Parallel and distributed processing in state estimation of power system energy

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. >

Power system state estimation plays a fundamental and critical role in the energy management system (EMS). To achieve a high performance and accurate system states estimation, a graph computing based distributed state estimation approach is proposed in this paper. Firstly, a power system network is divided into multiple areas. Reference buses are selected with PMUs being installed at these buses for each area. Then, the system network is converted into multiple independent areas. In this way, the power system state estimation could be conducted in parallel for each area and the estimated system states are obtained without compromise of accuracy. IEEE 118-bus system and MP 10790-bus system are employed to verify the results accuracy and present the promising computation performance.

Chapter 8 - Dynamic state estimation in electric power systems

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.

Growth in power systems has led to an increase in operational complexity, highlighting the importance of maintaining them in optimal conditions. State estimation in electric power systems is a crucial tool for determining the state of transmission networks through the use of sensors and topology information. This information is then utilized for contingency analysis and error detection/identification to ensure system reliability. To maintain optimal power system operations, state estimation and its associated techniques are critical components. This work focuses on analyzing DC state estimation using a statistical method and weighted least squares methodology. Anomalous measurements are filtered and corrected using Chi-Square. The algorithm was developed in MATLAB and verified in DIgSILENT PowerFactory. The IEEE 14-bus system, with added wind turbines, was used as a test system to determine security in the power grid through estimator confidence parameters. The results provide valuable insights into the efficacy of the DC state estimation process.

In this paper, we propose a high speed communication based state estimator for the future power system, smart grid. The proposed state estimator is carried out by a distributed/parallel algorithm, which is formed by combining successive quadratic programming with the dual method, to solve the power system's state estimation problem. We have tested the proposed state estimator on the IEEE 30-bus system. Numerous simulation cases are performed. The simulation results demonstrate the accuracy and the computational efficiency of the proposed state estimator.

This paper is concerned about the state estimation of power systems, which contain flexible ac transmission system devices. Specifically, the formulation of state estimation in order to incorporate the detailed steady state models of the unified power-flow controller (UPFC) is investigated. This formulation involves the use of equality and inequality constraints that account for the limits associated with the device operation and ratings. Furthermore, in addition to its use as an estimator, the developed estimator can also be used to determine the required settings for the control parameters of UPFCs for a given operating condition. Computational issues concerning initialization, step length, and matrix conditioning are also discussed and practical solutions are offered. Simulations on IEEE test systems of sizes ranging from 14 to 118 buses are presented to demonstrate the use of the developed program both as a state estimator and as a tool for setting the parameters of unified power-flow controllers.

This paper presents a new power system state estimation method based on pseudo-measurements. The conventional and synchronizing phasor measurements are employed to calculate the pseudo-measurements of voltage and current phasors. Using pseudo-measurement, the measurement matrix which describes relations between measurements and state variables contains only 0 or 1. In this work, the state estimation problem is formulated based on the weighted least absolute value (WLAV) criteria. The solutions are obtained by an interior point method considering equality and inequality constraints. Comparisons with the conventional hybrid method are also performed on the IEEE 14-bus system in the presence of bad data. Results indicate that the proposed method can provide accurate solutions but with smaller computation time.

Hybrid measurement of the phasor measurement units (PMU) and the supervisory control and data acquisition (SCADA) system is used for state estimation of power transmission system when PMU deployment can not cover all buses. The traditional iterative solution process of state estimation is low efficient and may cause truncation error. A fast state estimation method based on pseudo-measurement modeling of extreme learning machine (ELM) for power transmission system state estimation is proposed. The injected powers measured by SCADA are used as input, the real and imaginary parts of bus voltages are used as output. The pseudo-measurement value model and pseudo-measurement error model are trained using historical data. In the new time, the pseudo-measurements obtained from the trained model are combined with the PMU measurements for fast linear state estimation. The final state estimation results can be obtained quickly. Simulation results of IEEE 14 system show that the proposed method not only improves the accuracy of state estimation, but also greatly reduces the calculation time. It can provide basic information for other applications in EMS accurately and quickly.

This paper investigates the state estimation problem of power systems. A novel, fast and accurate state estimation algorithm is presented to solve this problem based on the one-dimensional denoising autoencoder and deep support vector machine (1D DA–DSVM). Besides, for further reducing the computation burden, a partitioning method is presented to divide the power system into several sub-networks and the proposed algorithm can be applied to each sub-network. A hybrid computing architecture of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) is employed in the overall state estimation, in which the GPU is used to estimate each sub-network and the CPU is used to integrate all the calculation results and output the state estimate. Simulation results show that the proposed method can effectively improve the accuracy and computational efficiency of the state estimation of power systems.

Vulnerability analysis of distributed state estimation under joint deception attacks

Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids

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.

This paper presents an algorithm for state estimation in power systems that include FACTS devices and PMUs. The FACTS devices are equipments of special purpose capable of changing the natural behavior of transmission systems. They may be able to influence the voltage, the active power or the reactive power flows based on predefined targets so that the classical power flow and steady state calculations must be adapted. Furthermore, PMU measurements are increasing in number so that the accuracy in steady state calculations can be improved. A state estimation algorithm was developed by considering the behavior of TCSC and SVC devices, while including some improvements due to the integration of synchronized measurements. A Matlab application was developed and simulations were performed on various test networks. In this paper, results obtained on the IEEE 30 bus test system only will be presented.