With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power elect...With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.展开更多
The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expan...The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expansion of renewable power generations such as wind and solar energy in a power system has increased their uncertainty,and仃aditional techniques are limited in capturing their variable behavior.This leads to greater needs of new techniques and methodologies to properly quan tify the voltage stability of power systems.展开更多
Donor-acceptor co-doped rutile TiO_(2) ceramics with colossal permittivity(CP)have been extensively investigated in recent years due to their potential applications in modern microelectronics.In addition to CP and low...Donor-acceptor co-doped rutile TiO_(2) ceramics with colossal permittivity(CP)have been extensively investigated in recent years due to their potential applications in modern microelectronics.In addition to CP and low dielectric loss,voltage stability is an essential property for CP materials utilized in high-power and high-energy density storage devices.Unfortunately,the voltage stability of CP materials based on codoped TiO_(2) does not catch enough attention.Here,we propose a strategy to enhance the voltage stability of co-doped TiO_(2),where different ionic defect clusters are formed by two acceptor ions with different radii to localize free carriers and result in high performance CP materials.The(Ta+Al+La)co-doped TiO_(2) ceramic with suitable La/Al ratio exhibits colossal permittivity with excellent temperature stability as well as outstanding dc bias stability.The density functional theory analysis suggests that La^(3+)Al^(3+)V_(0)Ti^(3+)defect clusters and Ta^(5+)-Al^(3+)pairs are responsible for the excellent dielectric properties in(Ta+Al+La)co-doped TiO_(2).The results and mechanisms presented in this work open up a feasible route to design high performance CP materials via defect engineering.展开更多
The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonl...The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.展开更多
With the increasing development of wind power,the scale of wind farms and unit capacity of wind turbines are getting larger and larger,and the impact of wind integration on power systems cannot be ignored.However,in m...With the increasing development of wind power,the scale of wind farms and unit capacity of wind turbines are getting larger and larger,and the impact of wind integration on power systems cannot be ignored.However,in most cases,the areas with a plenty of wind resources do not have strong grid structures.Furthermore,the characteristics of wind power dictate that wind turbines need to absorb reactive power during operation.Because of the strong correlation between voltage stability and systems' reactive power,the impacts of wind integration on voltage stability has become an important issue.Based on the power system simulation software DIgSILENT and combined analysis of actual practice,this paper investigates the impacts of two types of wind farms on voltage stability:namely a type of wind farms which are constituted by constant speed wind turbines based on common induction generators(IG) and another type of wind farms which are constituted by VSCF wind turbines based on doubly-fed induction generators(DFIG).Through investigation the critical fault clearing time is presented for different outputs of wind farms.Moreover,the impacts of static var compensator(SVC) and static synchronous compensator(STATCOM) on transient voltage stability in IG-based wind farms are studied to improve the security and stability of the Jiangsu power grid after the integration of large scale wind power.展开更多
Li-rich Mn-based oxides(LRMOs)hold great promise as next-generation cathode materials for high-energy Li-ion batteries because of their low cost and high capacity.Nevertheless,the practical application of LRMOs is imp...Li-rich Mn-based oxides(LRMOs)hold great promise as next-generation cathode materials for high-energy Li-ion batteries because of their low cost and high capacity.Nevertheless,the practical application of LRMOs is impeded by their low initial Coulombic efficiency and rapid voltage decay.Herein,a V-doped layered-spinel coherent layer is constructed on the surface of a Co-free LRMO through a simple treatment with NH_(4)VO_(3).The layered-spinel coherent layer with 3D ion channels enhanced Li+diffusion efficiency,mitigates surface-inter-face reactions and suppresses irreversible oxygen release.Notably,V-doping significantly reduces the Bader charge of oxygen atoms,thereby impeding excessive oxidation of oxygen ions and further enhancing the stability of O-redox.The modified LRMO exhibites a remarkable initial Coulombic efficiency of 91.6%,signifi-cantly surpassing that of the original LRMO(74.4%).Furthermore,the treated sample showes an impressive capacity retention rate of 91.9%after 200 cycles,accompanied by a voltage decay of merely 0.47 mV per cycle.The proposed treatment approach is straightforward and significantly improves the initial Coulombic efficiency,voltage stability,and capacity stability of LRMO cathode materials,thus holding considerable promise for the development of high-energy Li-ion batteries.展开更多
