Mechanical reliable,NIR light-induced rapid self-healing hydrogel electrolyte towards flexible zinc-ion hybrid supercapacitors with low-temperature adaptability and long service life

Hydrogel electrolytes hold great potential in flexible zinc ion supercapacitors(ZICs)due to their high conductivity,good safety,and flexibility.However,freezing of electrolytes at low temperature(subzero)leads to drastic reduction in ionic conductivity and mechanical properties that deteriorates the performance of flexible ZICs.Besides,the mechanical fracture during arbitrary deformations significantly prunes out the lifespan of the flexible device.Herein,a Zn^(2+)and Li^(+)co-doped,polypyrrole-dopamine decorated Sb_(2)S_(3)incorporated,and polyvinyl alcohol/poly(N-(2-hydroxyethyl)acrylamide)double-network hydrogel electrolyte is constructed with favorable mechanical reliability,anti-freezing,and self-healing ability.In addition,it delivers ultra-high ionic conductivity of 8.6 and 3.7 S m^(-1)at 20 and−30°C,respectively,and displays excellent mechanical properties to withstand tensile stress of 1.85 MPa with tensile elongation of 760%,together with fracture energy of 5.14 MJ m^(-3).Notably,the fractured hydrogel electrolyte can recover itself after only 90 s of infrared illumination,while regaining 83%of its tensile strain and almost 100%of its ionic conductivity during−30–60°C.Moreover,ZICs coupled with this hydrogel electrolyte not only show a wide voltage window(up to 2 V),but also provide high energy density of 230 Wh kg^(-1)at power density of 500 W kg^(-1)with a capacity retention of 86.7%after 20,000 cycles under 20°C.Furthermore,the ZICs are able to retain excellent capacity even under various mechanical deformation at−30°C.This contribution will open up new insights into design of advanced wearable flexible electronics with environmental adaptability and long-life span.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Mechanically skin-like and water-resistant self-healing bioelastomer for high-tension wound healing

The biomedical application of self-healing materials in wet or(under)water environments is quite challenging because the insulation and dissociation effects of water molecules significantly reduce the reconstruction of material–interface interactions.Rapid closure with uniform tension of high-tension wounds is often difficult,leading to further deterioration and scarring.Herein,a new type of thermosetting water-resistant self-healing bioelastomer(WRSHE)was designed by synergistically incorporating a stable polyglycerol sebacate(PGS)covalent crosslinking network and triple hybrid dynamic networks consisting of reversible disulfide metathesis(SS),and dimethylglyoxime urethane(Dou)and hydrogen bonds.And a resveratrol-loaded WRSHE(Res@WRSHE)was developed by a swelling,absorption,and crosslinked network locking strategy.WRSHEs exhibited skin-like mechanical properties in terms of nonlinear modulus behavior,biomimetic softness,high stretchability,and good elasticity,and they also achieved ultrafast and highly efficient self-healing in various liquid environments.For wound-healing applications of high-tension full-thickness skin defects,the convenient surface assembly by self-healing of WRSHEs provides uniform contraction stress to facilitate tight closure.Moreover,Res@WRSHEs gradually release resveratrol,which helps inflammatory response reduction,promotes blood vessel regeneration,and accelerates wound repair.

A Self-Healing Optoacoustic Patch with High Damage Threshold and Conversion Efficiency for Biomedical Applications

Compared with traditional piezoelectric ultrasonic devices,optoacoustic devices have unique advantages such as a simple preparation process,anti-electromagnetic interference,and wireless long-distance power supply.However,current optoacoustic devices remain limited due to a low damage threshold and energy conversion efficiency,which seriously hinder their widespread applications.In this study,using a self-healing polydimethylsiloxane(PDMS,Fe-Hpdca-PDMS)and carbon nanotube composite,a flexible optoacoustic patch is developed,which possesses the self-healing capability at room temperature,and can even recover from damage induced by cutting or laser irradiation.Moreover,this patch can generate high-intensity ultrasound(>25 MPa)without the focusing structure.The laser damage threshold is greater than 183.44 mJ cm^(-2),and the optoacoustic energy conversion efficiency reaches a major achievement at 10.66×10^(-3),compared with other carbon-based nanomaterials and PDMS composites.This patch is also been successfully examined in the application of acoustic flow,thrombolysis,and wireless energy harvesting.All findings in this study provides new insight into designing and fabricating of novel ultrasound devices for biomedical applications.

