CN106765052A - A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature - Google Patents
A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature Download PDFInfo
- Publication number
- CN106765052A CN106765052A CN201611039592.3A CN201611039592A CN106765052A CN 106765052 A CN106765052 A CN 106765052A CN 201611039592 A CN201611039592 A CN 201611039592A CN 106765052 A CN106765052 A CN 106765052A
- Authority
- CN
- China
- Prior art keywords
- model
- steam
- temperature
- vapor
- 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.)
- Granted
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F22—STEAM GENERATION
- F22G—SUPERHEATING OF STEAM
- F22G5/00—Controlling superheat temperature
- F22G5/20—Controlling superheat temperature by combined controlling procedures
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature, it comprises the following steps：Step 1. determines the sampling time interval T of PREDICTIVE CONTROLs；Step 2. identification unit operates in the partial model G that the input and output difference equation form of vapor (steam) temperature system under M typical load operating mode is describedm；Step 3. is using Model Predictive Control to all partial model GmCarry out the tracing control emulation of square-wave signal；Step 4. is by all partial model GmControl emulation data be used for training smart computation model, form Intelligent predictive control device IPC；Step 5. recognizes the nonlinear inverse model of control valve；Step 6. will train the Intelligent predictive control device IPC for obtaining as real-time controller, obtain enforceable desuperheating waters valve opening actual instruction u (t) of current sample time t.It is an advantage of the invention that be avoided that the vapor (steam) temperature object with large delay, big inertia and time-varying characteristics causing the not good problem of vapor (steam) temperature control performance as unit load changes.
Description
Technical field
The present invention relates to a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature, it belongs to boiler and controls automatically
Technical field processed, it is adaptable to the superheat steam temperature of fired power generating unit and automatically controlling for reheat steam temperature.
Background technology
By the control of station boiler vapor (steam) temperature in the range of set value for allowing, including superheat steam temperature and reheated steam temperature
Degree, is the economic benefit for improving generating set, the indispensable link for ensureing unit safety operation, is increasingly subject to power plant
Family and the great attention of scientific research personnel.
Nowadays almost all of large-scale power station unit will participate in peak load regulation network, and centering adjusts Automatic Generation Control (AGC) to refer to
The response speed of order is greatly related to the economic benefit and social benefit of power plant.And before making quick response to AGC instructions
Carry be unit itself each parameter can be good maintain in safe range, this needs unit to possess the automatic control of function admirable
System processed is ensured.
The complexity of particularity and the jet chimney construction yet with thermal power generation production process, to vapor (steam) temperature object
Control it is relatively difficult.Wherein because the randomness of unit load fluctuates, cause disturbance frequently and disturbance quantity is larger, it is especially right
In unit load or the equivalently disturbance of steam flow, cause steam temperature often to fluctuate, main cause be Steam Temperature System for
The change of unit load has obvious time-varying and nonlinear characteristic.Generally when load increases, system performance function can be caused
Inertia time constant and static gain be all presented significant change, total steam temperature is then raised and increased with load.It is conventional fixed
The vapor (steam) temperature cas PID control scheme of parameter is difficult to obtain satisfied control effect in full working scope scope.
For this, some more advanced control programs are suggested successively.
Such as Chinese patent " a kind of station boiler main steam temperature whole-process control method " (201310576564.5) is directed to
Steam temperature plant characteristic random groups load is presented the problem of obvious nonlinear change, it is proposed that it is a kind of according to unit operation operating mode from
The method of dynamic scheduling PID controller parameter, but it is because its scheme is still limited to conventional cas PID control scheme thus right
The improvement of control system performance is limited, because theoretically may certify that PID controller can for single order or second-order system
To obtain optimal definite value tracing control performance, but can only then be obtained as far as possible for the high order system as Steam Temperature for Boiler system
Satisfied control performance.
Also some patents and document are described using the multiple model predictive control of segmenting or using with global property
The neural network model of ability is predicted control design case, but they are examined from the angle of model descriptive system self character
Worry problem, it is still desirable to which the online PREDICTIVE CONTROL for carrying out complexity is calculated, and is unfavorable in now widely used scattered control system
(DCS) realized on, even if being realized by Special industrial controller such as PLC etc., these schemes need to take substantial amounts of memory cost,
It is also very unfavorable for the PLC device of limited memory, so as to limit the popularization and application of control program.
