Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportio...Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme. For the characteristics of the main steam pressure in coal-fired power plant boiler, the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement. First, internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay, and sliding mode controller (SMCr) is designed to overcome the model mismatch. Simulation results show the effectiveness of the proposed controller compared with conventional ones.展开更多
Based on the research on domestic and international automatic technical development in fossil power plant,the paper analyses the recent situation of the coordinate control system between turbine and boiler of domestic...Based on the research on domestic and international automatic technical development in fossil power plant,the paper analyses the recent situation of the coordinate control system between turbine and boiler of domestic fossil Power Plant,provides the development thought of coordinate control system between turbine and boiler,and describes the application prospect in control system of fossil power plant combining with the application experience.展开更多
In recent years,developing Artificial Intelligence(AI)models for complex system has become a popular research area.There have been several successful AI models for predicting the Selective Non-Catalytic Reduction(SNCR...In recent years,developing Artificial Intelligence(AI)models for complex system has become a popular research area.There have been several successful AI models for predicting the Selective Non-Catalytic Reduction(SNCR)system in power plants and large boilers.However,all these models are in essence black box models and lack of explainability,which are not able to give new knowledge.In this study,a novel explainable AI(XAI)model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics(SINDy)model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant.This proposed model identifies the system’s governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables.In addition,the explainable AI model achieves a considerable accuracy with less than 21%deviation from base-line models of partial least squares model and artificial neural network model.展开更多
基金Supported by the National Natural Science Foundation of China (61174059, 60934007, 61233004)the National Basic Research Program of China (2013CB035406)Shanghai Rising-Star Tracking Program (11QH1401300)
文摘Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying, strongly coupled, and nonlinear, it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme. For the characteristics of the main steam pressure in coal-fired power plant boiler, the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement. First, internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay, and sliding mode controller (SMCr) is designed to overcome the model mismatch. Simulation results show the effectiveness of the proposed controller compared with conventional ones.
文摘Based on the research on domestic and international automatic technical development in fossil power plant,the paper analyses the recent situation of the coordinate control system between turbine and boiler of domestic fossil Power Plant,provides the development thought of coordinate control system between turbine and boiler,and describes the application prospect in control system of fossil power plant combining with the application experience.
文摘In recent years,developing Artificial Intelligence(AI)models for complex system has become a popular research area.There have been several successful AI models for predicting the Selective Non-Catalytic Reduction(SNCR)system in power plants and large boilers.However,all these models are in essence black box models and lack of explainability,which are not able to give new knowledge.In this study,a novel explainable AI(XAI)model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics(SINDy)model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant.This proposed model identifies the system’s governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables.In addition,the explainable AI model achieves a considerable accuracy with less than 21%deviation from base-line models of partial least squares model and artificial neural network model.
