Considering that the measurement devices of the distribution network are becoming more and more abundant, on the basis of the traditional Supervisory Control And Data Acquisition (SCADA) measurement system, Phasor mea...Considering that the measurement devices of the distribution network are becoming more and more abundant, on the basis of the traditional Supervisory Control And Data Acquisition (SCADA) measurement system, Phasor measurement unit (PMU) devices are also gradually applied to the distribution network. So when estimating the state of the distribution network, the above two devices need to be used. However, because the data of different measurement systems are different, it is necessary to balance this difference so that the data of different systems can be compatible to achieve the purpose of effective utilization of the estimated power distribution state. To this end, this paper starts with three aspects of data accuracy of the two measurement systems, data time section and data refresh frequency to eliminate the differences between system data, and then considers the actual situation of the three-phase asymmetry of the distribution network. The three-phase state estimation equations are constructed by the branch current method, and finally the state estimation results are solved by the weighted least square method.展开更多
Distribution network state estimation provided complete and reliable information for the distribution management system (DMS) and was a prerequisite for other advanced management and control applications in the power ...Distribution network state estimation provided complete and reliable information for the distribution management system (DMS) and was a prerequisite for other advanced management and control applications in the power distribution network. This paper first introduced the basic principles of the state estimation algorithm and sorted out the research status of the distribution network state estimation from least squares, gross error resistance etc. <span style="font-family:Verdana;">Finally, this paper summarized the key problems faced by the high-dimensional</span><span style="font-family:Verdana;"> multi-power flow active distribution network state estimation and discussed prospects for future research hotspots and developments.</span>展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performa...This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.展开更多
The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the reg...The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.展开更多
The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was trans...The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was transformed into a non-constrained optimization problem using mass conservation. Then,through one dimensional optimization and scale matrix establishment,the feasible direction of iteration was obtained,and the values of state variables could be calculated. After several iterations,the optimal estimates of state variables were worked out and state simulation of water distribution networks was achieved as a result. A program of DFP algorithm is developed with Delphi 7 for verification. By running on a designed network,which is composed of 55 nodes,94 pipes and 40 loops,it is proved that DFP algorithm can quickly get the convergence. After 36 iterations,the root mean square of all nodal head errors is reduced by 90.84% from 5.57 to 0.51 m,and the maximum error is only 1.30 m. Compared to Marquardt algorithm,the procedure of DFP algorithm is more stable,and the initial values have less influences on calculation accuracy. Therefore,DFP algorithm can be used for real-time simulation of water distribution networks.展开更多
Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy w...Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.展开更多
This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and...This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.展开更多
Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling me...Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.展开更多
This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric c...This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.展开更多
Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs wit...Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are derived.Based on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also presented.The simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.展开更多
The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs...The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.展开更多
Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance...Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.展开更多
Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The curren...Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.展开更多
Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system s...Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.展开更多
This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available i...This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.展开更多
The accuracy of distribution system state estimation(DDSE)is reduced when phasor measurement unit(PMU)measurements contain outliers because of cyber attacks or global positioning system spoofing attacks.Therefore,to e...The accuracy of distribution system state estimation(DDSE)is reduced when phasor measurement unit(PMU)measurements contain outliers because of cyber attacks or global positioning system spoofing attacks.Therefore,to enhance the robustness of DDSE to measurement outliers,approximate the target distribution of Metropolis-Hastings(MH)sampling,and judge the prediction of the long short-term memory(LSTM)network,this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling(E-LM model),motivated by the characteristics of the chronological correlations of PMU measurements.First,the target distribution of outlier reconstruction is derived using a kernel density estimation function.Subsequently,the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view.The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations.Moreover,the proposed MH-based forecasting of the LSTM can judge each LSTM prediction,which is independent of its true value.Finally,simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.展开更多
