Distributed State Estimator Advances and Demonstration

Vulnerability analysis of distributed state estimation under joint deception attacks

The AC-DC hybrid microgrid is a credible evolution path for the microgrid. State estimation in complex distribution network is a significant foundation for the safe operation. In AC-DC hybrid microgrid, centralised state estimation algorithms have quite a few deficiencies such as large communication capacity and privacy protection. In response to these obstacles, this paper proposed a novel distributed state estimation algorithm. First, a three-stage state estimation model was established for AC-DC hybrid microgrid, in which the second stage is a nonlinear transform for the linearisation of the nonlinear state estimation progress so that the first and third stage can solve the linear state estimation problem. Furthermore, the Lagrangian relaxation method was ultilised for the distributed problem solution. In the proposed algorithm, AC subsystems and DC subsystems respectively execute state estimation through local information, and the overall consistency of the system state estimation can be eventually achieved according to the transfer iteration of the boundary information. The computational efficiency and convergence of the proposed method was ensured owing to the combination of bilinear theory and Lagrangian relaxation. Simulation results indicate that the proposed method can achieve its goal and be superior to existing method in efficiency.

The fast development in electrical power networks and this consequent complexity, impose a more useful state estimation operational scheme, shortening the time interval between consecutive state estimations. Technical developments in distributed energy management systems (EMS), based on fast data communication networks, open up the possibility of parallel and distributed state estimation implementation. In this paper, some ideas for the implementation of parallel and distributed state estimation in electrical power systems are introduced and discussed. A solution methodology based on conventional state estimation algorithms applicable in local networks is introduced and results obtained with an IEEE test system are analyzed in order to appreciate the performances of the proposed method. An evaluation of the degree of natural decoupling in the state estimation problem is also performed and is compared with the results of computational experiments with standard WLS methods, in the integral and distributed version.

State estimation is an important topic in the study of dynamical systems. The problem of estimation can be structured into three categories: 1 centralised scheme; 2 decentralised scheme; 3 distributed scheme. Distributed estimation is a compromise between completely centralised and decentralised versions of estimation. In this paper, we will provide an assessment of distributed estimation based on Kalman filtering techniques for large-scale or sensor networks. In simulation, a second order dynamical system is employed in a scenario of ten sensor nodes. The sensor nodes attempt to estimate the states of the dynamical system with embedded consensus filters. The results show that the distributed estimation algorithm effectively approximates the central Kalman filter. It is concluded that the distributed estimation techniques for distributed dynamical system requires further extensive research.

Distributed agent-based state estimation considering controlled coordination layer

With the increasing integration of renewable energy, the inter-regional electrical connection is strengthened. Due to the large scale of power grid analysis and calculation, the traditional state estimation cannot meet the real-time requirements of the cloud platform. To this end, this paper proposes a distributed state estimation method based on parallelized stream computing, and decoupling the interconnected power grid with tie line to perform sub-region state estimation. The method divides the parallel computing sub-region based on the regulatory subregion of the actual grid and designs a regional tie line state estimation method based on the calculation speed and precision balance. After the completion of distributed parallel sub-regions and tie line state estimation, the sub-region calculations are corrected according to the sensitivity matrix coordination algorithm and the regional tie line state estimation result. The overall estimation result is then obtained through parallel normalization. Numerical tests on IEEE standard system and a large interprovincial interconnected power system have been done to verify that the proposed method can not only significantly improve the speed of state estimation calculation, but also reduce the interregional convergence correlation and the residual pollution.

This paper investigates the distributed state estimation problem for spacecraft thermal experiments over sensor networks. The state estimators are designed in a distributed fashion to estimate the surplus temperature of the target node in the spacecraft. In particular, the randomly occurring time-varying sensor transmission delays are considered during the state estimation, which is more practical under networked environment. By employing the Lyapunov–Krasovskii functional approach, sufficient criteria are established and the desired distributed state estimator gains can be derived, such that the estimation error system can be asymptotically stable. Finally, the illustrative example is provided for demonstrating the effectiveness of the proposed method.

SUMMARYIn 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.

