Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network

Abstract

Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multilayer feed-forward ANN and training was performed with operational data using backpropagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.

Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model

“…The accurate analysis of thermodynamic power plants using mathematical models requires high number of parameters and assumptions, in order to represent the actual system unpredictability [1] [2]. Instead of mathematical modelling the system's thermodynamics, machine learning approaches can be used [3] [4].…”

“…However, they have been increasingly studied and applied with the recent existence of higher computational power and the availability of datasets [5]. In a typical modern power plant, a large amount of parametric data is stored over long periods of time; therefore, a large data based on the operational data is always ready without any additional cost [2].…”

“…Many researchers reported the feasibility and reliability of ANN models as simulation and analysis tool for power plant processes and components [14]- [22]. Relatively few studies have considered the Steam Turbine (ST) in a combined cycle power plant (CCPP) [2] [23] [24] [25]. In [1] the total power output of a cogeneration power plant with three gas turbine, three HRSGs and one steam turbine were predicted.…”

“…Niu [24] studied the control strategy of the gas turbine in a combined cycle power plant using a linearization model technique. Samani [2] used two subsequent artificial neural networks to model combined cycle power plant with inputs as the relative humidity, atmospheric pressure, ambient temperature and the exhaust vacuum of the steam turbine. The exhaust steam pressure alone is a function of ambient conditions and is not a deterministic parameter.…”

Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

“…This method uses many assumptions and parameters to solve thousands of nonlinear equations; its elucidation takes too much effort and computational time. Sometimes, it is not easy to solve these equations without these assumptions [1,2]. To eradicate this barrier, machine learning (ML) methods are common substitutes for thermodynamical methods and mathematical modeling to study random output and input patterns [1].…”