neural estimator of the rotor angle of synchronous generators from measurement of voltage and current at terminals
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In this work, a black-box model for the estimation of the rotor angle of a synchronous generator is developed by using voltage and current measurements at the terminals. The model is based on neural networks of the multilayer perceptron (MLP) type. From time-domain simulations for a basic power system, which consists of a generator connected to an infinite bus bar, voltage and current records are collected, which are used as a database to train and validate the neural network that is proposed for the estimation of the rotor angle. It is found that the neural-network based model adapts itself very well to the classic model of the generator, exhibiting a mean square error of 1´10-10 . The results show the validity of the estimation method and foster its potential use for stability studies of the synchronous generator.
- Juan J. Mora, Lucas P. Pérez, Sandra M. Pérez, Comparative evaluation of KBANN networks and ANFIS systems for fault location in electrical power distribution networks , Ingeniería y Competitividad: Vol. 10 No. 2 (2008)
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