Milton Brown Do Coutto Filho

Enhanced Bad Data Processing by Phasor-Aided State Estimation

The utilization of all the available data sources is a permanent objective of state estimation (SE). In this sense, forecasting-aided state estimation (FASE) is an important alternative to conventional state estimation, especially regarding its expeditious data validation scheme, which comprises bad data (BD) smearing effect elimination, block identification, and adequate replacement. Also, with the availability of synchrophasor measurements, there has been a growing interest in building a phasor-aided state estimation (PHASE) process. This paper presents a novel way of processing BD, whose features are similar to those found in the data validation routines of FASE. The proposed PHASE approach has the advantage of leaving the existing SE application software intact, complementing it with an extra estimation module, capable of processing phasor measurements separately and judging whether the measurement set contains anomalies. The results of a proof of concept study performed on the IEEE 14-bus benchmark system demonstrate the application of the proposed methodology. Also, PMU-observability issues are addressed and illustrated through simulation studies conducted on the IEEE 118-bus system.

Quantifying Observability in State Estimation

Observability means the aptitude for estimating the system state in its entirety from data currently available. As such, the problem of quantifying this aptitude assumes practical interest. Dealing with observable grids, this paper concentrates on proposing indicators capable of establishing unobservability risks. These indicators are based on measurement criticality analyses (with the aid of Venn diagrams) for a given network configuration. They are obtained in terms of the probability of unobservability, assuming that an event has occurred, such as the unavailability of: a single measurement; one pair of measurements; one k-tuple of measurements; a single metering unit; a single network branch; one pair of network branches. The potential/practical application of the proposed indicators is illustrated by the introduction of metric patterns, capable of reducing the information on unobservability risks to a single quantity. Numerical results obtained with the IEEE 14- and 118-bus test systems exemplify the computation of the proposed indicators.

A genetic-based methodology for evaluating requested outages of power network elements

The evaluation process of requested outages in power grids comprises a harmonious adjustment of maintenance needs placed by market participants, in which priorities and operational constraints are taken into account. It is a critical process for independent system operators (ISOs) since they were conceived for handling a huge amount of transactions, without affecting system reliability and market prices. This paper presents a methodology for the evaluation of requested outages, viewed as a constrained optimization problem due to its combinatorial nature. A genetic algorithm technique is adopted to obtain high-quality solutions. Simulation results with the IEEE 14-bus test system and part of a Brazilian system are presented to illustrate the proposed methodology.

Critical measuring units for state estimation

Power system state estimation (SE) performance is very much influenced by the availability of redundant, varied, well-located measurements. Insufficient supplies of qualified measurements can compromise network observability, making the SE process incompetent to detect/identify spurious data. Individual or group of measurements whose unavailability renders unobservability are known as critical for the SE purpose. One can define a measuring unit as critical, if the set of measurements it provides is essential to the network observability. This paper focuses on the identification of critical measuring units, which is achieved by processing selected measurements in the estimation residual covariance matrix. The proposed identification method is straightforward and reliable, being easily implemented into any existing state estimator. With the IEEE 30-bus test system, illustrative examples are presented to show how the proposed approach can be used to evaluate measurement plans.

Sobre o problema da integração generalizada de dados

This paper presents a set of methods which use quantitative and qualitative data, gathered from various heterogeneous sources of information, aiming at combining them in a process of data fusion. Different forms of uncertainty representation and modelling are discussed. Also, this work proposes a taxonomy for the data fusion processes according to the nature and redundancy of the processed data.

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.

Correcting electrical network parameters

This paper proposes efficient methods for correcting suspicious power system network parameters. State estimation is used for tackling the parameter estimation problem. The proposed methods explore the concept of irrelevant/barely relevant branches to eliminate/mitigate temporarily the participation of suspicious parameters in the state estimation process, until the suspicions are cleared up. Different situations, e.g. the occurrence of single/multiple parameter errors under unavailability of measurements, are handled. The methods distinguish themselves by the construction of a parameter estimation algorithm which does not modify an existing state estimation process, but interacts with it. Results with the IEEE-14 bus system, benchmark network for state estimation studies, and with a real power system are presented to illustrate the application of the proposed methods.

Parameter Estimation through a Genetic Algorithm

Network parameter errors may come from many different sources, such as: imprecise data provided by manufacturers, poor estimation of transmission line lengths, changes in the transmission network design which are not adequately updated in the corresponding database, etc. Network parameter data are used by almost all power system analysis tools, from real time monitoring to long term planning. Parameter errors may contaminate the obtained results and compromise decision making processes. This work proposes a methodology that combines genetic algorithms and power system state estimation to correct single or multiple network parameter errors. Simulations with the IEEE 14-bus test system are performed to illustrate the proposed method.

Indicators of critical conditions for state estimation

This paper proposes the computation of indices capable of indicating global/local data deficiencies for state estimation. These indices reflect more appropriately the current condition of a metering system to cover the entire power network. For a given measurement set, critical conditions for state estimation (in terms of network observability and bad data processing) are analyzed. Numerical results obtained with the IEEE 24-bus test system are presented to illustrate the application of the proposed indicators.