To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided b...With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.展开更多
We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce t...We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce the concept of K-woven generators for unitary systems,by means of which we investigate the weaving properties of K-frame generators for unitary systems.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
Moist-electric power generation is an emerging energy technology that collects energy from the environment and converts it into electrical energy through the interaction of moisture with materials.Although most of the...Moist-electric power generation is an emerging energy technology that collects energy from the environment and converts it into electrical energy through the interaction of moisture with materials.Although most of the moist-electric generators(MEGs)have achieved continuous breakthroughs in open-circuit voltage(V_(OC))and duration at present,it has been proven to be a challenge to maintain a continuous relatively high short-circuit current(ISC).Herein,electrospun nanofiber-based Janus heterogeneous film with both moisture absorption and moisture evaporation characteristics is prepared,and excellent power output performance MEGs have been fabricated by setting perforated electrode at each side respectively.Results have demonstrated the Janus nanofiber moist-electric generator(JFMEG)can generate a V_(OC)of 0.6 V with a continuous power generation time of up to 30 d and a maximum I_(SC)of about 44µA cm^(−2)at 95%relative humidity.In addition,the I_(SC)maintenance time above 10µA cm^(−2)is close to 40 h The integrated device can power commercial equipment and can be used for self-powered breath detection.Additionally,the self-powered field-effect transistor by JFMEG has been fabricated,demonstrating excellent output characteristics.The detailed working mechanism of JFMEG and the influencing factors of power generation performance are systematically analyzed,which can provide reference for the performance improvement of similar moist-electric devices.展开更多
Moisture can be utilized as a tremendous source of electricity by emerging moisture-electric generator (MEG). The directional moving of water molecules, which can be driven by gradient of functional groups and water e...Moisture can be utilized as a tremendous source of electricity by emerging moisture-electric generator (MEG). The directional moving of water molecules, which can be driven by gradient of functional groups and water evaporation, is vital for the electricity generation. Here, MEG composed of Graphene Oxide (GO-MEG) with gradient channels is constructed by one-step ice-templating technique, achieving a voltage of 0.48 V and a current of ~ 5.64 µA under humid condition. The gradient channels introduce Laplace pressure difference to the absorbed water droplets and electric potential between two side of the GO-MEG, facilitating the charge flow. Output voltage can be easily enhanced by increasing the structural gradient, reducing the channel size, incorporation of chemical gradient, or scaling up the number of GO-MEG units in series. This work not only provides insight for the working mechanism of GO-MEG with structural gradient, which can be applied to other functional materials, but also establishes a convenient and ecofriendly strategy to construct and finely tune the structural gradient in porous materials.展开更多
An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overloa...An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.展开更多
The ocean,as one of Earth’s largest natural resources,covers over 70% of the planet’s surface and holds vast water energy potential.Building on this context,this study designs a hybrid generator(WWR-TENG)that integr...The ocean,as one of Earth’s largest natural resources,covers over 70% of the planet’s surface and holds vast water energy potential.Building on this context,this study designs a hybrid generator(WWR-TENG)that integrates a triboelectric nanogenerator(TENG)and an electromagnetic generator(EMG).TENG is a new technology that can capture mechanical energy from the environment and convert it into electrical energy,and is particularly suitable for common natural or man-made power sources such as human movement,wind power,and water flow.EMG is a device that converts mechanical energy into electrical energy through the principle of electromagnetic induction and can usually provide stable power output.The composite design leverages the complementary advantages of both technologies to efficiently capture and convert marine wave energy.By combining the TENG’s high energy conversion efficiency,lowcost,lightweight structure,and simple designwith the EMG’s capabilities,the systemprovides a sustainable solution for marine energy development.Experimental results demonstrate that at a rotational speed of 3.0 r/s,the TENG component of the WWR-TENG achieves an open-circuit voltage of approximately 280 V and a shortcircuit current of 20μA.At the same time,the EMG unit exhibits an open-circuit voltage of 14 V and a short-circuit current of 14 mA.Furthermore,when integrated with a power management circuit,the WWR-TENG charges a 680μF capacitor to 3 V within 10 s at a rotational speed of 3.0 r/s.A simulated wave environment platform was established,enabling the WWR-TENG to maintain the thermo-hygrometer in normal operation under simulated wave conditions.These findings validate the hybrid system’s effectiveness in harnessing and storingwave energy,highlighting its potential for practical marine energy applications.展开更多
