CN104482525B - The control method of extra-supercritical unit reheat steam temperature and system - Google Patents
The control method of extra-supercritical unit reheat steam temperature and system Download PDFInfo
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Abstract
The invention discloses the control method of a kind of extra-supercritical unit reheat steam temperature and system, obtain the stage linear model of the Disturbance Model of each disturbance variable, each control variable, and it is converted into transmission function, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL, detect the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information; The Optimized model that detection information and the substitution of described step response model are preset is optimized and solves, generate optimal solution; Control variable in described optimal solution is applied to extra-supercritical unit, carries out reheat steam temperature and regulate and control. Implement the present invention, quickly and accurately the reheating temperature of described extra-supercritical unit can be regulated and controled to default effective temperature-control range, improve the control efficiency of reheated steam steam temperature.
Description
[technical field]
The present invention relates to thermal control technology field, particularly relate to control method and the system of a kind of extra-supercritical unit reheat steam temperature.
[background technology]
In the automatic control system of heat power engineering system, it is a big time delay process that reheated steam steam temperature regulates, and reheat steam temperature object has the feature of large delay, big inertia. The heating surface of reheated steam is at the tail end of exhaust gases passes, and flue-gas temperature is relatively low, adds the adjustment difficulty of reheated steam steam temperature. In addition reheat steam temperature is easily by the impact of various interference factors, and under various perturbation actions, steam temperature object has the characteristics such as non-linear, time-varying, makes control difficulty strengthen. It is loaded into steam temperature bigger by the impact of the variable such as load, spray water flux, owing to the vapour pressure of reheated steam is low, flow is little, heat transfer coefficient is little, so reheater is arranged among vertical gas pass or horizontal flue more, belong to pure convection heating surface, thus reheat steam temperature by boiler load change impact greatly. Especially as the raising of unit capacity and parameter, steam superheating heating surface ratio strengthens so that it is delay bigger with inertia, thus increasing the difficulty of control further.
Reheat steam temperature regulates to regulate with main steam steam temperature relatively big difference, reheat steam temperature desuperheating water water spray regulates, easily increase in steam turbine, the flow of low pressure (LP) cylinder, in adding accordingly, the power of low pressure (LP) cylinder, if aggregate capacity (load) remains unchanged, certainly will reducing power and the flow of high pressure cylinder, this is equal to replace high steam circulation with the circulation of part low-pressure steam, causing that whole monoblock thermal efficiency of cycle reduces, heat-economy is deteriorated. Therefore unit economy is but had large effect by the control of reheated steam temperature. 1000MW extra-supercritical unit adopts a resuperheat system mostly.
Reheat steam temperature controls system typically via normal regulating means as reheater outlet temperature of the pendulum angle of oscillating nozzle or gas bypass baffle plate, and spray desuperheating is as auxiliary adjustment means. Owing to the dynamic characteristic of the reheat steam temperature controlled device of extra-supercritical unit changes with the change of boiler load, in actual motion environment, various inside and outside disturbances are also compared many. The controller that this type of control system adopts at present is mostly still for PID (ProportionIntegrationDifferentiation, proportional-integral derivative controller) type controller, or the self-adaptive PID formed after taking some Adaptive steps on the PID basis controlled, cascade controller. Owing to PID is inherently delayed adjustment, thus just result in the control efficiency of PID type reheated steam Stream Temperature Control System relatively low.
[summary of the invention]
Based on this, it is necessary to for the problem that the control efficiency of PID type reheated steam Stream Temperature Control System is relatively low, it is provided that the control method of a kind of extra-supercritical unit reheat steam temperature and system.
The control method of a kind of extra-supercritical unit reheat steam temperature, comprises the following steps:
Obtaining the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generate the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information;
Obtain the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generate the second Disturbance Model of each disturbance variable;
Obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generate the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate;
Obtain the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generate the second stage linear model of each control variable;
By method of least square, the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable are converted into the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL;
Detect the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information;
The detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, is optimized and solves, generate optimal solution;
Control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled.
