CN103543763B - Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control - Google Patents
Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control Download PDFInfo
- Publication number
- CN103543763B CN103543763B CN201310518040.0A CN201310518040A CN103543763B CN 103543763 B CN103543763 B CN 103543763B CN 201310518040 A CN201310518040 A CN 201310518040A CN 103543763 B CN103543763 B CN 103543763B
- Authority
- CN
- China
- Prior art keywords
- kth moment
- fuzzy
- action coefficient
- integral action
- increment delta
- 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
Links
- 230000003053 immunization Effects 0.000 title claims abstract description 73
- 238000002649 immunization Methods 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000008569 process Effects 0.000 title claims abstract description 12
- 239000000446 fuel Substances 0.000 claims abstract description 22
- 230000008859 change Effects 0.000 claims abstract description 20
- 230000009471 action Effects 0.000 claims description 156
- 230000006870 function Effects 0.000 claims description 54
- 238000012360 testing method Methods 0.000 claims description 20
- 238000013461 design Methods 0.000 claims description 18
- 239000000427 antigen Substances 0.000 claims description 15
- 102000036639 antigens Human genes 0.000 claims description 15
- 108091007433 antigens Proteins 0.000 claims description 15
- 238000005070 sampling Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000010304 firing Methods 0.000 claims description 3
- 230000002401 inhibitory effect Effects 0.000 claims description 3
- 230000000638 stimulation Effects 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 230000003068 static effect Effects 0.000 abstract 1
- 230000004044 response Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- RZVAJINKPMORJF-UHFFFAOYSA-N Acetaminophen Chemical compound CC(=O)NC1=CC=C(O)C=C1 RZVAJINKPMORJF-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000009123 feedback regulation Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 210000000987 immune system Anatomy 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000007363 regulatory process Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Landscapes
- Electrical Control Of Air Or Fuel Supplied To Internal-Combustion Engine (AREA)
- Feedback Control In General (AREA)
Abstract
A kind of heavy duty gas turbine temperature-controlled process based on fuzzy immunization proportional plus integral control, relate to gas turbine temperature to control, the method is with reference to certain single-rotor gas turbine modeling actual exhaust air temperature, the deviation of outlet air temperature set value and actual exhaust air temperature is sent into fuzzy immunization pi controller, by changing fuel stroke benchmark, change fuel quantity.The method combines fuzzy control, immune control, proportional plus integral control, temperature can be made to control to adapt to the problems such as non-linear, uncertain, have good dynamic and static state performance.Compared with classic method, there is higher control accuracy, less overshoot, stronger antijamming capability, stronger robustness, prevent because temperature before turbine is too high and damage turbine blade, contribute to the serviceable life of improving unit.
Description
Technical field
The present invention relates to a kind of heavy duty gas turbine temperature-controlled process based on fuzzy immunization proportional plus integral control online, relate to the temperature controlled fuzzy immunization proportional plus integral control of gas turbine.
Background technology
The turbo wheel of gas turbine and blade etc. all work under high temperature and high speed, subject high temperature and huge centrifugal force, the strength of materials of turbo wheel and blade obviously reduces along with the rising of temperature, the intensity surplus of these high-temperature components is not very large, turbine intake air temperature must be controlled in certain scope so be in operation.Otherwise too high temperature can make turbine heating part service life reduction, turbine blade is even caused to burn, the major accidents such as fracture.
The high limit of gas turbine turbine internal temperature is at first order nozzle place, and working temperature is here very high, cannot directly measure and control, and indirectly can only control by measuring combustion turbine exhaustion temperature.When working temperature exceedes maximum temperature restriction, by adjust fuel amount, control delivery temperature, thus control working temperature, make unit energy have maximum exerting oneself and the highest efficiency, and can run safely and reliably under the maximum operating temperature allowed.
The temperature of gas turbine controls to be maximum temperature restriction system.Conventional temperature controls to adopt traditional pi controller, and controller is once determining no longer to change.In real process, due to non-linear, the reason such as load variations or disturbing factor, plant characteristic parameter and structure may change, and controller quality also can decline thereupon.Severe one gas turbine temperature controller lost efficacy, and gas turbine enters overtemperature prote and causes unit to interdict, and the lighter's temperature control effect is not good, it seems for a long time the serviceable life that can affect high-temperature component of gas turbine.
Summary of the invention
The object of the invention is the not good problem of Control platform caused to solve the reasons such as non-linear, uncertain in existing gas turbine temperature controlled processes, disturbing factor, use fuzzy immunization proportional plus integral control, comprise fuzzy control on-line tuning integral action coefficient, also comprise immune fuzzy and control on-line tuning proportional action coefficient, make controller parameter automatically adapt to complexity, time become, nonlinear controlled device, improve control accuracy and dynamic property.
