RESUMO
ABSTRACT A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 node model. The electrical system considered has automatic switches capable of identifying a momentary failure in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backpropagation, was used to classify the type of failure and determine the best reconfiguration of the distribution network. Initially, five power failure scenarios were simulated in certain different parts of the power grid, and power flow analysis via OpenDSS was performed. Next, the most suitable switching was observed within the shortest time interval to restore the power supply. With the purpose of better visualization to identify the reclosing, an implementation was carried out via ELIPSE SCADA. In this way, it is possible to identify the faulted segment in order to isolate it, leaving the smallest number of consumers without power supply in shortest possible time. With the results of the simulations, tests and analyzes were performed to verify their robustness and speed, in the expectation that the model developed be faster than an experienced Operating Distribution Center.
Assuntos
Redes Neurais de Computação , Instalação Elétrica , Eletricidade , Otimização de ProcessosRESUMO
ABSTRACT This study presents a system of conversion of mechanical energy produced by physical activity into electric energy obtained by a CC generator coupled to the pedal of an ergometric bicycle. It presents the converter that will be used to adjust the voltage and power coming from the system, as well as the details of the converter design, the simulation and the primary experimental results of the structure. The methodological procedures related to the development of the converter and data acquisition through simulation were carried out based on the bibliographic research. The study is documentary as equipment manuals were used.
