Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hyb...To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.展开更多
In order to enhance the off-peak performance of gas turbine combined cycle(GTCC)units,a novel collaborative power generation system(CPG)was proposed.During off-peak operation periods,the remaining power of the GTCC wa...In order to enhance the off-peak performance of gas turbine combined cycle(GTCC)units,a novel collaborative power generation system(CPG)was proposed.During off-peak operation periods,the remaining power of the GTCC was used to drive the adiabatic compressed air energy storage(ACAES),while the intake air of the GTCC was heated by the compression heat of theACAES.Based on a 67.3MW GTCC,under specific demand load distribution,a CPG system and a benchmark system(BS)were designed,both of which used 9.388% of the GTCC output power to drive the ACAES.The performance of the CPG and the BS without intake air heating was compared.The results show that the load rate of the GTCC in the CPG system during off-peak periods is significantly enhanced,and the average operating efficiency of the GTCC is increased by 1.19 percentage points.However,in the BS system,due to the single collaborativemethod of load shifting,the GTCC operative efficiency is almost increased by 1.00 percentage points under different ambient temperatures.In a roundtrip cycle at an ambient temperature of 288.15K,the systemefficiency of the CPG reaches 0.5010,which is 0.62 percentage points higher than the operative efficiency of 0.4948 in the standalone GTCC;while the system efficiency of the BS is slightly inferior to that of the standalone GTCC.The findings confirm the technical feasibility and performance improvement of the ACAES-GTCC collaborative power generation system.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact vario...The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact various aspects of the system,including renewable-energy resource potential,power-system planning and operation,and electricity markets.The Intergovernmental Panel on Climate Change(IPCC)has pointed out that as climate change accelerates,extreme weather events will continue to become more frequent and severe.展开更多
In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)d...In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)during disturbances.Moreover,due to the frequency decoupling between the two ends of the MMCHVDC,the sending-end wind farm(SEWF)cannot obtain the frequency variation information of the REG to provide inertia response.Therefore,this paper proposes a novel coordinated source-network-storage inertia control strategy based on wind power transmission via MMC-HVDC system.First,the grid-side MMC station(GS-MMC)maps the frequency variations of the REG to direct current(DC)voltage variations through the frequency mapping control,and uses submodule capacitor energy to provide inertial power.Then,the wind farm-side MMC station(WF-MMC)restores the DC voltage variations to frequency variations through the frequency restoration control and power loss compensation,providing real-time frequency information for the wind farm.Finally,based on real-time frequency information,thewind farmutilizes the rotor kinetic energy and energy storage to provide fast and lasting power support through the wind-storage coordinated inertia control strategy.Meanwhile,when the wind turbines withdraw from the inertia response phase,the energy storage can increase the power output to compensate for the power deficit,preventing secondary frequency drops.Furthermore,this paper uses small-signal analysis to determine the appropriate values for the key parameters of the proposed control strategy.A simulation model of the wind power transmission via MMCHVDC system is built in MATLAB/Simulink environment to validate and evaluate the proposed method.The results show that the proposed coordinated control strategy can effectively improve the system inertia level and avoid the secondary frequency drop under the load sudden increase condition.展开更多
Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged desi...Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.展开更多
The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,...The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,photovoltaic power generation is unstable and random,resulting in a low utilization rate and directly affecting the stability of the power grid.To solve this problem,this paper proposes a coordinated control strategy for a newenergy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit.Firstly,the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal,which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system.Secondly,the fuzzy control algorithm is introduced to balance the power between energy storage.In this paper,the actual data is used for simulation,and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit,effectively avoiding problems such as overshoot and over-discharge,and can significantly improve the stability of the photovoltaic power generation systemand the stability of the Direct Current bus.It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.展开更多
Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sen...Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.展开更多
Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationship...Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.展开更多
New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed s...New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power ...Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources.Utilizing the Coupled Model Intercomparison Project Phase 6 datasets,a predictive model for low-output wind power was employed to investigate regional trends worldwide.The frequency and duration of low-output wind-power events exhibited increasing trends globally,particularly in East Asia and South America,but not in North America.By 2060,the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d,and the maximum continuous duration of low-output wind power could reach 5 and 2 d,respectively.As wind power becomes a primary elec-tricity source,such low output could lead to shortages in energy supply within the power system,trig-gering large-scale power outages.This issue calls for critical attention when establishing power systems with a high share of renewable energy sources.The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.展开更多