The present invention compared with prior art, have the following advantages and breakthrough technique effect: 1. adopt immune fuzzy to control, according to the feedback regulation principle of Immune System, use fuzzy controller Nonlinear Function Approximation, according to the Drazin inverse proportional action coefficient of controller, the adaptive ability of system is strengthened greatly, ensure that the stability of system, in Dynamic Regulating Process, eliminate the deviation of target with the fastest speed.2. in order to error that compensate for disturbances causes, fuzzy control is adopted according to the deviation of outlet air temperature set value and measured value and deviation variation rate, automatically the optimum apjustment of integral action coefficient is realized, can become during self-adaptation, the gas turbine complication system that non-linear, disturbing factor is many, there is higher control accuracy, less overshoot, stronger antijamming capability, stronger robustness.
In order to realize above-mentioned object, technical scheme of the present invention is as follows:
1) install N number of exhaust gas temperature sensor at equal intervals at gas turbine turbine exhaust mouth, wherein, the kth moment delivery temperature of the 1st ~ N number of exhaust gas temperature sensor measurement is designated as T respectively
survey 1(k), T
survey 2(k) ... T
survey Nk (), N gets the positive integer being more than or equal to 16, and the delivery temperature mean value in kth moment is designated as T
on averagek (), the outlet air temperature set value in kth moment is designated as T
if(k), the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () is designated as e (k), as the input value of fuzzy immunization pi controller;
2) at the integral action coefficient of line computation fuzzy immunization pi controller: design fuzzy controller, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagethe deviation e (k) of (k) as the input value of fuzzy controller, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () obtains deviation variation rate through differential and is designated as ec (k), also as the input value of fuzzy controller;
Determine kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagethe fuzzy domain of the deviation of (k)-3 ,-2 ,-1,0,1,2,3}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagethe fuzzy domain {-3 ,-2 ,-1,0,1 of the deviation variation rate of (k), 2,3}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, the membership function of deviation and deviation variation rate is triangle, determine the fuzzy domain {-0.06 ,-0.04 ,-0.02,0,0.02 of integral action coefficient increment, 0.04,0.06}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, membership function adopts triangle;
According to engineering experience and gas turbine temperature control requirement, design fuzzy control integral action coefficient increment:
If e (k) is NB, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is NB,
If e (k) is NB, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is NB,
If e (k) is NB, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is NB, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is NB, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is NB, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is NB, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is NM, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is NB,
If e (k) is NM, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is NB,
If e (k) is NM, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is NM, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is NM, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is NM, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is NM, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is NS, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is NB,
If e (k) is NS, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is NS, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is NS, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is NS, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is NS, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is NS, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is Z, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is Z, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is Z, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is Z, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is Z, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is Z, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is Z, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is PS, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is NM,
If e (k) is PS, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is NS,
If e (k) is PS, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is PS, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is PS, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is PS, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is PS, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is PB,
If e (k) is PM, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is PM, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is PM, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is PM, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is PM, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is PM, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is PB,
If e (k) is PM, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is PB,
If e (k) is PB, and ec (k) is NB, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is PB, and ec (k) is NM, then integral action coefficient increment △ ki (k) in kth moment is Z,
If e (k) is PB, and ec (k) is NS, then integral action coefficient increment △ ki (k) in kth moment is PS,
If e (k) is PB, and ec (k) is Z, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is PB, and ec (k) is PS, then integral action coefficient increment △ ki (k) in kth moment is PM,
If e (k) is PB, and ec (k) is PM, then integral action coefficient increment △ ki (k) in kth moment is PB,
If e (k) is PB, and ec (k) is PB, then integral action coefficient increment △ ki (k) in kth moment is PB;
Kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of (), deviation variation rate ec (k) are multiplied by quantizing factor respectively and are quantified as fuzzy quantity on fuzzy domain, according to fuzzy control rule, obtain the fuzzy control quantity of integral action coefficient increment △ ki (k) in kth moment, be multiplied by quantizing factor by its precision, add the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out, obtain the integral action coefficient of kth moment fuzzy immunization pi controller
ki(k)＝ki0+△ki(k)×α
Wherein, the integral action coefficient of the fuzzy immunization pi controller that ki (k) is the kth moment, ki0 is the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out, and α is the quantizing factor of integral action coefficient increment;
