Performance Analysis of Static and Dynamic State Estimation Incorporating Synchro Phasor Measurements

Guest Editorial: Situational awareness of integrated energy systems

Accurate power system state estimation facilitates power system control, optimization, and security analysis. In this paper, a joint filter approach is proposed to estimate both the static states (voltage magnitude and phase angle of bus) and the dynamic states (rotor angle, rotor speed, and transient voltages of generator) utilizing the measurements of phasor measurement unit. First, due to the complexity and dynamics of power system, the power system model is usually not known accurately. The uncertainties include both model uncertainty and noise statistics uncertainty. Therefore, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is applied to reliable estimation of the static states. As a link between the static state estimation and the dynamic state estimation, the estimated static states are fed to dynamic state estimation. Next, to deal with the nonlinearity of synchronous generator model, an extended Kalman particle filter is employed to accurately estimate the dynamic states. The numerical tests on IEEE 30-bus system show that the proposed method is into both the static state estimation and the dynamic state estimation.

Monitoring and controlling the electrical distribution system for real time is becoming very important to improve its operating performance after transition to active distribution systems. So, many sensors are needed to monitor all parts in the systems. But if sensors are installed at all buses, investment cost becomes huge. To reduce the number of sensors, state estimation approach can be used to estimate the voltage of buses, which do not have sensors. State Estimation (SE) algorithms are broadly classified into Static State Estimator (SSE) and Dynamic State Estimator (DSE). This chapter classifies most algorithms used in active distribution networks, also State estimation in unbalanced distribution systems, and Role of PMU in Distribution System State Estimation.

Static state estimators have been in use in control centres for over four decades now. Recent availability of synchronized phasor measurements for bus voltages and line currents allows development of dynamic state estimators which can track the frequency and synchronous machine state variables and complement the information provided by static state estimators on the network states. This chapter provides a brief overview of the static state estimation problem followed by an introduction to dynamic state estimation. The problem of dynamic state estimation is formulated and a dynamic state estimator whose implementation lends itself to large size power grids is described. The chapter also contains a discussion of observability analysis for dynamic state estimators which presents additional challenges due to the time varying and nonlinear nature of the system and measurement equations.

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

State estimator is an important tool for online monitoring, analysis and control of power systems. With the increased use of phasor measurement units in a power grid, utilization of synchro phasor measurements in State Estimation (SE) becomes essential. Conventional State Estimation (CSE) has been done from measurements obtained by Supervisory Control And Data Acquisition (SCADA) systems. Linear State Estimation (LSE) is done using phasor measurements alone. The paper presents a hybrid method called Two Stage State Estimation (TSSE), wherein the states of the system are estimated by incorporating both the SCADA measurements and phasor measurements. Simulations are done on IEEE 14 bus, IEEE 30 bus and IEEE 118 bus systems and results are compared.

To address fast-increasing security incidents of drones, in this paper, we investigate fundamental solutions to defend against unsophisticated commercial drones. We have examined several common flight control and navigation algorithms and identified weaknesses in the prevalent EKF-based dynamic state estimation schemes. Exploiting the unique circumstances and characteristics of these drone algorithms, we have developed a theoretical framework of False Data Injection (FDI) specific to drone dynamic state estimation, different from existing FDI attacks on static state estimation. We have further defined two FDI attacks that allow compromised magnetometer “measurements” to pass the drone anomaly detection for misleading a drone’s dynamic state estimation, such that its navigation and flight control are misguided. We have evaluated two practical scenarios to show the effectiveness of the proposed attacks with simulations on the popular ArduPilot flight control system. Our evaluation shows that the proposed attacks on these common algorithms can seriously affect drone navigation, stability, and power consumption. We believe this work is the first FDI attack against common drone dynamic state estimation schemes, which opens a new area of research. We are currently expanding our investigation on exploiting other related issues in this direction.

According to the contradiction between the static power system state estimation and the actual operation of power system, based on the study of the classical algorithms of power system state estimation, apply the gray system theory of power system based on GM (1, 1) mode to the state estimation of power system, and create a new dynamic power system state estimation algorithm, for example to a four-node power system network test for State estimation simulation. Measurement of a known algorithm used to predict the gray system theory. On the other hand, measurement of unknown quantity, using the result that come from the known quantity, then get all parameters with character of power system state through statistical analysis of simulation results that the normal measurement system, the method is feasible and has a good filtering effect. The power system state estimation algorithm based on the gray system theory of Power System based on GM (1, 1) model has high estimating precision, fast calculation, use small memory and the adaptability of the measurement system is very strong, so it has good prospects.

DECENTRALIZED ROBUST BAD-DATA PRE-CLEANING WITH SUBSEQUENT HIERARCHICAL FAST RECURSIVE ESTIMATION

Hierarchical and Fast Recursive State Estimation with Robust Bad Data Pre-Cleaning for Large-Scale Power Systems

Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm

Recent introduction of microcomputers in substations to assume all substation tasks including protections makes possible accurate real-time measurements of positive sequence voltage phasors. Since static state estimators use measurements of line flows and injections to estimate the positive sequence voltage phasors it is clear that direct measurement of the state vector may be used to enhance state estimation. In order to obtain simultaneous measurements of different buses it is necessary to synchronize sampling clocks at different locations. After reviewing the techniques for obtaining positive sequence phasors from samples. techniques for clock synchronization are reviewed. Data volume reductions possible with direct measurement of state are examined and static state estimators which incorporate direct measurements are presented. Simulation results for the IEEE 118 bus system are presented.

Chapter 8 - Dynamic state estimation in electric power systems

State estimation is often a challenging task in distribution systems due to deploying a limited number of measurement devices. Moreover, the integration of precise distribution-level phasor measurement unites, a.k.a micro-PMUs, along with inaccurate pseudo-measurements in state estimation introduces another challenge. These issues might decrease the efficiency of traditional standard weighted least squares (WLS) for distribution system state estimation. This paper proposes a novel linear distribution system state estimation based on limited number of synchrophasor measurements and pseudo-measurements. To involve pseudo-measurements into the linear state estimation, Taylor's approximation is adopted to reformulate pseudo-measurement functions in a linear form. The results obtained via numerical simulations demonstrate that the proposed linear state estimation method performs as good as the standard nonlinear WLS estimator. Moreover, the sensitivity analyses results show that our method has a better performance compared to WLS once a limited number of highly precise micro-PMUs are accompanied with inaccurate pseudo-measurements in state estimation.

This paper presents a new approach for detection and identification of ultiple bad data --- estimation-identification approach which has been developed in order to improve the performance of static-state estimators of power systems. It describes the important function of detection and identification of bad data which is of crucial importance in designing a static-state estimator as a basic element in power system real-time control; it also discusses tthe basic principle, method of solution and programing technique of so called "Estimation-Identificaition Approach" which is based on the better utilization of the resources of residues and the analysis of the property of its sensitivity matrix; and some test results of simulation on a model system and a practical searching system of doubtful data are given. Test results of identifying bad data is good, it is usually successful especially in the case of the occurence of multi-bad data. At the end of this paper comments concerning the advantages of this approach and its recommendation as a prospective practical approach in real-time applications are given.