Another is utilized in line Adaptive Identification for the solution of Steam Temperature System random groups load time-varying feature
System model, then for model Photographing On-line adaptive controller (including Model Predictive Control) of identification.But due to vapour
Warm system is affected by various factors at any time, and the result of on-line identification tends not to reflection or even completely offsets from real steam temperature
System performance, control effect now cannot just meet desired Control platform certainly.As seen from the above analysis, existing boiler
Vapor (steam) temperature control method still suffers from certain limitation or defect.
The content of the invention
The technical problems to be solved by the invention there is provided that a kind of degree of accuracy is high, reaction is quick, on-line calculation is small, compile
The intelligence computation forecast Control Algorithm of the simple station boiler vapor (steam) temperatures of Cheng Shixian.
The present invention is adopted the following technical scheme that：
A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature, comprises the following steps that：
Step 1. determines the sampling time interval T of PREDICTIVE CONTROLs；
Step 2. identification unit operates in the input-output difference equation of vapor (steam) temperature system under M typical load operating mode
The partial model G of form descriptionm；
Step 3. is using Model Predictive Control to all partial model GmCarry out the tracing control emulation of square-wave signal；
Step 4. is by all partial model GmControl emulation data be used for training smart computation model, so as to form intelligence
Predictive controller IPC；
Step 5. recognizes the nonlinear inverse model of control valve, i.e. u (t)=f [v (t)]；
Step 6. will train the Intelligent predictive control device IPC that obtains as real-time controller in the step 4；By collection
The input signal of needs simultaneously sends into Intelligent predictive control device IPC, the Intelligent predictive control device IPC and is calculated automatically from any work
Attemperation water flow v (t) needed for adjusting steam temperature under condition, and attemperation water flow v (t) is substituted into regulating valve in the step 5
Nonlinear inverse model u (the t)=f [v (t)] of door, can obtain the desuperheating water valve opening calculated value of current sample time t,
Then limited through rate limit and amplitude, obtain enforceable desuperheating waters valve opening actual instruction u (t) of current sample time t.
Further, the sampling time interval T in the step 1s, characteristic speed according to boiler steam temperature system with
And the configuring condition of controller calculation server, its span is Ts=3~10 seconds.
Further, the M number of typical load operating mode in the step 2, it is interval in the main operating load of unit
Interior uniform value M=4~7.
Further, M office of the input-output difference equation form description of the vapor (steam) temperature system in the step 2
Portion model GmIncluding inner looping model Gm,qvWith external loop model Gm,yq, inner looping model Gm,qvWith external loop model Gm,yqRespectively such as
Under：
Inner looping model Gm,qv：
External loop model Gm,yq：
Wherein, t represents that present sample controls the moment；
ndAnd neRepresent inner looping model Gm,qvOrder, take identical value for M different typical load operating modes, take
Value scope nd=ne=2~3；
naAnd nbRepresent external loop model Gm,yqOrder, take identical value for M different typical load operating modes, take
Value scope na=nb=3~6；
di、ej、ai、bjModel coefficient is represented, is obtained using least squares identification, the typical load work different for M
Condition gets different values；
Q (t) represents the leading steam temperature predicted value of t；
V (t-j) represents the attemperation water flow at t-j moment；
Y (t) represents the outlet steam temperature predicted value of t.
Further, the Model Predictive Control in the step 3 is based on inner looping object function Jqv(t) and external loop target
Function JyqT () is solved using GPC algorithm, wherein inner looping object function JqvT () is as follows：
External loop object function JyqT () is as follows：
Wherein, t represents that present sample controls the moment；
PqAnd PyThe prediction time domain of inner looping and external loop, M are represented respectivelyvAnd MqInner looping and external loop are represented respectively
Control time domain；
qr(t+i), q (t+i) represents the leading steam temperature set-point and predicted value at t+i moment respectively；
yr(t+i), y (t+i) represents the outlet steam temperature set-point and predicted value at t+i moment respectively；
Δ v (t+j) represents t+j moment desuperheating water PREDICTIVE CONTROL increment to be calculated；
Δqr(t+j) t+j moment leading steam temperature set-point increment to be calculated is represented.
Further, the square-wave signal in the step 3, takes unit square-wave signal, and its cycle is 500Ts~1000Ts.
Further, the intelligent computation model that Intelligent predictive control device IPC designs are used in the step 4 uses radial direction base
Function Neural Network model, generally also known as RBF neural；In the Intelligent predictive control device IPC obtained after training includes
Loop IPC2 and external loop IPC1.
Further, nonlinear inverse model u (the t)=f [v (t)] of the control valve in the step 5 is desuperheating water regulation
The inversion model of valve opening u (t) (%)-attemperation water flow v (t) (kg/s) characteristic, in the controls to the non-of valve
Linear adjustment characteristic compensates linearisation, is described using quadratic function, i.e. u (t)=c2v(t)2+c1v(t)+c0, wherein c2、
c1、c0It is the valve characteristic coefficient of determination to be identified.
Further, the discrimination method in the step 5 uses least square method.
Further, in the step 6 rate limit and amplitude limitation, are respectively | Δ u (t) |≤Δ umaxAnd umin≤
u(t)≤umax, wherein Δ u (t) is the Desuperheating water regulating valve door aperture controlling increment that current sample time is calculated；ΔumaxIt is speed
Rate limits value, according to specific valve design requirement, span 3%~10%；uminAnd umaxRespectively under amplitude limitation
Limit and the upper limit, take the physical limit of valve location, i.e., 0 and 100%.
Beneficial effects of the present invention are as follows：
Instant invention overcomes the deficiency of existing vapor (steam) temperature control method, vapor (steam) temperature is including superheat steam temperature and again
Vapours temperature, there is provided a kind of degree of accuracy is high, reaction is quick, on-line calculation is small, the simple station boiler of programming realization steams
The direct predictive control method of stripping temperature, it is to avoid the vapor (steam) temperature object with large delay, big inertia and time-varying characteristics is with machine
Organize load variations and cause the not good problem of vapor (steam) temperature control performance.
In the range of the full working scope of unit operation, the direct predictive control of vapor (steam) temperature is realized, improve vapor (steam) temperature control
Dynamic regulation performance and stability, while programming realization is simple, be easy to the configuration in the widely used DCS of fired power generating unit to realize
And other soft hardware equipments need not be increased.
Brief description of the drawings
Fig. 1 is conventional vapor (steam) temperature cas PID control systematic schematic diagram.
Fig. 2 is the vapor (steam) temperature tandem intelligent Prediction Control System schematic diagram that the present invention is provided.
Symbol description in Fig. 1-Fig. 2：Y is vapor (steam) temperature measured value；
yrIt is vapor (steam) temperature setting value；
Q is leading steam temperature measured value；
qrIt is leading steam temperature setting value；
U is instructed for desuperheating water valve opening；
V is attemperation water flow signal；
X is unit load；
Gm,yqIt is vapor (steam) temperature inertia block transitive function；
Gm,qvIt is vapor (steam) temperature leading block transitive function；
V=g (u) is nonlinear characteristic function of the desuperheating water valve opening to attemperation water flow；
U=f (v) is nonlinear characteristic inverse function of the attemperation water flow to desuperheating water valve opening；
vaIt is predictive controller output signal, desuperheat is accurately ideally equal in aforementioned non-linear characteristic inverse function
Water flow signal v；
PID is external loop controller, and PI is inner looping controller, and IPC1 is external loop Intelligent predictive control device, and IPC2 is
Inner looping Intelligent predictive control device；
In Fig. 1 and Fig. 2 the controller realized on computers, right side dotted line frame (b) are partly represented in left-hand broken line frame (a)
Interior part represents live steam temperature process controlled device.
Specific embodiment
The present invention is described in more detail below in conjunction with Fig. 1 and Fig. 2 and specific embodiment.
As Figure 1-Figure 2, the present embodiment is related to a kind of power station pot that control law study is predicted based on intelligence computation
The forecast Control Algorithm of stove vapor (steam) temperature, comprises the following steps that：
Step 1. determines the sampling time interval T of PREDICTIVE CONTROLs；
Step 2. identification unit operates in the input-output difference equation of vapor (steam) temperature system under M typical load operating mode
The partial model G of form descriptionm；
Step 3. is using Model Predictive Control to all partial model GmCarry out the tracing control emulation of square-wave signal；Step
4. by all partial model GmControl emulation data be used for training smart computation model, so as to form Intelligent predictive control device
IPC；
Step 5. recognizes the nonlinear inverse model of control valve, i.e. u (t)=f [v (t)]；
Step 6. will train the Intelligent predictive control device IPC that obtains as real-time controller in the step 4；By collection
The input signal of needs simultaneously sends into Intelligent predictive control device IPC, the Intelligent predictive control device IPC and is calculated automatically from any work
Attemperation water flow v (t) needed for adjusting steam temperature under condition, and attemperation water flow v (t) is substituted into regulating valve in the step 5
Nonlinear inverse model u (the t)=f [v (t)] of door, can obtain the desuperheating water valve opening calculated value of current sample time t,
Then limited through rate limit and amplitude, obtain enforceable desuperheating waters valve opening actual instruction u (t) of current sample time t.
Further, the sampling time interval T in the step 1s, characteristic speed according to boiler steam temperature system with
And the configuring condition of controller calculation server, its span is Ts=3~10 seconds.
Further, the M number of typical load operating mode in the step 2, it is interval in the main operating load of unit
Interior uniform value M=4~7.
Further, M office of the input-output difference equation form description of the vapor (steam) temperature system in the step 2
Portion model GmIncluding inner looping model Gm,qvWith external loop model Gm,yq, inner looping model Gm,qvWith external loop model Gm,yqRespectively such as
Under：
Inner looping model Gm,qv：
External loop model Gm,yq：
Wherein, t represents that present sample controls the moment；
ndAnd neRepresent inner looping model Gm,qvOrder, take identical value for M different typical load operating modes, take
Value scope nd=ne=2~3；
naAnd nbRepresent external loop model Gm,yqOrder, take identical value for M different typical load operating modes, take
Value scope na=nb=3~6；
di、ej、ai、bjModel coefficient is represented, is obtained using least squares identification, the typical load work different for M
Condition gets different values；
Q (t) represents the leading steam temperature predicted value of t；
V (t-j) represents the attemperation water flow at t-j moment；
Y (t) represents the outlet steam temperature predicted value of t.
Further, the Model Predictive Control in the step 3 is based on inner looping object function Jqv(t) and external loop target
Function JyqT () is solved using GPC algorithm, wherein inner looping object function JqvT () is as follows：
External loop object function JyqT () is as follows：
Wherein, t represents that present sample controls the moment；
PqAnd PyThe prediction time domain of inner looping and external loop, M are represented respectivelyvAnd MqInner looping and external loop are represented respectively
Control time domain；
qr(t+i), q (t+i) represents the leading steam temperature set-point and predicted value at t+i moment respectively；
yr(t+i), y (t+i) represents the outlet steam temperature set-point and predicted value at t+i moment respectively；
Δ v (t+j) represents t+j moment desuperheating water PREDICTIVE CONTROL increment to be calculated；
Δqr(t+j) t+j moment leading steam temperature set-point increment to be calculated is represented.
Further, the square-wave signal in the step 3, takes unit square-wave signal, and its cycle is 500Ts~1000Ts.
Further, the intelligent computation model that Intelligent predictive control device IPC designs are used in the step 4 uses Radial Basis Function neural
Network model, generally also known as RBF neural；The Intelligent predictive control device IPC obtained after training includes inner looping IPC2
With external loop IPC1.
Further, nonlinear inverse model u (the t)=f [v (t)] of the control valve in the step 5 is desuperheating water regulation
The inversion model of valve opening u (t) (%)-attemperation water flow v (t) (kg/s) characteristic, in the controls to the non-of valve
Linear adjustment characteristic compensates linearisation, is described using quadratic function, i.e. u (t)=c2v(t)2+c1v(t)+c0, wherein c2、
c1、c0It is the valve characteristic coefficient of determination to be identified.
Further, the discrimination method in the step 5 uses least square method.
Further, in the step 6 rate limit and amplitude limitation, are respectively | Δ u (t) |≤Δ umaxAnd umin≤
u(t)≤umax, wherein Δ u (t) is the Desuperheating water regulating valve door aperture controlling increment that current sample time is calculated；ΔumaxIt is speed
Rate limits value, according to specific valve design requirement, span 3%~10%；uminAnd umaxRespectively under amplitude limitation
Limit and the upper limit, take the physical limit of valve location, i.e., 0 and 100%.
Operation principle of the invention is as follows：
Because the present invention has only learnt M of inner looping under M different typical load operating mode with a RBF neural
The function of predictive controller, has also only learnt the M of external loop under M different typical load operating mode with a RBF neural
The function of individual predictive controller, and learning process is to carry out offline, be in line computation two train without
Line optimization calculate RBF neural predictive controller IPC2 and IPC1, thus typical load operating mode number M can take compared with
It is big so as to the control performance of strengthening system, and common multiple model predictive control should not then take excessive M with avoid it is excessive
Line optimizes amount of calculation.
Technical characteristics of the invention：Performance model is recognized, and carries out station boiler vapor (steam) temperature to corresponding model
The neutral net integrated learning of the simulation calculation and result of calculation of predictive controller parameter.
Above-mentioned detailed description is directed to illustrating for possible embodiments of the present invention, and the embodiment simultaneously is not used to limit this hair
Bright the scope of the claims, it is all without departing from equivalence enforcement of the invention or change, such as：The prediction of other forms is used during specific implementation
Control algolithm, including Model Algorithmic contral MAC, dynamic matrix control DMC, Predictive function control PFC, instead of originally applying the wide of example
Adopted PREDICTIVE CONTROL GPC, carries out local control calculating and emulation, and replaces originally applying using the intelligent computation model of other forms
The study that the RBF neural RBFNN of example carries out global controller is integrated, including BP neural network BPNN, Wavelet Neural Network
Network WNN, support vector machines, support vector regression SVR, least square method supporting vector machine LSSVM etc., are intended to be limited solely by
In the scope of patent protection of this case.
Claims (10)
1. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature, it is characterised in that it comprises the following steps：
Step 1. determines the sampling time interval T of PREDICTIVE CONTROLs；
Step 2. identification unit operates in the input-output difference equation form of vapor (steam) temperature system under M typical load operating mode
The partial model G of descriptionm；
Step 3. is using Model Predictive Control to all partial model GmCarry out the tracing control emulation of square-wave signal；
Step 4. is by all partial model GmControl emulation data be used for training smart computation model, so as to form intelligent predicting
Controller IPC；
Step 5. recognizes the nonlinear inverse model of control valve, i.e. u (t)=f [v (t)]；
Step 6. will train the Intelligent predictive control device IPC that obtains as real-time controller in the step 4；Needed by gathering
Input signal and send into Intelligent predictive control device IPC, the Intelligent predictive control device IPC is calculated automatically under any operating mode
Attemperation water flow v (t) needed for regulation steam temperature, and attemperation water flow v (t) is substituted into control valve in the step 5
Nonlinear inverse model u (t)=f [v (t)], can obtain the desuperheating water valve opening calculated value of current sample time t, then
Limited through rate limit and amplitude, obtain enforceable desuperheating waters valve opening actual instruction u (t) of current sample time t.
2. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：Sampling time interval T in the step 1s, characteristic speed and controller according to boiler steam temperature system calculate clothes
The configuring condition of business device, its span is Ts=3~10 seconds.
3. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：The M number of typical load operating mode in the step 2, the uniform value M=4 in the main operating load of unit is interval
~7.
4. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：M partial model G of the input-output difference equation form description of the vapor (steam) temperature system in the step 2mIncluding interior
Loop model Gm,qvWith external loop model Gm,yq, inner looping model Gm,qvWith external loop model Gm,yqIt is as follows respectively：
Inner looping model Gm,qv：
External loop model Gm,yq：
Wherein, t represents that present sample controls the moment；
ndAnd neRepresent inner looping model Gm,qvOrder, take identical value, value model for M different typical load operating modes
Enclose nd=ne=2~3；
naAnd nbRepresent external loop model Gm,yqOrder, take identical value, value model for M different typical load operating modes
Enclose na=nb=3~6；
di、ej、ai、bjModel coefficient is represented, is obtained using least squares identification, taken for M different typical load operating mode
To different values；
Q (t) represents the leading steam temperature predicted value of t；
V (t-j) represents the attemperation water flow at t-j moment；
Y (t) represents the outlet steam temperature predicted value of t.
5. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：Model Predictive Control in the step 3 is based on inner looping object function Jqv(t) and external loop object function JyqT () uses
GPC algorithm is solved, wherein inner looping object function JqvT () is as follows：
External loop object function JyqT () is as follows：
Wherein, t represents that present sample controls the moment；
PqAnd PyThe prediction time domain of inner looping and external loop, M are represented respectivelyvAnd MqRespectively during the control of expression inner looping and external loop
Domain；
qr(t+i), q (t+i) represents the leading steam temperature set-point and predicted value at t+i moment respectively；
yr(t+i), y (t+i) represents the outlet steam temperature set-point and predicted value at t+i moment respectively；
Δ v (t+j) represents t+j moment desuperheating water PREDICTIVE CONTROL increment to be calculated；
Δqr(t+j) t+j moment leading steam temperature set-point increment to be calculated is represented.
6. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：Square-wave signal in the step 3, takes unit square-wave signal, and its cycle is 500Ts~1000Ts.
7. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：The intelligent computation model for being used for Intelligent predictive control device IPC designs in the step 4 uses radial basis function neural network mould
Type；The Intelligent predictive control device IPC obtained after training includes inner looping IPC2 and external loop IPC1.
8. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：Nonlinear inverse model u (t) of the control valve in the step 5=f [v (t)] is Desuperheating water regulating valve door aperture u (t)
The inversion model of (%)-attemperation water flow v (t) (kg/s) characteristic, is described, i.e. u (t)=c using quadratic function2v(t)2+c1v(t)+
c0, wherein c2、c1、c0It is the valve characteristic coefficient of determination to be identified.
9. a kind of intelligence computation forecast Control Algorithm of the station boiler vapor (steam) temperature according to claim 1 or 8, its feature
It is：Discrimination method in the step 5 uses least square method.
10. a kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature according to claim 1, its feature exists
In：Rate limit and amplitude limitation in the step 6, is respectively | Δ u (t) |≤Δ umaxAnd umin≤u(t)≤umax, wherein
Δ u (t) is the Desuperheating water regulating valve door aperture controlling increment that current sample time is calculated；ΔumaxIt is rate limitation value, according to tool
The valve design requirement of body, span 3%~10%；uminAnd umaxRespectively the lower and upper limit of amplitude limitation, take valve
The physical limit of position, i.e., 0 and 100%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611039592.3A CN106765052B (en) | 2016-11-21 | 2016-11-21 | A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611039592.3A CN106765052B (en) | 2016-11-21 | 2016-11-21 | A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106765052A true CN106765052A (en) | 2017-05-31 |
| CN106765052B CN106765052B (en) | 2019-01-29 |
Family
ID=58974897
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611039592.3A Expired - Fee Related CN106765052B (en) | 2016-11-21 | 2016-11-21 | A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106765052B (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108490790A (en) * | 2018-05-09 | 2018-09-04 | 东南大学 | A kind of overheating steam temperature active disturbance rejection cascade control method based on multiple-objection optimization |
| CN109116726A (en) * | 2017-06-22 | 2019-01-01 | 通用电气公司 | Method and system for plan forecast lead compensation |
| CN110285403A (en) * | 2019-06-10 | 2019-09-27 | 华北电力大学 | Main Steam Temperature Control method based on controlled parameter prediction |
| CN111102559A (en) * | 2019-11-28 | 2020-05-05 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Screen type superheater steam temperature control method based on double neural network inverse model |
| CN111123770A (en) * | 2019-12-13 | 2020-05-08 | 山东中实易通集团有限公司 | Method and device for determining opening of bypass model under FCB working condition |
| CN111708333A (en) * | 2020-06-01 | 2020-09-25 | 王铁军 | Intelligent prediction coordination control system of power plant |
| CN112799297A (en) * | 2020-11-11 | 2021-05-14 | 华能国际电力股份有限公司营口电厂 | Temperature prediction control method, system, equipment and readable storage medium |
| CN113531510A (en) * | 2021-06-18 | 2021-10-22 | 杭州电子科技大学 | Power station boiler main steam temperature control method |
| CN114167727A (en) * | 2021-12-08 | 2022-03-11 | 中电华创电力技术研究有限公司 | Boiler control method based on optimization of boiler superheated steam temperature model parameter identification process |
| CN114265317A (en) * | 2021-12-28 | 2022-04-01 | 华北电力科学研究院有限责任公司 | Main steam temperature multi-model stepped dynamic matrix control method |
| CN114326395A (en) * | 2021-12-23 | 2022-04-12 | 华北电力大学 | Intelligent generator set control model online updating method based on working condition judgment |
| CN114383130A (en) * | 2022-01-12 | 2022-04-22 | 浙江中智达科技有限公司 | Method, device and equipment for controlling temperature of superheated steam of boiler and storage medium |
| CN114562713A (en) * | 2022-01-17 | 2022-05-31 | 中冶华天南京工程技术有限公司 | Main steam temperature control method and system for power generation boiler |
| CN114673982A (en) * | 2022-03-30 | 2022-06-28 | 中冶华天工程技术有限公司 | Thermal power generation boiler main steam temperature control system based on hybrid intelligent optimization algorithm |
| CN114895567A (en) * | 2022-05-24 | 2022-08-12 | 国家能源集团科学技术研究院有限公司 | Supercritical unit overheating prediction control method based on PSO-ELM |
| CN115480478A (en) * | 2021-06-16 | 2022-12-16 | 中国科学院沈阳自动化研究所 | DMC-PID-based constant-speed variable-temperature process control method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07332602A (en) * | 1994-06-07 | 1995-12-22 | Mitsubishi Heavy Ind Ltd | Steam temperature prediction control device |
| CN103225799A (en) * | 2013-05-09 | 2013-07-31 | 北京四方继保自动化股份有限公司 | Method for controlling main steam temperature in thermal power plant |
| CN105318312A (en) * | 2015-11-26 | 2016-02-10 | 广东省粤电集团有限公司 | Method of controlling steam temperature of power station boiler through multi-step advance prediction of full working conditions |
-
2016
- 2016-11-21 CN CN201611039592.3A patent/CN106765052B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07332602A (en) * | 1994-06-07 | 1995-12-22 | Mitsubishi Heavy Ind Ltd | Steam temperature prediction control device |
| CN103225799A (en) * | 2013-05-09 | 2013-07-31 | 北京四方继保自动化股份有限公司 | Method for controlling main steam temperature in thermal power plant |
| CN105318312A (en) * | 2015-11-26 | 2016-02-10 | 广东省粤电集团有限公司 | Method of controlling steam temperature of power station boiler through multi-step advance prediction of full working conditions |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109116726B (en) * | 2017-06-22 | 2022-08-16 | 通用电气公司 | Method and system for planning predictive lead compensation |
| CN109116726A (en) * | 2017-06-22 | 2019-01-01 | 通用电气公司 | Method and system for plan forecast lead compensation |
| CN108490790A (en) * | 2018-05-09 | 2018-09-04 | 东南大学 | A kind of overheating steam temperature active disturbance rejection cascade control method based on multiple-objection optimization |
| CN110285403A (en) * | 2019-06-10 | 2019-09-27 | 华北电力大学 | Main Steam Temperature Control method based on controlled parameter prediction |
| CN111102559A (en) * | 2019-11-28 | 2020-05-05 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Screen type superheater steam temperature control method based on double neural network inverse model |
| CN111123770A (en) * | 2019-12-13 | 2020-05-08 | 山东中实易通集团有限公司 | Method and device for determining opening of bypass model under FCB working condition |
| CN111708333A (en) * | 2020-06-01 | 2020-09-25 | 王铁军 | Intelligent prediction coordination control system of power plant |
| CN112799297A (en) * | 2020-11-11 | 2021-05-14 | 华能国际电力股份有限公司营口电厂 | Temperature prediction control method, system, equipment and readable storage medium |
| CN115480478A (en) * | 2021-06-16 | 2022-12-16 | 中国科学院沈阳自动化研究所 | DMC-PID-based constant-speed variable-temperature process control method |
| CN113531510A (en) * | 2021-06-18 | 2021-10-22 | 杭州电子科技大学 | Power station boiler main steam temperature control method |
| CN114167727A (en) * | 2021-12-08 | 2022-03-11 | 中电华创电力技术研究有限公司 | Boiler control method based on optimization of boiler superheated steam temperature model parameter identification process |
| CN114326395A (en) * | 2021-12-23 | 2022-04-12 | 华北电力大学 | Intelligent generator set control model online updating method based on working condition judgment |
| CN114326395B (en) * | 2021-12-23 | 2024-03-19 | 华北电力大学 | Intelligent generator set control model online updating method based on working condition discrimination |
| CN114265317A (en) * | 2021-12-28 | 2022-04-01 | 华北电力科学研究院有限责任公司 | Main steam temperature multi-model stepped dynamic matrix control method |
| CN114383130B (en) * | 2022-01-12 | 2022-08-05 | 浙江中智达科技有限公司 | Method, device and equipment for controlling temperature of superheated steam of boiler and storage medium |
| CN114383130A (en) * | 2022-01-12 | 2022-04-22 | 浙江中智达科技有限公司 | Method, device and equipment for controlling temperature of superheated steam of boiler and storage medium |
| CN114562713A (en) * | 2022-01-17 | 2022-05-31 | 中冶华天南京工程技术有限公司 | Main steam temperature control method and system for power generation boiler |
| CN114562713B (en) * | 2022-01-17 | 2024-04-09 | 中冶华天南京工程技术有限公司 | Main steam temperature control method and system for power generation boiler |
| CN114673982A (en) * | 2022-03-30 | 2022-06-28 | 中冶华天工程技术有限公司 | Thermal power generation boiler main steam temperature control system based on hybrid intelligent optimization algorithm |
| CN114673982B (en) * | 2022-03-30 | 2024-01-02 | 中冶华天工程技术有限公司 | Main steam temperature control system of thermal power generation boiler based on hybrid intelligent optimization algorithm |
| CN114895567A (en) * | 2022-05-24 | 2022-08-12 | 国家能源集团科学技术研究院有限公司 | Supercritical unit overheating prediction control method based on PSO-ELM |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106765052B (en) | 2019-01-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106765052B (en) | A kind of intelligence computation forecast Control Algorithm of station boiler vapor (steam) temperature | |
| CN106406101B (en) | A kind of intelligence computation forecast Control Algorithm of fired power generating unit coordinated control system | |
| CN104482525B (en) | The control method of extra-supercritical unit reheat steam temperature and system | |
| CN102707743B (en) | Supercritical machine set steam temperature control method and system based on multivariable predictive control | |
| Daren et al. | Nonlinear coordinated control of drum boiler power unit based on feedback linearization | |
| CN104090491B (en) | Gas steam combined cycle unit multivariable constrained prediction function load control method | |
| CN104714526B (en) | The load control system estimated and method are adjusted based on condensate throttling | |
| Ma et al. | Intelligent coordinated controller design for a 600 MW supercritical boiler unit based on expanded-structure neural network inverse models | |
| CN106019939A (en) | Nonlinear intelligent predictive control technology-based super critical unit reheating steam temperature optimal control system | |
| CN107908106B (en) | Double reheat power generation sets reheat steam temperature concentrates Prediction Control system from depression of order multiloop | |
| CN107831652A (en) | A kind of unit load intelligent optimized control method based on cold end system energy storage | |
| Rivas-Perez et al. | Design of a fractional order PI controller for steam pressure in the steam drum of a bagasse fired boiler | |
| CN110703718A (en) | Industrial process control method based on signal compensation | |
| CN104076831B (en) | The high water tank control method optimized based on generalized predictive control | |
| CN104456513B (en) | A kind of thermal power plant Stream temperature estimation optimizing control method | |
| CN109032117B (en) | ARMA model-based single-loop control system performance evaluation method | |
| CN113048017A (en) | Wind turbine generator active power control optimization method and system based on internal model control | |
| CN108762086B (en) | Secondary reheat steam temperature control device and control system based on model predictive control | |
| CN106610589A (en) | Online hardware closed-loop network source coordination linear active-disturbance-rejection control method | |
| CN110094838A (en) | A kind of variable element MFA control method based on air-conditioning system | |
| CN105955030A (en) | Turbine and boiler coordination control method based on improved input weighted prediction controller | |
| CN105240822B (en) | A kind of the damper control method of boiler three and system based on neutral net | |
| Muravyova et al. | A Neural Network-Based Control System Using PID Controller to Control the Deaerator | |
| CN110631003B (en) | Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control | |
| CN104898419B (en) | Dyad tuning controller and its Optimization Design based on trapezoidal joint instruction |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190129 Termination date: 20191121 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |