Distributed State Estimation Based on the Realtime Dispatch and Control Cloud Platform

Summary The article presents a robust real-life distribution state estimation (DSE) that is integrated in the distribution management system. The DSE optimization procedure is developed in full accordance with the nature of distribution networks and their back/forward sweep–based load flow calculation. Thus, DSE is conceptually different from the traditional (transmission) state estimation. The article proves that the proposed DSE, even with relatively low level of telemetry and large dimensions of distribution networks, not only is possible as a function with value of its own but also provides a foundation for other important distribution management system functions. The procedure is the result of both research studies carried out in the last several years and real-life applications worldwide. Thus, this article offers DSE as an industry-grade product.

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.

Distributed agent-based state estimation considering controlled coordination layer

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.

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.

This paper exploits the possibility of fully distributed bad data processing for wide area state estimation in large interconnected power systems. Built upon our recent work on fully distributed state estimation, in which no central coordinator is needed for provable convergence of state estimation with the centralized results, this paper presents a new method to detect and identify bad measurement data in such a distributed setting. Based on the concept of error residual spread decomposition, each administrative control area performs its own Chi-squares test and largest normalized residual test. Only the largest normalized residual for each control area is exchanged with a predetermined set of control areas, all of which belong to the same error residual spread area. The communication burden for the control areas is moderate, therefore, the proposed method is applicable as part of real-time wide area monitoring tools. Numerical examples using the IEEE 14-bus system show that the proposed distributed bad data processing algorithm leads to the same results as centralized methods.

A state estimator including conventional and synchronized phasor measurements

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.

Global state estimation for whole transmission and distribution networks

Smart distribution grids with renewable energy based generators and demand response resources (DRR) requires accurate state estimators for real time control. Distribution grid state estimators are normally based on accumulated smart meter measurements. However, increase of measurements in the physical grid can enforce significant stress not only on the communication infrastructure but also in the control algorithms. This paper aims to propose a methodology to analyze needed real time smart meter data from low voltage distribution grids and their applicability in distribution state estimation. Different scenarios are created to observe the algorithm performance and effects on the estimation quality. The CIGRE benchmark network for low voltage distribution grids is used for simulation and analysis. This work also investigates the necessity of proper load modelling to reduce the stress due to huge amount of measurement data by utilizing them smartly via state estimation.

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 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.

Many large interconnected power systems such as the US eastern interconnection and the US western power system are characterized by many power transfer paths with high loading. Disruptions of these transfer paths frequently lead to increased loading on neighboring transfer paths, which themselves will become less secure and could cause further disruptions. State estimators have limited performance under large system disruptions, because of low sampling rates and potentially poor solution quality due to topology errors. Furthermore, disruptions in external power systems cannot be readily seen by control room operators because most state estimators only use reduced models for external systems. A system of well-placed phasor measurement units (PMUs) that provide voltage and current magnitude and phase at a high sampling rate can provide useful system dynamic security information. In this paper we apply energy function analysis using phasor data to monitor the dynamic status of power transfer paths. The ideas will be illustrated for a power transfer path using actual PMU data in the US western power system

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.