Both the modeling and the load regulation capacity prediction of a supercritical power plant are investigated in this paper. Firstly, an indirect identification method based on subspace identification method is propos...Both the modeling and the load regulation capacity prediction of a supercritical power plant are investigated in this paper. Firstly, an indirect identification method based on subspace identification method is proposed. The obtained identification model is verified by the actual operation data and the dynamic characteristics of the system are well reproduced. Secondly, the model is used to predict the load regulation capacity of thermal power unit. The power, main steam pressure, main steam temperature and other parameters are simulated respectively when the unit load is going up and down. Under the actual constraints, the load regulation capacity of thermal power unit can be predicted quickly.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
After a thorough demonstration in Panshan Thermal Power Plant, the 500 MW super critical pressure unit simulator developed by the Simulation & Control Institute under the North China University of Electric Power w...After a thorough demonstration in Panshan Thermal Power Plant, the 500 MW super critical pressure unit simulator developed by the Simulation & Control Institute under the North China University of Electric Power was accepted by experts from the North China Electric Power Group Company on 3rd August 1996.展开更多
Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal di...Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.展开更多
Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structu...Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structural problems of wind-solar power and thermoelectric. To solve these problems, this paper proposes a plurality of units together to ensure supply of heat load on the premise, by building a thermoelectric power peaking considering thermal load unit group dynamic scheduling model, to achieve the potential of different thermoelectric properties peaking units of the excavation. Simulation examples show, if the unit group exists obvious relationship thermoelectric individual differences, the thermal load dynamic scheduling can be more significantly improved overall performance peaking unit group, effectively increase clean energy consumptive.展开更多
文摘Both the modeling and the load regulation capacity prediction of a supercritical power plant are investigated in this paper. Firstly, an indirect identification method based on subspace identification method is proposed. The obtained identification model is verified by the actual operation data and the dynamic characteristics of the system are well reproduced. Secondly, the model is used to predict the load regulation capacity of thermal power unit. The power, main steam pressure, main steam temperature and other parameters are simulated respectively when the unit load is going up and down. Under the actual constraints, the load regulation capacity of thermal power unit can be predicted quickly.
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.
文摘After a thorough demonstration in Panshan Thermal Power Plant, the 500 MW super critical pressure unit simulator developed by the Simulation & Control Institute under the North China University of Electric Power was accepted by experts from the North China Electric Power Group Company on 3rd August 1996.
文摘Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.
文摘Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structural problems of wind-solar power and thermoelectric. To solve these problems, this paper proposes a plurality of units together to ensure supply of heat load on the premise, by building a thermoelectric power peaking considering thermal load unit group dynamic scheduling model, to achieve the potential of different thermoelectric properties peaking units of the excavation. Simulation examples show, if the unit group exists obvious relationship thermoelectric individual differences, the thermal load dynamic scheduling can be more significantly improved overall performance peaking unit group, effectively increase clean energy consumptive.