A kind of control system of extra-supercritical unit reheat steam temperature, including:
First disturbance module, for obtaining the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generating the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information;
Second disturbance module, for obtaining the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generates the second Disturbance Model of each disturbance variable;
First stage module, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generating the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate;
Second stage module, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generates the second stage linear model of each control variable;
Step response module, for the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable being converted into by method of least square the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL;
Detection module, for detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information;
Solve module, for the detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, it is optimized and solves, generate optimal solution;
Control module, for the control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled.
The control method of above-mentioned extra-supercritical unit reheat steam temperature and system, by detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information; The detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, is optimized and solves, generate optimal solution; Control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled, quickly and accurately the reheating temperature of described extra-supercritical unit can be regulated and controled to default effective temperature-control range, improve the control efficiency of reheated steam steam temperature.
[accompanying drawing explanation]
Fig. 1 is the schematic flow sheet of the control method of extra-supercritical unit reheat steam temperature of the present invention;
Fig. 2 be extra-supercritical unit reheat steam temperature of the present invention control method in the schematic diagram of stage linear model;
Fig. 3 is the structural representation of the control system of extra-supercritical unit reheat steam temperature of the present invention.
[detailed description of the invention]
In order to make the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the present invention is described in further detail.
Refer to the schematic flow sheet that Fig. 1, Fig. 1 are the control methods of extra-supercritical unit reheat steam temperature of the present invention.
The control method of the extra-supercritical unit reheat steam temperature described in present embodiment, it may include following steps:
Step 101, obtain the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generating the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information.
Step S102, obtains the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generates the second Disturbance Model of each disturbance variable.
Step S103, obtain the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generating the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate.
Step S104, obtains the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generates the second stage linear model of each control variable.
Step S105, by method of least square, the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable are converted into the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL.
Step S106, detects the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information.
Step S107, the detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, it is optimized and solves, generate optimal solution.
Step S108, is applied to described extra-supercritical unit by the control variable in described optimal solution, reheat steam temperature is regulated and controled.
Present embodiment, by detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information; The detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, is optimized and solves, generate optimal solution; According to the performance variable that the control variable in described optimal solution is corresponding, the reheat steam temperature of described extra-supercritical unit is regulated and controled, quickly and accurately the reheating temperature of described extra-supercritical unit can be regulated and controled to default effective temperature-control range, improve the control efficiency of reheated steam steam temperature.
Wherein, for step S101, extra-supercritical unit refers to the pressure of working medium in boiler, and the working medium in boiler is all water, and the critical parameters of water are: 22.129MPa and 374.15 DEG C; When this pressure and temperature, the density of water and steam is identical, just cry the critical point of water, in stove, power pressure is just subcritical boiler lower than this pressure, being exactly super critical boiler more than this pressure, in stove, vapor (steam) temperature is not less than 593 DEG C or steam pressure is not less than 31MPa and is referred to as ultra supercritical.
Preferably, the jet chimney of extra-supercritical unit reheat section is even number. Hot arc comprises two parts again, i.e. primary re-heater and final reheater, is reheat section desuperheat water spray link in the middle of the two reheater. At the end of exhaust gases passes, it is gas baffle, is all provided with the adjustment flue gas gas baffle by flow in reheat system both sides, by regulating the angle of gas baffle, distribute the ratio of the flue gas that flue gas passes through from overheated and reheat section. Primary re-heater outlet temperature and final reheater outlet temperature can pass through desuperheat water spray and gas baffle regulates.
Preferably, described soot blowing operation information can be soot blowing number of operations, and described ature of coal fluctuation information can be ature of coal undulate quantity.
For step S102, the first Disturbance Model of each disturbance variable that can prestore or the second Disturbance Model. Also can the impact of the real-time identification each disturbance variable primary re-heater outlet temperature on extra-supercritical unit and final reheater outlet temperature, generate the first Disturbance Model or the second Disturbance Model.
For step S103, it is preferable that described reheating desuperheat injection flow rate includes the desuperheat injection flow rate of unit both sides (i.e. A side and B side), such as A side desuperheat water spray and B side desuperheat water spray.
Preferably, when obtaining described first stage linear model and described second stage linear model, the load variations characteristic of stronger nonlinear characteristic and ultra supercritical coal-fired unit different phase can be had according to general ultra supercritical coal-fired unit, adopt piecewise nonlinear, the operating mode of unit is divided into main several operating modes, one corresponding one group of stage linear model (described first stage linear model and described second stage linear model) of operating mode, is approximately stage linear model by the model of reheated steam.
