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Published:
Nov 6, 2023Article Details
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Main Article Content
Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study
Abstract
This work proposes deployment of machine learning in the
maintenance of individual constituent parts of steam power plant
assemblages. With the condenser vacuum of a steam turbine (in a
six-turbine plant assemblage) taken as a case study, information
on the past operating parameters of the selected plant component
was used to forecast its future working condition. Based on
Exponential Gaussian Process of Regression, a model was
developed, trained using the diachronic operational data, and
employed in determining the future. A quantitative evaluation was
employed to provide the distribution of the test values of the data
about the lines of regression, as well as to measure the prediction
accuracy of the model. The results show MAE and RMSE values
are 6.1602 and 7.9286 respectively during the training; while for
the prediction, the values are 92.6544 and 92.7235 respectively.
It is concluded that modern power plants with myriads of
instrumentation and data acquisition mechanisms can leverage
on the approach of this study to model and plan the
maintenance scheme that best suits and fits individual
component units of power plants, since understanding of the
anticipatory values of operational parameters helps to determine
the likelihood of components failures.