With the rapid development of large-scale regional interconnected power grids,the risk of cascading failures under extreme condi-tions,such as natural disasters and military strikes,has increased significantly.To enha...With the rapid development of large-scale regional interconnected power grids,the risk of cascading failures under extreme condi-tions,such as natural disasters and military strikes,has increased significantly.To enhance the response capability of power systems to extreme events,this study focuses on a method for generator coherency detection.To overcome the shortcomings of the traditional slow coherency method,this paper introduces a novel coherent group identification algorithm based on the theory of nonlinear dynam-ical systems.By analyzing the changing trend of the Euclidean norm of the state variable derivatives in the reduced system,the algorithm can accurately identify the magnitude of the disturbances.Based on the slow coherency methods,the algorithm can correctly recognize coherent generator groups by analyzing system characteristics under varying disturbance magnitudes.This improvement enhances the applicability and accuracy of the coherency detection algorithm under extreme conditions,providing support for emergency control and protection in the power system.Simulations and comparison analyses on IEEE 39-bus system are conducted to validate the accuracy and superiority of the proposed coherent generator group identification method under extreme conditions.展开更多
The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protectio...The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protection schemes.The current amplitude differential protection is an effective means to solve this problem,but the existing criterions rarely consider both sensitivity to high-resistance faults and low requirements for data synchronization.Therefore,the general variation laws of the amplitude difference between the current steady-state components at both terminals and the phase differences between current fault components at both terminals are revealed.For external faults,the steady-state-component current amplitude difference is around zero and the fault-component current phase difference is around 180◦.For internal faults,either the amplitude difference is large or the phase difference is small.Accordingly,a current differential protection scheme based on the pre-fault and postfault steady-state current is proposed.The amplitude and phase of current at both terminals of the protected line are required in the proposed scheme,which has low requirements for data synchronization.The simulation results show that the proposed protection scheme is not affected by the fault type,position,resistance and capacity of the IIDGs.It can also be applied to radial distribution networks with IIDGs.展开更多
With the development of science and technology,the social demand for energy is also increasing.However,the traditional method of energy supply primarily relies on non-renewable resources for energy conversion.While th...With the development of science and technology,the social demand for energy is also increasing.However,the traditional method of energy supply primarily relies on non-renewable resources for energy conversion.While this conventional approach can expedite the energy conversion process,it also results in irreversible ecological hazards.To solve the above problems,the use of renewable clean energy is proposed.In this paper,a droplet generator is proposed to integrate the rotating structure with the body effect power generation for the tiny energy of raindrops.This droplet generator can increase the speed of droplets leaving the dielectric layer and reduce the effect of continuously falling droplets on the droplet-based electricity generator(DEG).It is demonstrated that the instantaneous power of the generator can reach 0.9 mW,which can be a good solution to the power supply needs of some small power supply equipment,and thereafter is beneficial to the self-powering of the equipment in rainy days.展开更多
We employed random distributions and gradient descent methods for the Generator Coordinate Method(GCM)to identify effective basis wave functions,taking halo nuclei ^(6)He and ^(6)Li as examples.By comparing the ground...We employed random distributions and gradient descent methods for the Generator Coordinate Method(GCM)to identify effective basis wave functions,taking halo nuclei ^(6)He and ^(6)Li as examples.By comparing the ground state(0^(+))energy of ^(6)He and the excited state(0^(+))energy of 6 Li calculated with various random distributions and manually selected generation coordinates,we found that the heavy tail characteristic of the logistic distribution better describes the features of the halo nuclei.Subsequently,the Adam algorithm from machine learning was applied to optimize the basis wave functions,indicating that a limited number of basis wave functions can approximate the converged values.These results offer some empirical insights for selecting basis wave functions and contribute to the broader application of machine learning methods in predicting effective basis wave functions.展开更多
