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Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

Xiao Wu1, Jiong Shen1, Yiguo Li1

  • 1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou #2, Nanjing 210096, China.

ISA Transactions
|February 25, 2014
PubMed
Summary

This study introduces a new data-driven fuzzy modeling and predictive control strategy for boiler-turbine units. The approach effectively handles nonlinear dynamics, improving control performance and system representation.

Keywords:
Boiler–turbine unitData-driven modeling and controlFuzzy clusteringFuzzy modelPredictive controlSubspace identification

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Area of Science:

  • * Engineering and Control Systems
  • * Data-Driven Modeling
  • * Fuzzy Logic Systems

Background:

  • * Boiler-turbine units exhibit complex nonlinear dynamics, posing challenges for traditional control methods.
  • * Accurate modeling is crucial for effective predictive control in power generation systems.

Purpose of the Study:

  • * To develop a novel data-driven fuzzy modeling strategy for boiler-turbine units.
  • * To design a predictive controller based on the developed fuzzy model.
  • * To explore a direct data-driven fuzzy predictive control approach.

Main Methods:

  • * Fuzzy clustering for operation region division and fuzzy model structure development.
  • * Subspace Identification (SID) method extended with fuzzy membership functions for local state-space model extraction.
  • * Development of a fuzzy model predictive controller and a direct data-driven fuzzy predictive controller.

Main Results:

  • * The proposed fuzzy model accurately represents the nonlinear behavior of the boiler-turbine unit.
  • * The fuzzy model predictive controller demonstrates effectiveness in simulations.
  • * The direct data-driven approach utilizing intermediate subspace matrices also shows promise.

Conclusions:

  • * The novel data-driven fuzzy modeling and predictive control strategy is effective for boiler-turbine units.
  • * The combination of fuzzy clustering and subspace identification offers a powerful approach for complex system modeling.
  • * The proposed methods provide a robust and accurate control solution for power generation systems.