3) at the proportional action coefficient of line computation fuzzy immunization pi controller: design immune fuzzy controller, if the antigen levels in the i-th generation is ε (i), the output of antigen stimulated cells is T
h(i), T
h(i)=k
1ε (i), k
1for exitation factor, the output of antigen T suppression cell is T
s(i), T
s(i)=k
2f () ε (i), k
2for inhibiting factor, f () is nonlinear function, and total stimulation of cell antigen is S (i)=T
h(i)-T
s(i)=(k
1-k
2f ()) ε (i)=K (1-η f ()) ε (i), with kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of () is as antigen levels ε (i), then immune fuzzy controller exports as K (1-η f ()) e (k), the proportional action coefficient of fuzzy immunization pi controller is K (1-η f ()), wherein, K=k
1control reaction velocity,
control stablizing effect, f () is selected nonlinear function;
Adopt method Nonlinear Function Approximation f () of fuzzy reasoning, the fuzzy immunization pi controller in kth moment exports and is designated as G (k), fuzzy immunization pi controller exports and obtains exporting change rate through differential, the value in kth moment is designated as △ G (k), G (k), △ G (k) is all as the input value of immune fuzzy controller, determine the fuzzy domain {-1 that fuzzy immunization pi controller exports, 1}, fuzzy language value { N, P}, the fuzzy domain {-1 of fuzzy immunization pi controller exporting change rate, 1}, fuzzy language value is { N, P}, the membership function of output and exporting change rate all adopts triangle, determine the fuzzy domain {-1 of nonlinear function f () value, 1}, fuzzy language value { N, Z, P}, membership function adopts triangle, design nonlinear function f ():
If G (k) is N, and △ G (k) is N, then the kth moment nonlinear function f (, k) be P;
If G (k) is N, and △ G (k) is P, then the kth moment nonlinear function f (, k) be Z;
If G (k) is P, and △ G (k) is N, then the kth moment nonlinear function f (, k) be Z;
If G (k) is P, and △ G (k) is P, then the kth moment nonlinear function f (, k) be N;
The fuzzy immunization pi controller in kth moment exports G (k), exporting change rate △ G (k) is multiplied by quantizing factor obfuscation respectively, according to above-mentioned fuzzy rule, obtain kth moment nonlinear function f (, k) fuzzy control quantity, is multiplied by quantizing factor by its precision, obtain kth moment nonlinear function f (, k) exact value, thus the proportional action coefficient obtaining kth moment fuzzy immunization pi controller
kp(k)＝K(1-ηf(·,k))
Wherein, the proportional action coefficient of the fuzzy immunization pi controller that kp (k) is the kth moment, K, η are parameter;
4) the fuzzy immunization pi controller calculating the kth moment exports,
Wherein, T represents the sampling time;
5) output of the fuzzy immunization pi controller in kth moment is temperature control fuel stroke benchmark G (k) and sends into fuel stroke selection of reference frame module, participate in the control of fuel flow rate, obtain the fuel quantity w in kth moment
fk (), sends into firing chamber by fuel control valve door.
For step 2), kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the quantizing factor of the deviation of () adopts the method for gathering through test to determine, its scope is 0.01 ~ 0.1, kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the quantizing factor of the deviation variation rate of () adopts the method for gathering through test to determine, its scope is 0.01 ~ 0.05, the quantizing factor of kth moment fuzzy immunization pi controller integral action coefficient increment adopts the method for gathering through test to obtain, and its scope is 0.01 ~ 0.03;
For step 3), the quantizing factor that kth moment fuzzy immunization pi controller exports adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, the quantizing factor of kth moment fuzzy immunization pi controller exporting change rate adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, kth moment nonlinear function f (, k) quantizing factor adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, parameter K in the proportional action coefficient K (1-η f ()) of fuzzy immunization pi controller, η adopts the method for gathering through test to obtain, the scope of K is 0.0005 ~ 0.0007, the scope of η is 0.1 ~ 0.3,
For step 4), sampling time T value is 0.001 second.
Accompanying drawing explanation
Fig. 1 heavy duty gas turbine temperature controlled fuzzy immunization proportional plus integral control process flow diagram
The membership function of Fig. 2 delivery temperature deviation
The membership function of Fig. 3 delivery temperature deviation variation rate
The membership function of Fig. 4 integral action coefficient increment
The membership function that Fig. 5 fuzzy immunization pi controller exports
The membership function of Fig. 6 fuzzy immunization pi controller exporting change rate
The membership function of nonlinear function during Fig. 7 immune fuzzy controls
The response curve of Fig. 8 proportional action coefficient
The response curve of Fig. 9 integral action coefficient
The output response curve of Figure 10 fuzzy immune control
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described further
Fig. 1 is heavy duty gas turbine temperature controlled fuzzy immunization proportional plus integral control process flow diagram, and its specific implementation method comprises the following steps:
1) install N number of exhaust gas temperature sensor at equal intervals at gas turbine turbine exhaust mouth, wherein, the delivery temperature of the 1st ~ N number of exhaust gas temperature sensor measurement is designated as T respectively
survey 1, T
survey 2t
survey N, N gets the positive integer being more than or equal to 16, and delivery temperature mean value is designated as T
on average, the delivery temperature mean value T in kth moment
on averagek () value is N number of measurement value sensor T in kth moment
survey 1(k), T
survey 2(k) ... T
survey Nk () removes maximal value and the later mean value of minimum value, outlet air temperature set value is designated as T
if, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () is designated as e (k), as the input value of fuzzy immunization pi controller;
2) outlet air temperature set value is designated as T
if, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () is designated as e (k), as the input value of fuzzy immunization pi controller, the k moment is the timer time that gas turbine brings into operation, k=0,1,2,, suppose that the sampling time is 0.01 second, then the 1st moment was the 0.01st second, 2nd moment was the 0.02nd second, by that analogy;
3) at the integral action coefficient of line computation fuzzy immunization pi controller: design fuzzy controller, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagethe deviation e (k) of (k) as the input value of fuzzy controller, the outlet air temperature set value T in kth moment
ifwith delivery temperature mean value T
on averagedeviation obtain deviation variation rate through differential and be designated as ec (k), also as the input value of fuzzy controller;
Determine outlet air temperature set value T
ifwith delivery temperature mean value T
on averagethe fuzzy domain of deviation {-3 ,-2 ,-1,0,1,2,3}, { NB, NM, NS, Z, PS, PM, PB} also can be expressed as { negative large, in negative, negative little, zero, just little, center, honest }, outlet air temperature set value T fuzzy language value
ifwith delivery temperature mean value T
on averagethe fuzzy domain {-3 of deviation variation rate,-2,-1, 0, 1, 2, 3}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, also can be expressed as { negative large, in negative, negative little, zero, just little, center, honest }, the membership function of deviation and deviation variation rate is triangle, respectively as shown in Figures 2 and 3, determine the fuzzy domain {-0.06 of integral action coefficient increment,-0.04,-0.02, 0, 0.02, 0.04, 0.06}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, also can be expressed as { negative large, in negative, negative little, zero, just little, center, honest }, membership function adopts triangle, as shown in Figure 4,
According to engineering experience and gas turbine temperature control requirement, the rule of design fuzzy control integral action coefficient increment, principle of design is as follows: when 1. e (k) is larger, integral action can make too by force system overshoot strengthen, to be limited integral action, be got less △ ki (k); 2., during e (k) median size, △ ki (k) value is moderate; 3., when e (k) is less, for reducing steady-state error, △ ki (k) value should be more greatly.Design fuzzy control integral action coefficient increment:
Kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of (), deviation variation rate ec (k) are multiplied by quantizing factor respectively and are quantified as fuzzy quantity on fuzzy domain, quantizing factor computing method: the 1. quantizing factor Ke=i/e (max) of deviation, wherein i is the quantification gradation of deviation, the domain that e (max) is deviation.2. the quantizing factor Kec=m/ec (max) of deviation variation rate, wherein m is the quantification gradation of deviation variation rate, the domain that ec (max) is deviation variation rate, in implementation process, with reference to above-mentioned computing method, adopt the method determination outlet air temperature set value T gathered through test according to engineering experience
ifwith delivery temperature mean value T
on averagek the quantizing factor of the deviation of (), its scope is 0.01 ~ 0.1, outlet air temperature set value T
ifwith delivery temperature mean value T
on averagethe quantizing factor of the deviation variation rate of (k), its scope is 0.01 ~ 0.05, e (k), after ec (k) obfuscation, search e (k), the position of ec (k) in fuzzy control rule table, obtain the fuzzy control quantity of integral action coefficient increment △ ki (k) in kth moment, be multiplied by quantizing factor, wherein the quantizing factor of integral action coefficient increment adopts the method for gathering through test to obtain, its scope is 0.01 ~ 0.03, the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out is added after precision, obtain the integral action coefficient of kth moment fuzzy immunization pi controller
ki(k)＝ki0+△ki(k)×α
Wherein, the integral action coefficient of the fuzzy immunization pi controller that ki (k) is the kth moment, ki0 is the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out, and α is the quantizing factor of integral action coefficient increment;
4) at the proportional action coefficient of line computation fuzzy immunization pi controller: design immune fuzzy controller, if the antigen levels in the i-th generation is ε (i), the output of antigen stimulated cells is T
h(i), T
h(i)=k
1ε (i), k
1for exitation factor, the output of antigen T suppression cell is T
s(i), T
s(i)=k
2f () ε (i), k
2for inhibiting factor, f () is nonlinear function, and total stimulation of cell antigen is S (i)=T
h(i)-T
s(i)=(k
1-k
2f ()) ε (i)=K (1-η f ()) ε (i), with kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of () is as antigen levels ε (i), then immune fuzzy controller exports as K (1-η f ()) e (k), the proportional action coefficient of fuzzy immunization pi controller is K (1-η f ()), wherein, K=k
1control reaction velocity,
control stablizing effect, f () is selected nonlinear function;
Adopt method Nonlinear Function Approximation f () of fuzzy reasoning, the fuzzy immunization pi controller in kth moment exports and is designated as G (k), fuzzy immunization pi controller exports and obtains exporting change rate through differential, the value in kth moment is designated as △ G (k), G (k), △ G (k) is all as the input value of immune fuzzy controller, determine the fuzzy domain {-1 that fuzzy immunization pi controller exports, 1}, fuzzy language value { N, P}, also can be expressed as { negative, just }, the fuzzy domain {-1 of fuzzy immunization pi controller exporting change rate, 1}, fuzzy language value is { N, P}, also can be expressed as { negative, just }, the membership function of output and exporting change rate all adopts triangle, respectively as shown in Figure 5 and Figure 6, determine the fuzzy domain {-1 of nonlinear function f () value, 1}, fuzzy language value { N, Z, P}, also can be expressed as { negative, zero, just }, membership function adopts triangle, as shown in Figure 7, design nonlinear function f ():
The fuzzy immunization pi controller in kth moment exports G (k), exporting change rate △ G (k) is multiplied by quantizing factor obfuscation respectively, the quantizing factor that fuzzy immunization pi controller exports adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, the quantizing factor of fuzzy immunization pi controller exporting change rate adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, rule list is looked into after obfuscation, obtain kth moment nonlinear function f (, k) fuzzy control quantity, be multiplied by the quantizing factor of f (), the quantizing factor of nonlinear function f () adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, obtain after precision kth moment nonlinear function f (, k) exact value, thus obtain the proportional action coefficient of the fuzzy immunization pi controller in kth moment
kp(k)＝K(1-ηf(·,k))
Wherein, the proportional action coefficient of the fuzzy immunization pi controller that kp (k) is the kth moment, K, η adopt the method for gathering through test to obtain, and the scope of K is the scope of 0.0005 ~ 0.0007, η is 0.1 ~ 0.3;
5) the fuzzy immunization pi controller calculating the kth moment exports G (k)
Wherein, T represents the sampling time, and value is 0.001 second.