Further, the running of the described extra-supercritical unit load with 10% is multiple operating mode for dividing partition of the scale, a corresponding one group of step response model of operating mode.
In one embodiment, obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, the step of the first stage linear model generating each control variable comprises the following steps:
Arbitrary control variable of described extra-supercritical unit is regulated to each default control value successively, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described primary re-heater outlet temperature each default control value corresponding;
Response data according to each default control value each default control value corresponding to the described primary re-heater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described primary re-heater outlet temperature, generate the first stage linear model of described arbitrary control variable.
For step S104, the first stage linear model of each control variable that can prestore or second stage linear model. Also can each control variable of real-time identification on the impact between the primary re-heater outlet temperature of extra-supercritical unit and final reheater outlet temperature, generate first stage linear model or second stage linear model.
In one embodiment, the corresponding relation between each control variable of the described acquisition extra-supercritical unit of difference and described final reheater outlet temperature, the step of the second stage linear model generating each control variable comprises the following steps:
Arbitrary control variable of described extra-supercritical unit is regulated to each default control value successively, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described final reheater outlet temperature each default control value corresponding;
Response data according to each default control value each default control value corresponding to the described final reheater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described final reheater outlet temperature, generate the second stage linear model of described arbitrary control variable.
Preferably, for 900MW operating mode, set steady is operated in 900MW, each control variable is applied to the biasing of 5%, other control variable remain unchanged, the response data of record primary re-heater outlet temperature and final reheater outlet temperature, sets up the corresponding relation between control variable and primary re-heater outlet temperature or final reheater outlet temperature.As in figure 2 it is shown, in Fig. 2, X1 is the corresponding relation between gas baffle aperture and final reheater outlet temperature, X4 is the corresponding relation between gas baffle aperture and primary re-heater outlet temperature; X2 be desuperheat water spray A side and final reheater outlet temperature between corresponding relation, X5 be desuperheat water spray A side and primary re-heater outlet temperature between corresponding relation; X3 be desuperheat water spray B side and final reheater outlet temperature between corresponding relation, X6 be desuperheat water spray B side and primary re-heater outlet temperature between corresponding relation.
For step S105, first stage linear model and the second linear model are applied step disturbance, generates the step response model of Multivariable Constrained PREDICTIVE CONTROL.
In one embodiment, based on the PREDICTIVE CONTROL of DMC, adopt step response model form, be created as such as the step response model of formula (1) Multivariable Constrained PREDICTIVE CONTROL:
Yk+1|k=Yk+1|k-1+A��Uk(1)
Wherein, Yk+1|k-1It it is the zero input response of reheat steam temperature;
Model prediction exports:
Output free response vector:
Controlling increment vector:
Dynamic matrix:
Kth moment dynamic matrix:
Inscribe system output during kth and i-th controlled the step response model of input:
In formula, U is control variable, and m is the number of control variable, and n is the number of controlled variable. P is prediction time domain length, and M controls time domain length.
Preferably, first stage linear model and second stage linear model are filtered and after normalization, again through method of least square, the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable are converted into the transmission function of described extra-supercritical unit.
Further, traditional least square method is utilized to set up transmission function, control variable applies step signal, record controlled variable signal (primary re-heater outlet temperature and final reheater outlet temperature), obtaining into the step response model of Multivariable Constrained PREDICTIVE CONTROL, the selectively actuatable relative stage of stable development tests accordingly.
Further, the numerical range of the weight that final reheater outlet temperature described in described step response model is corresponding is 800 to 1000, the numerical range of the weight that gas baffle aperture described in described step response model is corresponding is 1 to 10, and in described step response model, the numerical range of the weight that reheating desuperheat injection flow rate is corresponding is 500 to 1000.
For step S106, it is preferable that the currently real-time primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information can be detected. It is optimized by Optimized model and solves, be predicted controlling.
For step S107, it is preferable that described default Optimized model can be with constrained rolling optimization model.
