Prediction of Hourly Generated Electric Power Using Artificial Neural Network for Combined Cycle Power Plant

2016 ◽  
Vol 4 (2) ◽  
pp. 91-95
Author(s):  
Bayram Akdemir ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Electric Power ◽  
Combined Cycle ◽  
Combined Cycle Power Plant ◽  
Artificial Neural

scholarly journals Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1022
Author(s):  
Yondha Dwika Arferiandi ◽  
Wahyu Caesarendra ◽  
Herry Nugraha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Input Parameter ◽  
Heat Rate ◽  
Combined Cycle ◽  
Ann Model ◽  
Combined Cycle Power Plant ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.

Electric power regulation and modeling of a central tower receiver power plant based on artificial neural network technique

2018 ◽  
Vol 10 (4) ◽  
pp. 043706 ◽  
Author(s):  
Ibrahim Moukhtar ◽  
Adel A. Elbaset ◽  
Adel Z. El Dein ◽  
Yaser Qudaih ◽  
Evgeny Blagin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Electric Power ◽  
Power Regulation ◽  
Artificial Neural

scholarly journals Thermodynamic analysis and simulation of a new combined power and refrigeration cycle using artificial neural network

2011 ◽  
Vol 15 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Abdolreza Fazeli ◽  
Hossein Rezvantalab ◽  
Farshad Kowsary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Source ◽  
Exergy Efficiency ◽  
Combined Cycle ◽  
Cycle Performance ◽  
Working Fluid ◽  
Refrigeration Cycle ◽  
Water Mixture ◽  
Artificial Neural

In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN) is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.

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