The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-va...The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-varying characteristics of the node voltage during dynamic processes.As a result,traditional calcu-lation methods for and characteristics of the power response of the network based on phasor and impe-dance lose their validity.Therefore,this paper undertakes mathematical calculations to reveal the power response of a network under excitation by voltage with time-varying amplitude and frequency(TVAF),relying on the original mathematical relationships and superimposed step response.Then,the multi-timescale characteristics of both the active and reactive power of the network are explored physically.Additionally,this paper reveals a new phenomenon of storing and releasing the active and reactive power of the network.To meet practical engineering requirements,a simplified power expression is presented.Finally,the theoretical analysis is validated through time-domain simulations.展开更多
Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy...Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy storage.In this paper,we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy.The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation,denoted as the frequency-fixed zone.With the proposed strategy,the system frequency is almost always fixed at its rated value,achieving system active power balance independent of frequency,and intentional power flow adjustments are implemented through direct phase angle control.This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations.Furthermore,synchronization dynamics are significantly diminished,and synchronization stability is enhanced.The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.展开更多
Photovoltaic(PV)systems are being increasingly implemented in the grid,and their intermittent output fluctuations threaten the stability of the grid,thereby requiring effective power ramp control(PRRC)strategies.In th...Photovoltaic(PV)systems are being increasingly implemented in the grid,and their intermittent output fluctuations threaten the stability of the grid,thereby requiring effective power ramp control(PRRC)strategies.In this study,we proposed a power fluctuation identification method to optimize the PRRC strategy.The K-means++cluster based on DTW used in this method,which clusters the historical PV power generation data into power curves corresponding to a specific weather type(sunny,cloudy,and rainy)in a time zone.Subsequently,wavelet decomposition is applied to discretize the power curves with extreme RR overrun to accurately identify the extreme fluctuation time zones.We conducted an analysis using minute-level data from a 100 kW PV plant in Arizona,which demonstrates that the proposed method can effectively identify high-risk periods.Weather patterns within the time zones were quantitatively identified using a weather probability model.A hardware-in-the-loop experimental platform was employed to validate two days of actual power data in Arizona,demonstrating the weather zoning accuracy of the method and the reasonableness of the control.The proposed methodology contributes significantly to PRRC strategy selection and parameter optimization(e.g.,ESS capacity storage allocation and APC power reserveΔP)in different time zones and weather conditions.展开更多
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by Science and Technology Project of the headquarters of the State Grid Corporation of China(No.5500-202324492A-3-2-ZN).
文摘To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.
文摘In order to enhance the off-peak performance of gas turbine combined cycle(GTCC)units,a novel collaborative power generation system(CPG)was proposed.During off-peak operation periods,the remaining power of the GTCC was used to drive the adiabatic compressed air energy storage(ACAES),while the intake air of the GTCC was heated by the compression heat of theACAES.Based on a 67.3MW GTCC,under specific demand load distribution,a CPG system and a benchmark system(BS)were designed,both of which used 9.388% of the GTCC output power to drive the ACAES.The performance of the CPG and the BS without intake air heating was compared.The results show that the load rate of the GTCC in the CPG system during off-peak periods is significantly enhanced,and the average operating efficiency of the GTCC is increased by 1.19 percentage points.However,in the BS system,due to the single collaborativemethod of load shifting,the GTCC operative efficiency is almost increased by 1.00 percentage points under different ambient temperatures.In a roundtrip cycle at an ambient temperature of 288.15K,the systemefficiency of the CPG reaches 0.5010,which is 0.62 percentage points higher than the operative efficiency of 0.4948 in the standalone GTCC;while the system efficiency of the BS is slightly inferior to that of the standalone GTCC.The findings confirm the technical feasibility and performance improvement of the ACAES-GTCC collaborative power generation system.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
文摘The rapid growth in the proportion of renewable-energy gener-ation,such as wind and solar power,has significantly heightened the power system’s dependence on climate.Global climate change will profoundly impact various aspects of the system,including renewable-energy resource potential,power-system planning and operation,and electricity markets.The Intergovernmental Panel on Climate Change(IPCC)has pointed out that as climate change accelerates,extreme weather events will continue to become more frequent and severe.
基金funded by State Grid Corporation of China Central Branch Technology Project(52140024000C).
文摘In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)during disturbances.Moreover,due to the frequency decoupling between the two ends of the MMCHVDC,the sending-end wind farm(SEWF)cannot obtain the frequency variation information of the REG to provide inertia response.Therefore,this paper proposes a novel coordinated source-network-storage inertia control strategy based on wind power transmission via MMC-HVDC system.First,the grid-side MMC station(GS-MMC)maps the frequency variations of the REG to direct current(DC)voltage variations through the frequency mapping control,and uses submodule capacitor energy to provide inertial power.Then,the wind farm-side MMC station(WF-MMC)restores the DC voltage variations to frequency variations through the frequency restoration control and power loss compensation,providing real-time frequency information for the wind farm.Finally,based on real-time frequency information,thewind farmutilizes the rotor kinetic energy and energy storage to provide fast and lasting power support through the wind-storage coordinated inertia control strategy.Meanwhile,when the wind turbines withdraw from the inertia response phase,the energy storage can increase the power output to compensate for the power deficit,preventing secondary frequency drops.Furthermore,this paper uses small-signal analysis to determine the appropriate values for the key parameters of the proposed control strategy.A simulation model of the wind power transmission via MMCHVDC system is built in MATLAB/Simulink environment to validate and evaluate the proposed method.The results show that the proposed coordinated control strategy can effectively improve the system inertia level and avoid the secondary frequency drop under the load sudden increase condition.
基金supported by the National Natural Science Foundation of China(No.52375031)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers Research and Development Plan of Zhejiang Province(No.2023C01045)。
文摘Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.
基金supported by the State Grid Corporation of China Science and Technology Project,grant number 52270723000900K.
文摘The new energy power generation is becoming increasingly important in the power system.Such as photovoltaic power generation has become a research hotspot,however,due to the characteristics of light radiation changes,photovoltaic power generation is unstable and random,resulting in a low utilization rate and directly affecting the stability of the power grid.To solve this problem,this paper proposes a coordinated control strategy for a newenergy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit.Firstly,the variational mode decomposition algorithm is used to separate the high and low frequencies of the power signal,which is conducive to the rapid and accurate suppression of the power fluctuation of the energy storage system.Secondly,the fuzzy control algorithm is introduced to balance the power between energy storage.In this paper,the actual data is used for simulation,and the simulation results show that the strategy realizes the effective suppression of the bus voltage fluctuation and the accurate control of the internal state of the energy storage unit,effectively avoiding problems such as overshoot and over-discharge,and can significantly improve the stability of the photovoltaic power generation systemand the stability of the Direct Current bus.It is of great significance to promote the development of collaborative control technology for photovoltaic hybrid energy storage units.
基金supported by the National Natural Science Foundation of China(52272177,12204010)the Foundation for the Introduction of High-Level Talents of Anhui University(S020118002/097)+1 种基金the University Synergy Innovation Program of Anhui Province(GXXT-2023-066)the Scientific Research Project of Anhui Provincial Higher Education Institution(2023AH040008)。
文摘Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.
基金funded by the Inner Mongolia Nature Foundation Project,Project number:2023JQ04.
