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An effective multi-model based nonlinear control for USC power plant.

Chuanliang Cheng1, Chen Peng1, Xiangpeng Xie2

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China.

ISA Transactions
|February 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonlinear control method for ultra supercritical (USC) power plants. The integrated approach enhances energy efficiency, ensuring fast and stable operation even during significant load changes.

Keywords:
Generalized predictive controlHuman learning optimization algorithmInternal model controlLong short-term memory neural networkUltra supercritical unit

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Area of Science:

  • Power Generation Engineering
  • Control Systems Theory
  • Artificial Intelligence in Energy

Background:

  • Ultra supercritical (USC) boiler-turbine units are critical for efficient power generation.
  • Nonlinearity and slow dynamic response are significant challenges in USC unit control.
  • Optimizing energy efficiency in USC units is a key concern for the power industry.

Purpose of the Study:

  • To develop a robust nonlinear control method for USC boiler-turbine units.
  • To address the challenges of nonlinearity and slow dynamics in USC systems.
  • To improve the energy efficiency and operational stability of power plants.

Main Methods:

  • Integration of Internal Model Control (IMC) and Generalized Predictive Control (GPC) into a unified nonlinear control framework.
  • Utilizing a Long Short-Term Memory (LSTM) neural network for the IMC component to improve response speed.
  • Employing a Composite Weighted Human Learning Optimization (CWHLO) network for the nonlinear GPC component to achieve high-accuracy tracking.

Main Results:

  • The LSTM-based IMC demonstrated rapid convergence to setpoints, significantly enhancing system response speed.
  • The CWHLO-GPC achieved high-accuracy tracking performance.
  • Simulations on a 1000MW USC power plant confirmed the method's ability to provide fast and stable dynamic responses under large load variations.

Conclusions:

  • The proposed integrated IMC-GPC nonlinear control method effectively optimizes energy efficiency in USC boiler-turbine units.
  • The novel approach successfully mitigates nonlinearity and slow dynamics, leading to improved operational performance.
  • This control strategy offers a promising solution for enhancing the stability and efficiency of large-scale power generation systems.