An optimized nonlinear generalized predictive control for steam temperature in an ultra supercritical unit

Area of Science:

• * Power Engineering
• * Control Systems

Background:

• * Ultra supercritical (USC) units require precise control for safety and economic operation.
• * Intermediate point temperature control in USC units is challenging due to nonlinearity, large scale, and significant delays.
• * Conventional control methods often struggle to achieve effective regulation.

Purpose of the Study:

• * To develop an advanced nonlinear generalized predictive control (GPC) strategy for improving intermediate point temperature control in USC units.
• * To enhance the safety and economic performance of USC units through optimized temperature regulation.

Main Methods:

• * A composite weighted human learning optimization (CWHLO) network is developed, incorporating heuristic information from onsite data.
• * The CWHLO network utilizes local linear models and a scheduling program to form a global controller.
• * The proposed CWHLO-GPC integrates nonlinear CWHLO models into the quadratic programming (QP) routine of local linear GPC, addressing non-convex problems.

Main Results:

• * The CWHLO-GPC effectively solves the non-convex problem inherent in traditional GPC for this application.
• * Simulations demonstrate superior set point tracking capabilities compared to classical GPC.
• * The strategy shows enhanced resistance to interference, validating its efficiency.

Conclusions:

• * The proposed CWHLO-GPC offers a significant improvement in intermediate point temperature control for USC units.
• * This advanced control strategy enhances the operational safety and economic efficiency of power generation.
• * The method provides a robust solution for complex industrial control challenges.