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distributed state estimation of active distribution network considering mixed-frequency measurement data hierarchical encryption

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APPLIED ENERGY 2025年 388卷

作者： Tian, Shuxin Zhu, Feng Shen, Jinhua Yang, Xijun Fu, Yang Mi, Yang Ling, Ping Shanghai Univ Elect Power Minist Educ Engn Res Ctr Offshore Wind Power Technol Shanghai 200090 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China State Grid Shanghai Elect Power Res Inst Shanghai 200437 Peoples R China

distributed state estimation is necessary for active distribution networks to calculate high-frequency and highdynamic random fluctuations of distributed generations and controlled loads. Given the limitations of traditional measurements and the potential problem of false data injection attacks (FDIA), it is imperative that high accuracy phasor measurement units (PMUs) with information security are implemented in active distribution networks. A novel distributed state estimation method is proposed in this paper. This method involves merging data from the PMU and supervisory control and data acquisition (SCADA) and encrypting subsequent communications. Firstly, a PMU-centric active distribution network partition topology model is proposed based on the principles of parallel computing load balancing and local communication efficiency. Secondly, to improve the observability of the system, a data fusion strategy for mixed-frequency data measured by SCADA and PMU is proposed. Thirdly, a distributed state estimation method is proposed that considers an encryption model and a distributed square root cubature Kalman-Gaussian mixture probability hypothesis density attacks (DSRCK-GMPHD) algorithm. The proposed state estimation method can improve the speed and accuracy of multi-subdomain state tracking and minimize the mismatch between the measured and actual states of the active distribution network. Finally, extensive tests were performed on the improved PG&E69 system. The test results show that the method proposed in this paper is able to accurately capture the real-time state of multiple subareas of an active distribution network and improve the security of the multi-source measurement data transmission process.

关键词： Phasor measurement unit distributed state estimation Mixed-frequency measurement Communication encryption Probability hypothesis density algorithm

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distributed state estimation and fault diagnosis for networked systems under jointly connected switching networks

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AUTOMATICA 2025年 177卷

作者： Liang, Limei Su, Rong Liu, Shuai Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, the distributed state estimation of the target system and the distributed fault diagnosis of the sensor network are investigated for networked systems under jointly connected switching networks. In this scenario, to eliminate the effect of external disturbance, we first construct a distributed observer system consisting of N distributed unknown input observers (UIOs), each of which is located on the sensor and can only access the partial input and output information of the target system and the transmitted state estimation from its neighbors. Owing to the limitation of communication conditions, the network topology of the distributed observer system is often timevarying and disconnected. To solve this problem, a novel observer parameter design method is proposed, which overcomes the limitation that the existing techniques can only rely on fast switching to estimate the state of the target system. On this basis, a distributed sensor fault diagnosis scheme based on the observer system is designed and its performance is analyzed. In addition, a faulty sensor replacement strategy based on graph theory and system detectability is proposed to ensure that the faulty sensors will not affect the state estimation performance of the distributed observer system. Finally, an illustrative example is introduced to verify the effectiveness of the proposed scheme. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： distributed state estimation Fault diagnosis Jointly connected switching networks distributed UIO

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Approximating the Lower Bound on the Arithmetic Average-Based distributed state estimation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4642-4657页

作者： Yuan, Ye Liu, Xinyu Li, Wujun Yi, Wei Varshney, Pramod K. Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In multisensor state estimation, existing posterior Cram & eacute;r-Rao lower bounds (PCRLBs) are generally derived under the measurement independence assumption. When correlations among measurements are unknown, the true PCRLB cannot be computed due to the inability to compute the likelihood function corresponding to the local measurements. Motivated by the random-variables-based arithmetic average (RV-AA) fusion rule, which combines the local variables with unknown correlations as a linear mixture variable (LMV), an LMV-based approximation to the performance lower bound (LMV-A-LB) on RV-AA is derived in this article to provide a more refined performance indicator for it. This approximation uses the posterior obtained from the LMV along with the Bayes' rule to estimate the true but unavailable likelihood function. We show that the LMV-A-LB generally lacks a closed-form solution, so it is approximated using sequential Monte Carlo approaches. Furthermore, by limiting the system model to the commonly used additive Gaussian noise case, we compute the exact expression of the derived bound with the aid of the Kalman filters. Numerical simulation experiments demonstrate that the LMV-A-LB obtains a tighter performance lower bound for RV-AA fusion by comparing with existing bounds.

