J.C.S. de Souza

Forecasting-Aided State Estimation—Part I: Panorama

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.

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

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.

Optimal metering systems for monitoring power networks under multiple topological scenarios

Summary form only given. This work presents a methodology for designing optimal metering systems for real-time power system monitoring, taking into account different topologies that the network may experiment. Genetic algorithms are employed to achieve a trade-off between investment costs and reliability of the state estimation process under many different topology scenarios. This is done by formulating a fitness function where the cost of the metering system is minimized, while no critical measurements and/or critical sets are allowed in the optimal solution. An efficient algorithm for the identification of critical measurements and sets (irrespective of state estimation runs) is employed during the evaluation of the fitness function. Simulation results illustrate the performance of the proposed method

On-line voltage stability monitoring

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.

Forecasting-Aided State Estimation—Part II: Implementation

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.

Exploring fuzzy relations for alarm processing and fault location in electrical power systems

This work investigates the construction of fuzzy relations for alarm processing and fault location in electrical power systems. Several data aggregation classes are tested and compared. Fuzzy relations are established with the aid of the knowledge on protection devices operation for faults involving different system components. Tests are performed with a 7-bus test system and with part of a real Brazilian power system.

Generating High Quality Pseudo-Measurements to Keep State Estimation Capabilities

This paper proposes a methodology for providing real-time high quality pseudo-measurements to be used by the state estimation function. A forecasting step is added to the estimation process in which one-step-ahead forecasts, obtained considering recent past state estimation results, are adopted as pseudo-measurements. The model used to make forecasts is based on an artificial neural network. Test results using the IEEE-24 bus test system are presented to illustrate the performance of the proposed methodology.

Planning metering systems for power distribution systems monitoring

This work presents an optimal meter placement methodology for real-time power distribution systems monitoring. The proposed methodology is flexible and enables to take into account aspects such as: different network configurations, use of pseudomeasurements, already existent meters and remote terminal units, among others. The Genetic algorithms technique is employed to achieve a trade-off between investment costs and real-time monitoring capability.

Extracting a probabilistic criterion from a blackout

This paper presents all reliability evaluation hypotheses and practical results, which were obtained, based on the blackout analysis. A preliminary probabilistic criterion is then suggested, deemed useful in the short-term power systems operations environment.

Photorefractive moiré-like patterns for the multifringe projection method in Fourier transform profilometry

In the present paper, the method of simultaneous moire-like fringe pattern projection for Fourier transform profilometry is described. The photorefractive holographic interferometric process produces controlled moire-like patterns with two or more different variation directions. Each low spatial frequency fringe pattern is experimentally obtained as a result of the superposition of two high spatial frequency sinusoidal gratings, with slightly different pitches, for each fringe variation direction. These dynamic moire-like patterns are induced due to an optical holographic beating of the sinusoidal induced gratings in the volume of the photorefractive Bi₁₂TiO₂₀ (BTO) crystal sample used as dynamic holographic medium. Two or more moire-like fringe patterns, with at least two different variation directions, simultaneously (or not), are projected onto the object surface. Thus, this is the 2D fringe projection stage of our proposed Fourier transform procedure to determine the profile of a simple object.