In one embodiment, it is with shown in constrained rolling optimization model such as formula (2):
st.Cdu��Uk��bdu,CyYk��by
Wherein, JkIt is optimization aim, WkIt it is the reference locus set. Q and R is controlled variable and the control variable corresponding weight in quadratic programming respectively. Cdu��bduAnd Cy��byIt is �� UkAnd YkInequality constraints coefficient and restrained boundary.
In another embodiment, by default optimized algorithm, described step response model being solved, the step generating optimal solution comprises the following steps:
Under non-morbid state state, by method of Lagrange multipliers, described step response model is solved, generate optimal solution.
For step S108, optimization solution includes control variable.
In one embodiment, described control variable can be gas baffle aperture or the adjustment amount of reheating desuperheat injection flow rate.
In another embodiment, the control mode selecting primary re-heater outlet temperature is Region control mode, and namely controlled variable is left out setting value, as long as setting the interval of its change. Final reheater outlet temperature adopts customization to control, to ensure the precision controlled.
Refer to the structural representation that Fig. 3, Fig. 3 are the control systems of extra-supercritical unit reheat steam temperature of the present invention.
The control system of the extra-supercritical unit reheat steam temperature described in present embodiment, first disturbance module the 100, second disturbance module 200, first stage module 300, second stage module 400, step response module 500, detection module 600 can be included, solve module 700 and control module 800, wherein:
First disturbance module 100, for obtaining the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generating the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information;
Second disturbance module 200, for obtaining the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generates the second Disturbance Model of each disturbance variable;
First stage module 300, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generating the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate;
Second stage module 400, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generates the second stage linear model of each control variable;
Step response module 500, for the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable being converted into by method of least square the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL;
Detection module 600, for detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information;
Solve module 700, for the detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, it is optimized and solves, generate optimal solution;
Controlling module 800, for the control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled, wherein, described performance variable is gas baffle aperture or the adjustment amount of reheating desuperheat injection flow rate.
Present embodiment, by detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information; The detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, is optimized and solves, generate optimal solution;According to the performance variable that the control variable in described optimal solution is corresponding, the reheat steam temperature of described extra-supercritical unit is regulated and controled, quickly and accurately the reheating temperature of described extra-supercritical unit can be regulated and controled to default effective temperature-control range, improve the control efficiency of reheated steam steam temperature.
Wherein, for the first disturbance module 100, extra-supercritical unit refers to the pressure of working medium in boiler, and the working medium in boiler is all water, and the critical parameters of water are: 22.129MPa and 374.15 DEG C; When this pressure and temperature, the density of water and steam is identical, just cry the critical point of water, in stove, power pressure is just subcritical boiler lower than this pressure, being exactly super critical boiler more than this pressure, in stove, vapor (steam) temperature is not less than 593 DEG C or steam pressure is not less than 31MPa and is referred to as ultra supercritical.
Preferably, the jet chimney of extra-supercritical unit reheat section is even number. Hot arc comprises two parts again, i.e. primary re-heater and final reheater, is reheat section desuperheat water spray link in the middle of the two reheater. At the end of exhaust gases passes, it is gas baffle, is all provided with the adjustment flue gas gas baffle by flow in reheat system both sides, by regulating the angle of gas baffle, distribute the ratio of the flue gas that flue gas passes through from overheated and reheat section. Primary re-heater outlet temperature and final reheater outlet temperature can pass through desuperheat water spray and gas baffle regulates.
Preferably, described soot blowing operation information can be soot blowing number of operations, and described ature of coal fluctuation information can be ature of coal undulate quantity.
For the second disturbance module 200, the first Disturbance Model of each disturbance variable that can prestore or the second Disturbance Model. Also can the impact of the real-time identification each disturbance variable primary re-heater outlet temperature on extra-supercritical unit and final reheater outlet temperature, generate the first Disturbance Model or the second Disturbance Model.
For first stage module 300, it is preferable that described reheating desuperheat injection flow rate includes the desuperheat injection flow rate of unit both sides (i.e. A side and B side), such as A side desuperheat injection flow rate and B side desuperheat injection flow rate.
