The Prediction and Operational Control System of the Cofiring Combined Cycle Power Plant Using Deep Learning Methods to Improve Power Generation Performance

Addien Wahyu Wiranata, Dimas Anton Asfani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cofiring is one step in accelerating the mix of new renewable energy. The utilization of biomass is substituted in Coal is used as fuel in boilers in the process of transitioning thermal plants to biomass. However, the use of substantial amounts of biomass will have an impact on thermal and kinetic changes in turbine equipment so that it affects the output of the generator and speed drop governor. Using cofiring test performance data parameters of 3%, 5%, 100/0, 250/0, 50%, and 100% will be processed using machine learning, namely with the deep learning for time series forecasting (DL- TSF) method to obtain generator output optimization and speed drop governor. The use of deep learning will combine multilayer perceptron, convolutional neural network, and long short-term memory algorithms with adaptive moment estimation (ADAM) optimization and will be validated with mean square error (MSE) for each process. The output of this machine learning will later provide output optimization of generator loading and governor openings above 70% of the capable power capacity. The use of the long short-term memory algorithm will also be able to function as an estimator in the control speed droop governor function to maintain equipment work efficiency. In the future, the use of cofiring through deep learning process control for time series forecasting will be able to increase the efficiency of generator output with various uses of biomass types and increasing percentages so that the distribution of new renewable energy mix can continue to increase.

Original languageEnglish
Title of host publicationICT-PEP 2024 - International Conference on Technology and Policy in Energy and Electric Power
Subtitle of host publicationResilient Power Systems: Navigating the Clean Energy Transition, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages324-329
Number of pages6
ISBN (Electronic)9798331518646
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2024 - Bali, Indonesia
Duration: 3 Sept 20245 Sept 2024

Publication series

NameICT-PEP 2024 - International Conference on Technology and Policy in Energy and Electric Power: Resilient Power Systems: Navigating the Clean Energy Transition, Proceedings

Conference

Conference2024 International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2024
Country/TerritoryIndonesia
CityBali
Period3/09/245/09/24

Keywords

  • Deep Learning for Time Series Forecasting (DL-TSF)
  • Speed Drop Governor
  • Thermal-Biomass Power Plant

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