The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault character...The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion.Focusing on large-scale renewable energy transmission lines,the sensitivity of traditional current differential protection and distance protection may be reduced,and there is even the risk of maloperation.Therefore,a suitable transmission line model is established,which considers the distributed capacitance.Af terward,a novel dynamic state estimation based protection(DSEBP)for large-scale renewable energy transmission lines is proposed.The proposed DSEBP adopts instantaneous measure ments and additional protection criteria to ensure the quick ac tion and reliability.Finally,faults are identified by checking the matching degree between the actual measurements and the es tablished transmission line model.The performance of the pro posed DSEBP is verified through PSCAD/EMTDC and realtime digital simulator(RTDS)hardware-in-loop tests.The re sults demonstrate that the proposed DSEBP can identify vari ous types of faults quickly and reliably.Meanwhile,the pro posed DSEBP has a better capability to withstand fault resis tance and disturbance.展开更多
Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular...Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular computational networks(CCNs)structure.The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture.In the prediction layer,CCNs are employed to reduce the system scale by considering only relevant generators.In the correction layer,a novel adaptive filter is utilized to increase data abundance.Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency.Additionally,it offers easy scalability and strong robustness against uncertainties.The proposed method has potential applications in online dynamic state estimation and real-time security monitoring.展开更多
This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation sys...This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.展开更多
In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to es...In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.展开更多
In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first...In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.展开更多
High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges ar...High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.展开更多
In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the ...In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.展开更多
Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG...Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.展开更多
The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical appro...The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.展开更多
With the application of communication network technology in power systems,traditional power grids have gradually developed into cyber-physical systems(CPS),and false data injection(FDI)attacks have become a hidden dan...With the application of communication network technology in power systems,traditional power grids have gradually developed into cyber-physical systems(CPS),and false data injection(FDI)attacks have become a hidden danger that affects the operation of power CPS.To solve the problems of low estimation accuracy and model uncertainty when the extended Kalman filter(EKF)algorithm is subjected to FDI attacks in power systems,an interpolation adaptive H_(∞)extended Kalman filter(IAHEKF)algorithm is proposed for the dynamic state estimation of power systems under FDI attacks.The new algorithm uses an interpolation strategy to reduce the linearization error of the EKF algorithm and introduces adaptive H_(∞)theory to update the error covariance,minimizing the error upper bound caused by model uncertainty;moreover,it uses the Sage-Husa estimator to calculate the noise covariance,reducing the impact of unknown noise on state estimation.Finally,tests are conducted on the IEEE-14 node system and IEEE-30 node system,and the results show that the IAHEKF algorithm has higher estimation accuracy under different attack scenarios.展开更多
The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demand...The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.展开更多
We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory...We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.展开更多
With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they se...With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).展开更多
Wide-area damping controllers(WADCs)help in damping poorly damped inter-area oscillations(IAOs)using wide-area measurements.However,the vulnerability of the communication network makes the WADC susceptible to maliciou...Wide-area damping controllers(WADCs)help in damping poorly damped inter-area oscillations(IAOs)using wide-area measurements.However,the vulnerability of the communication network makes the WADC susceptible to malicious dynamic attacks.Existing cyber-resilient WADC solutions rely on accurate power system models or extensive simulation data for training the machine learning(ML)model,which are difficult to obtain for large-scale power system.This paper proposes a novel non-intrusive hybrid two-stage detection framework that mitigates these limitations by eliminating the need for realtime access to large system data or attack samples for training the ML model.In the first stage,an autoencoder is deployed at the actuator location to detect dynamic attacks with sharp gradient variations,e.g.,triangular,saw-tooth,pulse,ramp,and random attack signals.In the second stage,an unscented Kalman filter with unknown input estimation at the control center identifies smoothly varying dynamic attacks by estimating the control signal received by the actuator using synchrophasor measurements.A modified cosine similarity(MCS)metric is proposed to compare and quantify the similarity between the estimated control signal and the control signal sent by the WADC placed at the control center to detect any dynamic attacks.The MCS is designed to differentiate between events and dynamic attacks.The performance of the proposed framework has been validated on a hardware-in-the-loop(HIL)cyber-physical testbed built by using the OPAL-RT simulator and industry-grade hardware.展开更多
基金supported by National Natural Science Foundation of ChinaState Grid Corporation Joint Fund for Smart Grid(No.U2066210).
文摘The development of low-carbon energy systems and renewable energy sources(RESs)are critical to solving the energy crisis around the world.However,renewable energy gen eration control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion.Focusing on large-scale renewable energy transmission lines,the sensitivity of traditional current differential protection and distance protection may be reduced,and there is even the risk of maloperation.Therefore,a suitable transmission line model is established,which considers the distributed capacitance.Af terward,a novel dynamic state estimation based protection(DSEBP)for large-scale renewable energy transmission lines is proposed.The proposed DSEBP adopts instantaneous measure ments and additional protection criteria to ensure the quick ac tion and reliability.Finally,faults are identified by checking the matching degree between the actual measurements and the es tablished transmission line model.The performance of the pro posed DSEBP is verified through PSCAD/EMTDC and realtime digital simulator(RTDS)hardware-in-loop tests.The re sults demonstrate that the proposed DSEBP can identify vari ous types of faults quickly and reliably.Meanwhile,the pro posed DSEBP has a better capability to withstand fault resis tance and disturbance.