This paper studies on the change mechanisms of the voltage stability caused by the grid connection of front-end speed-controlled wind turbines(FSCWT)integrating into power system.First of all,the differential algebrai...This paper studies on the change mechanisms of the voltage stability caused by the grid connection of front-end speed-controlled wind turbines(FSCWT)integrating into power system.First of all,the differential algebraic equations describing the dynamic characteristics of wind turbines are illustrated.Then,under the guidance of IEEE3 node system model,the influence of the angular velocity of wind turbines,the reactive power and the active power at load bus on the voltage stability of grid-connection has been analyzed by using bifurcation theory.Finally,the method of linear-state feedback control has been applied to the original system in accordance with the bifurcation phenomenon of grid-connected voltage caused by the increase in the active power at load bus.Research shows that voltage at the grid-connected point would be changed with the fluctuation of turbines angular velocity.And increasing its reactive power can enhance voltage at the grid-connected point;problem of bifurcation at the grid-connected point can be delayed when increasing the gain k s of feedback controller within a certain range.展开更多
This paper presents a newly developed proximity indicator for voltage stability assessment which can be used to predict critical real system load and voltages at various load buses at critical loading point.The proxim...This paper presents a newly developed proximity indicator for voltage stability assessment which can be used to predict critical real system load and voltages at various load buses at critical loading point.The proximity indicator varies almost parabolic with total real load demand and reaches orthogonally to real load axis.This relation has been utilized to predict critical loading point.It has been shown that two operating points are needed for estimating critical point and proper selection of operating points and variation of proximity indicator near collapse point highly affect the accuracy of estimation.Simulation is based on load flow equations and system real and reactive loadings have been increased in proportion with base case scenario for IEEE 14 and IEEE 25 bus test systems to demonstrate the behaviour of proposed proximity indicator.CPF has been used as benchmark to check the accuracy of estimation.展开更多
The static voltage stability of the power system integrating wind farms adopting different kinds of wind turbines is analyzed. Through the simulation of one certain local power grid in Xinjiang Uygur Autonomous Region...The static voltage stability of the power system integrating wind farms adopting different kinds of wind turbines is analyzed. Through the simulation of one certain local power grid in Xinjiang Uygur Autonomous Region, the PV curves at the point of common coupling (PCC), key buses and important substations are plotted; the variation of voltage as well as the limit and margin of static stability are analyzed. It is resulted from the simulation that the limit of static voltage at weak nodes is lower, and the static voltage of the power system with wind farms adopting doubly-fed induction generators (DFIG) is more stable than that with wind farms adopting common asynchronous generators.展开更多
Power system operations can be optimized using power electronics based FACTS devices. The location of these devices at appropriate transmission line plays a major role in their performance. In this paper, two bio-insp...Power system operations can be optimized using power electronics based FACTS devices. The location of these devices at appropriate transmission line plays a major role in their performance. In this paper, two bio-inspired algorithms are used to optimally locate two FACTS devices: UPFC and STATCOM, so as to reduce the voltage collapse and real power losses. Particle swarm optimization and BAT algorithms are chosen as their behaviour is similar. VCPI index is used as a metric to calculate the voltage collapse scenario of the power system. The algorithm is tested on two benchmark power systems: IEEE 118 and the Indian UPSEB 75 bus system. Performance metrics are compared with the system without FACTS devices. Application of PSO and BAT algorithms to optimally locate the FACTS devices reduces the VCPI index and real power losses in the system.展开更多
The growth of wind energy penetration level in distribution system raises the concern about its impact on the operation of the power system, especially voltage stability and power loss. Among the major concerns, this ...The growth of wind energy penetration level in distribution system raises the concern about its impact on the operation of the power system, especially voltage stability and power loss. Among the major concerns, this paper studied the impact of connecting wind Turbine (WT) in radial distribution system with different penetration levels and different power factor (lead and lag) on power system voltage stability and power loss reduction. Load flow calculation was carried out using forward-backward sweep method. The analysis proceeds on 9- and 33-bus radial distribution systems. Results show that voltage stability enhancement and power loss reduction should be considered as WT installation objective.展开更多
