Edwin Benito Mitacc Meza

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.

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.

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.

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.

Utilização de um modelo neuro-fuzzy para a localização de defeitos em sistemas de potência

This work presents the application of a neuro-fuzzy model for alarm processing and fault location in power systems. Different techniques to establish the fuzzy relations among alarm patterns and fault occurrences in power systems are examined. Fuzzy relations are constructed and form a database that is employed to train artificial neural networks. The artificial neural networks have alarm patterns as inputs and each output neuron is responsible for estimating the degree of membership of a specific system component into the class of faulted components. Tests are performed with a 7-bus test system and with part of a real brazilian system.

Simulation model for Cutting, Loading and Transportation of sugar cane - a case study in the State of Rio de Janeiro

Neste trabalho foi desenvolvido um modelo de simulação para o Sistema de Corte, Carregamento e Transporte da cana-deaçúcar (conhecido como CCT) das frentes de corte até uma usina localizada no norte do Estado do Rio de Janeiro. O objetivo do modelo foi analisar parâmetros do sistema, a partir do carregamento de cana-de-açúcar, sendo estes influenciados por operações de descarregamento na usina. Foi avaliado, então, o impacto do tempo de descarregamento da cana na usina e as suas implicações no sistema de corte, carregamento e transporte, considerando diferentes cenários.