Generator System Research Articles

Transient Stability Analysis for Paralleled System of Virtual Synchronous Generators Based on Damping Energy Visualization and Approximation

Transient Stability Analysis for Paralleled System of Virtual Synchronous Generators Based on Damping Energy Visualization and Approximation

Recyclable polyurethane from castor oil based on dynamic disulfide bonds and multiple hydrogen bonds as adhesive and photothermal conversion materials

As an important polymer material, polyurethane brings convenience to daily life but also causes environmental problems, and the manufacturing of bio-based repairable, re-processable and sturdy materials can effectively reduce environmental pressure. Herein a low temperature recyclable polyurethane (PU) was developed with castor oil (CO) by combining isophorone diisocyanate (IPDI), dynamic disulfide bonds and hydrogen bonds. The castor oil-based PU showed impressive tensile strength (16.1MPa) remarkable elongation at break (1055.8%), and high bonding power (up to ~6MPa) with bonding wood chips. The dynamic disulfide bonds and hydrogen bonds imparted the bio-based PU with outstanding elastic recovery, impressive self-healing capability (up to ~90%), short relaxation time (5~6min at 180°C), favorable shape memory behavior, and multiple recyclability. By mixing different proportions of carbon nanotubes (CNTs), recyclable and stretchable conductive composites are realized. In addition, an integrated system of high-efficiency bio-based solar photovoltaic generator is demonstrated for simulating the ambient sunlight-heat-electricity conversion, which provides some guidance for the efficient use of solar energy.

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework.

Generative systems for graphical assets have the potential to provide users with high quality assets at the push of a button. However, there are many forms of assets, and many approaches for producing them. Quantitative evaluation of these methods is necessary if practitioners wish to validate or compare their implementations. Furthermore, providing benchmarks for new methods to strive for or surpass. While most methods are validated using tried-and-tested metrics within their own domains, there is no unified method of finding the most appropriate. We present a framework based on a literature pool of close to 200 papers, that provides guidance in selecting metrics to evaluate the validity and quality of artefacts produced, and the operational capabilities of the method.

Design of a Hybrid Excitation Controller for Multimachine Infinite Bus Power System

Abstract: A significant portion of contemporary power networks is still conventional power system that uses synchronous generators to produce electricity. Conventional power systems are operated under various operating situations by means of excitation systems and turbine-governor systems of synchronous generators. This paper proposes an excitation controller for synchronous generators (SGs) in a multi machine infinite bus (MMIB) power system through combining the idea of the nonlinear backstepping (BS) and partial feedback linearization methods. The model of third-order has been linearized into a second-order linearized system that is independent of the rotor angle which is unmeasurable, using the partial feedback linearization approach. In this case, the BS is applied to the partially linearized system. The idea is to find a control input that drives the system towards a desired behaviour while ensuring the transformed states converge to their target values. Lastly, simulation research is carried out on an MMIB system to analyse how the proposed control mechanism (BS-PFLC) performs in stabilizing the system under a three phase-to-ground fault conditions of power networks. To demonstrate the effectiveness of the BS-PFLC, an existing excitation controller has been used to compare the performance.

Mechanism analysis on a type of limit induced oscillation for single‐machine infinite‐bus system of PMSG with positive damping

AbstractThe mechanism of oscillations that occur in the system connected to renewable energy sources is complex, generally including negative damping oscillation, limit induced oscillation etc. Combining with the dynamic characteristics of the low‐dimensional system after limit working, this paper analyses the mechanism of a type of limit induced oscillation that occurs when the output of the DC voltage outer loop continuously touches the limit for single‐machine infinite‐bus system of permanent magnet synchronous generator with positive damping under a large disturbance. Specific study as follows: firstly, a non‐smooth state space model of single‐machine infinite‐bus system of permanent magnet synchronous generator with nonlinear state limit (clarified as a Filippov system) is established. Secondly, the small disturbance characteristics of the system are analysed when the limit does not work. Thirdly, the piecewise dynamic description of the system is analysed with the limit working, and the reason that the final contraction of the system to an eight‐dimensional manifold is explained based on mathematical derivation and physical characteristics. Finally, the characteristics of equilibrium point of the system in a low‐dimensional manifold are analysed when the limit continuously working, revealing that the existence of a pair of conjugate unstable eigenvalues in the low‐dimensional system is the reason of system oscillation.

Understanding Generative AI: From Basic Principles to Real-World Applications

This comprehensive article examines the foundational principles, technical implementations, and societal implications of generative artificial intelligence (GenAI), a transformative technology that has revolutionized the creation of synthetic content across multiple domains. The article explores the architectural frameworks underpinning GenAI, focusing on Generative Adversarial Networks (GANs) and Transformer-based models, while detailing the sophisticated training methodologies and evaluation metrics that enable their functionality. Through an in-depth analysis of real-world applications in healthcare, creative industries, and software development, this article illuminates the technology's potential to enhance human capabilities and drive innovation. The investigation extends to critical ethical considerations, addressing security concerns surrounding deepfakes, challenges in bias mitigation, and complex intellectual property issues. Furthermore, the article presents a forward-looking perspective on research opportunities, policy implications, and industry best practices, emphasizing the importance of responsible development and deployment of generative AI systems. This article contributes to the growing body of knowledge on GenAI by providing a holistic understanding of its current state, challenges, and future directions, while highlighting the crucial balance between technological advancement and ethical considerations in shaping its evolution.