基金supported in part by the National Natural Science Foundation of China(No.62203395)in part by the China Postdoctoral Science Foundation(No.2023TQ0306)+1 种基金in part by the Natural Science Foundation of Henan Province(No.242300421167)in part by the Postdoctoral Research Project of Henan Province(No.202101011).
文摘Accurate generator information is crucial for the efficient control and operation of a power system.This study proposes a hierarchical data-driven approach for dynamic state estimation(DSE)of generators using cellular computational networks(CCNs)structure.The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture.In the prediction layer,CCNs are employed to reduce the system scale by considering only relevant generators.In the correction layer,a novel adaptive filter is utilized to increase data abundance.Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency.Additionally,it offers easy scalability and strong robustness against uncertainties.The proposed method has potential applications in online dynamic state estimation and real-time security monitoring.
基金supported by the National Natural Science Foundation of China(No.62073121)the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202)+1 种基金the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004)the Natural Sciences and Engineering Research Council(NSERC)of Canada。
文摘This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.
基金This work was supported in part by National Natural Science Foundation of China(51777067)and(52077076)in part by funding from the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(LAPS2021-18).
文摘In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.
基金supported by the National Natural Science Foundation of China(No.62073121)the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202)the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004)。
文摘In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.
基金This work is supported by Electric Power Research Institute(EPRI).Its support is greatly appreciated.
文摘High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.
文摘In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.
基金supported by the National Natural Science Foundation of China(No.51725702)。
文摘Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.
基金supported by the National Key Basic Research Program of China(973 Program)(No.613294)the Natural Science Foundation of China(No.51877211)
文摘The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.
基金Supported by the National Key Research and Development Plan Project of China(2021YFB2601304)Shaanxi Provincial Key Research and Development Plan Project(2022GY-193)the Innovation Capability Support Program of Shaanxi(2021TD-28,2022KXJ-144).
文摘With the application of communication network technology in power systems,traditional power grids have gradually developed into cyber-physical systems(CPS),and false data injection(FDI)attacks have become a hidden danger that affects the operation of power CPS.To solve the problems of low estimation accuracy and model uncertainty when the extended Kalman filter(EKF)algorithm is subjected to FDI attacks in power systems,an interpolation adaptive H_(∞)extended Kalman filter(IAHEKF)algorithm is proposed for the dynamic state estimation of power systems under FDI attacks.The new algorithm uses an interpolation strategy to reduce the linearization error of the EKF algorithm and introduces adaptive H_(∞)theory to update the error covariance,minimizing the error upper bound caused by model uncertainty;moreover,it uses the Sage-Husa estimator to calculate the noise covariance,reducing the impact of unknown noise on state estimation.Finally,tests are conducted on the IEEE-14 node system and IEEE-30 node system,and the results show that the IAHEKF algorithm has higher estimation accuracy under different attack scenarios.
基金National Natural Sciences Foundation of China,Grant/Award Numbers:62125302,62203087Sci-Tech Talent Innovation Support Program of Dalian,Grant/Award Number:2022RG03+1 种基金Liaoning Revitalization Talents Program,Grant/Award Number:XLYC2002087Young Elite Scientist Sponsorship Program by CAST,Grant/Award Number:YESS20220018。
文摘The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.
文摘We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.
基金supported in part by the National Natural Science Foundation of China under Grant 62203395in part by the Natural Science Foundation of Henan under Grant 242300421167in part by the China Postdoctoral Science Foundation under Grant 2023TQ0306.
文摘With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).
基金supported in part by ANRF(No.CRG/2021/003827/EEC)SERB(No.SIRE/SIR/2022/000984)。
文摘Wide-area damping controllers(WADCs)help in damping poorly damped inter-area oscillations(IAOs)using wide-area measurements.However,the vulnerability of the communication network makes the WADC susceptible to malicious dynamic attacks.Existing cyber-resilient WADC solutions rely on accurate power system models or extensive simulation data for training the machine learning(ML)model,which are difficult to obtain for large-scale power system.This paper proposes a novel non-intrusive hybrid two-stage detection framework that mitigates these limitations by eliminating the need for realtime access to large system data or attack samples for training the ML model.In the first stage,an autoencoder is deployed at the actuator location to detect dynamic attacks with sharp gradient variations,e.g.,triangular,saw-tooth,pulse,ramp,and random attack signals.In the second stage,an unscented Kalman filter with unknown input estimation at the control center identifies smoothly varying dynamic attacks by estimating the control signal received by the actuator using synchrophasor measurements.A modified cosine similarity(MCS)metric is proposed to compare and quantify the similarity between the estimated control signal and the control signal sent by the WADC placed at the control center to detect any dynamic attacks.The MCS is designed to differentiate between events and dynamic attacks.The performance of the proposed framework has been validated on a hardware-in-the-loop(HIL)cyber-physical testbed built by using the OPAL-RT simulator and industry-grade hardware.