Preferably, when obtaining described first stage linear model and described second stage linear model, the load variations characteristic of stronger nonlinear characteristic and ultra supercritical coal-fired unit different phase can be had according to general ultra supercritical coal-fired unit, adopt piecewise nonlinear, the operating mode of unit is divided into main several operating modes, one corresponding one group of stage linear model (described first stage linear model and described second stage linear model) of operating mode, is approximately stage linear model by the model of reheated steam.
Further, the running of the described extra-supercritical unit load with 10% is multiple operating mode for dividing partition of the scale, a corresponding one group of step response model of operating mode.
In one embodiment, first stage module 300 may also include logging modle and the first linear block, wherein:
Described logging modle is for being regulated to each default control value successively by arbitrary control variable of described extra-supercritical unit, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described primary re-heater outlet temperature each default control value corresponding;
Described first linear block is for the response data according to each default control value each default control value corresponding to the described primary re-heater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described primary re-heater outlet temperature, generate the first stage linear model of described arbitrary control variable.
For second stage module 400, the first stage linear model of each control variable that can prestore or second stage linear model. Also can each control variable of real-time identification on the impact between the primary re-heater outlet temperature of extra-supercritical unit and final reheater outlet temperature, generate first stage linear model or second stage linear model.
In one embodiment, second stage module 400 may also include the second logging modle and the second linear block, wherein:
Described second logging modle is for being regulated to each default control value successively by arbitrary control variable of described extra-supercritical unit, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described final reheater outlet temperature each default control value corresponding.
Described second linear block is for the response data according to each default control value each default control value corresponding to the described final reheater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described final reheater outlet temperature, generate the second stage linear model of described arbitrary control variable.
Preferably, for 900MW operating mode, set steady is operated in 900MW, each control variable is applied to the biasing of 5%, other control variable remain unchanged, the response data of record primary re-heater outlet temperature and final reheater outlet temperature, sets up the corresponding relation between control variable and primary re-heater outlet temperature or final reheater outlet temperature. As in figure 2 it is shown, in Fig. 2, X1 is the corresponding relation between gas baffle aperture and final reheater outlet temperature, X4 is the corresponding relation between gas baffle aperture and primary re-heater outlet temperature; X2 be desuperheat water spray A side and final reheater outlet temperature between corresponding relation, X5 be desuperheat water spray A side and primary re-heater outlet temperature between corresponding relation; X3 be desuperheat water spray B side and final reheater outlet temperature between corresponding relation, X6 be desuperheat water spray B side and primary re-heater outlet temperature between corresponding relation.
For step response module 500, first stage linear model and the second linear model are applied step disturbance, generates the step response model of Multivariable Constrained PREDICTIVE CONTROL.
In one embodiment, step response module 500 based on the PREDICTIVE CONTROL of DMC, can adopt step response model form, be created as such as the step response model of formula (1) Multivariable Constrained PREDICTIVE CONTROL:
Yk+1|k=Yk+1|k-1+A��Uk(1)
Wherein, Yk+1|k-1It it is the zero input response of reheat steam temperature;
Model prediction exports:
Output free response vector:
Controlling increment vector:
Dynamic matrix:
Kth moment dynamic matrix:
Inscribe system output during kth and i-th controlled the step response model of input:
In formula, U is control variable, and m is the number of control variable, and n is the number of controlled variable. P is prediction time domain length, and M controls time domain length.
Preferably, first stage linear model and second stage linear model are filtered and after normalization, again through method of least square, the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable are converted into the transmission function of described extra-supercritical unit.
Further, utilize traditional least square method to set up transmission function, control variable applies step signal, records controlled variable signal, obtaining into the step response model of Multivariable Constrained PREDICTIVE CONTROL, the selectively actuatable relative stage of stable development tests accordingly.
Further, the numerical range of the weight that final reheater outlet temperature described in described step response model is corresponding is 800 to 1000, the numerical range of the weight that gas baffle aperture described in described step response model is corresponding is 1 to 10, and in described step response model, the numerical range of the weight that reheating desuperheat injection flow rate is corresponding is 500 to 1000.
For detection module 600, it is preferable that the currently real-time primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information can be detected. It is optimized by Optimized model and solves, be predicted controlling.
For solving module 700, it is preferable that described default Optimized model can be with constrained rolling optimization model.
