相关论文
2007 - 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007
Considering the uncertainty on the network parameters in the optimal planning of measurement systems for Distribution State Estimation

This paper proposes an optimization algorithm based on the techniques of the dynamic programming and suitable to choose the optimal number and position of the measurement devices in distribution state estimation procedures used in modern electric distribution network. The goal is to guarantee at the same time the minimum cost and the accuracy required to the measured data needed to operate management and control issues, such as energy dispatching and protection coordination. Both the uncertainty introduced by the measurement devices and the tolerance in the knowledge of the network parameters (line impedances) are taken into account in the proposed approach. Random changes of the loads are considered to establish adequate reference conditions for the tests. Tests relevant to a real size distribution network are presented to show the validity of the proposed approach. The results emphasize how the influence of the tolerance on the network parameters can be dramatically minimized by suitably choosing the algorithm to be implemented to solve the distribution state estimation problem.

2001 - IEEE Transactions on Power Systems
A robust method for equality constrained state estimation

This paper presents a robust method for the solution of the equality constrained state estimation problem. The problem is formulated as a weighted least squares (WLS) problem with equality constraints. Traditionally the problem is solved iteratively by the normal equations with equality constraints (NE/C). One problem of NE/C is that the gain matrices are not positive definite and extra logic is required to process the zero pivots during Gaussian elimination. In this. paper, positive definite gain matrices are formulated and triangularization is performed symbolically using sparsity techniques. The method is illustrated with a six-bus system and the IEEE 14-bus system.

1975 - Automatica
Exact and approximate state estimation for nonlinear dynamic systems

This paper presents a modified approach to solve state estimation problems of nonlinear dynamic systems involving noise free, uncorrelated and correlated state and measurement noise processes. The basic approach makes use of the matrix minimum principle together with the Kolmogorov and Kushner's equations to minimize the error-variance, taken to be the estimation criterion. The filtering equations obtained for nonlinear systems with white noise process are exact, but for non-white noise processes the results obtained are approximate. For systems with polynomial or product types non-linearities, the proposed algorithms can be evaluated without the need of approximation under the assumption that the estimator errors are Gaussian. Such an assumption is significantly different from the most commonly used assumption that the state is Gaussian. Simulation results obtained from the proposed filtering algorithms are compared to various other approximate nonlinear filters. The results indicate the superiority of the proposed filter over those of other filters investigated.

2008 - Seventh International Conference on Networking (icn 2008)
Using Network Density as a New Parameter to Estimate Distance

A wireless sensor network consists of a large quantity of small, low-cost sensor nodes that are limited in terms of memory, available energy and processing capacity. Generally, these sensor nodes are distributed in space to obtain physical parameters such as temperature, humidity, vibration or light conditions, and transmit the measured values to a central entity. The measurements are tagged with the corresponding location of the nodes in the network and the time of sampling, to enable a view on the value distribution in space and time later on. Positioning of wireless sensor nodes without dedicated hardware is an open research question. Especially in the domain of embedded networked sensors, many applications rely on spatial information to relate collected data to the location of its origin. As a first step towards localization, an estimation of the distance between two nodes is often carried out to determine their positions. So far, the majority of approaches therefore explore physical properties of radio signals such as the strength of a received signal or its trip time. However, this is problematic since either the complexity on the software or on the hardware side is not adequate for embedded systems, or the approaches lack the required accuracy. In this paper we present the WDNI algorithm (Weighted Density of Node Intersection) to determine the distance between two nodes, relying solely on the investigation of local node densities. To evaluate the accuracy of this algorithm, we ran extensive simulations and experimented with different testbed setups using real sensor nodes, and finally compared WDNI to a range-free distance estimation algorithm based the analysis of RSSI values.

2009 - 2009 IEEE Power & Energy Society General Meeting
A framework for estimation of power systems based on synchronized phasor measurement data

This paper presents a new approach for synchronized phasor measurement-based state estimation, which starts from the buses with synchronized phasor data and enables highly accurate estimation of portions of the power system with connectivity to these phasor data buses. Observability analysis is used to assemble the buses for this Phasor State Estimator (PSE). The state estimation is formulated as an iterative least-squares problem, with bus voltage and line current magnitudes and phases as the estimated variables. An important aspect of this formulation is its ability to correct for constant or random phase biases that may exist in some phasor measurement equipment. Beside being a standalone state estimator, the PSE can enhance the reliability of a conventional state estimator. The PSE methodology is demonstrated for a power transfer path in the New York power system.

