Prediction of power output of a coal-fired power plant by artificial neural network

Research output: Contribution to journalArticle

Abstract

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.

Details

Authors
  • J. Smrekar
  • D. Pandit
  • Magnus Fast
  • Mohsen Assadi
  • Sudipta De
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Energy Engineering

Keywords

  • Real plant, ANN model, Steam turbine, Power plant, Coal-fired boiler, Interpolation, data, Extrapolation
Original languageEnglish
Pages (from-to)725-740
JournalNeural Computing & Applications
Volume19
Issue number5
Publication statusPublished - 2010
Publication categoryResearch
Peer-reviewedYes