This paper presents is to develop and compare neural network and conventional based controllers for a boiler of steam power plant. Designs of two different controllers for pressure and temperature are presented for keeping the boiler working in normal condition and improve efficiency. These controllers consist of NARMA controller of ANN and a conventional proportional-integrator-derivative (PID) controller. These parameters are adjusted by built a model and implementation in MATLAB program according to the requisite of the steam power plant and the control objectives. The results show a neural network is best controlled and superior performances of power plant from PID controller artificial neural network and PID have been applied in Al-Dura power plant in Baghdad. Therefore, neural networks have been extensively utilized in many industrial applications. Порівняльний аналіз застосування пропорційно-інтегрально-диференціального регулятора і штучної нейронної мережі для керування паровим котлом електростанціїСалім Х., Султан Х. Ф., Джавад Р. * Технологічний університет, вул. аль-Сінаа., 10066, м. Багдад, Ірак Анотація. У статті представлено розроблену методику проведення порівняльного аналізу застосування нейронних мереж і контролерів для традиційних котлів парових електростанцій. Представлені схеми двох різних контролерів для тиску і температури для підтримки роботи котла в нормальному стані та підвищення ефективності. Ці контролери складаються з нелінійного NARMA-контролера штучної нейронної мережі та традиційного пропорційно-інтегрально-диференціального регулятора. Ці параметри коригуються шляхом побудови моделі та подальшої реалізації у програмі MATLAB відповідно до вимог парової електростанції та цілей управління. Результати свідчать, що нейронна мережа контролюється краще, а на електростанції Аль-Дюра у м. Багдад застосовуються характеристики відповідної моделі електростанції з використанням PID-контролера і штучної нейронної мережі, що може бути черговим підтвердження ефективності застосування нейронних мереж у багатьох галузях промисловості.Ключові слова: штучна нейронна мережа, керування, PID-контролер, NARMA.
Design and implantation of electric circuit for enhanced performance of steam power plant and artificial neural networks technique are used to control turbine. Artificial neural networks technique is used to control a lot of industrial models practically. Artificial neural network has been applied to control the important variables of turbine in AL–Dura power plant in Baghdad such as pressure, temperature, speed, and humidity. In this study Simulink model was applied in MATLAB program (v 2014 a) by using artificial neural network (ANN). The method of controlling model is by using NARMA to generate data and train network. ANN is offline. ANN requires data to obtain results and for comparison with actual power plant. The values of the input variables have a large effect on the number of nodes and epochs and in hidden layer of the artificial neural network they also affect performance of ANN. The electric circuit of sensors consists of transformer, DC bridge, and voltage regulator. Comparing the results from modeling by ANN and electric circuit with experimental data reveals a good agreement and the maximum deviation between the experimental data and predicted results from ANN and circuit design is less than 1%. The novelty in this paper is applying NARMA controller for the purpose of enhancement of turbine performance.
In the present-day decade, the world has regarded an expansion in the use of non-linear loads. These a lot draw harmonic non-sinusoidal currents and voltages in the connection factor with the utility and distribute them with the useful resource of the overall performance of it. The propagation of these currents and voltages into the grids have an effect on the electricity constructions in addition to the one of various client equipment. As a result, the electrical strength notable has come to be critical trouble for each client and distributor of electrical power. Active electrical electricity filters have been proposed as environment splendid gear for electrical power pinnacle notch enchantment and reactive electrical strength compensation. Active Power Filters (APFs) have Flipped out to be a possible wish in mitigating the harmonics and reactive electrical electricity compensation in single-phase and three-phase AC electrical energy networks with Non-Linear Loads (NLLs). Conventionally, this paper applied Ant Colony Algorithm(ACO) for tuning PI and reduce Total Harmonic Distortion (THD). The result show reduces THD at 2.33%.
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.
This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller’s design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.
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