2010 - Comput. Math. Appl.
Decentralized robust set-valued state estimation in networked multiple sensor systems

This paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder-estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations.

2011 - International Journal of Systems Science
Optimal consensus-based distributed estimation with intermittent communication

In this article, we consider the problem of distributed state estimation over a wireless sensor network (WSN). We firstly propose a distributed algorithm to estimate the state of a system by modelling the WSN as a directed network. Based on the Kalman filter, we introduce a consensus scheme for each sensor by including the estimates received from its neighbour sensors. We also consider intermittent and random data packet drops which are frequently seen in wireless networks. A sufficient condition is derived for the convergence of the state estimation error, and a upper bound is obtained for the estimation error covariance. We further consider minimising the estimation error by finding an optimal consensus gain for a given fixed network. The performance and effectiveness of the proposed algorithm are compared with existing well-known results from the literature.

2013 - IEEE Transactions on Neural Networks and Learning Systems
On Stabilization of Stochastic Cohen-Grossberg Neural Networks With Mode-Dependent Mixed Time-Delays and Markovian Switching

The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. By introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive delay-dependent criteria for the exponential stabilization problem. Three numerical examples are carried out to demonstrate the feasibility of our delay-dependent stabilization criteria.

2013 - 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm)
Detection and localization of targeted attacks on fully distributed power system state estimation

Distributed state estimation will play a central role in the efficient and reliable operation of interconnected power systems. Therefore, its security is of major concern. In this work we show that an attacker that compromises a single control center in an interconnected system could launch a denial of service attack against state-of-the-art distributed state estimation by injecting false data, and consequently, it could blind the entire system. We propose a fully distributed attack detection scheme based on local measurements to detect such a denial of service attack. We then propose a fully distributed attack localization scheme that relies on the regions' beliefs about the attack location, and performs inference on the power system topology to identify the most likely attack location. We validate both algorithms on the IEEE 118 bus power system.

2017 - Asian Journal of Control
Testing and Defending Methods Against DOS Attack in State Estimation

In this paper, we consider a state estimation problem. In this problem, a sensor measures the state of a linear discrete-time system and sends measurements to an estimator via a packet-dropping communication link. We are concerned with the effect of Denial-of-Service (DoS) attacks on stability of the estimation system, and particularly focus on how to examine whether the communication channel is under DoS attack or not as well as how to defend accordingly, if defense is possible. We formulate the detection problem as a hypothesis testing problem provided that the statistics of the communication channel is known a priori. Two defense countermeasures are proposed: one of which uses a secured packet coding approach to partly compensate the previous packet loss; and in the other the sensor's transmission power is raised to resist the jamming effect brought by the DoS attack. Simulations are provided to demonstrate the main results.

2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Distributed state estimation in multi-agent networks

In this paper, we consider the problem of state estimation of a dynamical system in a multi-agent network. The agents are sparsely connected and each of them observes a strict subset of the state vector. The distributed algorithm that we propose enables each agent to estimate any arbitrary linear dynamical system with bounded mean-squared error. To achieve this, the ratio of the algebraic connectivity and the largest eigenvalue of the graph Laplacian has to be larger than a lower bound determined by the spectral radius of the system's dynamics matrix. This extends the notion of Network Tracking Capacity introduced by other authors in prior work. We accomplish this by introducing a new class of estimation algorithm of dynamical systems that, besides a (consensus + innovations) term, also includes consensus on the innovations.

1983 - IEEE Power Engineering Review
Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems

This paper intends to give a unified survey of methods appropriate for solving the state estimation problem in large-scale electric power systems. After a first overview of the various approaches proposed up to now, the most suitable among them are described, examined and compared. The comparisons are carried out on the basis of selected criteria evolving estimation properties of the resulting algorithms, along with their organization possibilities and their capabilities of handling some important satellite functions.

1982 - IEEE Transactions on Power Apparatus and Systems
Power System State Estimation with Measurement Deficiency: An Algorithm that Determines the Maximal Observable Subnetwork

Measurement system failures may make it impossible for the power system state estimator to estimate bus voltage angles and magnitudes at all buses in the power system. When confronted with such a measurement deficiency, the state estimator can estimate bus voltages over only a portion of the entire network. This paper presents an algorithm that determines the largest region where bus voltages may be estimated-the maximum observable subnetwork. The theoretical basis of the algorithm is developed, and the underlying concepts are illustrated on an example network.