文摘Power quality is a crucial area of research in contemporary power systems,particularly given the rapid proliferation of intermittent renewable energy sources such as wind power.This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra,PSD,and random signal power spectra.The relationship was derived,validated through experiments and simulations,and subsequently applied to multi-objective optimization.Various optimization algorithms were compared to achieve optimal system power quality.The findings revealed that the relationships between power quality indices and PSD were influenced by variations in the order of the power spectral estimation model.An increase in the order of the AR model resulted in a 36%improvement in the number of optimal solutions.Regarding optimal solution distribution,NSGA-II demonstrated superior diversity,while MOEA/D exhibited better convergence.However,practical applications showed that while MOEA/D had higher convergence,NSGA-II produced superior optimal solutions,achieving the best power quality indices(THDi at 4.62%,d%at 3.51%,and cosφat 96%).These results suggest that the proposed method holds significant potential for optimizing power quality in practical applications.
基金supported by the science and technology project of State Grid Shanghai Municipal Electric Power Company(No.52094023003L).
文摘New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
基金supported by the Joint Research Fund in Smart Grid(U1966601)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and the State Grid Cor-poration of China(SGCC).
文摘Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy.This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources.Utilizing the Coupled Model Intercomparison Project Phase 6 datasets,a predictive model for low-output wind power was employed to investigate regional trends worldwide.The frequency and duration of low-output wind-power events exhibited increasing trends globally,particularly in East Asia and South America,but not in North America.By 2060,the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d,and the maximum continuous duration of low-output wind power could reach 5 and 2 d,respectively.As wind power becomes a primary elec-tricity source,such low output could lead to shortages in energy supply within the power system,trig-gering large-scale power outages.This issue calls for critical attention when establishing power systems with a high share of renewable energy sources.The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.
基金supported in part by the National Natural Science Fundation of China(52225704 and 52107096).
文摘The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-varying characteristics of the node voltage during dynamic processes.As a result,traditional calcu-lation methods for and characteristics of the power response of the network based on phasor and impe-dance lose their validity.Therefore,this paper undertakes mathematical calculations to reveal the power response of a network under excitation by voltage with time-varying amplitude and frequency(TVAF),relying on the original mathematical relationships and superimposed step response.Then,the multi-timescale characteristics of both the active and reactive power of the network are explored physically.Additionally,this paper reveals a new phenomenon of storing and releasing the active and reactive power of the network.To meet practical engineering requirements,a simplified power expression is presented.Finally,the theoretical analysis is validated through time-domain simulations.
基金supported by the National Key Research&Development Program of China under Grant 2024YFB2408900.
文摘Grid-forming(GFM)control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation(CIG),including wind power,photovoltaic,and battery energy storage.In this paper,we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy.The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation,denoted as the frequency-fixed zone.With the proposed strategy,the system frequency is almost always fixed at its rated value,achieving system active power balance independent of frequency,and intentional power flow adjustments are implemented through direct phase angle control.This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations.Furthermore,synchronization dynamics are significantly diminished,and synchronization stability is enhanced.The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.
基金supported by the Natural Science Research Project of Jiangsu Higher Education Institutions(23KJB470019)the Natural Science Foundation of Jiangsu Province under Grant BK20240594.
文摘Photovoltaic(PV)systems are being increasingly implemented in the grid,and their intermittent output fluctuations threaten the stability of the grid,thereby requiring effective power ramp control(PRRC)strategies.In this study,we proposed a power fluctuation identification method to optimize the PRRC strategy.The K-means++cluster based on DTW used in this method,which clusters the historical PV power generation data into power curves corresponding to a specific weather type(sunny,cloudy,and rainy)in a time zone.Subsequently,wavelet decomposition is applied to discretize the power curves with extreme RR overrun to accurately identify the extreme fluctuation time zones.We conducted an analysis using minute-level data from a 100 kW PV plant in Arizona,which demonstrates that the proposed method can effectively identify high-risk periods.Weather patterns within the time zones were quantitatively identified using a weather probability model.A hardware-in-the-loop experimental platform was employed to validate two days of actual power data in Arizona,demonstrating the weather zoning accuracy of the method and the reasonableness of the control.The proposed methodology contributes significantly to PRRC strategy selection and parameter optimization(e.g.,ESS capacity storage allocation and APC power reserveΔP)in different time zones and weather conditions.
