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Performance Comparison of distributed state estimation Algorithms for Power Systems

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Journal of Systems Science & Complexity 2017年第3期30卷 595-615页

作者： SUN Yibing FU Minyue ZHANG Huanshui School of Control Science and Engineering Shandong University School of Electrical Engineering and Computer Science University of Newcastle School of Automation Guangdong University of Technology

A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.

关键词： distributed MAP estimation distributed state estimation extended Kalman filter power systems.

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Optimal Network Partition and Edge Server Placement for distributed state estimation

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1637-1647页

作者： Lyuzerui Yuan Jie Gu Jinghuan Ma Honglin Wen Zhijian Jin School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

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 *** of edge server are constrained against underload and *** 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 *** 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 *** optimized result contributes significantly to real-time distributed state estimation.

关键词： Network partition edge server placement distributed state estimation edge computing non-dominated sorting genetic algorithm(NSGA)

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ADMM-based distributed state estimation for Integrated Energy System

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CSEE Journal of Power and Energy Systems 2019年第2期5卷 275-283页

作者： Yaxin Du Wen Zhang Tingting Zhang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250061China

In order to have an accurate knowledge of system-wide operation states,it is necessary to perform state estimation for the integrated energy system(IES)as the basis of energy man-agement and *** state estimation is practically infeasible for IES due to the unreliability of communication,the barrier on privacy,and the large scale of integrated *** paper proposes a distributed state estimation algorithm based on the alternating direction method of multipliers(ADMM)for IES containing electricity,heat,and natural *** coupling units are taken into full consideration in modeling of IES state estimation to reflect the harmonization of multi *** the basis of bilinear measurement model,the state estimation considering nonlinear measurements can be replaced by an equivalent three-stage problem containing two linear state estimations and an intermediate transformation to avoid non-convex *** three-stage procedure for IES state estimation can be further decoupled over three sub-systems with coordination on coupling units,yielding a fully distributed scheme based on ADMM.A modified ADMM with the self-adjusting penalty parameter is also adopted to enhance the *** results demonstrate the validity and superiority of the proposed algorithm.

关键词： Alternating direction method of multipliers coupling units distributed state estimation integrated energy system

distributed state estimation for Heterogeneous Mobile Sensor Networks with Varying Nodes

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Distributed State Estimation for Heterogeneous Mobile Sensor...

第三十九届中国控制会议

作者： Yingrong Yu Jie Ren Xiwang Dong Qingdong Li Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Aerospace System Engineering Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

On background of cooperative air combat, state estimation of aerial maneuvering target with multi-sensor cooperative reconnaissance is discussed in this paper. Multiple moving platforms equipped with different kinds of detectors construct heterogeneous mobile sensor networks. Graph theory is adopted to describe the communication topology of the networks. In consideration of detection range limit, the number of sensors participating in the detection changes over time. An interactive multiple model algorithm（IMM） is utilized for target motion modeling. The local observation information is processed by Cubature Information Filter（CIF）. A weighted average consensus-based state estimation method for heterogeneous mobile sensor networks with varying nodes is put forward to fuse information from different sensors and obtain a consistent and precise tracking result. Moreover, the stability analysis of proposed method is provided detailed. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.

关键词： Cubature Information Filter consensus theory distributed state estimation mobile sensor networks stochastic boundedness

Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph

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Science China(Information Sciences) 2023年第9期66卷 78-93页

作者： Mingyan ZHU Rui WANG Xiaodong MIAO Tianju SUI School of Control Science and Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Aeronautics and Astronautics Dalian University of Technology Key Laboratory of Advanced Technology for Aerospace Vehicles School of Mechanical and Power Engineering Nanjing Tech University

This paper addresses a distributed dynamic state estimation problem in large-scale systems characterized by a cyclic network graph. The objective is to develop a distributed estimation algorithm for each node to generate local state estimations, based on the coupled measurements and boundary information exchanged with neighboring nodes. Our proposed approach is grounded in the maximum a posteriori(MAP)estimation method, which yields suboptimal results in acyclic network graphs compared with the centralized MAP approach. We extend this approach to systems with a cyclic network graph. Furthermore, we provide an accuracy analysis by deriving bounds for the differences in estimation error covariance and state estimation between the proposed distributed algorithm and the suboptimal centralized MAP method. These bounds apply to a specific category of systems that satisfy certain conditions, including cyclic topology and sparse connections. We demonstrate that these bounds converge asymptotically, with the rate of convergence determined by the loop-free depth of the graph. The loop-free depth of the graph refers to the maximum number of nodes that can be traversed in a cycle without revisiting any node. Finally, we demonstrate the validity of the algorithm through numerical examples.

