The control of superheater steam temperature in power plants using model predictive controller

Abstract

The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to regulate temperature levels and guarantee stability. Using the proposed method, model-based predictive control aims to enhance control quality and identify the optimal value. By employing this approach, the superheater can attain the optimal configurations for the steam temperature plant, thereby simultaneously enhancing the stability of the plant. The principal aim of this manuscript is to attain a reduction in the Integral Absolute Error to 40.6%. The simulation was conducted using the MATLAB/Simulink platform, and the outcomes demonstrate that the proposed method exhibits superior performance compared to those that have been documented in the literature.