In one embodiment, it is with shown in constrained rolling optimization model such as formula (2):
st.Cdu��Uk��bdu,CyYk��by
Wherein, JkIt is optimization aim, WkIt it is the reference locus set. Q and R is controlled variable and the control variable corresponding weight in quadratic programming respectively. Cdu��bduAnd Cy��byIt is �� UkAnd YkInequality constraints coefficient and restrained boundary.
In another embodiment, solve module 700 to can be used for, under non-morbid state state, by method of Lagrange multipliers, described step response model being solved, generating optimal solution.
For controlling module 800, optimization solution includes control variable.
In one embodiment, described control variable can be gas baffle aperture or the adjustment amount of reheating desuperheat injection flow rate.
In another embodiment, the control mode selecting primary re-heater outlet temperature is Region control mode, and namely controlled variable is left out setting value, as long as setting the interval of its change. Final reheater outlet temperature adopts customization to control, to ensure the precision controlled.
The reheat steam temperature of extra-supercritical unit using the described step response model forecast model as Multivariable Constrained predictive controller, can be controlled by the present invention by described Multivariable Constrained predictive controller.
The following stated is set up the Multivariable Constrained predictive controller one detailed process being forecast model with described step response model:
Step 1: preliminary preparation
First process object is familiar with, it is necessary to be familiar with the relevant knowledge of the reheated steam process of ultra supercritical coal-fired unit, it is determined that the number of the control variable of Multivariable Constrained predictive controller, disturbance variable and controlled variable. And confirming that whether the signal of variable involved by Multivariable Constrained predictive controller is normal, transmitter or valve involved by variable will be repaired if there is fault, to guarantee the smooth input of Multivariable Constrained predictive controller.
Step 2: the determination of control program and control logic Modification
After being familiar with fired power generating unit reheat temperature process, it is determined that the structure of robust multivariable predictive control device and form, and communication modes, it is determined that final control program. According to control program, the control logical scheme of design DCS system, and after obtaining the confirmation of plant engineer, carry out the control logic Modification of DCS system.
Step 3: determine multi-model and duty parameter according to part throttle characteristics
The reheated steam process of fired power generating unit is a system with relatively strong nonlinearity. The mode being generally adopted multi-model is controlled. It is thus desirable to the operation characteristic of more unit, the process of whole service being divided into multiple operating mode, the load of general recommendations 10% divides operating mode, and sets up the model in each stage. Determine the thermal power unit operation parameter of each operating mode, including the Main change scope of the control variable of each operating mode and controlled variable, the parameter manipulation characteristic of disturbance variable.
Step 4: the identification of process multi-model
After determining multi-model and duty parameter, carry out the test of process object further. Test is by each input variable is carried out upset test, record simultaneously, the data of gatherer process.The test job of process object is very crucial, if test is accurately, then the model of the object obtained is just more accurate. After data test completes, by utilizing the data that object test obtains to carry out the Model Distinguish of object, data obtained when being tested by object obtain system transter matrix by system identification, and then set up object step response model.
Step 5: disturb the Model Distinguish of variable
Disturbance variable is the important guarantee of this programme and necessary means, it is necessary to set up disturbance variable model, sets up the Disturbance Model of unit load, soot blowing operation and ature of coal fluctuation and primary re-heater outlet temperature and final reheater outlet temperature. Select to operate the data of the relative stage of stable development, utilize traditional least square method to set up transmission function, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL in step signal.
Step 6: Multivariable Constrained predictive controller parameter configuration
After reheated steam process model is set up, it is necessary to configuration Multivariable Constrained predictive controller, mainly include model length, control step-length, in the control cycle, set the bound of disturbance variable, controlled variable setting value, control variable and controlled variable and the Q of the R of control variable and controlled variable. And by simple simulation run, parameter can be adjusted after the performance of the controller of assessment further to obtain desired performance.
Step 7: Multivariable Constrained predictive controller trail run and debugging
First Multivariable Constrained predictive controller On-line Control program is run with pretest operational mode, whether properly functioning carrys out testing procedure, simultaneously the accuracy of testing model. When trail run, controller will complete various computing, but the output of controller is not added in controlled device, mainly to follow the tracks of PID operation. All of control variable bound by be fixed on in current set value scope closely. Start Multivariable Constrained predictive controller, control variable is applied on object, by evaluating and testing the control performance of controller, adjust again if desired, repeatedly repeatedly. If all gone well, recalling to control variable bound, system formally puts into operation.
