Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design
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 t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2018/8042498 |
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| author | Hosham Salim Khalid Faisal Raheel Jawad |
| author_facet | Hosham Salim Khalid Faisal Raheel Jawad |
| author_sort | Hosham Salim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-10176e96c8ee4c22849c337865e9c612 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-10176e96c8ee4c22849c337865e9c6122025-02-03T06:01:37ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322018-01-01201810.1155/2018/80424988042498Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit DesignHosham Salim0Khalid Faisal1Raheel Jawad2Electro Mechanical. Eng. Dept., University of Technology, Baghdad, IraqElectro Mechanical. Eng. Dept., University of Technology, Baghdad, IraqElectro Mechanical. Eng. Dept., University of Technology, Baghdad, IraqDesign 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.http://dx.doi.org/10.1155/2018/8042498 |
| spellingShingle | Hosham Salim Khalid Faisal Raheel Jawad Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design Applied Computational Intelligence and Soft Computing |
| title | Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design |
| title_full | Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design |
| title_fullStr | Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design |
| title_full_unstemmed | Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design |
| title_short | Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design |
| title_sort | enhancement of performance for steam turbine in thermal power plants using artificial neural network and electric circuit design |
| url | http://dx.doi.org/10.1155/2018/8042498 |
| work_keys_str_mv | AT hoshamsalim enhancementofperformanceforsteamturbineinthermalpowerplantsusingartificialneuralnetworkandelectriccircuitdesign AT khalidfaisal enhancementofperformanceforsteamturbineinthermalpowerplantsusingartificialneuralnetworkandelectriccircuitdesign AT raheeljawad enhancementofperformanceforsteamturbineinthermalpowerplantsusingartificialneuralnetworkandelectriccircuitdesign |