关键词： distributed state estimation maximum a posteriori estimation cyclic network graph

distributed H∞ state estimation of Large-scale Power Grid Under Mixed-type Attacks

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Distributed H∞ State Estimation of Large-scale Power Grid U...

第32届中国控制与决策会议

作者： Yunfa Wu Jun-yi Li Bin Zhang Wenshuai Lin Guangdong University of Technology

This paper studies the distributed state estimation problem for large-scale power grid under mixed-type *** topology is introduced to describe the relationship among distributed sensor *** separate Bernoulli sequences are used to describe the situations that denial of service attack and deception attack occur randomly and independently of each *** on the Lyapunov method and stochastic technique,a sufficient condition is obtained which guarantees the mean-square stability and H performance of the nonlinear large-scale power grid system under the mixed-type *** state estimators are designed to maintain a small estimation error under mixed-type ***,the effectiveness of the designed estimators is verified by numerical simulation.

关键词： Large-scale power grid Mixed-type attacks distributed state estimation

Fully distributed variational Bayesian non-linear filter with unknown measurement noise in sensor networks

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Science China(Information Sciences) 2020年第11期63卷 24-37页

作者： Yu LIU Jun LIU Congan XU Gang LI You HE Research Institute of Information Fusion Naval Aviation University Department of Electronic Engineering Tsinghua University

In practical applications, the measurement noise statistics is usually unknown or may change over time. However, most existing distributed filtering algorithms for sensor networks are constructed based on exact knowledge of measurement noise statistics. Therefore, under situations with measurement uncertainty, the existing algorithms may result in deteriorated performance. To solve such problems, a distributed adaptive cubature information filter based on variational Bayesian(VB-DACIF) is proposed here. Firstly,the predicted estimates of interest from inclusive neighbours are fused by minimizing the weighted KullbackLeibler average, in which the cubature rule is utilized to tackle system nonlinearity. Then, the free form variational Bayesian approximation is applied to recursively update both the local estimate and the precision matrices of sensing nodes. Finally, the posterior Cram′er-Rao lower bound is exploited to evaluate performance of the proposed VB-DACIF. Simulation results with a maneuvering target tracking scenario validates the feasibility and superiority of the proposed VB-DACIF.

关键词： sensor networks distributed state estimation cubature Kalman filter variational Bayesian inference Kullback-Leibler divergence

Partial diffusion Kalman filter for communication bandwidth constraints 36

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Partial diffusion Kalman filter for communication bandwidth ...

第36届中国控制会议

作者： Guoqing Wang Ning Li Yonggang Zhang Zhiyong Wang College of Automation Harbin Engineering University China Academy of Space Technology

ISBN: (纸本)9781538629185

In this paper,we propose a novel distributed Kalman filter（KF） over sensor networks using partial diffusion strategy,where each sensor only communicates with its neighbours and individual estimation vectors are partially transmitted to obtain local ones through convex combinations under communication bandwidth *** solution can obtain a trade-off between communications burden and estimation *** results show the effectiveness of our algorithm,and it can obtain similar estimation accuracy with less information exchange when compared with the weighted KF.

关键词： sensor networks distributed state estimation partial diffusion Kalman filter

Multi-level Diffusion Kalman Filter Algorithm for Adaptive Networks 36

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Multi-level Diffusion Kalman Filter Algorithm for Adaptive N...

第36届中国控制会议

作者： Xiaorui Yang Wenxuan Han Lijuan Jia Shunshoku Kanae School of Information and Electronics Beijing Institute of Technology Fukui University of Technology

ISBN: (纸本)9781538629185

We study the problem of distributed state estimation over adaptive networks,where agents collaborate to estimate a common state parameter *** the sensing target area is too large or we want to improve the convergence speed of a large adaptive network,single-level diffusion algorithms do not have better performance,so we study the multi-level diffusion Kalman filter algorithm where a network running a diffusion strategy is enhanced by defining some special nodes called *** order to improve the estimation accuracy,we also study the weight-normalized diffusion Kalman filter *** results show that multi-level diffusion Kalman filter algorithm has better accuracy and convergence performance than single-level diffusion Kalman filter ***,in order to further improve the algorithm’s performance,we studied weightnormalized methods which are better than average weight methods.

关键词： distributed state estimation diffusion strategy Kalman filter adaptive networks