Step 8: the maintenance of Multivariable Constrained predictive controller
For any one Multivariable Constrained predictive controller, it is necessary to a certain amount of maintenance guarantees the optimum of performance. Can pass through to detect control variable and controlled variable operation bound, to determine that they are in allowed limits, remove unessential bound simultaneously.
Embodiment described above only have expressed the several embodiments of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention. It should be pointed out that, for the person of ordinary skill of the art, without departing from the inventive concept of the premise, it is also possible to making some deformation and improvement, these broadly fall into protection scope of the present invention. Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. the control method of an extra-supercritical unit reheat steam temperature, it is characterised in that comprise the following steps:
Obtaining the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generate the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information;
Obtain the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generate the second Disturbance Model of each disturbance variable;
Obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generate the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate;
Obtain the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generate the second stage linear model of each control variable;
By method of least square, the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable are converted into the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL;
Detect the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information;
The detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, is optimized and solves, generate optimal solution;
Control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled.
2. the control method of extra-supercritical unit reheat steam temperature according to claim 1, it is characterized in that, obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, the step of the first stage linear model generating each control variable comprises the following steps:
Arbitrary control variable of described extra-supercritical unit is regulated to each default control value successively, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described primary re-heater outlet temperature each default control value corresponding;
Response data according to each default control value each default control value corresponding to the described primary re-heater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described primary re-heater outlet temperature, generate the first stage linear model of described arbitrary control variable.
3. the control method of extra-supercritical unit reheat steam temperature according to claim 1, it is characterized in that, the numerical range of the weight that final reheater outlet temperature described in described step response model is corresponding is 800 to 1000, the numerical range of the weight that gas baffle aperture described in described step response model is corresponding is 1 to 10, and in described step response model, the numerical range of the weight that reheating desuperheat injection flow rate is corresponding is 500 to 1000.
4. the control method of extra-supercritical unit reheat steam temperature according to claim 1, it is characterised in that the running of the described extra-supercritical unit load with 10% is multiple operating mode for dividing partition of the scale, a corresponding one group of step response model of operating mode.
5. the control method of extra-supercritical unit reheat steam temperature as claimed in any of claims 1 to 4, it is characterized in that, the detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, being optimized and solve, the step generating optimal solution comprises the following steps:
Under non-morbid state state, by method of Lagrange multipliers described default Optimized model is optimized and solves, generate optimal solution.
6. the control system of an extra-supercritical unit reheat steam temperature, it is characterised in that including:
First disturbance module, for obtaining the corresponding relation between each disturbance variable of extra-supercritical unit and primary re-heater outlet temperature respectively, generating the first Disturbance Model of each disturbance variable, wherein, described disturbance variable includes unit load, soot blowing operation information and ature of coal fluctuation information;
Second disturbance module, for obtaining the corresponding relation between each disturbance variable of described extra-supercritical unit and final reheater outlet temperature respectively, generates the second Disturbance Model of each disturbance variable;
First stage module, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described primary re-heater outlet temperature respectively, generating the first stage linear model of each control variable, wherein, described control variable includes gas baffle aperture and reheating desuperheat injection flow rate;
Second stage module, for obtaining the corresponding relation between each control variable of described extra-supercritical unit and described final reheater outlet temperature respectively, generates the second stage linear model of each control variable;
Step response module, for the first Disturbance Model of each disturbance variable, the second Disturbance Model of each disturbance variable, the first stage linear model of each control variable and the second stage linear model of each control variable being converted into by method of least square the transmission function of described extra-supercritical unit, and in each control variable, apply step signal, record primary re-heater outlet temperature and final reheater outlet temperature, generate the step response model of Multivariable Constrained PREDICTIVE CONTROL;
Detection module, for detecting the primary re-heater outlet temperature of described extra-supercritical unit, final reheater outlet temperature, unit load, soot blowing operation information and ature of coal fluctuation information;
Solve module, for the detection primary re-heater outlet temperature of gained, final reheater outlet temperature, unit load, soot blowing operation information, ature of coal fluctuation information and described step response model are substituted into the Optimized model preset, it is optimized and solves, generate optimal solution;
Control module, for the control variable in described optimal solution is applied to described extra-supercritical unit, reheat steam temperature is regulated and controled.