6) the fuzzy immunization pi controller in kth moment is exported G (k) and send into fuel stroke selection of reference frame module, participate in the control of fuel flow rate, obtain the fuel quantity w in kth moment
fk (), makes valve opening reach corresponding fuel quantity by fuel valve control module, fuel is sent into firing chamber.
Embodiment:
With reference to certain single-rotor gas turbine model, actual exhaust air temperature is
Wherein, T
realfor delivery temperature actual value, unit DEG C, T
volumefor the specified setting value of delivery temperature, unit DEG C, w
ft fuel quantity that () is t, ε
tdfor turbine and exhaust system propagation delay time constant, n is rotating speed, L
iGVfor the stroke of compressor inlet adjustable guide vane topworks, T
afor environment temperature, unit DEG C.
Suppose that basic load stage gas turbine temperature controls to come into operation, the specified setting value T of delivery temperature
volumevalue 538.9 DEG C, at full speed, rotating speed n=1, compressor inlet adjustable guide vane reaches range L to unit
iGV=1, time delay ε
td=0.04 second, T
abe set to 15 DEG C, the fuel quantity w of t
f(t) be t fuel stroke benchmark G (t) after 0 ~ 100 amplitude limit, be multiplied by gain 0.77, add the value after compensation 0.23, outlet air temperature set value T
ifprovide step signal 548.9 DEG C to 538.9 DEG C at the initial time of emulation, add undesired signal at the intermediate time of emulation.
Use the parameter tuning function of MATLAB/SIMULINK proportional plus integral plus derivative controller, the initial parameter values kp0=0.000387 of fuzzy self-adaption pi controller of adjusting out, ki0=0.01749, arrange 0.001 second sampling time.
In design immune fuzzy pi controller process correlation parameter choose as follows, outlet air temperature set value T
ifwith delivery temperature actual value T
realthe quantizing factor value 0.01 of deviation, outlet air temperature set value T
ifwith delivery temperature actual value T
realthe quantizing factor value 0.01 of deviation variation rate, the quantizing factor value 0.03 of integral action coefficient increment, the quantizing factor value 1 that fuzzy immunization pi controller exports, the quantizing factor value 1 of fuzzy immunization pi controller exporting change rate, the quantizing factor value 1 of nonlinear function f (), K value 0.0006, η value 0.2 in proportional action coefficient formula.
Design fuzzy immunization pi controller, comprise design immune fuzzy to control, obtain line computation proportional action coefficient as shown in Figure 8, also comprise design fuzzy control, line computation integral action coefficient as shown in Figure 9, the output response curve obtained is as shown in Figure 10, fuzzy immunization proportional plus integral control has less overshoot compared with controlling with conventional proportional integral, higher control accuracy, stronger antijamming capability, better robustness.
Claims (2)
1., based on a heavy duty gas turbine temperature-controlled process for fuzzy immunization proportional plus integral control, it is characterized in that the method comprises the following steps:
1) install N number of exhaust gas temperature sensor at equal intervals at gas turbine turbine exhaust mouth, wherein, the kth moment delivery temperature of the 1st ~ N number of exhaust gas temperature sensor measurement is designated as T respectively
survey 1(k), T
survey 2(k) ... T
survey Nk (), N gets the positive integer being more than or equal to 16, and the delivery temperature mean value in kth moment is designated as T
on averagek (), the outlet air temperature set value in kth moment is designated as T
if(k), the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () is designated as e (k), as the input value of fuzzy immunization pi controller;
2) at the integral action coefficient of line computation fuzzy immunization pi controller: design fuzzy controller, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagethe deviation e (k) of (k) as the input value of fuzzy controller, the outlet air temperature set value T in kth moment
if(k) and delivery temperature mean value T
on averagek the deviation of () obtains deviation variation rate through differential and is designated as ec (k), also as the input value of fuzzy controller;
Determine kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagethe fuzzy domain of the deviation of (k)-3 ,-2 ,-1,0,1,2,3}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagethe fuzzy domain {-3 ,-2 ,-1,0,1 of the deviation variation rate of (k), 2,3}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, the membership function of deviation and deviation variation rate is triangle, determine the fuzzy domain {-0.06 ,-0.04 ,-0.02,0,0.02 of integral action coefficient increment, 0.04,0.06}, fuzzy language value { NB, NM, NS, Z, PS, PM, PB}, membership function adopts triangle;
According to engineering experience and gas turbine temperature control requirement, design fuzzy control integral action coefficient increment:
If e (k) is NB, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is NB,
If e (k) is NB, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is NB,
If e (k) is NB, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is NB, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is NB, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is NB, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is NB, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is NM, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is NB,
If e (k) is NM, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is NB,
If e (k) is NM, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is NM, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is NM, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is NM, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is NM, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is NS, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is NB,
If e (k) is NS, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is NS, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is NS, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is NS, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is NS, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is NS, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is Z, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is Z, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is Z, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is Z, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is Z, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is Z, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is Z, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is PS, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is NM,
If e (k) is PS, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is NS,
If e (k) is PS, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is PS, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is PS, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is PS, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is PS, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is PB,
If e (k) is PM, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is PM, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is PM, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is PM, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is PM, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is PM, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is PB,
If e (k) is PM, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is PB,
If e (k) is PB, and ec (k) is NB, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is PB, and ec (k) is NM, then integral action coefficient increment Delta ki (k) in kth moment is Z,
If e (k) is PB, and ec (k) is NS, then integral action coefficient increment Delta ki (k) in kth moment is PS,
If e (k) is PB, and ec (k) is Z, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is PB, and ec (k) is PS, then integral action coefficient increment Delta ki (k) in kth moment is PM,
If e (k) is PB, and ec (k) is PM, then integral action coefficient increment Delta ki (k) in kth moment is PB,
If e (k) is PB, and ec (k) is PB, then integral action coefficient increment Delta ki (k) in kth moment is PB;
Kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of (), deviation variation rate ec (k) are multiplied by quantizing factor respectively and are quantified as fuzzy quantity on fuzzy domain, according to fuzzy control rule, obtain the fuzzy control quantity of integral action coefficient increment Delta ki (k) in kth moment, be multiplied by quantizing factor by its precision, add the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out, obtain the integral action coefficient of kth moment fuzzy immunization pi controller
ki(k)＝ki0+Δki(k)×α
Wherein, the integral action coefficient of the fuzzy immunization pi controller that ki (k) is the kth moment, ki0 is the conventional proportional integral controller integral action coefficient initial value that conventional proportional integral attitude conirol method is adjusted out, and α is the quantizing factor of integral action coefficient increment;
3) at the proportional action coefficient of line computation fuzzy immunization pi controller: design immune fuzzy controller, if the antigen levels in the i-th generation is ε (i), the output of antigen stimulated cells is T
h(i), T
h(i)=k
1ε (i), k
1for exitation factor, the output of antigen T suppression cell is T
s(i), T
s(i)=k
2f () ε (i), k
2for inhibiting factor, f () is nonlinear function, and total stimulation of cell antigen is S (i)=T
h(i)-T
s(i)=(k
1-k
2f ()) ε (i)=K (1-η f ()) ε (i), with kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the deviation e (k) of () is as antigen levels ε (i), then immune fuzzy controller exports as K (1-η f ()) e (k), the proportional action coefficient of fuzzy immunization pi controller is K (1-η f ()), wherein, K=k
1control reaction velocity,
control stablizing effect, f () is selected nonlinear function;
Adopt method Nonlinear Function Approximation f () of fuzzy reasoning, the fuzzy immunization pi controller in kth moment exports and is designated as G (k), fuzzy immunization pi controller exports and obtains exporting change rate through differential, the value in kth moment is designated as Δ G (k), G (k), Δ G (k) is all as the input value of immune fuzzy controller, determine the fuzzy domain {-1 that fuzzy immunization pi controller exports, 1}, fuzzy language value { N, P}, the fuzzy domain {-1 of fuzzy immunization pi controller exporting change rate, 1}, fuzzy language value is { N, P}, the membership function of output and exporting change rate all adopts triangle, determine the fuzzy domain {-1 of nonlinear function f () value, 1}, fuzzy language value { N, Z, P}, membership function adopts triangle, design nonlinear function f ():
If G (k) is N, and Δ G (k) is N, then the kth moment nonlinear function f (, k) be P;
If G (k) is N, and Δ G (k) is P, then the kth moment nonlinear function f (, k) be Z;
If G (k) is P, and Δ G (k) is N, then the kth moment nonlinear function f (, k) be Z;
If G (k) is P, and Δ G (k) is P, then the kth moment nonlinear function f (, k) be N;
The fuzzy immunization pi controller in kth moment exports G (k), exporting change rate Δ G (k) is multiplied by quantizing factor obfuscation respectively, according to above-mentioned fuzzy rule, obtain kth moment nonlinear function f (, k) fuzzy control quantity, is multiplied by quantizing factor by its precision, obtain kth moment nonlinear function f (, k) exact value, thus the proportional action coefficient obtaining kth moment fuzzy immunization pi controller
kp(k)＝K(1-ηf(·,k))
Wherein, the proportional action coefficient of the fuzzy immunization pi controller that kp (k) is the kth moment, K, η are parameter;
4) the fuzzy immunization pi controller calculating the kth moment exports,
Wherein, T represents the sampling time;
5) output of the fuzzy immunization pi controller in kth moment is temperature control fuel stroke benchmark G (k) and sends into fuel stroke selection of reference frame module, participate in the control of fuel flow rate, obtain the fuel quantity w in kth moment
fk (), sends into firing chamber by fuel control valve door.