This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation.A constrained many-objective optimization problem is formulated to ...This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation.A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment,operation,and maintenance,avoid the difference in the partition sizes,reduce the level of coupling between connected partitions,and maximize the inner cohesion of each partition.Capacities of edge server are constrained against underload and overload.To efficiently solve the problem,an improved non-dominated sorting genetic algorithm III(NSGA-III)is developed,with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence.Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs,and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution.The optimized result contributes significantly to real-time distributed state estimation.展开更多
Modern distribution networks with high penetration of distributed energy resources(DERs)are undergoing continuous expansion in scale.However,the increasing complexity of network structure and the high installation cos...Modern distribution networks with high penetration of distributed energy resources(DERs)are undergoing continuous expansion in scale.However,the increasing complexity of network structure and the high installation cost of measurement equipment introduce operational challenges including state variability and measurement data incompleteness.Substantial data loss significantly compromises fault detection accuracy and network performance,creating obstacles for distributed energy management and posing critical challenges to distribution network state estimation.To address these issues,this paper proposes a hybrid state estimation framework(MC-VMD-ARIMA-LSTM)that integrates alternating-minimization matrix completion(MC)with variational mode decomposition(VMD),autoregressive integrated moving average(ARIMA)modeling,and long short-term memory(LSTM)neural networks for enhanced power flow analysis in low-observability distribution net-works.The methodology features a dual-timescale approach:(1)At individual time intervals,an alternating-minimization matrix completion model is formulated,incorporating linearized power flow constraints;(2)For multi-timescale analysis,the measurement dataset undergoes VMD-based decomposition,with subsequent specialized processing where ARIMA handles low-frequency components and LSTM manage high-frequency residuals.The results of state estimation are obtained through systematic component reconstruction.Compre-hensive evaluations using IEEE 33-bus distribution network and actual distribution system measurement datasets demonstrate the framework’s effectiveness in both multi-timescale data assimilation and state estimation ac-curacy under limited observability conditions.展开更多
In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（...In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（especially networked systems）. For networked systems, event-based transmission scheme is capable of improving the efficiency in resource utilization and prolonging the lifetime of the network components compared with the widely adopted periodic transmission scheme. As such, it would be interesting to 1） examining how the event-triggering mechanisms affect the control or filtering performance for networked systems, and 2） developing some suitable approaches for the controller and filter design problems. In this paper, a bibliographical review is presented on event-based control and filtering problems for various networked systems. First, the event-driven communication scheme is introduced in detail according to its engineering background, characteristic, and representative research frameworks. Then, different event-based control and filtering（or state estimation） problems are categorized and then discussed. Finally, we conclude the paper by outlining future research challenges for event-based networked systems.展开更多
文摘Considering that the measurement devices of the distribution network are becoming more and more abundant, on the basis of the traditional Supervisory Control And Data Acquisition (SCADA) measurement system, Phasor measurement unit (PMU) devices are also gradually applied to the distribution network. So when estimating the state of the distribution network, the above two devices need to be used. However, because the data of different measurement systems are different, it is necessary to balance this difference so that the data of different systems can be compatible to achieve the purpose of effective utilization of the estimated power distribution state. To this end, this paper starts with three aspects of data accuracy of the two measurement systems, data time section and data refresh frequency to eliminate the differences between system data, and then considers the actual situation of the three-phase asymmetry of the distribution network. The three-phase state estimation equations are constructed by the branch current method, and finally the state estimation results are solved by the weighted least square method.
文摘Distribution network state estimation provided complete and reliable information for the distribution management system (DMS) and was a prerequisite for other advanced management and control applications in the power distribution network. This paper first introduced the basic principles of the state estimation algorithm and sorted out the research status of the distribution network state estimation from least squares, gross error resistance etc. <span style="font-family:Verdana;">Finally, this paper summarized the key problems faced by the high-dimensional</span><span style="font-family:Verdana;"> multi-power flow active distribution network state estimation and discussed prospects for future research hotspots and developments.</span>
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金supported by the Fund from National Board of Higher Mathematics(NBHM),New Delhi(Grant No.2/48/10/2011-R&D-II/865)
文摘This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.
文摘The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.