Organic thermoelectric generators(TEGs)are flexible and lightweight,but they often have high electrical resistance,poor output power,and low mechanical durability,because of which their thermoelectric performance is p...Organic thermoelectric generators(TEGs)are flexible and lightweight,but they often have high electrical resistance,poor output power,and low mechanical durability,because of which their thermoelectric performance is poor.We used a facile and rapid solvent evaporation process to prepare a robust carbon nanotube/Bi0.45Sb1.55Te3(CNT/BST)foam with a high thermoelectric figure of merit(zT).The BST sub-micronparticles effectively create an electrically conductive network within the three-dimensional porous CNT foam to greatly improve the electrical conductivity and the Seebeck coefficient and reinforce the mechanical strength of the composite against applied stresses.The CNT/BST foam had a zT value of 7.8×10^(−3)at 300 K,which was 5.7 times higher than that of pristine CNT foam.We used the CNT/BST foam to fabricate a flexible TEG with an internal resistance of 12.3Ωand an output power of 15.7μW at a temperature difference of 21.8 K.The flexible TEG showed excellent stability and durability even after 10,000 bending cycles.Finally,we demonstrate the shapeability of the CNT/BST foam by fabricating a concave TEG with conformal contact on the surface of a cylindrical glass tube,which suggests its practical applicability as a thermal sensor.展开更多
In intricate aquatic environments, enhancing the sensory performance of underwater actuators to ensure successful task execution is a significant challenge. To address this, a biomimetic tactile multimodal sensing app...In intricate aquatic environments, enhancing the sensory performance of underwater actuators to ensure successful task execution is a significant challenge. To address this, a biomimetic tactile multimodal sensing approach is introduced in this study, based on TriboElectric NanoGenerator (TENG) and Micro-ThermoElectric Generator (MTEG). This method enables actuators to identify the material properties of underwater target objects and to sense grasping states, such as pressure and relative sliding. In this study, a multi-dimensional underwater bionic tactile perception theoretical model is established, and a bionic sensing prototype with a sandwich-type structure is designed. To verify the performance of pressure feedback and material perception, pertinent experiments are conducted. The experimental results reveal that within a pressure measurement range of 0–16 N, the detection error of the sensor is 1.81%, and the maximum pressure response accuracy achieves 2.672 V/N. The sensing response time of the sensor is 0.981 s. The recovery time of the sensor is 0.97 s. Furthermore, the exceptional fatigue resistance of the sensor is also demonstrated. Based on the frequency of the output voltage from the prototype, the sliding state of the target object relative to the actuator can be sensed. In terms of material identification, the temperature response accuracy of the sensor is 0.072 V/°C. With the assistance of machine learning methods, six characteristic materials are identified by the sensor under 7 N pressure, with a recognition accuracy of 92.4%. In complex marine environments, this method has great application potential in the field of underwater tactile perception.展开更多
The power-electronics-based DC microgrid system composed of new energy sources in railway field has low inertia,weak damping characteristics,and the voltage fluctuation microgrid systems caused by the power disturbanc...The power-electronics-based DC microgrid system composed of new energy sources in railway field has low inertia,weak damping characteristics,and the voltage fluctuation microgrid systems caused by the power disturbance of solar.In order to improve the inertia of the DC microgrid system,a virtual DC generator technology is adopted in the interface converter of photovoltaic(PV)power generation unit,so that it has the external characteristics of DC generator.However,the influence of PV maximum power point tracking(MPPT)is not considered in the traditional virtual DC generator control.Therefore,an improved control strategy for virtual DC generator is proposed,and its small signal model is established to analyze the influence of inertia and damping coefficient on stability.The results show that the proposed method effectively weakens the impact on DC bus voltage when the output of PV power unit changes suddenly,which improves the stability of the microgrid.Meanwhile,the correctness and feasibility of the method are verified.展开更多
This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an ...This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.展开更多
To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy stora...To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金financially supported by the Talent Initiation Fund of Wuxi University(550220008).
文摘With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.
基金Supported by NSFC(Nos.12361028,11761057)Science Foundation of Jiangxi Education Department(Nos.GJJ202302,GJJ202303,GJJ202319).
文摘We give a new result on the construction of K-frame generators for unitary systems by using the pseudo-inverses of involved operators,which provides an improvement to one known result on this topic.We also introduce the concept of K-woven generators for unitary systems,by means of which we investigate the weaving properties of K-frame generators for unitary systems.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金financially supported by the National Natural Science Foundation of China(Nos.11774001 and 52202156)the Scientific Research Project of Colleges and Universities in Anhui Province(No.2022AH050113)+2 种基金the University Synergy Innovation Program of Anhui Province(No.GXXT-2022-012)the China Postdoctoral Science Foundation(No.2024M760010)the Postdoctoral Daily Public Start-up Funds of Anhui University(No.S202418001/069).
文摘Moist-electric power generation is an emerging energy technology that collects energy from the environment and converts it into electrical energy through the interaction of moisture with materials.Although most of the moist-electric generators(MEGs)have achieved continuous breakthroughs in open-circuit voltage(V_(OC))and duration at present,it has been proven to be a challenge to maintain a continuous relatively high short-circuit current(ISC).Herein,electrospun nanofiber-based Janus heterogeneous film with both moisture absorption and moisture evaporation characteristics is prepared,and excellent power output performance MEGs have been fabricated by setting perforated electrode at each side respectively.Results have demonstrated the Janus nanofiber moist-electric generator(JFMEG)can generate a V_(OC)of 0.6 V with a continuous power generation time of up to 30 d and a maximum I_(SC)of about 44µA cm^(−2)at 95%relative humidity.In addition,the I_(SC)maintenance time above 10µA cm^(−2)is close to 40 h The integrated device can power commercial equipment and can be used for self-powered breath detection.Additionally,the self-powered field-effect transistor by JFMEG has been fabricated,demonstrating excellent output characteristics.The detailed working mechanism of JFMEG and the influencing factors of power generation performance are systematically analyzed,which can provide reference for the performance improvement of similar moist-electric devices.
基金supported by National Natural Science Foundation of China(52373119,52105296,62161160311)National Key R&D Program of China(2022YFB4701000)Open Fund of Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration(Wuhan University)(EMPI2023020).
文摘Moisture can be utilized as a tremendous source of electricity by emerging moisture-electric generator (MEG). The directional moving of water molecules, which can be driven by gradient of functional groups and water evaporation, is vital for the electricity generation. Here, MEG composed of Graphene Oxide (GO-MEG) with gradient channels is constructed by one-step ice-templating technique, achieving a voltage of 0.48 V and a current of ~ 5.64 µA under humid condition. The gradient channels introduce Laplace pressure difference to the absorbed water droplets and electric potential between two side of the GO-MEG, facilitating the charge flow. Output voltage can be easily enhanced by increasing the structural gradient, reducing the channel size, incorporation of chemical gradient, or scaling up the number of GO-MEG units in series. This work not only provides insight for the working mechanism of GO-MEG with structural gradient, which can be applied to other functional materials, but also establishes a convenient and ecofriendly strategy to construct and finely tune the structural gradient in porous materials.
基金supported in part by National Natural Science Foundation of China under Grant 52125705in part by National Natural Science Foundation of China under Grant 52107194.
文摘An islanded microgrid exhibits poor transient power sharing between synchronous generators(SGs)and inverterinterfaced distributed generators(IIDGs).This large error of transient power-sharing may result in the overload of generators and a large deviation in frequency.In this paper,the mechanism that leads to poor transient power sharing is revealed.Then,a parameter design and a coordinated control strategy are proposed to improve transient power sharing.A coordinated enhanced power-sharing(EPS)control strategy is proposed for IIDGs,which prevents the overload of IIDGs in grid-forming mode and is compatible with the existing power sharing strategies.By using a hierarchical control structure,accurate transient power sharing is achieved without the knowledge of connecting impedance.The analysis results and the proposed control method are validated by simulation.
文摘The ocean,as one of Earth’s largest natural resources,covers over 70% of the planet’s surface and holds vast water energy potential.Building on this context,this study designs a hybrid generator(WWR-TENG)that integrates a triboelectric nanogenerator(TENG)and an electromagnetic generator(EMG).TENG is a new technology that can capture mechanical energy from the environment and convert it into electrical energy,and is particularly suitable for common natural or man-made power sources such as human movement,wind power,and water flow.EMG is a device that converts mechanical energy into electrical energy through the principle of electromagnetic induction and can usually provide stable power output.The composite design leverages the complementary advantages of both technologies to efficiently capture and convert marine wave energy.By combining the TENG’s high energy conversion efficiency,lowcost,lightweight structure,and simple designwith the EMG’s capabilities,the systemprovides a sustainable solution for marine energy development.Experimental results demonstrate that at a rotational speed of 3.0 r/s,the TENG component of the WWR-TENG achieves an open-circuit voltage of approximately 280 V and a shortcircuit current of 20μA.At the same time,the EMG unit exhibits an open-circuit voltage of 14 V and a short-circuit current of 14 mA.Furthermore,when integrated with a power management circuit,the WWR-TENG charges a 680μF capacitor to 3 V within 10 s at a rotational speed of 3.0 r/s.A simulated wave environment platform was established,enabling the WWR-TENG to maintain the thermo-hygrometer in normal operation under simulated wave conditions.These findings validate the hybrid system’s effectiveness in harnessing and storingwave energy,highlighting its potential for practical marine energy applications.
基金supported by National Natural Science Foundation of China(Grant No:52477133)Science and Technology Project of China Southern Power Grid(Grant No.GDKJXM20231178(036100KC23110012)+1 种基金GDKJXM20240389(030000KC24040053))Sanya Yazhou Bay Science and Technology City(Grant No:SKJC-JYRC-2024-66).
文摘With the rapid development of large-scale regional interconnected power grids,the risk of cascading failures under extreme condi-tions,such as natural disasters and military strikes,has increased significantly.To enhance the response capability of power systems to extreme events,this study focuses on a method for generator coherency detection.To overcome the shortcomings of the traditional slow coherency method,this paper introduces a novel coherent group identification algorithm based on the theory of nonlinear dynam-ical systems.By analyzing the changing trend of the Euclidean norm of the state variable derivatives in the reduced system,the algorithm can accurately identify the magnitude of the disturbances.Based on the slow coherency methods,the algorithm can correctly recognize coherent generator groups by analyzing system characteristics under varying disturbance magnitudes.This improvement enhances the applicability and accuracy of the coherency detection algorithm under extreme conditions,providing support for emergency control and protection in the power system.Simulations and comparison analyses on IEEE 39-bus system are conducted to validate the accuracy and superiority of the proposed coherent generator group identification method under extreme conditions.
基金supported in part by Science and Technology Project of State Grid Corporation of China:Research on Key Protection Technologies for New-type Urban Distribution Network with Controllable Sources and Loads.
文摘The petal-shaped distribution network has high power supply reliability.However,the closed-loop operation mode and the access of inverter-interfaced distributed generators(IIDGs)bring great challenges to the protection schemes.The current amplitude differential protection is an effective means to solve this problem,but the existing criterions rarely consider both sensitivity to high-resistance faults and low requirements for data synchronization.Therefore,the general variation laws of the amplitude difference between the current steady-state components at both terminals and the phase differences between current fault components at both terminals are revealed.For external faults,the steady-state-component current amplitude difference is around zero and the fault-component current phase difference is around 180◦.For internal faults,either the amplitude difference is large or the phase difference is small.Accordingly,a current differential protection scheme based on the pre-fault and postfault steady-state current is proposed.The amplitude and phase of current at both terminals of the protected line are required in the proposed scheme,which has low requirements for data synchronization.The simulation results show that the proposed protection scheme is not affected by the fault type,position,resistance and capacity of the IIDGs.It can also be applied to radial distribution networks with IIDGs.
文摘With the development of science and technology,the social demand for energy is also increasing.However,the traditional method of energy supply primarily relies on non-renewable resources for energy conversion.While this conventional approach can expedite the energy conversion process,it also results in irreversible ecological hazards.To solve the above problems,the use of renewable clean energy is proposed.In this paper,a droplet generator is proposed to integrate the rotating structure with the body effect power generation for the tiny energy of raindrops.This droplet generator can increase the speed of droplets leaving the dielectric layer and reduce the effect of continuously falling droplets on the droplet-based electricity generator(DEG).It is demonstrated that the instantaneous power of the generator can reach 0.9 mW,which can be a good solution to the power supply needs of some small power supply equipment,and thereafter is beneficial to the self-powering of the equipment in rainy days.
基金supported by the National Key R&D Program of China(No.2023YFA1606701)the National Natural Science Foundation of China(Nos.12175042,11890710,11890714,12047514,12147101,and 12347106)+1 种基金Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)China National Key R&D Program(No.2022YFA1602402).
文摘We employed random distributions and gradient descent methods for the Generator Coordinate Method(GCM)to identify effective basis wave functions,taking halo nuclei ^(6)He and ^(6)Li as examples.By comparing the ground state(0^(+))energy of ^(6)He and the excited state(0^(+))energy of 6 Li calculated with various random distributions and manually selected generation coordinates,we found that the heavy tail characteristic of the logistic distribution better describes the features of the halo nuclei.Subsequently,the Adam algorithm from machine learning was applied to optimize the basis wave functions,indicating that a limited number of basis wave functions can approximate the converged values.These results offer some empirical insights for selecting basis wave functions and contribute to the broader application of machine learning methods in predicting effective basis wave functions.
文摘Organic thermoelectric generators(TEGs)are flexible and lightweight,but they often have high electrical resistance,poor output power,and low mechanical durability,because of which their thermoelectric performance is poor.We used a facile and rapid solvent evaporation process to prepare a robust carbon nanotube/Bi0.45Sb1.55Te3(CNT/BST)foam with a high thermoelectric figure of merit(zT).The BST sub-micronparticles effectively create an electrically conductive network within the three-dimensional porous CNT foam to greatly improve the electrical conductivity and the Seebeck coefficient and reinforce the mechanical strength of the composite against applied stresses.The CNT/BST foam had a zT value of 7.8×10^(−3)at 300 K,which was 5.7 times higher than that of pristine CNT foam.We used the CNT/BST foam to fabricate a flexible TEG with an internal resistance of 12.3Ωand an output power of 15.7μW at a temperature difference of 21.8 K.The flexible TEG showed excellent stability and durability even after 10,000 bending cycles.Finally,we demonstrate the shapeability of the CNT/BST foam by fabricating a concave TEG with conformal contact on the surface of a cylindrical glass tube,which suggests its practical applicability as a thermal sensor.
基金supported by the National Natural Science Foundation of China(62372077,61976124)supported by the Fundamental Research Funds for the National Key R&D Project from the Minister of Science and Technology(2021YFA1201604).
文摘In intricate aquatic environments, enhancing the sensory performance of underwater actuators to ensure successful task execution is a significant challenge. To address this, a biomimetic tactile multimodal sensing approach is introduced in this study, based on TriboElectric NanoGenerator (TENG) and Micro-ThermoElectric Generator (MTEG). This method enables actuators to identify the material properties of underwater target objects and to sense grasping states, such as pressure and relative sliding. In this study, a multi-dimensional underwater bionic tactile perception theoretical model is established, and a bionic sensing prototype with a sandwich-type structure is designed. To verify the performance of pressure feedback and material perception, pertinent experiments are conducted. The experimental results reveal that within a pressure measurement range of 0–16 N, the detection error of the sensor is 1.81%, and the maximum pressure response accuracy achieves 2.672 V/N. The sensing response time of the sensor is 0.981 s. The recovery time of the sensor is 0.97 s. Furthermore, the exceptional fatigue resistance of the sensor is also demonstrated. Based on the frequency of the output voltage from the prototype, the sliding state of the target object relative to the actuator can be sensed. In terms of material identification, the temperature response accuracy of the sensor is 0.072 V/°C. With the assistance of machine learning methods, six characteristic materials are identified by the sensor under 7 N pressure, with a recognition accuracy of 92.4%. In complex marine environments, this method has great application potential in the field of underwater tactile perception.
基金supported by National Natural Science Foundation of China(No.52067013)Natural Science Foundation of Gansu Province(No.20JR5RA395)Tianyou Innovation Team of Lanzhou Jiaotong University(No.TY202010).
文摘The power-electronics-based DC microgrid system composed of new energy sources in railway field has low inertia,weak damping characteristics,and the voltage fluctuation microgrid systems caused by the power disturbance of solar.In order to improve the inertia of the DC microgrid system,a virtual DC generator technology is adopted in the interface converter of photovoltaic(PV)power generation unit,so that it has the external characteristics of DC generator.However,the influence of PV maximum power point tracking(MPPT)is not considered in the traditional virtual DC generator control.Therefore,an improved control strategy for virtual DC generator is proposed,and its small signal model is established to analyze the influence of inertia and damping coefficient on stability.The results show that the proposed method effectively weakens the impact on DC bus voltage when the output of PV power unit changes suddenly,which improves the stability of the microgrid.Meanwhile,the correctness and feasibility of the method are verified.
文摘This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.
基金This study was supported by State Grid Corporation headquarters technology project(4000-202399368A-2-2-ZB).
文摘To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