关键词： Probability density function Sensors state estimation estimation Arithmetic Sensor fusion Data integration Computational modeling Bayes methods Current measurement distributed state estimation posterior Cram & eacute r-Rao lower bound (PCRLB) random variables-based arithmetic average (RV-AA) fusion

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Stealthy Attack Against distributed state estimation for Cyber-Physical Systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第3期35卷 1091-1099页

作者： Wang, Jie Liu, Yun Yuan, Hongbo Yang, Wen East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China

This article studies a stealthy attack strategy design for distributed state estimation from the perspective of an attacker in cyber-physical systems, where both internal and external attack scenarios are considered simultaneously. To enhance robustness against outliers and reduce communication burden, a distributed fusion estimator is developed by fusing the innovation residuals of neighboring smart sensor nodes without any attack. Based on the designed distributed fusion estimator, the evolution of the distributed estimation error covariance is analyzed, and its lower and upper bounds are obtained. Moreover, a stealthy attack framework embedding adjustable parameter is designed to weaken the estimation performance, where the constraints of the adjustable parameter are provided based on the desired attack effect. Finally, a simulation example is provided to manifest the validity of the main results.

关键词： cyber-physical systems distributed state estimation stealthy attack

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Byzantine-Resilient distributed state estimation: A Distance-Based Multivariable Filtering Mechanism

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13015-13029页

作者： Gao, Rui Yang, Guang-Hong Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper studies the problem of resilient distributed state estimation for a linear system using a network of agents, some of which are subject to the Byzantine attacks. First, by introducing the distance function to quantify the difference between the estimates of the neighboring agents, a multivariable filtering mechanism is designed such that the regular agents can extract the reliable information from the vectors sent by their in-neighbors. Then, using the properties of the distance-based multivariable filtering mechanism and the detectability decomposition, resilient distributed observers are designed for the regular agents to asymptotically estimate the state vector of the system despite the adversarial influence of the Byzantine attacks. Furthermore, a graph-dependent Lyapunov function is proposed to analyze the convergence of the proposed method. In contrast to the existing scalar filtering mechanism-based methods, the proposed method can reduce the complexity of the reliable information extraction, and does not require the existence of multiple individual agents to detect each unstable eigenvalue of the system matrix. Finally, an example is given to demonstrate the effectiveness of the proposed method. Note to Practitioners-This paper is motivated by the problem of collaboratively estimating the state vector of a dynamical system using a network of agents in an attack-prone environment. Existing methods for regular agents to extract the reliable information from the received vectors generally rely on the scalar filtering mechanism, which increase in complexity as the dimension of the system increases and require relatively conservative observability assumption. In contrast, we propose a multivariable filtering mechanism-based resilient distributed state estimation method that has lower complexity and works under milder observability assumption. In future research, we will consider the applications of the proposed method to address the related problems suc

关键词： Vectors Filtering Reliability Eigenvalues and eigenfunctions Data mining Automation Matrix decomposition Observers Time measurement Observability Byzantine attacks distributed state estimation resilient estimation

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Consensus Iterated Posterior Linearization Filter for distributed state estimation

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 561-565页

作者： Garcia-Fernandez, Angel F. Battistelli, Giorgio Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Firenze Dipartimento Ingn Informaz DINFO I-50139 Florence Italy

This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.

关键词： Covariance matrices Approximation algorithms Vectors Kalman filters Signal processing algorithms Sensors Consensus algorithm state estimation Current measurement Noise measurement Consensus distributed state estimation iterated posterior linearisation nonlinear filtering

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distributed state estimation for discrete-time uncertain linear systems over jointly connected switching networks

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AUTOMATICA 2025年 173卷

作者： Zhang, Lan Guay, Martin Lu, Maobin Wang, Shimin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Queens Univ Dept Chem Engn Kingston ON K7L 3N6 Canada

