also exhibit competitive effects. (3) TSD is transitioning from non-coordinated to coordinated development with economic and social progress at different scales. This research informs transportation facility evaluation in arid regions.

ChatGPT - A new milestone in the field of education

With the rapid development of artificial intelligence, natural language processing models like ChatGPT hold creative potential for applications in education. This study focuses on ChatGPT and explores its applications in the field of education. This research employs literature review and case analysis methods to systematically investigate the applications of ChatGPT in instructional assistance, personalized education, and writing support. By analyzing three cases involving ChatGPT's professional competence in medical exams, the construction of personalized learning systems, and writing support, the paper emphasizes ChatGPT's advantages in education, future development directions, as well as ethical and privacy concerns. The study finds that ChatGPT possesses strong semantic understanding and analytical capabilities. Within the context of general applicability, it can provide accurate answers to questions posed by users with certain specialized backgrounds. The paper not only addresses ChatGPT's role as a student's auxiliary tool but also explores collaborative models between ChatGPT and teachers. However, it's important to note that ChatGPT currently has limitations in terms of limited knowledge and inadequate emotional understanding. Additionally, in terms of ethics and privacy, considerations regarding information protection and moral aspects within the educational application of ChatGPT are crucial. In the future, it's worth further exploring specialized models for ChatGPT's tasks in both teacher and student roles, along with enhancing ChatGPT's emotional support capabilities.

A Frequency Control Strategy for EV Stations Based on MPC-VSG in Islanded Microgrids

The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multi-objective model predictive control (MPC) algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

A novel DC‐link voltage synchronous control with enhanced inertial capability for full‐scale power conversion wind turbine generators

AbstractThe new power system is characterized by high penetration of renewable energy sources and a high proportion of power electronics (namely, double‐high). The grid‐forming control is an effective method to improve the grid‐connected stability of wind turbine generators (WTGs) in the “double‐high” grid. The control method based on the DC‐link voltage can effectively realize the grid‐forming control for WTGs. However, there is a disadvantage that the DC‐link voltage cannot be maintained at the given value. To address this, the grid synchronization mechanism of DC‐link voltage is explored and the specific implementation of a novel DC‐link voltage synchronous control applicable to full‐scale power conversion WTGs is proposed. Then, the boundary of the inertial coefficient is probed through the state‐space method. And a compensation control is proposed to enlarge the inertial response capability based on the mechanism of damping characteristics. Finally, the PSCAD/EMTDC simulation and RTLAB hardware‐in‐loop experiment show that the synchronization frequency can accurately map the grid frequency changes in real‐time under the premise that the DC‐link voltage remains constant. In addition, the inertial coefficient can be increased by more than five times with the compensation strategy, which can enhance the support capability of the WTGs to the power grid.

An Extended Continuation Power Flow Method for Static Voltage Stability Assessment of Renewable Power Generation-Penetrated Power Systems

The continuation power flow method (CPF) has been extensively used for obtaining the static voltage stability limit of power systems, which provides important guidance for the stable operation. However, the conventional CPF cannot account for the dynamic characteristics of renewable power generation, thus resulting in prominent errors for renewable power generation-penetrated power systems. In this paper, we investigate the static voltage stability assessment (SVSA) of power systems with high penetration of rgb0,0,0renewable power generation (RPG). An extended Jacobian matrix is firstly established by considering the dynamic characteristics of synchronous generators (SG) and renewable power generation in constant AC voltage control mode (CVRPG). Based on the extended Jacobian matrix, an extended CPF method is proposed for SVSA of the power system and a reactive power sensitivity index is proposed to indicate the voltage support capability of nodes. Simulation results show that the proposed extended CPF method has higher accuracy than conventional CPF in assessing the static voltage stability of power systems with high penetration of RPG. It is also revealed that that the voltage support capability of each node rises as the proportion coefficient is increases.

Visualization Design for Predictive Models of Fractured Well Production

This paper explores the visualization design for predictive models of fractured well production to enhance the intuitiveness and comprehensibility of the prediction results. Initially, the theoretical foundation of predictive models for fractured well production is established, encompassing geomechanics, fluid mechanics, and statistics. Subsequently, the methodology of visualization design is elaborated, which includes three main steps: data preprocessing, model construction, and result presentation. During data preprocessing, standardization and normalization techniques are employed to ensure data consistency and accuracy. In the model construction phase, multiple linear regression and neural network models are introduced and compared to determine the optimal model. For result presentation, various charts and dynamic interactive interfaces, such as heat maps, trend charts, and 3D models, are designed to enhance user visual experience and decision support capabilities. Finally, the paper summarizes the positive role of visualization design in improving the efficiency and accuracy of predictive model applications and proposes future research directions, including model optimization, innovation in visualization techniques, and deepening interdisciplinary research. This research provides theoretical support and practical guidance for the visualization design of predictive models for fractured well production, contributing to technological advancement and application expansion in the related field.

Analysis and quantitative evaluation of wind turbine frequency support capabilities in power systems

With the rapid development of photovoltaic and wind power, the penetration rate of renewable energy in power system is gradually increasing. This escalation poses a challenge as it results in a continuous reduction in the inertia and damping of the power grid, accentuating the issue of frequency stability in power systems. It is one of the effective measures to deal with this risk that renewable energy sources such as wind turbines actively provide frequency support for power grid. This paper carries out research into the examination of wind turbines’ capacity to contribute to system frequency support, considering two aspects: inertia support and primary frequency regulation capabilities. Subsequently, the frequency control method of the wind turbine support system is analyzed, emphasizing the roles of rotor kinetic energy control and power reserve control in facilitating frequency support. The evaluation of the transient frequency support capability of wind turbines is introduced, incorporating factors such as control methods, controller parameters, and the duration of transient frequency support. Key metrics, including accumulated energy and frequency change rate indices during the transient frequency support stage, are proposed to quantitatively assess the transient frequency support capability of wind turbines. These indices provide a comprehensive framework for the quantitative evaluation of wind turbine transient frequency support capabilities.

Instrumenting OpenCTI with a Capability for Attack Attribution Support

In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information for guiding defenders’ security procedures and supporting incident response and remediation. However, the technical analysis involved in cyberattack attribution requires skills, experience, access to up-to-date Cyber Threat Intelligence, and significant investigator effort. Attribution results are not always reliable, and skillful attackers often work hard to hide or remove the traces of their operations and to mislead or confuse investigators. In this article, we translate the technical attack attribution problem to the supervised machine learning domain and present a tool designed to support technical attack attribution, implemented as a machine learning model extending the OpenCTI platform. We also discuss the tool’s performance in the investigation of recent cyberattacks, which shows its potential in increasing the effectiveness and efficiency of attribution operations.

Frequency support scheme of grid‐forming based hybrid cascaded HVDC integrated wind farms

AbstractThe grid‐following control‐based hybrid cascaded high‐voltage direct current (HC‐HVDC) links may contribute to the worsening of inertia decline and instability issues, especially in situations characterized by a weak grid. To address the challenge, this paper proposes a grid‐forming (GFM) based optimal frequency support scheme (OFSS) for HC‐HVDC integrated wind farms. Firstly, a GFM control for hybrid converter is proposed and its stability under weak grid conditions is evaluated through a small‐signal model. Then, DC current–voltage droop control is introduced to the line commutated converter (LCC). This control approach ensures the consistency of reactive power in the LCC by regulating the DC voltage and current in the same direction, thereby mitigating AC voltage fluctuations. It also facilitates active power control in the LCC in response to grid frequency variations. Furthermore, the OFSS can convey the frequency of receiving‐end grid to the rectifier and wind farms without communication for precise power control. Finally, to validate the effectiveness of OFSS, a point‐to‐point HC‐HVDC and a 4‐machine test model are constructed on PSCAD/EMTDC. The OFSS significantly enhances the frequency support capability of HC‐HVDC links and improves the robustness in weak grid.

