Parallel and distributed state estimation

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.

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.

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.

Distributed state estimation system is beneficial in power grids with complex structures and large quantities of measurements because of its advantages, such as high computational efficiency and enhanced reliability. This study investigates a novel multi-area distributed state estimation algorithm for large-scale interconnected power systems. The state estimation model of the entire power grid is effectively decomposed into a group of estimation models that can be locally solved using an area estimator. The global information required by an estimator in solving its estimation model is effectively synthesized as two average values, and an average consensus-based algorithm is developed for an area estimator to access the required global information in a distributed fashion. Bad data are identified and eliminated after the convergence of the iterative estimation process. Simulation results of the IEEE 14-bus test system are provided to show the correctness and effectiveness of the proposed algorithm. Solution accuracy, computational efficiency and state estimation speed are compared with those of existing centralized, hierarchical and distributed methods by the numerical example in the IEEE 300-bus test system.

While various industries heavily rely on electrochemical sensors for air and liquid quality monitoring and control, many electrochemical sensors are cross-sensitive to one or more interfering gases in addition to the target component, which may cause incorrect readings and lead to improper control measures. In this study, we proposed a direct algebraic approach-based state estimation and output decomposition algorithm which is suitable for state estimation for a class of nonlinear dynamic systems with output measurements that are highly cross-sensitive to multiple interfering gases. Using the cross-sensitive sensor measurement and its successive time derivatives, the proposed methodology can provide a closed-form solution for the actual system state estimation in a systematic way. The established state estimation algorithm was successfully validated in a numeric example. The proposed state estimation and output decomposition algorithm is applicable to a broad class of nonlinear dynamic systems which use sensors that are cross-sensitive to multiple interfering gases in order to improve the performance of the systems.

Vulnerability analysis of distributed state estimation under joint deception attacks

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.

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.

Distributed agent-based state estimation considering controlled coordination layer

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.

Present state estimator performance is not 100% reliable. Specifically, there is significant probability that the state estimators may not converge to an acceptable solution. On an industry wide basis, the probability of non convergence is about 5%. The reasons for this performance have been investigated and have been reported in earlier papers. In this paper we report on a new approach that alleviates the sources of state estimator unreliability and at the same time distributes the computational procedure to each substation of the system, assuming there is at least one GPS-synchronized device (relay, PMU, recorder, meter, etc.) at each substation. This results in a true distributed estimator. The results of the distributed state estimator are communicated to the control center where the overall system state is constructed. The approach has been implemented to two subsystems of two substations each. This paper describes the overall approach and provides results from the two pilot implementations. Glossary GPS: Global Positioning System PMU: Phasor Measurement Unit CT: Current Transformer VT: Voltage Transformer CCVT : Capacitor Coupled Voltage Transformer

Existing electricity distribution management systems (DMS) have been designed using operational and algorithmic procedures that are highly centralised. As more of the distribution network becomes active, accurately estimating the state of the system becomes essential and therefore DMS must include functions to achieve the required near to real-time state estimation. The objective of this paper is to evaluate and to compare the performances obtained with two different solutions developed by EDF R&D for the Distribution State Estimator (DSE) algorithm for MV networks: classical optimization resolution approach using a Newton resolution algorithm on one side and a Particle Swarm Optimization algorithm on the other side. The performances of these solutions are evaluated in terms of precisions obtained for the estimates related to the primary and secondary variables and of computation times.

Distributed state estimation (DSE) is effective for the analysis of multi-region state estimation problems, avoiding integrating complicated data sets and models. In this work, the peer-to-peer decomposition of the Karush-Kuhn-Tucker (KKT) condition is applied to formulate the fully distributed fast decoupled state estimation (FD2SE) algorithm. Then, a fixed-matrix form data-exchanging interface incorporating the fast decoupled algorithm is designed, reducing the communication costs and allowing it to be used for various system operating conditions. Further, a convergence detecting method for dispatch centers is proposed in order to infer, locally and asynchronously, about the global convergence. Case studies carried out on IEEE systems and a real power grid of China verify the accuracy and efficiency of the FD2SE algorithm.

The paper illustrates the development and the implementation of a Distribution State Estimation (DSE) algorithm for the Low Voltage Test Facility with Distributed Energy Resources of the Italian Research center RSE. The algorithm is integrated into a Distribution Management System within the control system of the Test Site organized as a microgrid. The Test Facility is equipped with conventional and renewable generation plants, storage systems and programmable loads. Switching boxes allow to obtain a very high number of different topology configurations. The paper shows details of the methodologies and theoretical bases adopted for the algorithm design, implementation and testing of a two level state estimation procedure. Simulation and comparison with field results allow the evaluation of the power contributions to total nodal injections, in all the possible grid configurations.