Christiano Lyra Filho

Application of Learning Classifier Systems to the On-Line Reconfiguration of Electric Power Distribution Networks

This work explores the use of Learning Classifier Systems (Holland, 1992), to obtain good solutions to the problem of loss reduction in electric power distribution systems with time-varying demands. Loss reduction in power distribution systems can be achieved by changing the status of distribution switches towards alternative power flows. Given a demand profile, finding the best status for switches is a difficult combinatorial problem. To date, no approach is guaranteed to achieve an optimal solution for real scale distribution systems in a reasonable time-scale. However, heuristic approaches have been able to provide approximate solutions. Larger gains can be achieved if on-line reconfiguration is allowed in order to consider varying demand profiles (Lee and Brooks, 1988). Few papers have attempted to address this dynamic problem (Vargas et al., 2001; Zhou et al., 1997), and additional adaptation skills are necessary when compared with the corresponding static problem.

Echo state networks for seasonal streamflow series forecasting

The prediction of seasonal streamflow series is very important in countries where power generation is predominantly done by hydroelectric plants. Echo state networks can be safely regarded as promising tools in forecasting because they are recurrent networks that have a simple and efficient training process based on linear regression. Recently, Boccato et al. proposed a new architecture in which the output layer is built using a principal component analysis and a Volterra filter. This work performs a comparative investigation between the performances of different ESNs in the context of the forecasting of seasonal streamflow series associated with Brazilian hydroelectric plants. Two possible reservoir design approaches were tested with the classical and the Volterra-based output layer structures, and a multilayer perceptron was also included to establish bases for comparison. The obtained results show the relevance of these networks and also contribute to a better understanding of their applicability to forecasting problems.

Redução de perdas em redes de distribuição de energia elétrica através de sistemas classificadores

The problem of minimising the technical energy losses in electric power distribution systems corresponds to the definition of the best network configuration, through the manipulation of sectionalising switches. Using a graph terminology, we have a combinatorial optimisation problem, whose solution asks for the definition of the minimum spanning tree for the network. With the purpose of treating the case of time-varying profiles of energy requirements, this paper presents the first results obtained with the application of learning classifier systems, an evolutionary computation approach to find network configurations close to the one associated with minimum energy losses. Its uttermost feature is the possibility of application to on-line network supervision, proposing new configurations that will reduce the level of energy losses when significant demand variations are detected.

Um método de suporte a decisões sobre investimento e comercialização de energia elétrica no Brasil

This paper presets a decision making method for investments, trading and portfolio evaluation on electrical energy in the wholesale Brazilian trading market. Scenarios for spot prices are estimated on mid-term horizon by a statistic approach. The probability curve of pay-off for several alternatives of trading is calculated. Risk aversion of the decision maker is introduced in the process by multicriteria optimization approach, so that decision support is carried out by the method according to specific decision maker risk sensitivity. A case study illustrates application of the methodology for decision making on trading electrical energy by a distribution utility, for a two-year horizon.