Dynamic Characteristic Analysis of Multi-Virtual Synchronous Generator Systems Considering Line Impedance in Multi-Node Microgrid

With the increasing integration of distributed energy resources into modern power systems, virtual synchronous generators (VSGs) have been a promising approach to imitate the inertial response of synchronous generators, thereby enhancing microgrid stability in a dynamic state. When many VSGs are integrated into microgrids, the dynamic characteristics of the system become increasingly complex. Current studies typically assume that different VSGs are connected to a common coupling point, focusing on analyzing the interaction characteristics, which may overlook the widely distributed line impedances in microgrids with distance between different facilities. This may lead to incomplete understanding of the interaction dynamics when VSGs are distributed over long feeder lines. Therefore, this paper proposes and investigates a multi-node, multi-VSG model incorporating line impedances among different nodes, establishing transfer function models for multi-node load disturbances and the frequency responses of individual VSGs. The study explores the dynamic response characteristics of VSGs under varying parameter influences and proposes principles for designing VSG port impedance and inertia parameters to optimize system dynamic frequency characteristics. The findings, validated through simulations in PSCAD v46, provide insights for enhancing the flexibility and reliability of grids incorporating VSGs.

A virtual data generator system for shape recognition in haptic robotics

A virtual data generator system for shape recognition in haptic robotics

Design of Coil-Wound Once-Through Steam Generator System for Concentrating Solar Power Plants

The viability of solar tower plants is endangered owing to multiple failures, mainly in their steam generators. These failures produce unscheduled shutdowns with significant economic losses that increase the financing costs of this technology due to its technological risk. On the other hand, if the flexibility of the steam generator rises, solar power tower plants could participate in the energy adjustment market, improving their returns, encouraging the penetration of variable renewable energies, and providing security to the power grid. A novel steam generator system design based on a once-through steam generator composed of two coil-wound heat exchangers is proposed for a highly reliable, flexible, and quick response steam generator. Coil-wound heat exchangers reduce thermal stress and allow part load operation, while once-through steam generators permit fast load changes and reduce the number of components. Compared with traditional shell and tube designs, the results indicate that the proposed steam generator reduces heat exchange area by 22%, molten salt pressure drop by 79%, and tube-to-tubesheet joints by 73%.

Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF

Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems.

Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports.

Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented. We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports. We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2. In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P<.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2's 38% (149/392, P<.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P<.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2. The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics.

Design and Performance of High-Capacity Magnesium–Air Battery for Power Generator System

Efforts to achieve carbon neutrality, which aims to reduce the net carbon emissions to zero by decreasing carbon emissions from human activities and increasing carbon absorption, are actively underway. Additionally, the search for clean energy alternatives to fossil fuels has become a global research trend. This paper presents research on metal–air batteries, focusing on the development of energy supply technologies that do not generate carbon emissions during power generation and require less space for power generation compared to existing renewable energy sources. The proposed Mg–air battery (MAB) in this study uses magnesium as the metal anode and theoretically offers a maximum open-circuit voltage of 3.1 V and a high energy density of 6.8 kWh/kg. While previous research has primarily focused on designing small-capacity cells and maximizing the performance of metal anodes, this study differentiates itself by designing a large-capacity MAB cell and optimizing its electrical performance. For the large-capacity cell design, the weight, shape, and size of the anode were designed based on MAB performance factors, and research was conducted on manufacturing methods to optimize the performance of the air cathode. Furthermore, to enhance usability and extend the lifespan of the MAB cell, it was designed to allow electrolyte circulation, and the electrolyte circulation performance was verified through simulations of fluid flow within the cell. Based on the study of the power performance of the newly designed large-capacity MAB cell, the feasibility of constructing a kW-class system using multiple Mg–air battery cell stacks was confirmed.

Creativity, credit, and copyright in the age of artificial art

ABSTRACT Generative artificial intelligence is transforming the way we make, and think about, art. With prompting from human users, these generative systems now produce aesthetically compelling and seemingly creative works in a variety of artistic domains. In doing so, they challenge the ways we think about artistic credit, about creativity, and about the mechanism of legal copyright, which is meant to protect and promote creativity in a capitalist art market. All of this is currently at play in the courtroom, as artists contest the ways in which their artworks can be rightfully fed into these artificial systems (“the problem of the inputs”), and other artists are challenged over whether they might be credited for the visual images these systems generate (“the problem of the outputs”). Here, we explore these problems as they arise in visual art. We argue that the contested legal landscape surrounding these artificial systems reflects the ways they challenge our received notions of artistic credit and creativity. And we suggest that clarifying or changing the application of copyright in light of their generative capacities will ultimately involve revising our conception of art.

Study on type of magnetic suspension rotor groove and wear of drop touchdown bearing

The active magnetic bearing (AMB) is widely used in the field of flywheel energy storage system (FESS) in wind power generation. This study mainly studies the magnetic suspension rotor groove (MSRG) of FESS and the wear of drop touchdown bearing (DTB). The mathematical model of air gap magnetic field waveform on stator surface and Plath equation of control in air gap region were established. Contrastive analysis of conventional, second harmonic and third harmonic ‘petal-shaped’ MSRG in AMB. The life test of DTB on magnetic suspension rotor was carried out for 24000r/min and lasted for 1500 h. The second harmonic ‘petal-shaped’ MSRG could not only generate reluctance torque and increased the torque density of the motor, but also increased the fundamental component and excitation torque component by reducing the peak value of the magnetic field. The temperature ranges of front and rear DTB were ‘30℃–46℃’ and ‘26℃–40℃’, respectively. After service, the maximum thickness of metamorphic layer in the DTB raceway was 6 μm. After service, the content of Fe in the metamorphic layer of DTB raceway decreased by 10.11% and the content of C increased by 2.41%. Raceway hardness of DTB after service was 59.67 HV higher than that before service.

Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or ``state'' across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a position framework for temporal alignment with configurable toggles. We present five alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world application scenarios. This paper discusses the potential benefits and challenges of each alignment method and positions the importance of a configurable system in advancing progress in stateful generative AI systems.

Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model.

This study addresses the academic gap in the adoption of generative AI systems by investigating the factors influencing technology acceptance and usage behavior in Korean firms. Although recent advancements in AI are accelerating digital transformation and innovation, empirical research on the adoption of these systems remains scarce. To fill this gap, this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model, surveying 300 employees from both large and small enterprises in South Korea. The findings reveal that effort expectancy and social influence significantly influence employees' behavioral intention to use generative AI systems. Specifically, effort expectancy plays a critical role in the early stages of adoption, while social influence, including support from supervisors and peers, strongly drives the adoption process. In contrast, performance expectancy and facilitating conditions show no significant impact. The study also highlights the differential effects of age and work experience on behavioral intention and usage behavior. For older employees, social support is a key factor in technology acceptance, whereas employees with more experience exhibit a more positive attitude toward adopting new technologies. Conversely, facilitating conditions are more critical for younger employees. This study contributes to the understanding of the interaction between various factors in AI technology adoption and offers strategic insights for the successful implementation of AI systems in Korean companies.

A scoping review of food consumer aspects in transitioning to a safe and just agrifood system

Abstract The agrifood system holds the key to identifying potential transformative pathways to achieve prosperity for all within the limits of the planet's natural resources, thereby fostering a safe and just operating system (SJOS) for future generations. The agrifood system is currently not on the right track to meet this ambition. Food-consumer processes such as preference shifts toward healthy diets and substantial reductions in food losses and waste could help to avoid severe environmental degradation and decrease overall mortality, although it remains unclear whether such transitional developments are entirely compatible with socially responsible thresholds. In this paper, we conduct a scoping review approach to map the evidence on the underlying drivers of such demand-side processes in the context of a SJOS with the aim to provide insights on how to transform the EU agrifood system. This review specifically examines how consumer aspects influence the SJOS, rather than exploring the bidirectional relationship. We used a scoping review approach to select relevant studies. The selected papers were subjected to quantitative and qualitative analyses. As a result, we extract insights and draw lessons from the role of food-consumer processes in the transition toward a more SJOS for the agrifood system.

Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

Control of transient power compensation for virtual impedance VSG in Quasi-Z-source photovoltaic grid integration system

The virtual impedance solution effectively addresses the power coupling issues and improves small-signal stability in grid-connected Virtual Synchronous Generator (VSG) systems but can deteriorate the system's transient synchronous stability, particularly in high-voltage lines with significant line inductance exacerbating this effect. To address these contradictions, the paper first analyzes the power coupling mechanisms of the system, highlighting how the line reactance ratio (R/X) and power angle (δ) affect system stability, and describes the transient stability of VSGs using the equal area criterion. Leveraging the advantages of quasi-Z-source inverters, they are applied to VSG grid-connected systems, proposing an improved VSG control strategy tailored for quasi-Z-source photovoltaic grid-connected systems. This strategy introduces virtual active power at the point of common coupling (PCC) and uses it as power feedback in the control loop, instead of using real active power. The control model is validated on an experimental platform, demonstrating that the VSG control strategy with transient power compensation significantly enhances system transient stability while preserving small-signal stability. This comprehensive approach bridges the gap between maintaining essential stability functions and enhancing dynamic performance under various operational conditions.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.