基金Project(IRT0853) supported by Changjiang Scholars and Innovative Research Team in UniversityProject(DB03086) supported by Talents Fund of Xi’an University of Architecture and TechnologyProject(50978213) supported by National Natural Science Foundation
文摘The improved weighted-least-square model was used for state simulation of water distribution networks. And DFP algorithm was applied to get the model solution. In order to fit DFP algorithm,the initial model was transformed into a non-constrained optimization problem using mass conservation. Then,through one dimensional optimization and scale matrix establishment,the feasible direction of iteration was obtained,and the values of state variables could be calculated. After several iterations,the optimal estimates of state variables were worked out and state simulation of water distribution networks was achieved as a result. A program of DFP algorithm is developed with Delphi 7 for verification. By running on a designed network,which is composed of 55 nodes,94 pipes and 40 loops,it is proved that DFP algorithm can quickly get the convergence. After 36 iterations,the root mean square of all nodal head errors is reduced by 90.84% from 5.57 to 0.51 m,and the maximum error is only 1.30 m. Compared to Marquardt algorithm,the procedure of DFP algorithm is more stable,and the initial values have less influences on calculation accuracy. Therefore,DFP algorithm can be used for real-time simulation of water distribution networks.
基金Supported by the National Natural Science Foundation of China(No.61701010,61571021,61601330)
文摘Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.
基金supported in part by Fundamental Research Funds for the Central Universities(No.ZYGX2024J014)in part by the National Natural Science Foundation of China(No.52277083).
文摘This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation(DSSE)against anomalous real-time measurements,as well as a deep auto-encoder(DAE)-based detector and a Gaussian process-aided residual learning(GARL)to deal with challenges arising from topology changes.A global-scanning jumping knowledge network(GSJKN)is first designed to establish the regression rule between the measurement data and state variables.The structural information of distribution system(DS)and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology,contributing to valid estimation precision in sparsely measured DSs.To monitor the topology changes of the network,a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology,which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error.When the topology change occurs,a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology.The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements,which enhances the robustness to typical data acquisition errors.The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change,as well as achieve effective quantification of the estimation uncertainties.Comparative tests on balanced and unbalanced systems demonstrate the accuracy,robustness,and adaptability of the proposed DSSE method.
基金supported in part by the National Key Research Program of China(2016YFB0900100)Key Project of Shanghai Science and Technology Committee(18DZ1100303).
文摘Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.
基金supported by National Natural Science Foundation of China(No.51277052)
文摘This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.
文摘Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are derived.Based on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also presented.The simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.
基金supported by the National Key R&D Program of China (No. 2020YFB0906000 and 2020YFB0906001)。
文摘The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.
基金supported in part by the Department of Energy(No.DE-AR-0001001,No.DE-EE0009355)the National Science Foundation(NSF)(No.ECCS-2145063)。
文摘Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.
基金supported by the National Natural Science Foundation of China(No.5217070269).
文摘Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.
基金supported in part by the National Key Research and Development Program of China(No.2017YFB0902900)the State Grid Corporation of China
文摘Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.
基金supported in part by the grant#2021/11380-5,Centro Paulista de Estudos da Transi??o Energética (CPTEn),São Paulo Research Foundation (FAPESP)the grant#88887.661856/2022-00,Coordenação de Aperfei?oamento de Pessoal de Nível Superior–Brasil (CAPES)the grant#88887.370014/2019-00,Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)。
文摘This paper presents a properly designed branchcurrent based state estimator(BCBSE)used as the main core ofan accurate fault location approach(FLA)devoted to distribution networks.Contrary to the approaches available in the literature,it uses only a limited set of conventional measurementsobtained from smart meters to accurately locate faults at busesor branches without requiring measurements provided by phasor measurement units(PMUs).This is possible due to themethods used to model the angular reference and the faultedbus,in addition to the proper choice of the weights in the stateestimator(SE).The proposed approach is based on a searchingprocedure composed of up to three stages:①the identificationof the faulted zones;②the identification of the bus closest tothe fault;and③the location of the fault itself,searching onbranches connected to the bus closest to the fault.Furthermore,this paper presents a comprehensive assessment of the proposedapproach,even considering the presence of distributed generation,and a sensitivity study on the proper weights required bythe SE for fault location purposes,which can not be found inthe literature.Results show that the proposed BCBSE-basedFLA is robust,accurate,and aligned with the requirements ofthe traditional and active distribution networks.