Short-term voltage stability(STVS)assessment is a critical monitoring technology in modern power systems.During daily operation,transmission lines may switch on or off due to scheduled maintenance or unexpected faults...Short-term voltage stability(STVS)assessment is a critical monitoring technology in modern power systems.During daily operation,transmission lines may switch on or off due to scheduled maintenance or unexpected faults,which poses challenges to the STVS assessment under varying topology change conditions.To adapt the STVS assessment to the system topology changes,we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies.The topology-adaptive voltage dynamic feature,extracted from streaming time-series data of phasor measurement units(PMUs),is used to characterize transient voltage stability.The voltage dynamic features depend on the balance of reactive power flow and system topology,effectively revealing both spatio-temporal patterns of post-disturbance system dynamics.The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance,with an accuracy of 99.65%in predicting voltage stability compared with the existing deep learning methods.The proposed model also performs well when applied to the larger IEEE 145-bus power system.The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems.It achieves an accuracy of 99.50%in predicting the STVS for the New England 39-bus power system with the transmission line alternation.Furthermore,the proposed model demonstrates strong robustness to noisy and missing data.展开更多
As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional A...As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional AI barely identify what the specific key factors in power systems are and how they influence STVS,thus providing limited practical information for engineers in on-site dispatch centers.Enlightened by the latest explainable artificial intelligence(XAI)techniques,this paper aims to unveil the mechanism underlying the complex STVS problem.First,the ground truth for STVS is established via qualitative analysis.Based on this,an explainability score is then devised to measure the trustworthiness of different XAI techniques,among which Local Interpretable Model-agnostic Explanations(LIME)exhibits the best performance in this study.Finally,a sequential approach is proposed to extend the local interpretation of LIME to a broader scope,which is applied to enhance STVS performance before a fault occurs in distribution system load shedding,serving as an example to demonstrate the application merits of the explored mechanism.Numerical results on a modified IEEE system demonstrate that this finding facilitates the identification of the most suitable XAI technique for STVS,while also providing an interpretable mechanism for the STVS,offering accessible guidance for stability-aware dispatch.展开更多
An optimal preventive-corrective control model for static voltage stability under multiple N-1 contingencies considering the wind power uncertainty is established in this paper.The objective is to minimize the control...An optimal preventive-corrective control model for static voltage stability under multiple N-1 contingencies considering the wind power uncertainty is established in this paper.The objective is to minimize the control variable adjustment cost including the load shedding cost of each contingency.The chance constraints of the static voltage stability margins(SvSMs)in the normal operation state and after each N-1 contingency are included.The approximate functions between the probability density functions(PDFs)of SVSMs and load shedding quantity with respect to preventive control variables are obtained to transform the expectation of load shedding quantity and the SvSM chance constraints into deterministic expressions.An approximate sequential convex quadratically constrained quadratic programming iteration method is proposed to solve the optimal control model.In each iteration,the approximate expressions and range are determined by the generated data samples.Moreover,a fast approximation calculation method of second-order matrices is proposed.By the naive Bayes classifier,the most severe N-1 contingencies are selected to replace all the contingencies to be added to the optimization model to improve the computational efficiency.Case studies on the IEEE-39 bus system and an actual provincial power grid demonstrate the effectiveness and efficiency of the proposed method.展开更多
With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)wa...With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)was developed to estimate EV charging load with available corresponding response capacity under different charging strategies.A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin(VSM)of power system above a predefined security level.Two control modes were used including the disconnection of EV charging load(‘V1G’mode)and the discharge of stored battery energy back to power grid(‘V2G’mode).A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy.Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation,but also guarantee the travelling comfort for EV owners.展开更多
The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irration...The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irrational regulations of reactive power compensation equipment have become the prominent problems of the regions where large-scale wind power integrated.In view of these problems,this paper proposed an optimal reactive power dispatch(ORPD)strategy of wind power plants cluster(WPPC)considering static voltage stability for lowcarbon power system.The control model of the ORPD strategy was built according to the wind power prediction,the present operation information and the historical operation information.By utilizing the automatic voltage control capability of wind power plants and central substations,the ORPD strategy can achieve differentiated management between the discrete devices and the dynamic devices of the WPPC.Simulation results of an actual WPPC in North China show that the ORPD strategy can improve the voltage control performance of the pilot nodes and coordinate the operation between discrete devices and the dynamic devices,thus maintaining the static voltage stability as well.展开更多
This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(...This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(SRP),based on a deep residual learning convolutional neural network,is employed to cope with the missing PMU measurements.The incremental broad learning(BL)is used to rapidly update the model to maintain and enhance the online application performance.Being different from the state-of-the-art methods,the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario.Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.展开更多
Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algor...Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.展开更多
Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed condi-tions.To prevent voltage collapse caused by such instability,accurate voltage collapse prediction is n...Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed condi-tions.To prevent voltage collapse caused by such instability,accurate voltage collapse prediction is necessary for power system planning and operation.This paper proposes a novel collapse prediction index(NCPI)to assess the volt-age stability conditions of the power system and the critical conditions of lines.The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices(Lmn,FVSI,LQP,NLSI,and VSLI)under several power system operations to validate its practicability and versatility.The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction.The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions.The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines,weak buses,and weak areas in medium and large networks during various power load operations and contingencies.A line interruption or generation unit outage in a power system can also lead to voltage collapse,and this is a contingency in the power system.Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage.The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.展开更多
In this paper, a new evaluation method of probabilistic TTC based on SVSR calculation is developed through a hierarchical simulation. A smooth technology based on the non-parametric kernel estimator is adapted to obta...In this paper, a new evaluation method of probabilistic TTC based on SVSR calculation is developed through a hierarchical simulation. A smooth technology based on the non-parametric kernel estimator is adapted to obtain the time-dependent probabilistic density function of the feeder-head load data. In order to describe possible operating change directions of the operating point, the original hyper-cone-like(HCL) model is constructed to consider the probabilistic distribution function(PDF) extracted from feeder-head load data to replace the simple Normal Distribution model and the uncertain generator outputs. To realize the fast TTC calculation of the current operating point in random conditions, a sub-hyper-cone-like(SHCL) model in full power injections space is proposed, which is a similarity transformation of the original one.展开更多
基金the State Grid Shanxi Electric Power Company Science and Technology Project“Smart distribution network with a high proportion of distributed wind storage adaptability assessment and improvement strategy research”(520530230024).
文摘With the increasing penetration of renewable energy in power systems,grid structures and operational paradigms are undergoing profound transformations.When subjected to disturbances,the interaction between power electronic devices and dynamic loads introduces strongly nonlinear dynamic characteristics in grid voltage responses,posing significant threats to system security and stability.To achieve reliable short-term voltage stability assessment under large-scale renewable integration,this paper innovatively proposes a response-driven online assessment method based on energy function theory.First,energy modeling of system components is performed based on energy function theory,followed by analysis of energy interaction mechanisms during voltage instability.To address the challenge of traditional energy functions in online applications,a convolutional neural network-long short-term memory(CNNLSTM)hybrid artificial Intelligence approach is introduced.By quantifying the contribution of each energy component to voltage stability,key energy terms are identified.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the voltage unstable equilibrium point(UEP)obtained from offline simulations is used as both the energy threshold and the output of the artificial intelligence model,enabling the construction of an artificial intelligence model for energy threshold prediction.The measurable electrical quantities corresponding to these key energies serve as inputs,while the energy at the unstable equilibrium point(UEP)obtained from offline simulations acts as the output,enabling the construction of an artificial intelligence model for energy threshold prediction.Real-time response data are fed into the model to predict the system's instantaneous energy threshold,which is then compared with the transient energy at fault clearance to evaluate stability.Validation on both a 3-machine,10-bus system and the New England 10-machine,39-bus system confirms the method's adaptability and accuracy.The simulation results demonstrate that the proposed short-term voltage stability assessment model outperforms other methods in both accuracy and computational efficiency.
文摘The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expansion of renewable power generations such as wind and solar energy in a power system has increased their uncertainty,and仃aditional techniques are limited in capturing their variable behavior.This leads to greater needs of new techniques and methodologies to properly quan tify the voltage stability of power systems.
基金financially supported by the Fundamental Research Foundation for University of Heilongjiang Province(No.2018-KYYWF-1628)the National Natural Science Foundation of China(Nos.51471057 and 51677033)。
文摘Donor-acceptor co-doped rutile TiO_(2) ceramics with colossal permittivity(CP)have been extensively investigated in recent years due to their potential applications in modern microelectronics.In addition to CP and low dielectric loss,voltage stability is an essential property for CP materials utilized in high-power and high-energy density storage devices.Unfortunately,the voltage stability of CP materials based on codoped TiO_(2) does not catch enough attention.Here,we propose a strategy to enhance the voltage stability of co-doped TiO_(2),where different ionic defect clusters are formed by two acceptor ions with different radii to localize free carriers and result in high performance CP materials.The(Ta+Al+La)co-doped TiO_(2) ceramic with suitable La/Al ratio exhibits colossal permittivity with excellent temperature stability as well as outstanding dc bias stability.The density functional theory analysis suggests that La^(3+)Al^(3+)V_(0)Ti^(3+)defect clusters and Ta^(5+)-Al^(3+)pairs are responsible for the excellent dielectric properties in(Ta+Al+La)co-doped TiO_(2).The results and mechanisms presented in this work open up a feasible route to design high performance CP materials via defect engineering.
基金Sponsored by the Scientific and Technological Project of Heilongjiang Province(Grant No.GD07A304)
文摘The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.
文摘With the increasing development of wind power,the scale of wind farms and unit capacity of wind turbines are getting larger and larger,and the impact of wind integration on power systems cannot be ignored.However,in most cases,the areas with a plenty of wind resources do not have strong grid structures.Furthermore,the characteristics of wind power dictate that wind turbines need to absorb reactive power during operation.Because of the strong correlation between voltage stability and systems' reactive power,the impacts of wind integration on voltage stability has become an important issue.Based on the power system simulation software DIgSILENT and combined analysis of actual practice,this paper investigates the impacts of two types of wind farms on voltage stability:namely a type of wind farms which are constituted by constant speed wind turbines based on common induction generators(IG) and another type of wind farms which are constituted by VSCF wind turbines based on doubly-fed induction generators(DFIG).Through investigation the critical fault clearing time is presented for different outputs of wind farms.Moreover,the impacts of static var compensator(SVC) and static synchronous compensator(STATCOM) on transient voltage stability in IG-based wind farms are studied to improve the security and stability of the Jiangsu power grid after the integration of large scale wind power.
基金Natural Science Research(Department of Education)Project of Higher Education Institutions in Guangdong Province(Grant No.2018KQNCX063)Applied Basic Research Fund of Guangdong Province(Grant No.2024B1515020071)+1 种基金National Natural Science Foundation of China(Grant Nos.52371217 and 52150410411)Guangdong Provincial Science and Technology Plan Project(Grant No.2023A0505020009)。
文摘Li-rich Mn-based oxides(LRMOs)hold great promise as next-generation cathode materials for high-energy Li-ion batteries because of their low cost and high capacity.Nevertheless,the practical application of LRMOs is impeded by their low initial Coulombic efficiency and rapid voltage decay.Herein,a V-doped layered-spinel coherent layer is constructed on the surface of a Co-free LRMO through a simple treatment with NH_(4)VO_(3).The layered-spinel coherent layer with 3D ion channels enhanced Li+diffusion efficiency,mitigates surface-inter-face reactions and suppresses irreversible oxygen release.Notably,V-doping significantly reduces the Bader charge of oxygen atoms,thereby impeding excessive oxidation of oxygen ions and further enhancing the stability of O-redox.The modified LRMO exhibites a remarkable initial Coulombic efficiency of 91.6%,signifi-cantly surpassing that of the original LRMO(74.4%).Furthermore,the treated sample showes an impressive capacity retention rate of 91.9%after 200 cycles,accompanied by a voltage decay of merely 0.47 mV per cycle.The proposed treatment approach is straightforward and significantly improves the initial Coulombic efficiency,voltage stability,and capacity stability of LRMO cathode materials,thus holding considerable promise for the development of high-energy Li-ion batteries.
基金National Natural Science Foundation of China(No.61663019)
文摘This paper studies on the change mechanisms of the voltage stability caused by the grid connection of front-end speed-controlled wind turbines(FSCWT)integrating into power system.First of all,the differential algebraic equations describing the dynamic characteristics of wind turbines are illustrated.Then,under the guidance of IEEE3 node system model,the influence of the angular velocity of wind turbines,the reactive power and the active power at load bus on the voltage stability of grid-connection has been analyzed by using bifurcation theory.Finally,the method of linear-state feedback control has been applied to the original system in accordance with the bifurcation phenomenon of grid-connected voltage caused by the increase in the active power at load bus.Research shows that voltage at the grid-connected point would be changed with the fluctuation of turbines angular velocity.And increasing its reactive power can enhance voltage at the grid-connected point;problem of bifurcation at the grid-connected point can be delayed when increasing the gain k s of feedback controller within a certain range.
文摘This paper presents a newly developed proximity indicator for voltage stability assessment which can be used to predict critical real system load and voltages at various load buses at critical loading point.The proximity indicator varies almost parabolic with total real load demand and reaches orthogonally to real load axis.This relation has been utilized to predict critical loading point.It has been shown that two operating points are needed for estimating critical point and proper selection of operating points and variation of proximity indicator near collapse point highly affect the accuracy of estimation.Simulation is based on load flow equations and system real and reactive loadings have been increased in proportion with base case scenario for IEEE 14 and IEEE 25 bus test systems to demonstrate the behaviour of proposed proximity indicator.CPF has been used as benchmark to check the accuracy of estimation.
基金National Natural Science Foundation of China(5076700350867004)Autonomous university research projects(XJEDU2007105)
文摘The static voltage stability of the power system integrating wind farms adopting different kinds of wind turbines is analyzed. Through the simulation of one certain local power grid in Xinjiang Uygur Autonomous Region, the PV curves at the point of common coupling (PCC), key buses and important substations are plotted; the variation of voltage as well as the limit and margin of static stability are analyzed. It is resulted from the simulation that the limit of static voltage at weak nodes is lower, and the static voltage of the power system with wind farms adopting doubly-fed induction generators (DFIG) is more stable than that with wind farms adopting common asynchronous generators.
文摘Power system operations can be optimized using power electronics based FACTS devices. The location of these devices at appropriate transmission line plays a major role in their performance. In this paper, two bio-inspired algorithms are used to optimally locate two FACTS devices: UPFC and STATCOM, so as to reduce the voltage collapse and real power losses. Particle swarm optimization and BAT algorithms are chosen as their behaviour is similar. VCPI index is used as a metric to calculate the voltage collapse scenario of the power system. The algorithm is tested on two benchmark power systems: IEEE 118 and the Indian UPSEB 75 bus system. Performance metrics are compared with the system without FACTS devices. Application of PSO and BAT algorithms to optimally locate the FACTS devices reduces the VCPI index and real power losses in the system.
文摘The growth of wind energy penetration level in distribution system raises the concern about its impact on the operation of the power system, especially voltage stability and power loss. Among the major concerns, this paper studied the impact of connecting wind Turbine (WT) in radial distribution system with different penetration levels and different power factor (lead and lag) on power system voltage stability and power loss reduction. Load flow calculation was carried out using forward-backward sweep method. The analysis proceeds on 9- and 33-bus radial distribution systems. Results show that voltage stability enhancement and power loss reduction should be considered as WT installation objective.
