A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed.

A Review on Distribution 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

This article provides a survey on state estimation (SE) in electric power grids and examines the impact on SE of the technological changes being proposed as a part of the smart grid development. Although SE at the transmission level has a long history, further research and development of innovative SE schemes, including those for distribution systems, are needed to meet the new challenges presented by the requirements of the future grid. This article also presents some example topics that signal processing (SP) research can contribute to help meet those challenges.

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State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid

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 is the second part of a two-part paper addressing power system forecasting-aided state estimation. Part I presented a comprehensive survey on the subject, including future prospects. Part II is devoted to presentation and discussion of numerical results obtained with the implementation of a forecasting-aided state estimator in the energy management system of LIGHT Services of Electricity, which is company that supplies Rio de Janeiro, Brazil. This estimator is submitted to different situations, such as: normal operating conditions, presence of spurious measurements (including those which are critical), and occurrence of unpredictable sudden changes in the state of the LIGHT system.

Forecasting-Aided State Estimation—Part II: Implementation

This work presents a methodology that combines the use of artificial neural networks and fuzzy logic for alarm processing and identification of faulted components in electrical power systems. Fuzzy relations are established and form a database employed to train artificial neural networks. The artificial neural networks inputs are alarm patterns, while each output neuron is responsible for estimating the degree of membership of a specific system component into the class of faulted components. The proposed method allows good interpretation of the results, even in the presence of difficult corrupted alarm patterns. Tests are performed with a test system and with part of a real Brazilian system.

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Alarm processing in electrical power systems through a neuro-fuzzy approach

In this Letter, a proposal addressing the problem of two-dimensional phase unwrapping based on the theory of residues is presented. Here, wrapped phase maps with shifted phase jumps are used to balance residue charges. With this approach, we seek to minimize processing time and residue connection, which is essential in the development of branch-cut algorithms. Finally, a phase-unwrapping algorithm is applied to these wrapped maps, generated by Fourier transform profilometry to obtain three-dimensional profiles of objects illuminated by photorefractive moire-like patterns generated in an experiment of real-time dynamic holography, and by fringe patterns generated with a Michelson interferometer.

Branch-cut algorithm for optical phase unwrapping

This paper presents a new methodology for online voltage stability assessment, consisting of two steps. Firstly, the time evolution of the power system operating state is modeled with the help of a forecasting-aided state estimator; secondly the voltage collapse point is determined through an extrapolation technique based on tangent vector behavior. Tests are carried out using the IEEE-14 bus system, where different operating scenarios are considered. The results show that monitoring system load trends may improve the assessment of voltage stability.

J.C.S. de Souza

Papers

Forecasting-Aided State Estimation—Part I: Panorama

Forecasting-Aided State Estimation—Part II: Implementation

Alarm processing in electrical power systems through a neuro-fuzzy approach

Branch-cut algorithm for optical phase unwrapping

On-line voltage stability monitoring