The need for higher frequency in state estimation execution covering larger supervised networks has led to the investigation of faster and numerically more stable state estimation algorithms. However, technical developments in distributed Energy Management Systems, based on fast data communication networks, open up the possibility of parallel or distributed state estimation implementation. In this paper, this possibility is exploited to derive a solution methodology based on conventional state estimation algorithms and a coupling constraints optimization technique. Numerical experiments show suitable performance of the proposed method with regard to estimation accuracy, convergence robustness and computational efficiency. The results of these experiments also indicate the decoupled nature of the state estimation problem. >

System state monitoring is crucial in order to maintain the system security, reliability and quality of a power system. Considering the limitation of monitoring devices in distribution network, state estimation technique could be employed. State estimation is an action of appraising an unknown system variables based on limited number of real-time measurements. The main objective of this paper is to find the suitable state estimation that able to estimate the state of the islanded distribution network. In this paper, state estimation for a distribution network is developed and enhanced by incorporating composite load model. By incorporating this model, the calculated state variables of the network will be more accurate (to obtain the most practical value of the system state variables). Moreover, the proposed state estimation results are compared with backward-forward sweep load flow technique to justify the system state variables.

The problem of malicious false data injection in power grid state estimators has recently gained considerable attention. Most of this attention, however, has been focused on the assumption of a centralised state estimator. In a next-generation smart grid environment incorporating distributed generation and highly variable demand induced by electric mobility, distributed state estimation is highly desirable to enhance overall grid robustness. We therefore consider the case of a bi-level hierarchical state estimator, which provides only partial observability to lower-tier state estimators.Using a formal observability model, we consider the case of an active adversary able to modify a set of measurements and derive bounds on the maximum number of manipulated measurements that can be tolerated, the composition of attack vectors, and give a formulation for identifying minimal sets of additional measurements to tolerate k-measurement attacks in this hierarchical state estimator. This allows us a more rigorous formulation over existing models.

This article focuses on the event‐triggered distributed state estimation for nonlinear dynamic systems over wireless sensor networks, where whether measurement should be transmitted from the sensor to the corresponding local estimator depends on a predesigned event‐triggered mechanism. To obtain a better estimation performance while saving the communication energy consumption, a novel event‐triggered nonlinear state estimator is designed by approximating the true posterior probability density function with minimum Kullback–Leibler divergence when the measurement is not transmitted. After local estimation results are produced in every local estimator through applying the cubature rule, according to a communication protocol and covariance intersection fusion strategy, an event‐triggered distributed cubature Kalman filtering (EDCKF) algorithm is developed. Compared with algorithms based on weighted average consensus, the proposed algorithm eliminates the disagreement between local estimators and obtains its best performance within a limited time. Moreover, sufficient conditions are obtained to prove the stability of the EDCKF algorithm. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed estimator and algorithm.

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

Remedial Action Schemes (RAS) provide automatic control action with high impact on system performance. In order to improve the resilience of the RAS against any single node failure and to guarantee the secured and reliable operation of the power systems, distributed implementations of the RAS is researched upon. The input data to such a distributed RAS must be of high quality. In addition, RAS operation must be fast. Traditional centralized state estimation, which feeds data to RAS is slow and cannot meet the requirements of RAS. New approach needs to be developed to provide the fast and accurate data to RAS. In order to solve this problem, distributed state estimation is developed as an alternative strategy to feed data to the RAS. This paper discusses the implementation of Distributed Linear State Estimation (DLSE) in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS). The DLSE algorithm is fully implemented in RIAPS platform and validated on a real-time testbed consisting of Real Time Digital Simulator, Phasor Measurement Units and BeagleBones. The effectiveness of the proposed approach is validated through online simulations on IEEE 14-bus test system under various cyber failures.

This paper presents a novel coordinated algorithm, Fast Distributed State Estimation with Linear Coordination (FDSELC), considering Phasor Measurement Units (PMUs) measurements at both subsystem and coordination levels. At the subsystem level, the estimators apply augmented Fast Decoupled State Estimation algorithm to reduce the computational time at the first step. At the coordination level, linear estimators based on PMUs measurements are able to coordinate the voltage states of boundary buses, which lead to the further improvement of computational performance. Tests on the IEEE 118-bus system demonstrate the performance of the novel FDSELC algorithms. The results are compared with centralized and other distributed state estimation algorithms in terms of both the estimation quality and computational cost. In addition, the proposed FDSELC algorithm with the distributed control architecture is suitable for future smart grids. In this situation, a large power grid can be decomposed into subsystems, and those subsystems can be estimated, monitored and controlled separately.