A hybrid technique to control superheater steam temperature in power plants using multi modeling and predictive sliding mode control

Abstract

One of the most critical variables to control in a steam power plant is superheater steam temperature. It must be carefully managed within a narrow temperature range; too high temperatures will cause material degradation. Temperature variation impacts the plant's efficiency, causing unplanned disturbances, because to frequent and intense load fluctuations and stringent requirement for performance and safety. To control the temperature level and maintain stability, the proposed hybrid approach is applied with MM-PSMC in this research. Fuzzy Logic Controller (FLC) and Golden Eagle Optimization [GEO] are combined in the suggested hybrid approach. Using the GEO technique, the FLC aims to increase control quality and identify the optimum value. This technique enables the superheater to attain the best-fit settings for the steam temperature plant while also increasing the plant's stability. The primary goal of this paper is to lower the IAE index at the superheater temperature plant. Finally, the MATLAB/Simulink platform provides steady-state values of the control levels of the superheater steam temperature under various scenarios. The suggested technique is compared to existing methods such as RNN with EHO, RNN with PSO, and ANN. Simulation results shows the proposed method gives good results Maximum 160 MW of power is generated and when the set value intelligent optimization compensation method is employed, the SST's control quality is dramatically improved—by 90 % when compared to the initial control—in terms of overshoot, control error, and relation to risk management. The proposed method controls the temperature at 535°C compared to existing methods.