2., as claimed in claim 1 based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control, it is characterized in that:
For step 2), kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the quantizing factor of the deviation of () adopts the method for gathering through test to determine, its scope is 0.01 ~ 0.1, kth moment outlet air temperature set value T
if(k) and delivery temperature mean value T
on averagek the quantizing factor of the deviation variation rate of () adopts the method for gathering through test to determine, its scope is 0.01 ~ 0.05, the quantizing factor of kth moment fuzzy immunization pi controller integral action coefficient increment adopts the method for gathering through test to obtain, and its scope is 0.01 ~ 0.03;
For step 3), the quantizing factor that kth moment fuzzy immunization pi controller exports adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, the quantizing factor of kth moment fuzzy immunization pi controller exporting change rate adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, kth moment nonlinear function f (, k) quantizing factor adopts the method for gathering through test to obtain, its scope is 0.01 ~ 1, parameter K in the proportional action coefficient K (1-η f ()) of fuzzy immunization pi controller, η adopts the method for gathering through test to obtain, the scope of K is 0.0005 ~ 0.0007, the scope of η is 0.1 ~ 0.3,
For step 4), sampling time T value is 0.001 second.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310518040.0A CN103543763B (en) | 2013-10-28 | 2013-10-28 | Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310518040.0A CN103543763B (en) | 2013-10-28 | 2013-10-28 | Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103543763A CN103543763A (en) | 2014-01-29 |
| CN103543763B true CN103543763B (en) | 2016-01-20 |
Family
ID=49967319
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310518040.0A Active CN103543763B (en) | 2013-10-28 | 2013-10-28 | Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103543763B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107219873A (en) * | 2016-03-21 | 2017-09-29 | 北京澳尔金石油技术开发有限公司 | A kind of gas turbine turbine exhaust temperature control equipment and method |
| CN108304678A (en) * | 2018-03-06 | 2018-07-20 | 中国船舶重工集团公司第七0三研究所 | The method for being directed to different fuel calculation gas turbine performance based on emulation platform |
| CN108644020A (en) * | 2018-04-27 | 2018-10-12 | 中国大唐集团科学技术研究院有限公司华东分公司 | A kind of improved combustion turbine exhaustion temprature control method |
| CN109270972A (en) * | 2018-10-11 | 2019-01-25 | 中国船舶重工集团公司第七〇九研究所 | A kind of thermostatically-controlled equipment |
| CN116466567A (en) * | 2023-04-23 | 2023-07-21 | 上海交通大学 | Self-adaptive control method and system for ship gas turbine under complex working condition |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009060889A1 (en) * | 2007-11-06 | 2009-05-14 | Mitsubishi Heavy Industries, Ltd. | Operation controller and operation control method of gas turbine |
| WO2010041552A1 (en) * | 2008-10-08 | 2010-04-15 | 三菱重工業株式会社 | Gas turbine and operating method therefor |
| CN101763035A (en) * | 2009-11-13 | 2010-06-30 | 上海电力学院 | Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization |
| CN102853069A (en) * | 2012-09-05 | 2013-01-02 | 江苏大学 | Fuzzy immune PID (proportion integration differentiation) control system and method based on hydro-viscous soft start device |
| CN103019103A (en) * | 2012-12-04 | 2013-04-03 | 清华大学 | Simulation optimization method of fuzzy control variable heating temperatures in directional solidification process |
| CN103195730A (en) * | 2013-04-07 | 2013-07-10 | 北京华清燃气轮机与煤气化联合循环工程技术有限公司 | Compressor inlet rotatable guide vane temperature control method of gas turbine |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120036863A1 (en) * | 2010-08-13 | 2012-02-16 | Joseph Kirzhner | Method, apparatus and system for delivery of wide range of turbine fuels for combustion |
-
2013
- 2013-10-28 CN CN201310518040.0A patent/CN103543763B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009060889A1 (en) * | 2007-11-06 | 2009-05-14 | Mitsubishi Heavy Industries, Ltd. | Operation controller and operation control method of gas turbine |
| WO2010041552A1 (en) * | 2008-10-08 | 2010-04-15 | 三菱重工業株式会社 | Gas turbine and operating method therefor |
| CN101763035A (en) * | 2009-11-13 | 2010-06-30 | 上海电力学院 | Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization |
| CN102853069A (en) * | 2012-09-05 | 2013-01-02 | 江苏大学 | Fuzzy immune PID (proportion integration differentiation) control system and method based on hydro-viscous soft start device |
| CN103019103A (en) * | 2012-12-04 | 2013-04-03 | 清华大学 | Simulation optimization method of fuzzy control variable heating temperatures in directional solidification process |
| CN103195730A (en) * | 2013-04-07 | 2013-07-10 | 北京华清燃气轮机与煤气化联合循环工程技术有限公司 | Compressor inlet rotatable guide vane temperature control method of gas turbine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103543763A (en) | 2014-01-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103543763B (en) | Based on the heavy duty gas turbine temperature-controlled process of fuzzy immunization proportional plus integral control | |
| CN107800146B (en) | Speed regulator parameter optimization method considering primary frequency modulation and ultralow frequency oscillation suppression | |
| US7191588B2 (en) | Combustor controller | |
| CN109524693A (en) | Fuel battery air feed system model predictive control method | |
| CN105874196A (en) | Power-ramping pitch feed-forward | |
| CN103401256B (en) | The optimization method of large-scale steam turbine power-frequency electro-hydraulic control system of thermal power plant parameter | |
| CN112240235B (en) | SCR control method and device | |
| CN103838920A (en) | Turboshaft engine dynamic real-time model establishing method and system | |
| US10100679B2 (en) | Control system for managing steam turbine rotor stress and method of use | |
| CN105700380A (en) | Secondary reheating unit steam turbine speed regulation system simulation model, and modeling method therefor | |
| CN103195730B (en) | Compressor inlet adjustable guide vane is used to control the method for combustion turbine exhaustion temperature | |
| CN108757192B (en) | Diesel engine electronic control speed regulation and test method based on fuzzy variable structure | |
| CN103362661A (en) | Dynamic control method and device of EGR valve opening, and EGR engine | |
| CN105404750A (en) | Turboshaft engine self-adaptive model building method based on modified Broyden algorithm to solve equation set | |
| Wang et al. | Optimization of air–fuel ratio control of fuel-powered UAV engine using adaptive fuzzy-PID | |
| CN114123238B (en) | Kalman filtering control method for electrolytic aluminum load to participate in frequency modulation of power system | |
| Haji et al. | Adaptive model predictive control design for the speed and temperature control of a V94. 2 gas turbine unit in a combined cycle power plant | |
| CN108131238A (en) | A kind of New PID Control method for inhibiting water hammer pressure fluctuation | |
| Shi et al. | Shaft speed control of the gas turbine based on active disturbance rejection control | |
| CN103593578A (en) | Flue suction force feedback setting method in coke oven heating combustion process | |
| CN110361974A (en) | Turbine Governor System optimization method based on BP-FOA hybrid algorithm | |
| CN116131288A (en) | Comprehensive energy system frequency control method and system considering wind and light fluctuation | |
| Hsu et al. | Adaptive Dynamic RBF Fuzzy Neural Controller Design with a Constructive Learning. | |
| CN115576189A (en) | PID control method of air inlet environment simulation system based on self-adaptive homogeneous differentiator | |
| Bresch-Pietri et al. | Prediction-based control of moisture in a convective flow |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Heavy duty gas turbine temperature-controlled process based on fuzzy immunization proportional plus integral control Effective date of registration: 20161104 Granted publication date: 20160120 Pledgee: Tsinghua Holdings Co., Ltd. Pledgor: Beijing Huatsing Gas Turbine & IGCC Technology Co., Ltd. Registration number: 2016990000853 |
|
| PLDC | Enforcement, change and cancellation of contracts on pledge of patent right or utility model | ||
| PP01 | Preservation of patent right |
Effective date of registration: 20190820 Granted publication date: 20160120 |
|
| PP01 | Preservation of patent right | ||
| PD01 | Discharge of preservation of patent |
Date of cancellation: 20191230 Granted publication date: 20160120 |
|
| PD01 | Discharge of preservation of patent | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20191211 Granted publication date: 20160120 Pledgee: Tsinghua Holdings Co.,Ltd. Pledgor: Beijing Huatsing Gas Turbine & IGCC Technology Co.,Ltd. Registration number: 2016990000853 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right |