Voltage dip propagation in renewable‐rich power systems utilizing grid‐forming converters

AbstractThe growing integration of converter‐interfaced renewable energy sources (RESs) utilizing Grid‐Following (GFL) converters has displaced conventional synchronous generators (SGs) in central generation units. This shift presents challenges, including diminished system inertia, lower fault levels, and implications for system strength and network resilience. The propagation of voltage dips, particularly during disturbances like system Short Circuit (SC) faults, is adversely affected by the increased penetration of such RESs. This is attributed to the limited support capability of these sources and their distinct SC response compared to SGs. In response to these challenges, Grid‐Forming (GFM) converters emerge as a promising technology equipped with advanced functionalities that emulate SG operation. Consequently, they hold potential for mitigating the effects of voltage dip propagation in renewable‐rich power systems. This study aims to assess the impact of employing GFM converters in renewable‐rich power systems on voltage dip propagation across the network. The authors’ investigation begins by examining the SC response of GFM converters and comparing it with the responses of traditional GFL converters and SGs. The paper proceeds to analyze voltage dip propagation, considering various penetration scenarios involving RESs based on GFL and GFM converters. The IEEE 9‐BUS test system, implemented in the DIgSILENT PowerFactory software, serves as the basis for these evaluations. Through extensive simulations and analysis, the authors’ research provides valuable insights into the effectiveness of GFM converters in enhancing the network's response to voltage dips.

Use of FACTS for Improving Voltage Stability in Mining Applications

FACTS systems provide a well proven solution for actual different power system problems. FACTS devices can be effectively used for reactive power support, enhance controllability, improve stability and increase power transfer capability of AC transmission systems. This paper analyses the utilisation of FACTS systems in the expansion project of a mine, as an environmental solution for optimizing existing exploitations instead of opening new mines.

Assessment of ChatGPT-3.5's Knowledge in Oncology: Comparative Study with ASCO-SEP Benchmarks.

ChatGPT (Open AI) is a state-of-the-art large language model that uses artificial intelligence (AI) to address questions across diverse topics. The American Society of Clinical Oncology Self-Evaluation Program (ASCO-SEP) created a comprehensive educational program to help physicians keep up to date with the many rapid advances in the field. The question bank consists of multiple choice questions addressing the many facets of cancer care, including diagnosis, treatment, and supportive care. As ChatGPT applications rapidly expand, it becomes vital to ascertain if the knowledge of ChatGPT-3.5 matches the established standards that oncologists are recommended to follow. This study aims to evaluate whether ChatGPT-3.5's knowledge aligns with the established benchmarks that oncologists are expected to adhere to. This will furnish us with a deeper understanding of the potential applications of this tool as a support for clinical decision-making. We conducted a systematic assessment of the performance of ChatGPT-3.5 on the ASCO-SEP, the leading educational and assessment tool for medical oncologists in training and practice. Over 1000 multiple choice questions covering the spectrum of cancer care were extracted. Questions were categorized by cancer type or discipline, with subcategorization as treatment, diagnosis, or other. Answers were scored as correct if ChatGPT-3.5 selected the answer as defined by ASCO-SEP. Overall, ChatGPT-3.5 achieved a score of 56.1% (583/1040) for the correct answers provided. The program demonstrated varying levels of accuracy across cancer types or disciplines. The highest accuracy was observed in questions related to developmental therapeutics (8/10; 80% correct), while the lowest accuracy was observed in questions related to gastrointestinal cancer (102/209; 48.8% correct). There was no significant difference in the program's performance across the predefined subcategories of diagnosis, treatment, and other (P=.16, which is greater than .05). This study evaluated ChatGPT-3.5's oncology knowledge using the ASCO-SEP, aiming to address uncertainties regarding AI tools like ChatGPT in clinical decision-making. Our findings suggest that while ChatGPT-3.5 offers a hopeful outlook for AI in oncology, its present performance in ASCO-SEP tests necessitates further refinement to reach the requisite competency levels. Future assessments could explore ChatGPT's clinical decision support capabilities with real-world clinical scenarios, its ease of integration into medical workflows, and its potential to foster interdisciplinary collaboration and patient engagement in health care settings.

