scholarly journals Application of Association Rules Data Mining in the Determination the Operation Target Values in the Thermal Power Plant

2014 ◽  
Author(s):  
Xiang Wan ◽  
Niansu Hu ◽  
Min Huo
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Association Rules ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Target Values

scholarly journals Performance evaluation of a large-scale thermal power plant based on the best industrial practices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yousef S. H. Najjar ◽  
Amer Abu-Shamleh
Keyword(s):  
Power Plant ◽  
Thermal Efficiency ◽  
Thermal Power Plant ◽  
Large Scale ◽  
Best Practice ◽  
Thermal Power ◽  
Capacity Factor ◽  
Target Value ◽  
Target Values ◽  
Installed Capacity

AbstractThe aim of this study is to assess and evaluate the performance of a large-scale thermal power plant (TPP). The performance rating was conducted in compliance with the statistical principles. The data for this analysis were obtained for a TPP with an installed capacity of 375 MW during a span of 8 years (2010–2017). Four parameters were used to evaluate the performance of the TPP including the availability, the reliability, the capacity factor, and the thermal efficiency. These parameters were calculated using a set of equations and then compared to the international best practices and target values. The results indicate that approximately 91% of the expected capacity was available throughout the studied period against the industry best practice of 95%. However, the average TPP’s reliability was found to be approximately 95% against the target value of 99.9%. Furthermore, the average capacity factor throughout the studied period is 70% as against the international value of 40–80%. Moreover, the thermal efficiency of the TPP is 40% against the target value of 49%. Due to the outage hours and malfunctions, the power losses throughout the studied period reached 846 MW. Overall, the analysis indicates that the studied TPP is not within the scope of the best industrial practices.

The Applications of Real-Time Data Mining Technology in Fault Prediction of Power Plant Generator

2015 ◽  
Vol 740 ◽  
pp. 351-354
Author(s):  
Feng Li ◽  
Hong Bin Wang ◽  
Dao Jun Deng ◽  
Yan Xia Zhang
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Fault Prediction ◽  
Time Data ◽  
Mining Technology ◽  
Online Security ◽  
Real Time Data

This paper mainly discusses the applications of real-time data mining technology in fault prediction of power plant generator. Massive real-time historical data of thermal power plant turbine generator equipment is stored to realize comprehensive quantitative assessment of thermal power plant turbine generator’s online security status and potential failure Early Warning. It is based on the Real-time data mining analysis and modeling techniques.

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