On the Analysis and Fault-Diagnosis Tools for Small-Scale Heat and Power Plants

Jaime Arriagada

Research output: ThesisDoctoral Thesis (compilation)

Abstract

The deregulation of the electricity market drives utilities and independent power producers to operate heat and power plants as profit centers. In order to keep the economic margins on the credit side, the preferred measures have been to improve the electrical efficiency through changes in the hardware and boost the overall efficiency through e.g. combined heat and power (CHP) generation. The better understanding of global environmental issues is also pushing the development toward more advanced power plant technology that at the introduction stage may represent a risky option for the plant owner. Recently, there is a growing interest in improving the plant operation instead, and therefore the focus has been put on aspects related to the RAM (reliability-availability-maintenance) of the plants.

Small- and mid-scale CHP plants, especially natural gas- and biomass-fueled, have been identified to be important to satisfy the needs of the energy market and help to mitigate the environmental factors in the short- and middle-term. One of the major challenges that these types of plants will face is attaining good RAM at the same time that they cannot support big O&M costs and a lot of personnel. Therefore the implementation of cheap and reliable IT-based tools that help to achieve this goal is essential.

Most power plants today are equipped with modern distributed control systems that through a considerable number of sensors deliver large amounts of data to the control room. This paves the way to the introduction of intelligent tools -derived from the artificial intelligence technology- such as artificial neural networks (ANNs) and genetic algorithms (GA). ANNs have a learning ability that makes them useful for the construction of powerful non-physical models based on data from the process, while GA has shown to be a robust optimization method based on the principle of the “survival-of-the-fittest”. Principally ANNs, but also to a lesser extent GA, have been applied to different case studies in this thesis, either alone or in conjunction with heat and mass balance programs (HMBPs) -the state-of-the-art tool in the field today. This thesis presents both theoretical and experimental case studies which have been validated with data from simulations and real plants, respectively. The studied tasks include design, optimization, system identification, and fault diagnosis of small- and medium-size heat and power plants. Some results of these case studies are powerful hybrid models that speed up calculations and fault diagnosis systems capable of recognizing developing faults and delivering early warnings to the plant operator.
Original languageEnglish
QualificationDoctor
Awarding Institution
  • Thermal Power Engineering
Supervisors/Advisors
  • [unknown], [unknown], Supervisor, External person
Award date2003 Dec 15
Publisher
ISBN (Print)91-628-5843-2
Publication statusPublished - 2003

Bibliographical note

Defence details

Date: 2003-12-15
Time: 10:15
Place: Room M:B of the M-building, Lund Institute of Technology

External reviewer(s)

Name: Karlsson, Agne
Title: Dr
Affiliation: Finspång

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Article: 1. Arriagada, J., Assadi, M., (2000): Air Bottoming Cycle for Gas Turbines. ISME-2000, Mechanical Engineering Conference, Teheran, Iran.

Article: 2. Arriagada, J., Rosén, P., Torisson, T., (2001): A novel gas turbine concept for combined power, heat and cooling generation. ASME Turbo Expo 2001, New Orleans, USA.

Article: 3. Arriagada, J., Azimian, A., Assadi, M., (2004): Generation of Steam Tables Using Artificial Neural Networks. Accepted for publication in Heat Transfer Engineering – An International Journal, Taylor and Francis Ltd, Vol. 25(2), February 2004.

Article: 4. Assadi, M., Mesbahi, E., Torisson, T., Lindquist, T., Arriagada, J., Olausson, P., (2001): A Novel Correction Technique for Simple Gas Turbine Parameters. ASME Turbo Expo 2001, New Orleans, USA.

Article: 5. Arriagada, J., Olausson, P., Selimovic, A., (2002): Artificial Neural Network Simulator for SOFC Performance Prediction. Journal of Power Sources, Elsevier Science, Vol. 112, pp. 54-60.

Article: 6. Arriagada, J., Costantini, M., Olausson, P., Assadi, M., Torisson, T., (2003): Artificial Neural Network model for a Biomass-fueled Boiler. ASME Turbo Expo 2003, Atlanta, USA.

Article: 7. Olausson, P., Häggståhl, D., Arriagada, J., Dahlquist, E., Assadi, M., (2003): Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks. ASME Turbo Expo - 2003, Atlanta, USA.

Article: 8. Mesbahi, E., Arriagada, J., Assadi, M., Ghorban, H. (2003): Diesel Engine Fault Diagnosis by Means of ANNs: A Comparison Between Fault Pattern and Residual Method. Submitted to Journal of Marine Design and Operations.

Article: 9. Arriagada, J., Genrup, M., Loberg, A., Assadi, M., (2003): Fault Diagnosis System for anIndustrial Gas Turbine by Means of Neural Networks. IGTC2003, International Gas Turbine Congress, Tokyo, Japan.

Article: 10. Fredriksson-Möller, B., Arriagada, J., Assadi, M., Potts, I. (2003): Optimization of a GT/SOFC System with CO2 Capture. Submitted to Journal Power Sources, Elsevier Science.

Subject classification (UKÄ)

  • Energy Engineering

Keywords

  • Thermal engineering
  • applied thermodynamics
  • Termisk teknik
  • termodynamik
  • genetic algorithms
  • neural networks
  • fault diagnosis
  • maintenance
  • availability
  • reliability
  • energy analysis
  • combined heat and power
  • Methods and tools
  • small scale

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