Performance improvement of power generation in gas turbines by using random forest and recurrent neural networks

Mrs. T. Sujatha , Mrs. S. Jayasree , Mr. M. Kannan

RNN, air turbine, random forest, recurrent neural networks, gas turbine, complexity.

In order to improve the power generating performance of gas turbines, machine learning based models are developed. In this paper, random forest and recurrent neural network algorithms are combined to improve the power generation. Average errors based on the operating characteristics are captured by this model. Furthermore, these models are established for capturing the gas turbine part-load performance and full-load performance. Simulation performance is computed by embedding the air compressor and air turbine and individual dataset are used for the validation of these models. The correction curves of gas turbine performance are constructed by predicting the full load performance of RNN model and it possesses reduced complexity and increased accuracy. The obtained curves of the RNN model to predict the part-load performance produces extreme results for continuous turbine monitoring and diagnosis of fault. This proposed method is suitable to any gas turbines and it will be aided to all the power plants for studying the quantitative performance degradation with respect to time.

Published Paper   E-Certificate

"Performance improvement of power generation in gas turbines by using random forest and recurrent neural networks", IJEDR - INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (www.IJEDR.org), ISSN:2321-9939, Vol.10, Issue 4, page no.46-49, December 2022, Available :https://rjwave.org/IJEDR/papers/IJEDR2204006.pdf

Volume 10 Issue 4, October-2022

Pages : 46-49

Paper Reg. ID: IJEDR_220218

Published Paper Id: IJEDR2204006

Research Area: Engineering

Country: chennai, tamil nadu, India

Published Paper PDF: https://rjwave.org/IJEDR/papers/IJEDR2204006

Published Paper URL: https://rjwave.org/IJEDR/viewpaperforall?paper=IJEDR2204006

ISSN: 2321-9939 | IMPACT FACTOR: 9.37 Calculated By Google Scholar | ESTD YEAR: 2013

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.37 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJEDR (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition