CN110631003B - Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control - Google Patents

Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control Download PDF

Info

Publication number
CN110631003B
CN110631003B CN201910913987.9A CN201910913987A CN110631003B CN 110631003 B CN110631003 B CN 110631003B CN 201910913987 A CN201910913987 A CN 201910913987A CN 110631003 B CN110631003 B CN 110631003B
Authority
CN
China
Prior art keywords
model
steam temperature
layer model
method based
predictive control
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910913987.9A
Other languages
Chinese (zh)
Other versions
CN110631003A (en
Inventor
刘桂生
赵重阳
王煜伟
丁永三
张建伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CHN Energy Jianbi Power Plant
Original Assignee
CHN Energy Jianbi Power Plant
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CHN Energy Jianbi Power Plant filed Critical CHN Energy Jianbi Power Plant
Priority to CN201910913987.9A priority Critical patent/CN110631003B/en
Publication of CN110631003A publication Critical patent/CN110631003A/en
Application granted granted Critical
Publication of CN110631003B publication Critical patent/CN110631003B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F22—STEAM GENERATION
    • F22G—SUPERHEATING OF STEAM
    • F22G5/00—Controlling superheat temperature
    • F22G5/04—Controlling superheat temperature by regulating flue gas flow, e.g. by proportioning or diverting

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control, which comprises the steps of respectively establishing a transfer function model of influence of a plurality of flue gas baffles on final-stage reheat steam temperature in a low load section, a middle load section and a high load section of a thermal power generating unit, designing a predictive controller according to the submodels, weighting control quantities obtained by calculation of all controllers, acting on a system, and switching different model sets according to different operation conditions of the unit so as to give consideration to both control precision and algorithm operation efficiency. The invention can improve the automation degree of the flue gas baffle, reduce the workload of operators, reduce the use of reheating desuperheating water when the load changes in a large range, and improve the control precision of the outlet steam temperature of the final-stage reheater.

Description

Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control
Technical Field
The invention relates to the field of thermal power generating unit boiler steam temperature control, in particular to a reheat steam temperature adjusting method, and belongs to the field of thermal engineering control.
Background
The stable control of the reheated steam temperature in the thermal power generating unit has important significance for improving the economical efficiency and safety of the thermal power generating unit, but the existing strategy for adjusting the reheated steam temperature of the thermal power generating unit cannot meet the requirement for adjusting the reheated steam temperature, accident water spraying is often used for adjusting, the steam inlet humidity of the steam turbine is increased, and the safe operation of the steam turbine is influenced. Because the flue gas baffle has great hysteresis and strong nonlinear characteristics to reheat steam temperature control, most power plants are difficult to put into automation.
The predictive control has a good effect on overcoming the large lag of the controlled object, but because the influence characteristic change of the flue gas baffles on the reheated steam temperature is large at different load sections, the predictive control designed by a common linear model cannot obtain a good control effect, the calculation amount of the nonlinear predictive control is large, and the nonlinear predictive control is difficult to apply on site.
In order to improve the automatic control rate of the flue gas baffle and the control quality of the reheated steam temperature, the reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control is provided, the calculation amount is reduced as far as possible while the calculation precision is ensured, the reheated steam temperature can be effectively controlled, and the use of accident desuperheating water is reduced.
Disclosure of Invention
The technical problem is as follows: the invention aims to solve the problems that the quality of control of the reheated steam temperature of a thermal power generating unit is poor, and the traditional prediction control is difficult to apply to engineering practice.
The technical scheme is as follows: in order to overcome the problems, the reheating steam temperature adjusting method based on hierarchical scheduling multi-model predictive control is provided, so that the system control gives consideration to both control precision and control speed.
A reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control comprises the following implementation steps:
step 1: final reheating steam temperature T of flue gas baffle pair at 50% -100% load section of thermal power generating unitreThe non-linear degree of influence, the maximum number n of the sub-models and the weight distribution omega of each sub-model in the corresponding load sectioni(Ne) I is 1,2, …, n, wherein wi(Ne) The weight distribution of the smoke baffle sub-model is a function related to the load, and the weight distribution satisfies w is more than or equal to 0i(Ne) Is less than or equal to 1, and
Figure GDA0002914360960000021
ωi(Ne) The larger the weight occupied by the controller is, the larger the influence on the whole system is; w is ai(Ne) Is a load NeThe function of (d) can be regarded as being independent of the time k, and online calculation is not needed.
Step 2: after the sub-model determined in the step 1 corresponds to the load points and the quantity, the reheating steam temperature T is measured according to the change of the smoke bafflereThe data obtained by the characteristic test of the flue gas baffle plate pair is established to the final reheating steam temperature TreThe set of transfer function models G(s).
And step 3: establishing three layers of model sets with different precisions, and respectively recording an upper layer model, a middle layer model and a lower layer model as L1，L2，L3The upper model has n1The submodel and the middle layer model have n2The submodels and the lower layer model have n3Submodels, and n1＜n2＜n3，n1+n2+n3N. And the upper layer model L1Sub-model numberNumber n1Less than or equal to 2, middle layer model L2Number n of submodels2Not more than 4, lower layer model L3Number n of submodels3≤6，
And 4, step 4: designing generalized predictive controllers according to each sub-model, and determining predictive control sampling time T of each controllersiPredicting step size NiAnd a control step size NuiWherein the time T is sampledsi2-5s, prediction step size NiControl step size N of 50-100ui＝2。
And 5: calculating final-stage reheated steam temperature TreDeviation e (k) from the set value, and calling the upper layer model L when the deviation e (k) is larger than or equal to a1The corresponding controller carries out coarse adjustment; calling the middle layer model L with higher precision when the deviation a is more than e (k) and more than b2A corresponding controller; invoking the finest lower layer model L when the deviation e (k) < b3And the corresponding controllers carry out fine adjustment, wherein a and b are respectively threshold values of the error e (k).
Step 6: and multiplying the controller output corresponding to each layer of model by the weight distribution to obtain a final smoke baffle instruction u (k). Weight p of controller output corresponding to each layer modeljiFor the weight distribution omega in step onei(Ne) And (5) converting the result. The formula is as follows:
Figure GDA0002914360960000031
wherein j is [1,2,3 ]]When i is equal to [1, n ]j]. The final output u (k) is:
Figure GDA0002914360960000032
by the scheme, the invention at least has the following advantages:
by utilizing the reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control, the system control can take control precision and control speed into consideration, the automation degree of the flue gas baffle plate is improved, the dynamic and steady state deviation in the reheated steam temperature control process is small, the accident water spraying usage amount is reduced, and the thermal cycle efficiency and the safety of a unit are improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate a certain embodiment of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram of reheat steam temperature control based on hierarchical scheduling multi-model predictive control.
Detailed Description
The invention is further described below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a diagram of a hierarchical scheduling multi-model predictive control structure, where yrIndicates a reheat steam temperature set value unjAccording to the selected LjAnd (3) the control quantity of the generalized predictive controller designed by the nth sub-model of the layer, u represents the weighted overall control quantity, namely the opening command of the flue gas damper, and y represents the actual reheated steam temperature under the action of the control quantity u. The reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control specifically comprises the following steps:
step 1: final reheating steam temperature T of flue gas baffle pair at 50% -100% load section of thermal power generating unitreThe non-linear degree of influence, the maximum number n of the sub-models and the weight distribution omega of each sub-model in the corresponding load sectioni(Ne) I is 1,2, …, n. Wherein ω isi(Ne) The weight distribution of the smoke baffle sub-model is a function related to the load, and the weight distribution satisfies w is more than or equal to 0i(Ne) Is less than or equal to 1, and
Figure GDA0002914360960000041
wi(Ne) The larger the weight occupied by the controller is, the larger the influence on the overall system is, wi(Ne) Is a load NeThe function of (d) can be regarded as being independent of the time k, and online calculation is not needed.
Step 2: after the sub-model determined in the step 1 corresponds to the load points and the quantity, the reheating steam temperature T is measured according to the change of the smoke bafflereThe data obtained by the characteristic test are fitted by adopting an MATLAB fitting tool box to establish the temperature T of the flue gas baffle plate to the final-stage reheated steamreThe set of transfer function models G(s).
And step 3: according to the temperature T of the final reheating steam of the flue gas baffle pairreThe transfer function model set G(s) establishes three layers of model sets with different precisions, and an upper layer model, a middle layer model and a lower layer model are respectively recorded as L1，L2，L3The upper model has n1The submodel and the middle layer model have n2The submodels and the lower layer model have n3Submodels, and n1＜n2＜n3，n1+n2+n3N. And the upper layer model L1Number n of submodels1Less than or equal to 2, middle layer model L2Number n of submodels2Not more than 4, lower layer model L3Number n of submodels3≤6，
And 4, step 4: designing generalized predictive controllers according to each sub-model, and determining predictive control sampling time T of each controllersiPredicting step size NiAnd a control step size NuiWherein the time T is sampledsi2-5s, prediction step size NiControl step size N of 50-100ui＝2。
And 5: calculating final-stage reheated steam temperature TreDeviation e (k) from the set value, and calling the upper layer model L when the deviation e (k) is larger than or equal to a1The corresponding controller carries out coarse adjustment; calling the middle layer model L with higher precision when the deviation a is more than e (k) and more than b2A corresponding controller; invoking the finest lower layer model L when the deviation e (k) < b3The corresponding controllers carry out fine adjustment, wherein a and b are respectively threshold values of the error e (k). In general, a is 10 to 20 ℃ and b is 3 to 6 ℃.
Step 6: and multiplying the controller output corresponding to each layer of model by the weight distribution to obtain a final smoke baffle instruction u (k). Weight p of controller output corresponding to each layer modeljiFor the weight distribution omega in step onei(Ne) And (5) converting the result. The formula is as follows:
Figure GDA0002914360960000051
wherein j is [1,2,3 ]]When i is equal to [1, n ]j]. The final output u (k) is:
Figure GDA0002914360960000052
the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (5)

1. A reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control is characterized by comprising the following steps:
the method comprises the following steps: final reheating steam temperature T of flue gas baffle pair at 50% -100% load section of thermal power generating unitreThe non-linear degree of influence, the maximum number n of the sub-models and the weight distribution omega of each sub-model in the corresponding load sectioni(Ne) I is 1,2, …, n, wherein wi(Ne) A function which is related to the load is distributed for the weight of the smoke baffle sub-model;
step two: determining the corresponding load points and the number of the sub-models according to the step one, and then, adjusting the reheating steam temperature T according to the change of the flue gas bafflereThe data obtained by the characteristic test of the flue gas baffle plate pair is established to the final reheating steam temperature TreA set of transfer function models G(s);
step three: establishing three layers of model sets with different precisions, and respectively recording an upper layer model, a middle layer model and a lower layer model as L1，L2，L3The upper model has n1The submodel and the middle layer model have n2The submodels and the lower layer model have n3Submodels, and n1＜n2＜n3，n1+n2+n3＝n；
Step four: designing generalized predictive controllers according to each sub-model, and determining predictive control sampling time T of each controllersiPredicting step size NiAnd a control step size Nui；
Step five: calculating final-stage reheated steam temperature TreDeviation e (k) from the set value, and calling the upper layer model L when the deviation e (k) is larger than or equal to a1The corresponding controller carries out coarse adjustment; deviation a>Calling the middle layer model L with higher precision when e (k) is more than or equal to b2A corresponding controller; deviation e (k)<b calls the finest lower layer model L3Fine-tuning the corresponding controller, wherein a and b are respectively threshold values of an error e (k);
step six: and multiplying the controller output corresponding to each layer of model by the weight distribution to obtain a final smoke baffle instruction u (k).
2. The reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control as claimed in claim 1, wherein:
the weight distribution in the step one satisfies w is more than or equal to 0i(Ne) Is less than or equal to 1, and
Figure FDA0002914360950000011
wi(Ne) The larger the weight occupied by the controller is, the larger the influence on the whole system is; w is ai(Ne) Is a load NeThe function of (d) can be regarded as being independent of the time k, and online calculation is not needed.
3. The reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control as claimed in claim 1, wherein:
the upper layer model L in the third step1Number n of submodels1Less than or equal to 2, middle layer model L2Number n of submodels2Not more than 4, lower layer model L3Number n of submodels3≤6。
4. The reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control as claimed in claim 1, wherein:
the sampling time T in the fourth stepsi2-5s, prediction step size NiControl step size N of 50-100ui＝2。
5. The reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control as claimed in claim 1, wherein:
the weight p output by the controller corresponding to each layer model in the sixth stepjiFor the weight distribution omega in step onei(Ne) The formula obtained by conversion is as follows:
Figure FDA0002914360950000021
wherein j is [1,2,3 ]]When i is equal to [1, n ]j]And finally, calculating to obtain the smoke baffle output u (k) as follows:
Figure FDA0002914360950000022
CN201910913987.9A 2019-09-25 2019-09-25 Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control Active CN110631003B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910913987.9A CN110631003B (en) 2019-09-25 2019-09-25 Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910913987.9A CN110631003B (en) 2019-09-25 2019-09-25 Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control

Publications (2)

Publication Number Publication Date
CN110631003A CN110631003A (en) 2019-12-31
CN110631003B true CN110631003B (en) 2021-03-19

Family

ID=68974428

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910913987.9A Active CN110631003B (en) 2019-09-25 2019-09-25 Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control

Country Status (1)

Country Link
CN (1) CN110631003B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111365703A (en) * 2020-03-23 2020-07-03 国电浙能宁东发电有限公司 Reheat steam temperature composite control method based on humanoid intelligent control

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103984242B (en) * 2014-05-19 2017-02-15 上海交通大学 Layering predictive control system and method based on model predictive control
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
CN106855691B (en) * 2016-12-06 2019-07-30 东南大学 The double-deck control system for supercritical thermal power unit machine furnace system Steam Generator in Load Follow
CN107479389B (en) * 2017-09-30 2020-04-14 东南大学 Fuzzy self-adaptive PID control method for thermal power generating unit superheated steam temperature prediction
CN109066810B (en) * 2018-09-12 2020-06-09 南京国电南自电网自动化有限公司 Optimal control method and system for battery energy storage to participate in frequency modulation of thermal power generating unit

Also Published As

Publication number Publication date
CN110631003A (en) 2019-12-31

Similar Documents

Publication Publication Date Title
CN110285403B (en) Main steam temperature control method based on controlled parameter estimation
CN108363295B (en) Thermal power generating unit AGC performance assessment index calculation and prediction method based on system identification
CN107515598A (en) Fired power generating unit distributed and coordinated control system based on multi-parameter dynamic matrix control
CN104090491B (en) Gas steam combined cycle unit multivariable constrained prediction function load control method
CN102841540A (en) MMPC-based supercritical unit coordination and control method
CN110376895B (en) Thermal power generating unit coordination control method based on hierarchical limited predictive control
CN105180136A (en) Thermal-power-plant boiler main steam temperature control method based on fractional order proportional-integral (PI) dynamic matrix
CN107908106B (en) Double reheat power generation sets reheat steam temperature concentrates Prediction Control system from depression of order multiloop
CN101788789A (en) Nonlinear predictive control method of unit plant based on chaos and hybrid optimization algorithm
CN109669355A (en) Miniature gas turbine combined cooling and power control system and control method based on generalized predictive control
CN110515304A (en) Overheating steam temperature PID forecast Control Algorithm based on ARX-Laguerre function model
CN109032117B (en) ARMA model-based single-loop control system performance evaluation method
CN101968832A (en) Coal ash fusion temperature forecasting method based on construction-pruning mixed optimizing RBF (Radial Basis Function) network
CN111520701A (en) CFB boiler main steam pressure control method based on improved predictive control
CN114326395B (en) Intelligent generator set control model online updating method based on working condition discrimination
CN105955014A (en) Method for controlling coke furnace chamber pressure based on distributed dynamic matrix control optimization
CN110631003B (en) Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control
CN106200379A (en) A kind of distributed dynamic matrix majorization method of Nonself-regulating plant
CN106444388A (en) Distributed PID type dynamic matrix control method for furnace pressure of coke furnace
CN106855691A (en) For the double-deck control system of supercritical thermal power unit machine furnace system Steam Generator in Load Follow
CN106610589A (en) Online hardware closed-loop network source coordination linear active-disturbance-rejection control method
CN105955030A (en) Turbine and boiler coordination control method based on improved input weighted prediction controller
CN108803342B (en) Unit unit load quick response prediction control method
CN106287659A (en) Reheat steam temperature degree control method and device
Muravyova et al. A Neural Network-Based Control System Using PID Controller to Control the Deaerator

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant