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
With the development of new energy,the primary frequency control(PFC)is becoming more and more important and complicated.To improve the reliability of the PFC,an evaluation method of primary frequency control ability(...With the development of new energy,the primary frequency control(PFC)is becoming more and more important and complicated.To improve the reliability of the PFC,an evaluation method of primary frequency control ability(PFCA)was proposed.First,based on the coupling model of the coordinated control system(CCS)and digital electro-hydraulic control system(DEH),principle and control mode of the PFC were introduced in detail.The simulation results showed that the PFC of the CCS and DEH was the most effective control mode.Then,the analysis of the CCS model and variable condition revealed the internal relationship among main steam pressure,valve opening and power.In term of this,the radial basis function(RBF)neural network was established to estimate the PFCA.Because the simulation curves fit well with the actual curves,the accuracy of the coupling model was verified.On this basis,simulation data was produced by coupling model to verify the proposed evaluation method.The low predication error of main steam pressure,power and the PFCA indicated that the method was effective.In addition,the actual data obtained from historical operation data were used to estimate the PFCA accurately,which was the strongest evidence for this method.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.
基金supported by the Electric Power Research Institute of State Grid Corporation of China in Zhejiang province。
文摘With the development of new energy,the primary frequency control(PFC)is becoming more and more important and complicated.To improve the reliability of the PFC,an evaluation method of primary frequency control ability(PFCA)was proposed.First,based on the coupling model of the coordinated control system(CCS)and digital electro-hydraulic control system(DEH),principle and control mode of the PFC were introduced in detail.The simulation results showed that the PFC of the CCS and DEH was the most effective control mode.Then,the analysis of the CCS model and variable condition revealed the internal relationship among main steam pressure,valve opening and power.In term of this,the radial basis function(RBF)neural network was established to estimate the PFCA.Because the simulation curves fit well with the actual curves,the accuracy of the coupling model was verified.On this basis,simulation data was produced by coupling model to verify the proposed evaluation method.The low predication error of main steam pressure,power and the PFCA indicated that the method was effective.In addition,the actual data obtained from historical operation data were used to estimate the PFCA accurately,which was the strongest evidence for this method.
