RBF NN based marine diesel engine generator modeling

Abstract

For building a real time marine power system simulator, models of fast calculation and high precision of marine power system are needed. Because there are abilities of learning and batch operation with artificial neural networks (ANN), it is fit for using ANN to build a real time marine diesel generator model for marine power system simulator. In this paper, radial basis function neural networks (RBF NN) was used for building model of marine diesel engine generator. RBF NN is a universal approximation neural network. There is an ability to approximate a nonlinear function with RBF NN. According to the working principles of diesel generator, parameters of excitation current/voltage and diesel engine mechanical torque are inputs of RBF NN, while parameters of terminal voltage current and frequency of generator are outputs for RBF NN training. The type of supervised learning of center selection strategy was used for the RBF NN learning method. An approximated model of marine diesel generator is built in high precision result with 99 hidden neurons of RBF NN.