Research On Modeling Method Of Reheated Steam Temperature System Based On Historical Data

During the operation of the unit,in order to ensure the safe and efficient operation environment of the unit,it is an important link to maintain reheated steam temperature within a certain range.With the popularization of the high parameter direct current boiler,the obvious large lag phenomenon makes the control more difficult,and it is difficult to obtain satisfactory control quality in the actual operation.For reheat steam temperature system,the quality of control can be improved in many ways,it can start from two aspects with the perspective of the establishment of the model :(1)Establish a more accurate model based on the actual unit operation.The accuracy of the model has a significant impact on the control of the system,the deviation between the established model and the actual operating condition will lead to a large deviation of the system control,thus leading to the deviation of the steam temperature from the maintenance range.In this paper,the reheat steam temperature system is modeled based on the historical data,at the same time,the established model is verified to verify the accuracy and effectiveness of the model.(2)According to the changing trend of reheated steam temperature,the predictive model is established,and the system is adjusted and controlled ahead of time.Due to the hysteresis of reheat steam temperature object,the control becomes complex,so it can be adjusted ahead of schedule according to the changing trend of steam temperature to achieve pre-control,thus alleviating the control effect caused by large delay.This paper uses support vector machine regression algorithm and deep learning algorithm to predict reheat steam temperature,the prediction model is established based on the trend of the factors affecting the reheat steam temperature.This paper shows a variety of methods for modeling reheated steam temperature system based on the historical data,the improved particle swarm optimization algorithm is used to identify the system.The model established in this paper can well adapt to the field conditions,and has a reference value for the control of reheated steam temperature.At the same time,using advanced intelligent algorithm to predict reheat steam temperature is an important means to improve control quality.According to the field operation data,the change trend is predicted,and the adjustment is advanced.It has a guiding significance for the on-site operation of the unit,and improves the control quality of reheated steam temperature.