This paper proposes a constructive distributed adaptive observer design approach for jointly observable discrete-time uncertain linear time-invariant (LTI) systems over time-varying communication networks. In comparison with existing works, the approach developed in this work can guarantee distributed state estimation in the presence of sparse sensor arrangement, modeling uncertainties, and unreliable network communication. A discrete-time linear system decomposition method is first developed to mitigate the impact of the unknown parameters. A fully distributed discrete- time adaptive nonlinear observer is then designed for the decomposed system. The observer is composed of two time-varying discrete-time dynamics and two nonlinear mappings that establish the connection between the observed system's state and parameters and the states of the two time-varying dynamics. By establishing a discrete-time parametric representation of the measurement output, the parameter estimation problem is converted to a parameter identification problem, which is solved by the gradient-descent adaptive law in the proposed observer dynamics. The analysis shows that the estimation error system is asymptotically stable. Thus, the distributed discrete-time adaptive observer holds under the jointly observable condition in spite of system uncertainties and jointly connected communication networks. The robustness properties of the proposed distributed discrete- time adaptive observer in the presence of measurement noise are established using an input-to-state stability analysis. (c) 2024 Published by Elsevier Ltd.

关键词： distributed state estimation Parameter estimation Adaptive control Linear system observers

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Encoding-decoding-based distributed state estimation over sensor networks with limited sensing range under DoS attacks

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NEUROCOMPUTING 2025年 611卷

作者： Lin, Xufeng Hu, Yanyan Zhang, Xuechun Peng, Kaixiang Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Unmanned Syst Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

This paper is concerned with encoding-decoding-based distributed state estimation over sensor networks under DoS attacks. Different from most of the existing research results on distributed state estimation for sensor networks, where all sensors are assumed to have sufficiently wide sensing ranges, this paper considers sensors with limited sensing ranges. Therefore, the problem studied has more practical significance. To save the limited bandwidth resources of sensor networks, a two-channel encoding-decoding scheme (EDS) based on probability is proposed for each node to compress the transmitted data to an acceptable range, where independent DoS attacks are launched randomly on the communication channels between nodes. Then, a distributed state estimator with limited sensing ranges under DoS attacks in the presence of both the sensor-estimator channel EDS and the node-node channel EDS is constructed under the criterion of minimum mean-square error. Furthermore, considering the real-time changes of the communication topology resulting from independent DoS attacks and the uncertainty introduced by the node-node channel EDS, the upper bound of the expected estimation error covariance is derived and the boundedness of the upper bound is analyzed under given assumption conditions. Finally, a numerical example is exhibited to illustrate the effectiveness of the designed algorithm.

关键词： distributed state estimation DoS attacks Limited sensing range Encoding-decoding scheme

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distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

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Science China(Information Sciences) 2016年第2期59卷 217-231页

作者： Zhi ZHANG Jianxun LI Liu LIU Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications

Low energy consumption and limited power supply are significant factors for wireless sensor networks(WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore,quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.

关键词： data fusion distributed state estimation target tracking Kalman filtering quantization wireless sensor networks

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distributed state estimation for discrete-time linear time invariant systems: A survey

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ANNUAL REVIEWS IN CONTROL 2019年 48卷 36-56页

作者： Rego, Francisco F. C. Pascoal, Antonio M. Aguiar, A. Pedro Jones, Colin N. Univ Lisbon ISR IST Lisbon Portugal Univ Porto Fac Engn Dept Elect & Comp Engn Porto Portugal Ecole Polytech Fed Lausanne Automat Control Lab Lausanne Switzerland

Motivated by the increasing availability and quality of miniaturized sensors, computers, and wireless communication devices arid given their enormous potential, the use of wireless sensor networks (WSN) has become widespread. Because in many applications of WSNs one is required to estimate at each local sensor unit the state of a system given the measurements acquired by multiple sensors, there has been a flurry of activity related to the theory of distributed state estimation. This article contains a literature survey of distributed state estimation for discrete-time linear time invariant systems. In order to obtain the proper historical context, we review the state of the art in this field and summarize previous work. To provide the mathematical intuition behind some of the methods, this survey paper reproduces some of the main results given in the literature. It also provides a critical appraisal of the state of the art and affords the reader a comprehensive presentation of the most relevant results published so far. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： distributed state estimation Linear systems Wireless sensor networks

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