7. the control system of extra-supercritical unit reheat steam temperature according to claim 6, it is characterised in that described first stage module also includes logging modle and the first linear block, wherein:
Described logging modle is for being regulated to each default control value successively by arbitrary control variable of described extra-supercritical unit, and other control variable except described arbitrary control variable are remained unchanged, record the response data of described primary re-heater outlet temperature each default control value corresponding;
Described first linear block is for the response data according to each default control value each default control value corresponding to the described primary re-heater outlet temperature of record, simulate the corresponding relation between described arbitrary control variable and described primary re-heater outlet temperature, generate the first stage linear model of described arbitrary control variable.
8. the control system of extra-supercritical unit reheat steam temperature according to claim 6, it is characterized in that, the numerical range of the weight that final reheater outlet temperature described in described step response model is corresponding is 800 to 1000, the numerical range of the weight that gas baffle aperture described in described step response model is corresponding is 1 to 10, and in described step response model, the numerical range of the weight that reheating desuperheat injection flow rate is corresponding is 500 to 1000.
9. the control system of extra-supercritical unit reheat steam temperature according to claim 6, it is characterised in that the running of the described extra-supercritical unit load with 10% is multiple operating mode for dividing partition of the scale, a corresponding one group of step response model of operating mode.
10. the control system of the extra-supercritical unit reheat steam temperature according to any one in claim 6 to 9, it is characterized in that, the described module that solves is additionally operable under non-morbid state state, by method of Lagrange multipliers described default Optimized model is optimized and solves, generates optimal solution.
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| CN105278507B (en) * | 2015-11-03 | 2017-12-12 | 广东电网有限责任公司电力科学研究院 | The procedure optimization control method and system of extra-supercritical unit main steam system |
| CN105278333B (en) * | 2015-11-03 | 2018-08-17 | 广东电网有限责任公司电力科学研究院 | The Data Modeling Method and system of extra-supercritical unit coordinated control system |
| CN105447256B (en) * | 2015-12-04 | 2018-07-31 | 国网河北能源技术服务有限公司 | A kind of enhancing excitation emulation genetic optimization method |
| CN106019939B (en) * | 2016-06-20 | 2018-09-07 | 东南大学 | Supercritical unit reheat steam temperature Optimal Control System based on Nonlinear Intelligent Prediction and Control Technology |
| CN106678031B (en) * | 2016-07-08 | 2018-08-14 | 国网山东省电力公司电力科学研究院 | The method for determining Boiler of Ultra-supercritical Unit feed pump maintenance maintenance interval |
| CN107065518B (en) * | 2016-11-28 | 2019-12-24 | 国网浙江省电力公司电力科学研究院 | Control algorithm for coordination prediction function of supercritical unit |
| CN107168059B (en) * | 2017-05-27 | 2020-08-18 | 杭州意能电力技术有限公司 | Thermal power generating unit predictive control strategy based on main steam pressure |
| CN107246604B (en) * | 2017-07-27 | 2023-10-17 | 华北电力科学研究院有限责任公司 | Method and system for adjusting reheat steam temperature of boiler |
| CN108361683B (en) * | 2018-05-14 | 2023-12-22 | 华能国际电力股份有限公司海门电厂 | Full load section reheat temperature intelligent control system |
| CN112130455B (en) * | 2020-08-18 | 2022-12-09 | 东南大学 | Control method, device, storage medium and system of coordination control system |
| CN113126549B (en) * | 2021-04-23 | 2022-06-28 | 广东电网有限责任公司电力科学研究院 | Basic controller and control method and device thereof |
| CN113835342B (en) * | 2021-09-18 | 2024-04-16 | 国网河北能源技术服务有限公司 | Disturbance rejection predictive control method for overheat steam temperature system |
| CN118426319B (en) * | 2024-07-05 | 2024-10-25 | 惠生清洁能源科技集团股份有限公司 | Cold box operation adjusting method and device in FLNG, computer equipment and storage medium |
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