Method to Control the Output Power of SRG at Low Speed with Appropriate Excitation Angles using Artificial Neural Network
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Abstract
Parameters of a switched reluctance generator (SRG) consist of DC bus voltage, rotor speed and excitation angles. When the DC bus voltage and rotor speed are constantly controlled, the output power of an SRG depends on the excitation angles. This paper presents a method for determining the appropriate excitation angles of an SRG using artificial neural network to generate the required output power, when the SRG operates at low speed. The artificial neural network used as a back-propagation type in combination with a Levenberg-Marquardt algorithm. The inputs used in the artificial neural network have two variables; rotor speed and output power. The desired output is the excitation angles. The data set for teaching artificial neural network was obtained from an experiment to collect the results of the output power and excitation angles at a constant speed between 1,000-2,000 rpm. Half bridge converter circuit is used to drive the SRG, which is processed by a dSPACE DS1202. The control program is designed through MATLAB/Simulink together with the ControlDesk program to control and display via the computer screen. The rotor speed and required output power were determined into the artificial neural network to predict the excitation angle. The experiment results, the output power generated by the SRG has an average error of 1.76% compared with the required output power. This result confirms the accuracy of the excitation angles processed by artificial neural network.
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