2015 - IEEE Transactions on Cybernetics
Event-Triggered State Estimation for Complex Networks With Mixed Time Delays via Sampled Data Information: The Continuous-Time Case

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called “event-triggered condition” is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results.

2003
Parallel and Distributed State Estimation

In this chapter, we discuss parallel and distributed techniques for state estimation. We cover the design of parallel state estimation, which will run on a parallel mainframe; then we design an asynchronous distributed state estimation method by taking the unpredictability of communication delays into account. The mathematical proofs and analyses for the convergence of asynchronous distributed state estimation algorithms are included. The proposed distributed state estimation algorithms will run on a distributed system that consists of computers at the ISO and RTO sites, and their satellite control centers.

2008 - Autom.
Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes

A moving-horizon state estimation problem is addressed for a class of nonlinear discrete-time systems with bounded noises acting on the system and measurement equations. As the statistics of such disturbances and of the initial state are assumed to be unknown, we use a generalized least-squares approach that consists in minimizing a quadratic estimation cost function defined on a recent batch of inputs and outputs according to a sliding-window strategy. For the resulting estimator, the existence of bounding sequences on the estimation error is proved. In the absence of noises, exponential convergence to zero is obtained. Moreover, suboptimal solutions are sought for which a certain error is admitted with respect to the optimal cost value. The approximate solution can be determined either on-line by directly minimizing the cost function or off-line by using a nonlinear parameterized function. Simulation results are presented to show the effectiveness of the proposed approach in comparison with the extended Kalman filter.

2016 - International Journal of General Systems
state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

This paper deals with the robust state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov–Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

2005 - IEEE Transactions on Neural Networks
State estimation for delayed neural networks

In this letter, the state estimation problem is studied for neural networks with time-varying delays. The interconnection matrix and the activation functions are assumed to be norm-bounded. The problem addressed is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally exponentially stable. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. In particular, we derive the conditions for the existence of the desired estimators for the delayed neural networks. We also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities (LMIs). Finally, it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks. Numerical examples are included to illustrate the applicability of the proposed design method.

2005 - SenSys '05
Data collection, storage, and retrieval with an underwater sensor network

In this paper we present a novel platform for underwater sensor networks to be used for long-term monitoring of coral reefs and fisheries. The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate point-to-point using a novel high-speed optical communication system integrated into the TinyOS stack, and they broadcast using an acoustic protocol integrated in the TinyOS stack. The nodes have a variety of sensing capabilities, including cameras, water temperature, and pressure. The mobile nodes can locate and hover above the static nodes for data muling, and they can perform network maintenance functions such as deployment, relocation, and recovery. In this paper we describe the hardware and software architecture of this underwater sensor network. We then describe the optical and acoustic networking protocols and present experimental networking and data collected in a pool, in rivers, and in the ocean. Finally, we describe our experiments with mobility for data muling in this network.

2016 - Neurocomputing
Non-fragile state estimation for discrete Markovian jumping neural networks

In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov-Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods.

Distributed state estimation for uncertain Markov‐type sensor networks with mode‐dependent distributed delays

Zidong Wang
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Xiaohui Liu
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Jinling Liang
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Zidong Wang
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Jinling Liang
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Xiaohui Liu

Abstract:SUMMARY In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete-time dynamical systems with Markovian jumping parameters and distributed time-delays. The sensor network consists of sensor nodes characterized by a directed graph with a nonnegative adjacency matrix that specifies the interconnection topology (or the distribution in the space) of the network. Both the parameters of the target plant and the sensor measurements are subject to the switches from one mode to another at different times according to a Markov chain. The parameter uncertainties are norm-bounded that enter into both the plant system as well as the network outputs. Furthermore, the distributed time-delays are considered, which are also dependent on the Markovian jumping mode. Through the measurements from a small fraction of the sensors, this paper aims to design state estimators that allow the nodes of the sensor network to track the states of the plant in a distributed way. It is verified that such state estimators do exist if a set of matrix inequalities is solvable. A numerical example is provided to demonstrate the effectiveness of the designed distributed state estimators. Copyright © 2011 John Wiley & Sons, Ltd.

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