Multifunctional phase change film with high recyclability, adjustable thermal responsiveness, and intrinsic self-healing ability for thermal energy storage

Phase change materials(PCMs) present promising potential for guaranteeing safety in thermal management systems.However,most reported PCMs have a single application in energy storage for thermal management systems,which does not meet the growing demand for multi-functional materials.In this paper,the flexible material and hydrogen-bonding function are innovatively combined to design and prepare a novel multi-functional flexible phase change film(PPL).The 0.2PPL-2 film exhibits solid-solid phase change behavior with energy storage density of 131.8 J/g at the transition temperature of42.1℃,thermal cycling stability(500 cycles),wide-temperature range flexibility(0-60℃) and selfhealing property.Notably,the PPL film can be recycled up to 98.5% by intrinsic remodeling.Moreover,the PPL film can be tailored to the desired colors and configurations and can be cleverly assembled on several thermal management systems at ambient temperature through its flexibility combined with shape-memory properties.More interestingly,the transmittance of PPL will be altered when the ambient temperature changes(60℃),conveying a clear thermal signal.Finally,the thermal energy storage performance of the PPL film is successfully tested by human thermotherapy and electronic device temperature control experiments.The proposed functional integration strategy provides innovative ideas to design PCMs for multifunctionality,and makes significant contributions in green chemistry,highefficiency thermal management,and energy sustainability.

Flexible,thermal processable,self-healing,and fully bio-based starch plastics by constructing dynamic imine network

The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.

Shear resistance of assembled bentonite interface after confined water saturation and interfacial self-healing capacity

The requisite functions of a bentonite buffer in a deep geological repository depend on the sealing/healing of bentonite interfaces,with particular emphasis on the self-healing(automatic healing upon wetting)of assembled bentonite-bentonite interfaces.This study determined the shear resistance(including the peak shear strength and secant modulus)of densely compacted Gaomiaozi(GMZ)bentonite and its assembled interface after confined water saturation.The effect of bentonite dry density and saturation time on the shear resistance of saturated healed interfaces was elucidated,and the interfacial self-healing capacity was assessed.The results indicate that the shear resistance of the saturated healed interfaces increased with the bentonite dry density but had a non-monotonic correlation with the saturation time.For a given dry density of the bentonite,the saturated healed interface exhibits a lower peak shear strength than the saturated intact bentonite but a higher peak shear strength than the saturated separated interface.The saturated healed and separated interfaces have comparable shear moduli(secant moduli),which are lower than that of the saturated intact bentonite.The saturated healed interfaces display smooth shear failure planes,while the saturated assembled interfaces and intact bentonite exhibit comparable frictional angles.This indicates that interfacial self-healing plays a pivotal role in enhancing interfacial peak shear strength by facilitating microstructural bonding at the assembled interface.Finally,it can be stated that densely compacted GMZ bentonite has a robust interfacial self-healing capacity in terms of shear resistance.These findings contribute to the design of the bentonite buffer and facilitate the evaluation of its safe operation at specified disposal ages.

High performance photodegradation resistant PVA@TiO_(2)/carboxyl-PES self-healing reactive ultrafiltration membrane

The occurrence of ultrafiltration(UF)membrane fouling frequently hampers the sustainable advancement of UF technology.Reactive self-cleaning UF membranes can effectively alleviate the problem of membrane fouling.Nevertheless,the self-cleaning process may accelerate membrane aging.Addressing these concerns,we present an innovative design concept for composite self-healing materials based on self-cleaning UF membranes.To begin,TiO_(2)nanoparticles were incorporated into the polymer molecular structure via molecular design,resulting in the synthesis of TiO_(2)/carboxyl-polyether sulfone(PES)hybrid materials.Subsequently,the nonsolvent-induced phase inversion technique was employed to prepare a novel of UF membrane.Lastly,a polyvinyl alcohol(PVA)hydrogel coating was applied to the hybrid UF membrane surface to create PVA@TiO_(2)/carboxyl-PES self-healing reactive UF membranes.By establishing a covalent bond,the TiO_(2)nanoparticles were effectively and uniformly dispersed within the UF membrane,leading to exceptional self-cleaning properties.Furthermore,the water-absorbing and swelling properties of PVA hydrogel,along with its capacity to form hydrogen bonds with water molecules,resulted in UF membranes with improved hydrophilicity and active self-healing abilities.The results demonstrated that the water contact angle of PVA@5%TiO_(2)/carboxyl-PES UF membrane was 43.1°.Following a 1-h exposure to simulated solar exposure,the water flux recovery ratio increased from 48.16%to 81.03%.Moreover,even after undergoing five cycles of 12-h simulated sunlight exposure,the UF membranes exhibited a consistent retention rate of over 97%,thus fully demonstrating their exceptional self-cleaning,antifouling,and selfhealing capabilities.We anticipate that the self-healing reactive UF membrane system will serve as a pioneering and comprehensive solution for the self-cleaning antifouling challenges encountered in UF membranes while also effectively mitigating the aging effects of reactive UF membranes.

