This paper discusses the state of the art in electric power system state estimation. Within energy management systems, state estimation is a key function for building a network real-time model. A real-time model is a quasi-static mathematical representation of the current conditions in an interconnected power network. This model is extracted at intervals from snapshots of real-time measurements (both analog and status). The new modeling needs associated with the introduction of new control devices and the changes induced by emerging energy markets are making state estimation and its related functions more important than ever.

Electric power system state estimation

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.

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Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

A new estimation method for power system dynamic state estimation, the unscented Kalman filter (UKF), is presented. It is based on the application of the unscented transformation (UT) combined with the Kalman filter theory. One of the challenges in the process of power system estimation is coping with a highly non-linear mathematical model of network equations, which is usually approximated through a linearisation. The new derivative free estimation method overcomes this limitation using the UT and achieves better accuracy with simpler implementation. The UKF is derived and demonstrated using three different test power systems under typical network and measurement conditions. Its performance is compared with the classical extended Kalman filter. The simplicity of the new estimator and its low computational demand make it a better option to be applied in the next generation of dynamic system estimators.

Unscented kalman filter for power system dynamic state estimation

In this paper, a computation algorithm using the exponential function to increase the robustness of the dynamic state estimation is proposed. The merit of this approach lies in its immunity to the polluted measurements, while the implementation of the method is not complicated when compared with other methods. To validate the effectiveness of the proposed method, it was tested through several example power systems under different scenarios that include normal operation, bad measurements, sudden load change, and topology error conditions. From test results, they help support the feasibility of the method for state estimation applications.

Application of a Robust Algorithm for Dynamic State Estimation of a Power System

The art of estimating future values of a random process, based upon previously observed or estimated values, is usually known as a priori estimation, prediction, or forecasting. Power system state estimation process can be enhanced if state/measurement forecasts are incorporated into it. Important research efforts have been made in this direction bringing a fresh perspective to the state estimation problem. This paper (Part I) presents a comprehensive survey of forecasting-aided state estimators. It gathers up-covering a period of three decades-research results on the main benefits achieved by state estimators with forecasting capability regarding: data redundancy, innovation analysis, observability, filtering, bad data, and network configuration and parameter error processing. Aspects of modeling, forecasting techniques, and computational effort are also addressed. The second of this two-paper series presents the results of the implementation of a forecasting-aided state estimator in the energy management system of the LIGHT Services of Electricity, a company which provides electric energy to Rio de Janeiro, Brazil.

Forecasting-Aided State Estimation—Part I: Panorama

This paper presents a comparison between the performance of dynamic and tracking estimators, in power systems operating under quasi-static conditions, concerning their characteristics of forecasting and filtering. From this comparison a new dynamic estimator which incorporates the main advantages of the previous estimators is proposed. Also, a new scheme of detection and identification of bad data properly built for dynamic algorithms is presented. Numerical results showing the performance of the new algorithm under different operational conditions are discussed.

J. M. C. Cantera

Papers

An Efficient Dynamic State Estimation Algorithm including Bad Data Processing