基金supported by the National Key Research and Development Program(No.2017YFB0902900).
文摘The accuracy of distribution system state estimation(DDSE)is reduced when phasor measurement unit(PMU)measurements contain outliers because of cyber attacks or global positioning system spoofing attacks.Therefore,to enhance the robustness of DDSE to measurement outliers,approximate the target distribution of Metropolis-Hastings(MH)sampling,and judge the prediction of the long short-term memory(LSTM)network,this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling(E-LM model),motivated by the characteristics of the chronological correlations of PMU measurements.First,the target distribution of outlier reconstruction is derived using a kernel density estimation function.Subsequently,the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view.The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations.Moreover,the proposed MH-based forecasting of the LSTM can judge each LSTM prediction,which is independent of its true value.Finally,simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.
基金supported by the Shanghai Sailing Program(No.19YF1423700)the National Key Research and Development Program of China(No.2016YFB0900100)the Key Project of Shanghai Science and Technology Committee(No.18DZ1100303).
文摘This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation.A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment,operation,and maintenance,avoid the difference in the partition sizes,reduce the level of coupling between connected partitions,and maximize the inner cohesion of each partition.Capacities of edge server are constrained against underload and overload.To efficiently solve the problem,an improved non-dominated sorting genetic algorithm III(NSGA-III)is developed,with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence.Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs,and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution.The optimized result contributes significantly to real-time distributed state estimation.
基金supported in part by the project of National Natural Science Foundation of China(Nos.52277116 and 52207133).
文摘Modern distribution networks with high penetration of distributed energy resources(DERs)are undergoing continuous expansion in scale.However,the increasing complexity of network structure and the high installation cost of measurement equipment introduce operational challenges including state variability and measurement data incompleteness.Substantial data loss significantly compromises fault detection accuracy and network performance,creating obstacles for distributed energy management and posing critical challenges to distribution network state estimation.To address these issues,this paper proposes a hybrid state estimation framework(MC-VMD-ARIMA-LSTM)that integrates alternating-minimization matrix completion(MC)with variational mode decomposition(VMD),autoregressive integrated moving average(ARIMA)modeling,and long short-term memory(LSTM)neural networks for enhanced power flow analysis in low-observability distribution net-works.The methodology features a dual-timescale approach:(1)At individual time intervals,an alternating-minimization matrix completion model is formulated,incorporating linearized power flow constraints;(2)For multi-timescale analysis,the measurement dataset undergoes VMD-based decomposition,with subsequent specialized processing where ARIMA handles low-frequency components and LSTM manage high-frequency residuals.The results of state estimation are obtained through systematic component reconstruction.Compre-hensive evaluations using IEEE 33-bus distribution network and actual distribution system measurement datasets demonstrate the framework’s effectiveness in both multi-timescale data assimilation and state estimation ac-curacy under limited observability conditions.
基金supported by National Natural Science Foundation of China(No.61329301)the Royal Society of the UK+2 种基金the Research Fund for the Taishan Scholar Project of Shandong Province of Chinathe China Postdoctoral Science Foundation(No.2016M600547)the Alexander von Humboldt Foundation of Germany
文摘In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（especially networked systems）. For networked systems, event-based transmission scheme is capable of improving the efficiency in resource utilization and prolonging the lifetime of the network components compared with the widely adopted periodic transmission scheme. As such, it would be interesting to 1） examining how the event-triggering mechanisms affect the control or filtering performance for networked systems, and 2） developing some suitable approaches for the controller and filter design problems. In this paper, a bibliographical review is presented on event-based control and filtering problems for various networked systems. First, the event-driven communication scheme is introduced in detail according to its engineering background, characteristic, and representative research frameworks. Then, different event-based control and filtering（or state estimation） problems are categorized and then discussed. Finally, we conclude the paper by outlining future research challenges for event-based networked systems.
