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Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

机译:利用人工神经网络和电路设计提高火力发电厂汽轮机性能。

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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.
机译:为了提高蒸汽发电厂的性能而设计和植入电路,并使用人工神经网络技术来控制涡轮机。人工神经网络技术被用来实际控制许多工业模型。人工神经网络已被用于控制巴格达AL–Dura电厂中涡轮机的重要变量,例如压力,温度,速度和湿度。在这项研究中,Simulink模型通过使用人工神经网络(ANN)应用于MATLAB程序(v 2014 a)。模型控制的方法是使用NARMA生成数据并训练网络。人工神经网络离线。人工神经网络需要数据来获得结果并与实际电厂进行比较。输入变量的值对节点和纪元的数量有很大影响,在人工神经网络的隐藏层中,它们也影响ANN的性能。传感器的电路由变压器,直流电桥和稳压器组成。将人工神经网络和电路建模的结果与实验数据进行比较,可以得出很好的一致性,并且实验数据与人工神经网络和电路设计的预测结果之间的最大偏差小于1%。本文的新颖之处在于将NARMA控制器应用于提高涡轮机性能的目的。

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