An intrinsically self-healing and anti-freezing molecular chains induced polyacrylamide-based hydrogel electrolytes for zinc manganese dioxide batteries

The anti-freezing strategy of hydrogels and their self-healing structure are often contradictory,it is vital to break through the molecular structure to design and construct hydrogels with intrinsic anti-freezing/self-healing for meeting the rapid development of flexible and wearable devices in diverse service conditions.Herein,we design a new hydrogel electrolyte(AF/SH-Hydrogel)with intrinsic anti-freezing/self-healing capabilities by introducing ethylene glycol molecules,dynamic chemical bonding(disulfide bond),and supramolecular interaction(multi-hydrogen bond)into the polyacrylamide molecular chain.Thanks to the exceptional freeze resistance(84%capacity retention at-20℃)and intrinsic self-healing capabilities(95%capacity retention after 5 cutting/self-healing cycles),the obtained AF/SH-Hydrogel makes the zinc||manganese dioxide cell an economically feasible battery for the state-of-the-art applications.The Zn||AF/SH-Hydrogel||MnO_(2)device offers a near-theoretical specific capacity of 285 m A h g^(-1)at 0.1 A g^(-1)(Coulombic efficiency≈100%),as well as good self-healing capability and mechanical flexibility in an ice bath.This work provides insight that can be utilized to develop multifunctional hydrogel electrolytes for application in next generation of self-healable and freeze-resistance smart aqueous energy storage devices.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Effect of drying cracks on swelling and self-healing of bentonite-sand blocks used as engineered barriers for radioactive waste disposal

Experiments were conducted to evaluate the healing of drying cracks in air-dried bentonite-sand blocks after hydration and swelling in groundwater,providing justifications to simplify the protection of blocks prior to installation in a high-level radioactive waste repository.Synthetic groundwater was prepared to represent the geochemistry of Beishan groundwater,and was used to hydrate the blocks during the swelling pressure and swelling strain measurements,as Beishan is the most promising site for China's repository.Healing of the surface cracks was recorded by photography,and healing of the internal cracks was visualized by CT images and hydraulic conductivity of air-dried blocks.The results indicate that the maximum swelling pressure and swelling strain are primarily affected by the geochemistry of Beishan groundwater,but not affected by the drying cracks.The maximum swelling pressure and swelling strain of air-dried blocks are comparable to or even higher than the pressure and strain of fresh blocks.The maximum swelling pressure measured in strong(i.e.high ion strength)Beishan groundwater was 44%of the pressure measured in deionized(DI)water,and the maximum swelling strain was reduced to 23%of the strain measured in DI water.Nevertheless,the remained swelling of the blocks hydrated in strong Beishan groundwater was sufficient to heal the surface and internal drying cracks,as demonstrated by the pictures of surface cracks and CT images.The hydraulic conductivity of the air-dried block permeated with strong groundwater was comparable(3.7×higher)to the hydraulic conductivity of the fresh block,indicating the self-healing of drying cracks after hydration and swelling in groundwater.A simplified method of protecting the block with plastic wraps before installation is recommended,since the remained swelling of the block hydrated in Beishan groundwater is sufficient to heal the drying cracks.

Distributed fiber optic sensors for tunnel monitoring:A state-of-the-art review

Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

A Distributed Photovoltaics Ordering Grid-Connected Method for Analyzing Voltage Impact in Radial Distribution Networks

In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.

Distributed Dynamic Load in Structural Dynamics by the Impulse-Based Force Estimation Algorithm

This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force,thereby achieving dimensionality reduction.The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain.Firstly,the algorithm establishes a recursion scheme based on convolution integral,enabling it to identify loads with a long history and rapidly changing forms over time.Secondly,the algorithm introduces moving mean and polynomial fitting to detrend,enhancing its applicability in load estimation.The aforementioned methodology successfully accomplishes the reconstruction of distributed,instead of centralized,dynamic loads on the continuum in the time domain by utilizing acceleration response.To validate the effectiveness of the method,computational and experimental verification were conducted.