Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

Zhenhao Tang,Ping Che,Shengxian Cao,Zhiyong Zhao,Haiyang Zhang

doi:10.1155/2017/8048962

Abstract

To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

Highlights

Fossil-fuel-fired power plants can produce stable and controllable energy
If the relative predictive error is bigger than 1% for continuous T sample period, the prediction model is reconstructed with updated sample data
Case 1 includes a step change in the boiler furnace temperature which can illustrate the sudden change in practical production

Summary

Introduction

Fossil-fuel-fired power plants can produce stable and controllable energy. Despite the quick development of sustainable power generation methods, such as solar power systems and wind power systems, the fossil-fuel-fired power generation system is and will be an import part of the power system. Heo et al [9] and Li et al [10] proposed intelligent control methods based on particle swarm optimization and genetic algorithm relatively These methods mainly focus on the application of these modern methods, and little attention is paid to high accuracy modeling. (1) The DoAMPC adopts LSSVM to construct the predictive model for the boiler furnace temperature with high accuracy rapidly. This model considered the main state variables and control variables. (3) The DoAMPC presents a new way to realize model predictive control in practical problems by utilizing data-driven algorithm and intelligent optimization algorithms.

Background

Computational Results

Conclusion