Open AccessAdaptive Control Using NARMA-L2 Model and Electric Circuit for Condenser of Thermal Power Plant
Author(s) -
Hosham S. Anead,
Khalid F. Sultan,
Raheel Jawad
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/765/1/012019
Subject(s) - artificial neural network , condenser (optics) , electric power , engineering , thermal power station , bridge circuit , voltage , control theory (sociology) , electronic engineering , computer science , electrical engineering , power (physics) , artificial intelligence , light source , physics , control (management) , quantum mechanics , optics
Design and implantation electric circuit for enhancement performance of steam power plant and artificial neural networks technique used to control of condenser. Artificial neural network has been applied to control of the important variables of condenser in Al–Dura power plant in Baghdad such as pressure, temperature. in this study, applied Simulink model in Matlab program (v 2014 a) by using artificial neural network toolbox. the model of condenser of neural network by using NARMA to generated data and train network and using back propagation algorithm for training neural network. The method of control by ANN is off line. the electric circuit of pressure sensor and temperature sensor consist of transformer, dc bridge and voltage regulator. Experimental data of actual power plant obtained from al-dura power plant. comparing results of modelling neural network and electric circuit with experimental data of actual power plant. the results shown the maximum deviation between the ANN and electric circuit with experimental data is less than 4%. artificial neural network can be used in many industrial and engineering application.