YOLOv5s maritime distress target detection method based on swin transformer

AbstractIn recent years, the task of maritime emergency rescue has increased, while the cost of time for traditional methods of search and rescue is pretty long with poor effect subject to the constraints of the complex circumstances around the sea, the effective conditions, and the support capability. This paper applies deep learning and proposes a YOLOv5s‐SwinDS algorithm for target detection in distress at sea. Firstly, the backbone network of the YOLOv5s algorithm is replaced by swin transformer, and a multi‐level feature fusion module is introduced to enhance the feature expression ability for maritime targets. Secondly, deformable convolutional networks v2 (DCNv2) is used instead of traditional convolution to improve the recognition capability for irregular targets when the neck network features are output. Finally, the CIoU loss function is replaced with SIoU to reduce the redundant box effectively while accelerating the convergence and regression of the predicted box. Experimenting on the publicly dataset SeaDronesSee, the , , and of YOLOv5s‐SwinDS model are 87.9%, 75.8%, 79.1% and 42.9%, respectively, which get higher results than the original YOLOv5s model, the YOLOv7 series of models, and the YOLOv8 series of models. The experiments verifies that the algorithm has good performance in detecting maritime distress targets.

Improved VSG strategy of grid-forming inverters for supporting inertia and damping

A virtual synchronous generator (VSG) strategy can introduce the rotational inertia and damping characteristics of the synchronous generator to the static inverter, e.g., PV, wind generation, and ESS, which are used to enhance the system frequency support characteristics of the micro-grid. However, under various operations of the VSG, the inertia and damping support capabilities are different, and the conventional VSG strategy with a fixed control coefficient sacrifices a certain degree of dynamic regulation performance with less robustness. This research proposes an improved VSG strategy with adaptive inertia and damping coefficients to increase the flexibility of VSG. To this end, a mathematical model is first established to analyze the impact of various parameters on the characteristics of VSG, and then the root trajectory is used to explore the impacts of various rotational inertia and damping coefficients on the stability of the VSG system. Second, an improved control strategy with adaptive inertia and damping coefficients is proposed based on the characteristics of the second-order system and the system frequency deviations. Finally, a simulation system with the VSG is constructed based on MATLAB/Simulink. The effectiveness of the proposed control strategy is verified by comparing the simulation results to the conventional control strategy with the fixed control coefficients under over-frequency and under-frequency disturbances.

Research on fast frequency response to AC grid of a battery‐free MVDC system incorporating a high proportion of distributed photovoltaic generation units with reserve capacity

AbstractWith the fast development of renewable energy, system operators intend to require renewable energy generation to have more grid support capabilities. This paper studies the grid‐connected control strategy of a battery‐free medium‐voltage direct current (MVDC) system with distributed photovoltaic generation units (DPVGUs), aiming to realize the rapid grid support function to AC grid frequency. Firstly, a scheme for the MVDC system can keep some reserve capacity is provided. At this moment, DPVGUs execute a master‐slave coordinated control strategy, that is, the master DPVGU adopts an improved DC constant voltage control strategy to maintain the MVDC bus voltage, and the remaining DPVGUs adopt an improved droop control strategy to quickly provide corresponding power. Secondly, when the frequency deviation of AC grid exceeds the deadband of primary frequency control, fast frequency response (FFR) control strategy of the modular multilevel converter is proposed, aiming to actively transmit some or all of the reserved power of the MVDC system to the AC grid and can quickly provide frequency support to AC grid. In FFR control strategy, an additional control strategy is added on the vector control strategy. Finally, simulation studies are carried out in RTDS to verify the effectiveness of the proposed control scheme.

Seasonal rockfall risk analysis in a touristic island: Application to the Tramuntana Range (Mallorca, Spain)

Rockfalls are an ever-present possibility in the mountainous context of the Tramuntana Region (Mallorca, Spain). Recent events have shown the high potential for direct and indirect impact on the safety of people and economic activities, lasting for weeks or even months. In the present study, we start from a probabilistic assessment of the rockfall hazard (spatial propensity and temporal recurrence), based on a detailed historical record of occurrences and rockfall modelling, which is subsequently superimposed on three exposure scenarios and on a social vulnerability assessment. Exposure considers the floating population at three seasons of the year, given the area's high tourist aptitude. Vulnerability considers on the one hand the intrinsic characteristics of individuals and, on the other, the characteristics of the surrounding territory that act to facilitate emergency operations, mitigate the immediate impact and enhance rapid recovery. Due to the characteristics of the island and the spatial distribution of tourism, the results show that the highest density of rockfall trajectories potentially affect areas of high exposure, whose access by emergency services is complex. Not being, in general, the areas of highest individual criticality, those areas have in most situations low support capability installed or nearby.The results constitute a useful tool for emergency and risk management planning in multiple sectors linked to risk governance. Despite the high geographic detail of the analysis, these studies do not replace exposure and vulnerability analysis at the building level, for which the contribution of georeferenced Census data is fundamental.

Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization.

Carbon emission reduction contribution analysis of electricity enterprises in urban green development: A quantum spherical fuzzy sets-based decision framework

As managers of urban power grids (UPGs), electricity enterprises are responsible for ensuring energy security and stability during the urban green transformation process. They are also a key driving force in promoting green power transmission and integration. Therefore, exploring the green attributes of UPGs and accurately enhancing their green support capabilities are of significant importance. This study addresses the perceived contributions of electricity enterprises to carbon emission reduction and proposes a decision framework within a quantum-spherical fuzzy (QSF) environment. First, from the perspectives of economy, technology, policy, and management, a carbon emission reduction contribution evaluation system for electric enterprises was constructed. In the QSF environment, this study identifies the importance of various attributes in the evaluation system. Subsequently, an electric enterprise carbon emission reduction perception model was developed based on the preference ranking organization method for enrichment evaluations and prospect theory (PROMETHEE-PT). An empirical validation of the proposed model was conducted in Hefei, China, demonstrating the robustness and effectiveness of the evaluation framework. The study's results were verified by comparing them with those of other methods.

Reasons for Introducing Homeworking and Different Perceptions of Homeworking Depending on the Size of the Company

Working from home (homeworking) is currently a much discussed topic and an area of working life that is already becoming a standard issue for employees. Homeworking (HW) provides employees with a very important degree of flexibility and freedom in their work organization and thus represents an important management tool for an effective work-life balance. The ability to work from home has proved essential in the face of the two-year Covid-19 pandemic, which has confronted the whole world and especially business managers. A whole range of work (especially administrative work) moved to employees’ homes very quickly. Homeworking has also become very widespread due to better IT support and online communication capabilities in most businesses and is poised to become even more prevalent in the future. The aim of this paper is to evaluate the aspects that influence the adoption of HW in the Czech Republic and to describe the different perceptions of HW depending on the size of the enterprise. The research was conducted towards the end of 2020 during the ongoing Covid-19 pandemic. The authors managed to collect data from 216 enterprises through a questionnaire survey. Managers as the Covid-19 crisis, the nature of HW-enabling work and the possibility of working most frequently cited the main reasons for HW adoption during light sickness. Important aspects influencing the adoption of HW from managers’ perspectives include the possibility of working in light sickness, the required availability of employees, as well as employee satisfaction and flexibility.

Factors influencing users’ retention of using human resources information system in the post-implementation stage in Vietnam, from human resources professionals’ perspective

This study aims to explore the factors influencing the retention of human resources information systems (HRIS) by users during the post-implementation stage in Vietnam. To obtain insights into the experiences of HR professionals with HRIS implementation and use, semi-structured interviews were conducted with eight professionals from three different organisations. The thematic analysis of the interview data revealed four key themes that impact the retention of HRIS, which are system features, management support, organisational management, and organisational capabilities. The study found that system features, such as user-friendliness, self-service, reliability, data aggregation, and integration, drive satisfaction and ongoing use of HRIS. Management support in the form of modelling usage and training also encourages retention. Organisational management practices, including communication and change management, facilitate adoption. Finally, digital transformation experience and financial investment enhance engagement with HRIS. The study concludes that user experience, leadership, communication, change management, and organisational elements all contribute to sustaining HRIS adoption after implementation. This study provides valuable insights into promoting continued HRIS utilisation through a holistic approach that addresses these various factors. Additionally, it offers practical recommendations to increase user retention during the post-implementation stage, based on real-world perspectives from HR professionals in Vietnam.