It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems(EMSs), is vulnerable to false data injection attacks due to the undetectability...It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems(EMSs), is vulnerable to false data injection attacks due to the undetectability of those attacks using standard bad data detection techniques,which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter(UKF) in conjunction with a weighted least square(WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.展开更多
In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fa...In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fast, and robust power system state estimation(PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states(i.e., nodal voltage phasors).Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed.Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.展开更多
State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady...State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.展开更多
This paper proposes an L_(p)(0<p<1)quasi norm state estimator for power system static state estimation.Compared with the existing L1 and L2 norm estimators,the proposed estimator can suppress the bad data more e...This paper proposes an L_(p)(0<p<1)quasi norm state estimator for power system static state estimation.Compared with the existing L1 and L2 norm estimators,the proposed estimator can suppress the bad data more effectively.The robustness of the proposed estimator is discussed,and an analysis shows that its ability to suppress bad data increases as p decreases.Moreover,an algorithm is suggested to solve the nonconvex state estimation problem.By introducing a relaxation factor in the mathematical model of the proposed estimator,the algorithm can prevent the solution from converging to a local optimum as much as possible.Finally,simulations on a 3-bus DC system,the IEEE 14-bus and IEEE 300-bus systems as well as a 1204-bus provincial system verify the high computation efficiency and robustness of the proposed estimator.展开更多
With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.Thi...With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.展开更多
The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature.Nevertheless,these two operations have been designed and optimized for the most part...The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature.Nevertheless,these two operations have been designed and optimized for the most part in isolation.Specifically,state estimators are optimized based on the minimum mean-square error criteria,which is only optimal when the source of distortions in the data is Gaussian random noise.Hence,the state estimators fail to perform optimality when the data is further contaminated by bad data,which cannot necessarily be modeled by additive Gaussian terms.The problem of power state estimation has been studied extensively.But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data(due to sensor failures)or structured bad data(due to cyber attacks,which are also referred to false data injection attacks).This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines.We aim to carry out the combined tasks of detecting the presence of random and structured bad data,and form accurate estimations for the state of power grid.This paper characterizes the optimal detectors and estimators.Furthermore,the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.展开更多
The generation of atomic entanglement is discussed in a system that atoms are trapped in separate cavities which are connected via optical fibres. Two distant atoms can be projected to Bell-state by synchronized turni...The generation of atomic entanglement is discussed in a system that atoms are trapped in separate cavities which are connected via optical fibres. Two distant atoms can be projected to Bell-state by synchronized turning off of the local laser fields and then performing a single quantum measurement by a distant controller. The distinct advantage of this scheme is that it works in a regime where Δ≈κ〉〉g, which makes the scheme insensitive to cavity strong leakage. Moreover, the fidelity is not affected by atomic spontaneous emission.展开更多
Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS...Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)applications.However,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports.Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and etc.To pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this paper.Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module.Two characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical measurements.We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.展开更多
为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非...为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非处处可导,因而无法利用基于梯度的方法进行求解。为此,给出了MEAV基本模型的等价模型,并详细推导了基于原-对偶内点算法的MEAV等价模型的求解方法。算例分析表明,MEAV在估计过程中可自动抑制多个强相关不良数据,显示了良好的抗差性和较高的计算效率,因而具有良好的工程应用前景。展开更多
基金supported by the Ministry of Education,Science and Technological Development of the Republic of Serbia and Schneider Electric DMS NS,Serbia(No.Ⅲ-42004)
文摘It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems(EMSs), is vulnerable to false data injection attacks due to the undetectability of those attacks using standard bad data detection techniques,which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter(UKF) in conjunction with a weighted least square(WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.
基金Wang G and Giannakis GB were supported by the National Natural Science Foundation of China(NSFC)(Nos.1514056,1505970,and 1711471)Chen J and Sun J were supported by the NSFC(Nos.61621063 and 61522303)+2 种基金the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(No.61720106011)the Projects of Major International(Regional)Joint Research Program NSFC(No.61720106011)the Program for Changjiang Scholars and Innovative Research Team in University(No.IRT1208)
文摘In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fast, and robust power system state estimation(PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states(i.e., nodal voltage phasors).Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed.Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.
基金This work was supported in part by the National Natural Science Foundation of China(61733010)the China Postdoctoral Science Foundation(2019M650675).
文摘State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.
基金This work was supported by the National Natural Science Foundation of China(No.51967002).
文摘This paper proposes an L_(p)(0<p<1)quasi norm state estimator for power system static state estimation.Compared with the existing L1 and L2 norm estimators,the proposed estimator can suppress the bad data more effectively.The robustness of the proposed estimator is discussed,and an analysis shows that its ability to suppress bad data increases as p decreases.Moreover,an algorithm is suggested to solve the nonconvex state estimation problem.By introducing a relaxation factor in the mathematical model of the proposed estimator,the algorithm can prevent the solution from converging to a local optimum as much as possible.Finally,simulations on a 3-bus DC system,the IEEE 14-bus and IEEE 300-bus systems as well as a 1204-bus provincial system verify the high computation efficiency and robustness of the proposed estimator.
基金supported by the Science and Technology Program of State Grid Corporation of China under project“AI based oscillation detection and control”(No.SGJS0000DKJS1801231)
文摘With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.
基金supported by the US NationalScience Foundation(No.ECCS-1554482).
文摘The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature.Nevertheless,these two operations have been designed and optimized for the most part in isolation.Specifically,state estimators are optimized based on the minimum mean-square error criteria,which is only optimal when the source of distortions in the data is Gaussian random noise.Hence,the state estimators fail to perform optimality when the data is further contaminated by bad data,which cannot necessarily be modeled by additive Gaussian terms.The problem of power state estimation has been studied extensively.But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data(due to sensor failures)or structured bad data(due to cyber attacks,which are also referred to false data injection attacks).This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines.We aim to carry out the combined tasks of detecting the presence of random and structured bad data,and form accurate estimations for the state of power grid.This paper characterizes the optimal detectors and estimators.Furthermore,the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.
基金Supported by the National Natural Science Foundation of China under Grant Nos 10647107 and 10575017.
文摘The generation of atomic entanglement is discussed in a system that atoms are trapped in separate cavities which are connected via optical fibres. Two distant atoms can be projected to Bell-state by synchronized turning off of the local laser fields and then performing a single quantum measurement by a distant controller. The distinct advantage of this scheme is that it works in a regime where Δ≈κ〉〉g, which makes the scheme insensitive to cavity strong leakage. Moreover, the fidelity is not affected by atomic spontaneous emission.
基金This work was supported by SGCC Science and Technology Program under project“AI-based oscillation detection and control”(SGJS0000DKJS1801231)。
文摘Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)applications.However,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports.Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and etc.To pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this paper.Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module.Two characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical measurements.We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.
文摘为提高状态估计的抗差性,提出一种基于最大指数绝对值目标函数的状态估计(maximum exponential absolute value state estimation,MEAV)方法。首先给出了MEAV的基本模型,并介绍了其理论基础和数学性质。由于MEAV基本模型的目标函数并非处处可导,因而无法利用基于梯度的方法进行求解。为此,给出了MEAV基本模型的等价模型,并详细推导了基于原-对偶内点算法的MEAV等价模型的求解方法。算例分析表明,MEAV在估计过程中可自动抑制多个强相关不良数据,显示了良好的抗差性和较高的计算效率,因而具有良好的工程应用前景。
