A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system.
J. Huang et al., "Identification of Induction Machines Stator Currents with Generalized Neurons," Proceedings of the IEEE Power Engineering Society Conference and Exposition in Africa, 2007. PowerAfrica '07, Institute of Electrical and Electronics Engineers (IEEE), Jul 2007.
The definitive version is available at http://dx.doi.org/10.1109/PESAFR.2007.4498040
IEEE Power Engineering Society Conference and Exposition in Africa, 2007. PowerAfrica '07
Electrical and Computer Engineering
Keywords and Phrases
Asynchronous Machines; Feedforward Neural Nets; Particle Swarm Optimisation; Stators
Article - Conference proceedings
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.