基金supported in part by the National Natural Science Foundation of China(No.21773182)the Fundamental Research Funds for the Central Universities(No.xtr052024009)the HPC Platform,Xi’an Jiaotong University。
文摘Short-term voltage stability(STVS)assessment is a critical monitoring technology in modern power systems.During daily operation,transmission lines may switch on or off due to scheduled maintenance or unexpected faults,which poses challenges to the STVS assessment under varying topology change conditions.To adapt the STVS assessment to the system topology changes,we propose a deep-learning-based STVS assessment model with the topology-adaptive voltage dynamic feature and the fine-tuning domain transfer for power systems with changing system topologies.The topology-adaptive voltage dynamic feature,extracted from streaming time-series data of phasor measurement units(PMUs),is used to characterize transient voltage stability.The voltage dynamic features depend on the balance of reactive power flow and system topology,effectively revealing both spatio-temporal patterns of post-disturbance system dynamics.The simulation results based on large disturbances in the New England 39-bus power system demonstrate that the proposed model achieves superior STVS assessment performance,with an accuracy of 99.65%in predicting voltage stability compared with the existing deep learning methods.The proposed model also performs well when applied to the larger IEEE 145-bus power system.The fine-tuning domain transfer of the proposed model adapts very well to system topology changes in power systems.It achieves an accuracy of 99.50%in predicting the STVS for the New England 39-bus power system with the transmission line alternation.Furthermore,the proposed model demonstrates strong robustness to noisy and missing data.
基金supported in part by the National Natural Science Foundation of China under Grant U23B6008in part by the Guangdong Basic and Applied Basic Research Foundation under Grants 2022A1515240075in part by the Italian Ministry of University and Research,Project NEST,Code PE0000021,CUP J33C22002890007.
文摘As the cornerstone for the safe operation of energy systems,short-term voltage stability(STVS)has been assessed effectively with the advance of artificial intelligence(AI).However,the black-box models of traditional AI barely identify what the specific key factors in power systems are and how they influence STVS,thus providing limited practical information for engineers in on-site dispatch centers.Enlightened by the latest explainable artificial intelligence(XAI)techniques,this paper aims to unveil the mechanism underlying the complex STVS problem.First,the ground truth for STVS is established via qualitative analysis.Based on this,an explainability score is then devised to measure the trustworthiness of different XAI techniques,among which Local Interpretable Model-agnostic Explanations(LIME)exhibits the best performance in this study.Finally,a sequential approach is proposed to extend the local interpretation of LIME to a broader scope,which is applied to enhance STVS performance before a fault occurs in distribution system load shedding,serving as an example to demonstrate the application merits of the explored mechanism.Numerical results on a modified IEEE system demonstrate that this finding facilitates the identification of the most suitable XAI technique for STVS,while also providing an interpretable mechanism for the STVS,offering accessible guidance for stability-aware dispatch.
基金supported by the National Natural Science Foundation of China under Grant 51977080the Natural Science Foundation of Guangdong Province(2023A1515240075).
文摘An optimal preventive-corrective control model for static voltage stability under multiple N-1 contingencies considering the wind power uncertainty is established in this paper.The objective is to minimize the control variable adjustment cost including the load shedding cost of each contingency.The chance constraints of the static voltage stability margins(SvSMs)in the normal operation state and after each N-1 contingency are included.The approximate functions between the probability density functions(PDFs)of SVSMs and load shedding quantity with respect to preventive control variables are obtained to transform the expectation of load shedding quantity and the SvSM chance constraints into deterministic expressions.An approximate sequential convex quadratically constrained quadratic programming iteration method is proposed to solve the optimal control model.In each iteration,the approximate expressions and range are determined by the generated data samples.Moreover,a fast approximation calculation method of second-order matrices is proposed.By the naive Bayes classifier,the most severe N-1 contingencies are selected to replace all the contingencies to be added to the optimization model to improve the computational efficiency.Case studies on the IEEE-39 bus system and an actual provincial power grid demonstrate the effectiveness and efficiency of the proposed method.
基金This work was supported in part by the National Natural Science Foundation of China(collaborating with EPSRC of UK)(Nos.51361130152 and EP/L001039/1)the National Science and Technology Support Program of China(No.2013BAA01B03)Research on Reactive Power Control and Comprehensive Evaluation Technique of Large Scale Integration of Wind/Photovoltaic Power Generation(No.NY71-14-035).
文摘With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)was developed to estimate EV charging load with available corresponding response capacity under different charging strategies.A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin(VSM)of power system above a predefined security level.Two control modes were used including the disconnection of EV charging load(‘V1G’mode)and the discharge of stored battery energy back to power grid(‘V2G’mode).A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy.Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation,but also guarantee the travelling comfort for EV owners.
基金This work was supported by the National Natural Science Foundation of China(No.51207145)the Science and Technology Project of State Grid Corporation of China(No.NY71-14-035).
文摘The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irrational regulations of reactive power compensation equipment have become the prominent problems of the regions where large-scale wind power integrated.In view of these problems,this paper proposed an optimal reactive power dispatch(ORPD)strategy of wind power plants cluster(WPPC)considering static voltage stability for lowcarbon power system.The control model of the ORPD strategy was built according to the wind power prediction,the present operation information and the historical operation information.By utilizing the automatic voltage control capability of wind power plants and central substations,the ORPD strategy can achieve differentiated management between the discrete devices and the dynamic devices of the WPPC.Simulation results of an actual WPPC in North China show that the ORPD strategy can improve the voltage control performance of the pilot nodes and coordinate the operation between discrete devices and the dynamic devices,thus maintaining the static voltage stability as well.
基金The work was supported in part by National Natural Science Foundation of China(51807009,71931003,72061147004).
文摘This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(SRP),based on a deep residual learning convolutional neural network,is employed to cope with the missing PMU measurements.The incremental broad learning(BL)is used to rapidly update the model to maintain and enhance the online application performance.Being different from the state-of-the-art methods,the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario.Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.
文摘Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant 52007032National Key R&D Program of China(2022YFB2703502)Basic Research Program of Jiangsu province under Grant BK20200385,China.
文摘Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed condi-tions.To prevent voltage collapse caused by such instability,accurate voltage collapse prediction is necessary for power system planning and operation.This paper proposes a novel collapse prediction index(NCPI)to assess the volt-age stability conditions of the power system and the critical conditions of lines.The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices(Lmn,FVSI,LQP,NLSI,and VSLI)under several power system operations to validate its practicability and versatility.The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction.The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions.The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines,weak buses,and weak areas in medium and large networks during various power load operations and contingencies.A line interruption or generation unit outage in a power system can also lead to voltage collapse,and this is a contingency in the power system.Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage.The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.
基金supported the National Hi-Tech Research and Development Program of China(Grant No.2015AA050403)the National Natural Science Foundation of China(Grant Nos.51277128+7 种基金51407125&51361135704)Statement of Collaboration between University of VictoriaCanadaTianjin UniversityChinaand"131"Talent&Innovative Team of Tianjin CityFundamental and Perspective Project of State Grid Corporation of China-"Study on the Energy Internet Technology Framework"Science and Technology Project of State Grid Corporation of China(Grant No.5217L0150004)
文摘In this paper, a new evaluation method of probabilistic TTC based on SVSR calculation is developed through a hierarchical simulation. A smooth technology based on the non-parametric kernel estimator is adapted to obtain the time-dependent probabilistic density function of the feeder-head load data. In order to describe possible operating change directions of the operating point, the original hyper-cone-like(HCL) model is constructed to consider the probabilistic distribution function(PDF) extracted from feeder-head load data to replace the simple Normal Distribution model and the uncertain generator outputs. To realize the fast TTC calculation of the current operating point in random conditions, a sub-hyper-cone-like(SHCL) model in full power injections space is proposed, which is a similarity transformation of the original one.
