CN112947049B - Thermal power generating unit control method, system and medium for hysteresis characteristic object - Google Patents
Thermal power generating unit control method, system and medium for hysteresis characteristic object Download PDFInfo
- Publication number
- CN112947049B CN112947049B CN202110120967.3A CN202110120967A CN112947049B CN 112947049 B CN112947049 B CN 112947049B CN 202110120967 A CN202110120967 A CN 202110120967A CN 112947049 B CN112947049 B CN 112947049B
- Authority
- CN
- China
- Prior art keywords
- deviation
- vector
- thermal power
- response instruction
- generating unit
- 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
- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000013598 vector Substances 0.000 claims abstract description 86
- 230000004044 response Effects 0.000 claims abstract description 72
- 238000004364 calculation method Methods 0.000 claims abstract description 22
- 230000009471 action Effects 0.000 claims abstract description 19
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 6
- 238000006880 cross-coupling reaction Methods 0.000 claims description 5
- 230000000670 limiting effect Effects 0.000 claims description 5
- 238000010168 coupling process Methods 0.000 abstract description 6
- 238000005859 coupling reaction Methods 0.000 abstract description 6
- 230000008878 coupling Effects 0.000 abstract description 5
- 230000006870 function Effects 0.000 description 10
- 238000004422 calculation algorithm Methods 0.000 description 8
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 239000003546 flue gas Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The application discloses a thermal power generating unit control method, a thermal power generating unit control system and a thermal power generating unit control medium aiming at a hysteresis characteristic object, wherein the thermal power generating unit control method comprises the steps of obtaining a measured value of a controlled object; calculating the deviation e of the measured value of the controlled object and the set value thereof and the first derivative of the deviation e with respect to time; constructing a feedback vector taking the deviation e and the first derivative of the deviation e with respect to time as elements; performing dot product calculation on the transpose of the response instruction vector with the feedback vector being the same as the feedback vector in dimension as an action instruction of a corresponding executor; and dynamically adjusting the values of the elements of the response instruction vector according to a preset self-learning rule of the response instruction vector. The response instruction obtained by the application has a self-learning function, and can be dynamically adjusted according to the feedback state of the controlled parameter, so that the control system has better response speed and response precision, and is suitable for a univariate control system with a hysteresis characteristic object and a multivariate coupling control system with the hysteresis characteristic object.
Description
Technical Field
The application relates to an automatic control technology of an industrial process, in particular to a thermal power generating unit control method, a thermal power generating unit control system and a thermal power generating unit control medium aiming at a hysteresis characteristic object.
Background
The control science and technology starts from a flyball speed regulator of a vapor machine of the application, and is in the development stage of intelligent control at present through classical control represented by Nyquist stability criteria and Evans root locus method and modern control represented by state space method, optimal control and optimal filtering. With respect to intelligent control, there is no recognized, unified definition to date. In general, the methods of fuzzy control, neural network control, expert control, hierarchical control, learning control, humanoid intelligent control and the like are all calculated as the category of intelligent control by people.
In the field of thermal power generation control, in order to reduce the power generation cost, pursue higher economic benefits, meet increasingly strict emission standards, and the thermal power generating unit gradually develops towards high capacity and high parameters. The ultra (super) critical unit occupies most of the thermal power unit. The coordination control system of the supercritical parameter unit has the characteristics of multivariable input and output, nonlinearity and strong coupling. Because of the platform limiting effect of the control system, the current thermal power control still adopts a classical control era algorithm, most of the related closed loop control problems still depend on a PID controller, and the ideal effect is difficult to obtain by coordinated control. With the increase of the operation years of the thermal power generating unit, after the main equipment and the auxiliary equipment are overhauled, the linear regulation characteristic is reduced year by year, and the regulation quality of the automatic control system is further reduced. Many power plants, especially older units, are still manual units with a low level of automation.
In recent years, scholars and engineering technicians develop researches from different aspects to try to improve the automation and intelligence level of the thermal power generating unit, and different intelligent control algorithms are continuously introduced into the field of thermal power generation control. However, various intelligent control technologies still have the following problems in engineering applications: firstly, when a neural network is adopted to establish a multivariable, strong-coupling and nonlinear time-varying object model, the accuracy, the effectiveness and the universality of sample data are critical to the establishment and the training of the model, more advanced algorithms are required to be researched, and the problem of limited sample quantity is solved; secondly, the intelligent optimization algorithm has large calculated amount and relatively slow searching speed, and is difficult to ensure that a global optimal solution is found in a limited time, so that the real-time performance of control is adversely affected; thirdly, the intelligent optimization algorithm is often used independently and is not organically integrated with the traditional PID control, and the accurate response characteristic is still to be improved; fourth, the modeling and optimizing process of the neural network and the intelligent optimization algorithm needs to be performed with complex calculation, is difficult to complete by using the existing configuration tool of the DCS, needs to develop an independent system, and limits popularization and application of the technology. At present, related intelligent algorithms are not widely applied to thermal power generating unit control, and particularly control objects with cross coupling influence and hysteresis characteristics such as main steam pressure, main steam temperature and the like, and control of the content of nitrogen oxides in flue gas with pure hysteresis characteristics are not provided with effective intelligent control means. The reason for this is mainly that the controlled object is complex, the nonlinear and hysteresis characteristics are obvious, the working conditions are quite different, the control parameters and the control loop lack self-learning function, cannot be dynamically adjusted, and are difficult to widely adapt to all working conditions. Therefore, it is necessary to study the self-learning function of the control system and the control parameters based on the characteristics of the controlled object, so that the control is more intelligent.
Disclosure of Invention
The application aims to solve the technical problems: aiming at the problems in the prior art, the application provides a thermal power generating unit control method, a thermal power generating unit control system and a thermal power generating unit control medium aiming at a hysteresis characteristic object.
In order to solve the technical problems, the application adopts the following technical scheme:
a thermal power generating unit control method for a hysteresis characteristic object, comprising:
1) Acquiring a measured value of a controlled object with hysteresis characteristics;
2) Calculating the deviation e of the measured value of the controlled object and the set value thereof and the first derivative of the deviation e with respect to time;
3) Constructing a feedback vector taking the deviation e and the first derivative of the deviation e with respect to time as elements;
4) Firstly, performing dot product calculation on the feedback vector and the transpose of the response instruction vector with the same dimension as the feedback vector, and taking the dot product calculation result as an action instruction of a corresponding executor; and then dynamically adjusting the values of the elements of the response instruction vector according to a preset self-learning rule of the response instruction vector.
Optionally, the controlled object is a single independent controlled object, and the feedback vector constructed in step 3) isWherein e is the deviation of the measured value of the controlled object and its set value, < >>Is the first derivative of the deviation e with respect to time.
Optionally, the response instruction vector of step 3) of the same dimension as the feedback vector is of form [ w ] 1 ,w 2 ]Wherein w is 1 ,w 2 Respectively, the elements in the response instruction vector, and element w 1 ,w 2 The initial value of (2) satisfies the following conditions: by e.w 1 ，When the corresponding actuator is driven as an action instruction, the deviation e is driven to approach to 0, wherein e is the deviation of the measured value of the controlled object and the set value thereof, and ∈>Is the first derivative of the deviation e with respect to time.
Optionally, the functional expression of the dot product calculation result in step 4) is:wherein e is the deviation of the measured value of the controlled object and its set value, < >>For the first derivative of the deviation e with respect to time, w 1 ,w 2 Each of which is an element in the response instruction vector.
Optionally, the controlled object is one of n controlled objects with cross coupling influence, and the feedback vector constructed in the step 3) isWherein e 1 ～e n The measured value of the 1 st to n th controlled object and the deviation of the set value of the measured value are respectively +.>Measured values of the 1 st to n th controlled objects and deviation e of set values thereof 1 ～e n The first derivative with respect to time.
Optionally, when dot product calculation is performed in step 4), the response instruction vector of any ith controlled object, which is the same as the feedback vector dimension, is [ w i1 ,w i2 ,……,w i2n ]Wherein w is i1 ,w i2 ,……,w i2n N response instructions in the response instruction vector of the ith controlled object respectively, and n response instructions w i1 ,w i2 ,……,w i2n The initial value of (2) satisfies the following conditions: by e 1 ·w i1 ，When the ith actuator is driven as an action command, the deviation e will be caused i Acts in a direction approaching 0.
Optionally, when dot product calculation is performed in step 4), the functional expression of the dot product calculation result of any i-th controlled object is:wherein e 1 ～e n Deviation of the measured value and the set value of the controlled object, respectively, < >>Respectively the deviation e 1 ～e n First derivative of time, w i1 ,w i2 ,……,w i2n And n response instructions in the response instruction vector of the ith controlled object respectively.
Optionally, in dynamically adjusting the values of the elements of the response instruction vector in step 4), any i-th element w of the response instruction vector is targeted i The processing steps of (a) comprise: judgment element w i Whether the preset adjustment condition is satisfied, if not, maintaining the element w i Is unchanged; otherwise according to w i+1 ＝w i +Δw update element w i And to update the new value w i+1 And performing amplitude limiting processing, wherein Deltaw is a preset adjustment step length of the element.
In addition, the application also provides a thermal power generating unit control system aiming at the hysteresis characteristic object, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the thermal power generating unit control method aiming at the hysteresis characteristic object.
Furthermore, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program programmed or configured to execute the thermal power generating unit control method aiming at the hysteresis characteristic object.
Compared with the prior art, the application has the following advantages:
1. the application takes the deviation of the set value of the controlled variable and the measured value and the first derivative of the deviation with respect to time as the feedback vector, and can monitor the absolute value and the change trend of the control deviation at the same time, so that the controller adjusting instruction based on the feedback vector has a prediction function, can quickly eliminate the control deviation, and effectively avoids the overshoot or undershoot phenomenon which easily occurs to the hysteresis characteristic object control.
2. The response instruction has a self-learning function, can be dynamically adjusted according to the control deviation and the change trend of the controlled variable, and the adjusting instruction of the actuator is designed to be the product of a corresponding instruction vector and a feedback vector, so that the adjusting instruction of the actuator is essentially responded by a track of a secondary nonlinear curve, and compared with the common linear adjustment, the response speed and the control precision are remarkably improved. When the deviation between the set value of the controlled object and the measured value is large and the expansion trend exists, the controller greatly outputs a command for enabling the controlled object to act in the deviation reducing direction, so that the speed is high; when the deviation between the set value and the measured value of the controlled object is reduced, the controller outputs an instruction for preventing overshoot in advance, and the controller has predictability, and the two characteristics are very suitable for nonlinear control objects with hysteresis characteristics.
3. The self-learning rule of the response instruction vector is designed with protective measures, so that dynamic adjustment is ensured to always act in the direction of reducing the control deviation of the controlled quantity, the divergence of a control system is effectively prevented, and the control system has good robustness and reliability.
4. According to the application, an independent adjustment step length and adjustment conditions are designed aiming at each element in the response instruction vector, so that the pertinence is stronger, the adjustment is more accurate, the self-learning ability is more excellent, and the control method can adapt to various working conditions which change rapidly in the running process, thereby solving the problems of poor adaptability and need of a large number of training samples of the existing control method.
5. The learning rule and the control algorithm designed by the application can be realized on any current control platform, can also be realized based on an independent platform, has good practicability, is easy to implement and popularize engineering, and solves the problems that the current part of intelligent control technology is complex and is difficult to implement in a highly modularized control system.
Drawings
FIG. 1 is a basic flow chart of a method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of the basic principle of the first embodiment of the present application.
Fig. 3 is a schematic diagram of the basic principle of the second embodiment of the present application.
Detailed Description
The application is directed to a thermal power generating unit control method for a hysteresis characteristic object, which is applicable to a single-variable control system with hysteresis characteristic and a multivariable coupling control system with hysteresis characteristic object, and further specifically described below with reference to the accompanying drawings and the detailed description. The thermal power plant control method according to the present application for hysteresis characteristics is applicable to other individual hysteresis characteristics or to two or more hysteresis characteristics coupled to each other, in addition to the two embodiments described below.
Embodiment one:
the embodiment is a thermal power generating unit control method of a single controlled object.
As shown in fig. 1, the thermal power generating unit control method for a hysteresis characteristic object according to the present embodiment includes:
1) Acquiring a measured value of a controlled object with hysteresis characteristics;
2) Calculating the deviation e of the measured value of the controlled object and the set value thereof and the first derivative of the deviation e with respect to time;
3) Constructing a feedback vector taking the deviation e and the first derivative of the deviation e with respect to time as elements;
4) Firstly, performing dot product calculation on the feedback vector and the transpose of the response instruction vector with the same dimension as the feedback vector, and taking the dot product calculation result as an action instruction of a corresponding executor; and then dynamically adjusting the values of the elements of the response instruction vector according to a preset self-learning rule of the response instruction vector.
In this embodiment, the controlled object is a single independent control object. As an alternative implementation manner, as shown in fig. 2, the single independent controlled object in this embodiment refers to the content of nitrogen oxides in the flue gas of the thermal power generating unit.
In this embodiment, the feedback vector constructed in step 3) isWherein e is the deviation of the measured value of the controlled object and its set value, < >>Is the first derivative of the deviation e with respect to time.
In this embodiment, the response instruction vector of step 3) having the same dimension as the feedback vector is of the form [ w ] 1 ,w 2 ]Wherein w is 1 ,w 2 Respectively, the elements in the response instruction vector, and element w 1 ,w 2 The initial value of (2) satisfies the following conditions: by e.w 1 ，When the corresponding actuator is driven as an action instruction, the deviation e is driven to approach to 0, wherein e is the deviation of the measured value of the controlled object and the set value thereof, < >>Is the first derivative of the deviation e with respect to time. In this embodiment, the deviation e=measured value-set value of the nox content, the deviation e acts in the direction approaching 0, i.e. when the set value of the nox content is greater than the measured value, the controller output command (i.e. the actuator driving command) should be reduced; when the setpoint for the nox content is less than the measured value, the controller output command should be increased (i.e., the actuator drive command).
As shown in fig. 2, the functional expression of the dot product calculation result in step 4) of the present embodiment is:wherein e is the deviation of the measured value of the controlled object and its set value, < >>For the first derivative of the deviation e with respect to time, w 1 ,w 2 Each of which is an element in the response instruction vector. The transpose of the response instruction vector obtained in the step 4) is used for dot product calculation, and the settlement result as an action instruction of the corresponding executor specifically refers to: will->As an actuator action command.
The initial steps of responding to the self-learning rule of the instruction vector include: 1) Designing an adjustment step size and an adjustment condition for each element in the response instruction vector; 2) When one element in the response instruction vector meets the adjustment condition, the element changes the corresponding adjustment step value on the original basis to obtain a new response instruction vector; 3) Clipping is designed for each element in the response instruction vector, and the action of an actuator is prevented from acting in a direction of expanding the set value of the controlled variable and the measurement deviation in the self-learning process. In this embodiment, when dynamically adjusting the values of the elements of the response instruction vector in step 4), the i-th element w is arbitrarily selected for the response instruction vector i The processing steps of (a) comprise: judgment element w i Whether the preset adjustment condition is satisfied, if not, maintaining the element w i Is unchanged; otherwise according to w i+1 ＝w i +Δw update element w i And to update the new value w i+1 And performing amplitude limiting processing, wherein Deltaw is a preset adjustment step length of the element. Specifically, in this embodiment, the first element w in the instruction vector is responded to 1 Setting the adjustment step length of (2) to 0.02, and adjusting the conditions to beW responsive to the second element in the instruction vector 2 Setting the adjustment step size of (2) to 0.01, the adjustment condition is +.>When->w 1(i+1) ＝w 1i +△w 1 Wherein Deltaw 1 Equal to 0.02; when->w 2(i+1) ＝w 2i ++ Deltaw, where Deltaw 2 Equal to 0.01. The adjustment process is as follows: in the ith operation, whenw 1(i+1) ＝w 1i +0.02, otherwise w 1(i+1) ＝w 1i When->w 2(i+1) ＝w 2i +0.01, otherwise w 2(i+1) ＝w 2i 。
In summary, the response command obtained by controlling the thermal power generating unit aiming at the hysteresis characteristic object has a self-learning function, and can be dynamically adjusted according to the feedback state of the controlled parameter, so that the control system has better response speed and response precision, and is suitable for a single-variable control system aiming at the hysteresis characteristic object and a multivariable coupling control system aiming at the hysteresis characteristic object.
In addition, the embodiment also provides a thermal power generating unit control system for the hysteresis characteristic object, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the thermal power generating unit control method for the hysteresis characteristic object.
Further, the present embodiment also provides a computer-readable storage medium in which a computer program programmed or configured to execute the foregoing thermal power generating unit control method for a hysteresis characteristic object is stored.
Embodiment two:
the method of this embodiment is basically the same as that of the embodiment, and the main differences include:
1. the controlled object is one of n controlled objects with cross coupling influence;
as an alternative implementation manner, as shown in fig. 3, the n controlled objects with cross coupling influence in this embodiment specifically refer to main steam pressure and main steam temperature of the thermal power generating unit.
2. The feedback vector constructed in step 3) of this embodiment isWherein e 1 ～e n The measured value of the 1 st to n th controlled object and the deviation of the set value of the measured value are respectively +.>Measured values of the 1 st to n th controlled objects and deviation e of set values thereof 1 ～e n The first derivative with respect to time.
3. In the dot product calculation in step 4) of this embodiment, the response instruction vector of any ith controlled object, which is the same as the feedback vector dimension, is [ w ] i1 ,w i2 ,……,w i2n ]Wherein w is i1 ,w i2 ,……,w i2n N response instructions in the response instruction vector of the ith controlled object respectively, and n response instructions w i1 ,w i2 ,……,w i2n The initial value of (2) satisfies the following conditions: by e 1 ·w i1 ，When the ith actuator is driven as an action command, the deviation e will be caused i Acts in a direction approaching 0. Namely: response instruction vector [ w ] corresponding to 1 st controlled variable 11 ,w 12 ,……,w 12n ]The value of (c) should be such that e 1 ·w 11 ，The 1 st actuator is driven to make the 1 st controlled variable set value deviate from the measured deviation e 1 The response instruction vector [ w ] corresponding to the 2 nd controlled variable is moved in the direction approaching 0 21 ,w 22 ,……,w 22n ]The value of (c) should be such that e 1 ·w 21 ，/>The value of (2) drives the 2 nd actuator to deviate the 2 nd controlled variable set point from the measured deviation e 2 The response command vector [ w ] corresponding to the n-th controlled variable is moved in the direction approaching 0 n1 ,w n2 ,……,w n2n ]The value of (c) should be such that e 1 ·w n1 ，/>The n-th actuator is driven to deviate the n-th controlled variable set value from the measured deviation e n Approaching 0. Deviation e of the i-th controlled variable set point from the measured value n The direction motion approaching 0 specifically means: when the main steam pressure set value is larger than the measured value, the corresponding response instruction vector [ w ] is 11 ,w 12 ,……,w 12n ]The value of (a) should be increased to the output value of the main steam pressure controller, and when the main steam pressure set value is smaller than the measured value, the corresponding response instruction vector [ w ] is obtained 11 ,w 12 ,……,w 12n ]The value of the main steam pressure controller is reduced; when the main steam temperature set value is larger than the measured value, the corresponding response instruction vector [ w ] is 21 ,w 22 ,……,w 22n ]The value of the main steam temperature controller is reduced, and when the main steam temperature set value is smaller than the measured value, the corresponding response instruction vector [ w ] is obtained 21 ,w 22 ,……,w 22n ]The value of (2) should be increased by the output value of the main steam temperature controller. The transpose of the response instruction vector obtained in the step 4) is used for dot product calculation, and the settlement result as an action instruction of the corresponding executor specifically refers to: will-> Takes the value of (1) as the action instruction of the 1 st actuator, namely the output of the main steam pressure controller, the valve is +.> The value of (2) is used as an action instruction of the 2 nd actuator, namely the output of the main steam temperature controller.
4. In the step 4) of this embodiment, when dot product calculation is performed, the functional expression of the dot product calculation result of any i-th controlled object is:wherein e 1 ～e n Deviation of the measured value and the set value of the controlled object, respectively, < >>Respectively the deviation e 1 ～e n First derivative of time, w i1 ,w i2 ,……,w i2n And n response instructions in the response instruction vector of the ith controlled object respectively. Namely: when the controlled variable is multi-variable, the control variable will be +.>Is taken as an action command of the 1 st actuator Is taken as the action command of the 2 nd actuator As an action command for the n-th actuator.
5. In step 4) of this embodiment, when dynamically adjusting the values of the elements of the response instruction vector, an arbitrary i-th element w of the response instruction vector is used i The processing steps of (a) comprise: judgment element w i Whether the preset adjustment condition is satisfied, if not, maintaining the element w i Is unchanged; otherwise according to w i+1 ＝w i +Δw update element w i And to update the new value w i+1 And performing amplitude limiting processing, wherein Deltaw is a preset adjustment step length of the element. Specifically, the 1 st controlled variable in this embodiment responds to the first element w in the instruction vector 11 Is set to 0.01,when (when)When w is 11(i+1) ＝w 11i +0.01, second element w 12 Is set to 0.005 when +.>w 12(i+1) ＝w 12i +0.005, third and fourth element w 13 ,w 14 The value of (2) is kept unchanged; the second controlled variable is responsive to the first element w in the instruction vector 21 Is set to 0.03, when +.>When w is 21(i+1) ＝w 21i +0.03, second element w 22 Is set to 0.015 whenw 22(i+1) ＝w 22i +0.015, third and fourth element w 23 ,w 24 The value of (2) remains unchanged. In the ith operation at the time of adjustment, when +.>When w is 11(i+1) ＝w 11i +0.01, otherwise w 11(i+1) ＝w 11i When->w 12(i+1) ＝w 12i +0.005 otherwise w 12(i+1) ＝w 12i Third and fourth elements w 13 ,w 14 The value of (2) is always unchanged; when->When w is 21(i+1) ＝w 21i +0.03, otherwise w 21(i+1) ＝w 21i When->w 22(i+1) ＝w 22i +0.015, otherwise, w 22(i+1) ＝w 22i Third and fourth elements w 23 ,w 24 The value of (2) is always unchanged.
In summary, the response command obtained by controlling the thermal power generating unit with respect to the hysteresis characteristic object in the embodiment has a self-learning function, and can be dynamically adjusted according to the feedback state of the controlled parameter, so that the control system has better response speed and response precision, and is suitable for both a single-variable control system with hysteresis characteristic and a multivariable coupling control system with hysteresis characteristic object.
In addition, the embodiment also provides a thermal power generating unit control system for the hysteresis characteristic object, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the thermal power generating unit control method for the hysteresis characteristic object.
Further, the present embodiment also provides a computer-readable storage medium in which a computer program programmed or configured to execute the foregoing thermal power generating unit control method for a hysteresis characteristic object is stored.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is directed to methods, apparatus (systems), and computer program products in accordance with embodiments of the present application that produce means for implementing the functions specified in the flowchart flow(s) and/or block diagram block or blocks, with reference to the instructions that execute in the flowchart and/or processor(s) of the computer program product. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the protection scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (8)
1. A thermal power generating unit control method for a hysteresis characteristic object, comprising:
1) Acquiring a measured value of a controlled object with hysteresis characteristics;
2) Calculating the deviation of the measured value and the set value of the controlled objecteDeviation ofeFirst derivative with respect to time;
3) Build to biaseDeviation ofeA feedback vector with the first derivative of time as an element; the controlled object isnOne of the controlled objects affected by the cross coupling; the feedback vector constructed is [ [e 1 , , e 2 , />, ……, e n , ]Whereine 1 ～e n The measured value of the 1 st to n th controlled object and the deviation of the set value of the measured value are respectively +.>Measured values of the 1 st to n th controlled objects and deviation of the set values thereof>First derivative with respect to time;
4) Firstly, performing dot product calculation on the feedback vector and the transpose of the response instruction vector with the same dimension as the feedback vector, and taking the dot product calculation result as an action instruction of a corresponding executor; then dynamically adjusting the values of elements of the response instruction vector according to a self-learning rule of the preset response instruction vector; and when dot product calculation is performed, arbitrary firstiThe response instruction vector of the controlled object, which is the same as the feedback vector in dimension, is [ w i1 , w i2 , ……, w i2n ]Wherein w is i1 , w i2 , ……, w i2n Respectively the firstiIn response instruction vectors of controlled objectsnEach responds to an instruction element, annResponse instruction w i1 , w i2 , ……, w i2n The initial value of (2) satisfies the following conditions: by e 1 ·w i1 ，·w i2 ，……，/>·w i2n Drive the first as an action commandiThe first actuator isiThe actuators will cause deviation after actuatione i Acts in a direction approaching 0.
2. A method according to claim 1 for hysteresisThe thermal power generating unit control method of the characteristic object is characterized in that the controlled object is a single independent controlled object, the feedback vector constructed in the step 3) is [ e,]wherein->Deviation of the measured value and the set value of the controlled object, < >>For deviation->The first derivative with respect to time.
3. The thermal power generating unit control method for hysteresis characteristics object according to claim 2, wherein the response instruction vector of the same dimension as the feedback vector in step 3) has the form of [ w ] 1 , w 2 ]Wherein w is 1 , w 2 Respectively, the elements in the response instruction vector, and element w 1 , w 2 The initial value of (a) satisfies the requirement of e.w 1 ，·w 2 When the corresponding actuator is driven as an action command, the deviation is causedeAction in the direction approaching 0, wherein +.>Deviation of the measured value and the set value of the controlled object, < >>For deviation->The first derivative with respect to time.
4. A thermal power generating unit control method for a hysteresis characteristic object according to claim 3, wherein the functional expression of the dot product calculation result in step 4) is: [ e ] the process is carried out,]·[w 1 , w 2 ] T wherein->Deviation of the measured value and the set value of the controlled object, < >>For deviation->First derivative of time, w 1 , w 2 Each of which is an element in the response instruction vector.
5. The thermal power generating unit control method for a hysteresis characteristic object according to claim 1, wherein when dot product calculation is performed in step 4), arbitrary firstiThe functional expression of the dot product calculation result of each controlled object is: [ e ] 1 , , e 2 , , ……, e n , />]·[w i1 , w i2 , ……, w i2n ] T Wherein e is 1 ～e n Deviation of the measured value and the set value of the controlled object, respectively, < >>Deviation->First derivative of time, w i1 , w i2 , ……, w i2n Respectively the firstiIn response instruction vectors of controlled objectsnThe response instruction element.
6. The thermal power generating unit control method according to claim 1, wherein, when dynamically adjusting the values of the elements of the response instruction vector in step 4), any i-th element w of the response instruction vector is used i The processing steps of (a) comprise: judgment element w i Whether the preset adjustment condition is satisfied, if not, maintaining the element w i Is unchanged; otherwise according to w i+1 =w i +Δw update element w i And to update the new value w i+1 And performing amplitude limiting processing, wherein Deltaw is a preset adjustment step length of the element.
7. A thermal power plant control system for a hysteresis characteristic object comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the steps of the thermal power plant control method for a hysteresis characteristic object according to any one of claims 1 to 6.
8. A computer-readable storage medium, wherein the computer-readable storage medium has stored therein a computer program programmed or configured to perform the thermal power plant control method for a hysteresis characteristic object according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110120967.3A CN112947049B (en) | 2021-01-28 | 2021-01-28 | Thermal power generating unit control method, system and medium for hysteresis characteristic object |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110120967.3A CN112947049B (en) | 2021-01-28 | 2021-01-28 | Thermal power generating unit control method, system and medium for hysteresis characteristic object |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112947049A CN112947049A (en) | 2021-06-11 |
| CN112947049B true CN112947049B (en) | 2023-10-27 |
Family
ID=76238955
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110120967.3A Active CN112947049B (en) | 2021-01-28 | 2021-01-28 | Thermal power generating unit control method, system and medium for hysteresis characteristic object |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112947049B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101131571A (en) * | 2007-08-15 | 2008-02-27 | 华北电力大学 | PID parameter setting method for monobloc generator group coordination and control system |
| CN102607053A (en) * | 2012-02-29 | 2012-07-25 | 东南大学 | Intermittent control method for eliminating static deviation of main steam pressure of fossil fuel fired power unit |
| CN105867125A (en) * | 2016-04-07 | 2016-08-17 | 中国中化股份有限公司 | Optimization control method and apparatus of refining apparatus coupling unit |
| CN105962408A (en) * | 2016-07-01 | 2016-09-28 | 云南烟叶复烤有限责任公司 | Multi-strategy composite control method for moisture of tobacco at outlet of tobacco redryer |
| CN107769210A (en) * | 2017-09-22 | 2018-03-06 | 华锐风电科技(集团)股份有限公司 | A kind of double-fed unit frequency modulation control method and system based on CS algorithms |
| CN109212970A (en) * | 2018-09-18 | 2019-01-15 | 西北工业大学 | A kind of neural network dynamic face control method of drive lacking rope system complex system |
| JP2019074375A (en) * | 2017-10-13 | 2019-05-16 | 株式会社リコー | Distance measuring device, moving body, distance measuring method, and program |
| CN110750047A (en) * | 2019-11-24 | 2020-02-04 | 曾喆昭 | Novel self-adaptive mutual coupling PID cooperative control theory method |
| CN110838725A (en) * | 2019-09-19 | 2020-02-25 | 国电新能源技术研究院有限公司 | Parameter setting method and device for wind power plant closed-loop PI controller |
| CN111255530A (en) * | 2020-03-19 | 2020-06-09 | 西安热工研究院有限公司 | Thermal power generating unit load adjusting system and method assisted by butterfly valve with low-pressure cylinder |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160209031A1 (en) * | 2015-01-20 | 2016-07-21 | Alstom Technology Ltd | Model-based controls for a furnace and method for controlling the furnace |
-
2021
- 2021-01-28 CN CN202110120967.3A patent/CN112947049B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101131571A (en) * | 2007-08-15 | 2008-02-27 | 华北电力大学 | PID parameter setting method for monobloc generator group coordination and control system |
| CN102607053A (en) * | 2012-02-29 | 2012-07-25 | 东南大学 | Intermittent control method for eliminating static deviation of main steam pressure of fossil fuel fired power unit |
| CN105867125A (en) * | 2016-04-07 | 2016-08-17 | 中国中化股份有限公司 | Optimization control method and apparatus of refining apparatus coupling unit |
| CN105962408A (en) * | 2016-07-01 | 2016-09-28 | 云南烟叶复烤有限责任公司 | Multi-strategy composite control method for moisture of tobacco at outlet of tobacco redryer |
| CN107769210A (en) * | 2017-09-22 | 2018-03-06 | 华锐风电科技(集团)股份有限公司 | A kind of double-fed unit frequency modulation control method and system based on CS algorithms |
| JP2019074375A (en) * | 2017-10-13 | 2019-05-16 | 株式会社リコー | Distance measuring device, moving body, distance measuring method, and program |
| CN109212970A (en) * | 2018-09-18 | 2019-01-15 | 西北工业大学 | A kind of neural network dynamic face control method of drive lacking rope system complex system |
| CN110838725A (en) * | 2019-09-19 | 2020-02-25 | 国电新能源技术研究院有限公司 | Parameter setting method and device for wind power plant closed-loop PI controller |
| CN110750047A (en) * | 2019-11-24 | 2020-02-04 | 曾喆昭 | Novel self-adaptive mutual coupling PID cooperative control theory method |
| CN111255530A (en) * | 2020-03-19 | 2020-06-09 | 西安热工研究院有限公司 | Thermal power generating unit load adjusting system and method assisted by butterfly valve with low-pressure cylinder |
Non-Patent Citations (2)
| Title |
|---|
| 单元机组协调控制系统模糊自适应控制研究;王爱军;张磊洋;邢宝钦;李娜;;汽轮机技术(第01期);全文 * |
| 基于大滞后控制技术的1000MW超超临界机组过热及再热汽温优化控制;程辉;苗国耀;;自动化技术与应用(第07期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112947049A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Superheated steam temperature control based on modified active disturbance rejection control | |
| Joseph et al. | Cohen-coon PID tuning method; A better option to Ziegler Nichols-PID tuning method | |
| Esfandyari et al. | Adaptive fuzzy tuning of PID controllers | |
| Pal et al. | Self-tuning fuzzy PI controller and its application to HVAC systems | |
| Khan et al. | Experimental evaluation of control performance of mpc as a regulatory controller | |
| Camacho et al. | Introduction to model predictive control | |
| Yaghoot et al. | Design Intelligent Robust Model-base Sliding Guidance Controller for Spherical Motor | |
| KR101576004B1 (en) | Boiler-Turbine coordinated control method and apparatus using Dynamic matrix control in thermal power plant | |
| CN112947049B (en) | Thermal power generating unit control method, system and medium for hysteresis characteristic object | |
| Sambariya et al. | Load frequency control of multi-area hydro thermal power system using elephant herding optimization technique | |
| Wang et al. | Survey of transient performance control | |
| Kubosawa et al. | Non-steady-state Control under Disturbances: Navigating Plant Operation via Simulation-Based Reinforcement Learning | |
| CN115981156A (en) | Aeroengine active limit protection control method under time-varying output constraint | |
| CN111538305B (en) | Thermal power generating unit water supply and fuel control intelligent optimization method, system and medium based on demand diagnosis | |
| Chew et al. | Optimized computational analysis of feedforward and feedback control scheme using genetic algorithm techniques | |
| Kaur et al. | H-infinity controller design for pneumatic servosystem: a comparative study | |
| Vansovits et al. | Identification of industrial water boiler for model predictive control of district heat plant | |
| CN108107713B (en) | Design method of proportional-differential advanced intelligent model set PID controller | |
| Huang et al. | Identification and internal model control of an ideal heat integrated distillation column (HIDiC) | |
| Sivagurunathan et al. | Fuzzy logic based self tuning of PI controller for a non linear spherical tank system | |
| CN111663032B (en) | Active disturbance rejection temperature control method for amorphous iron core annealing furnace | |
| Li et al. | Application of hierarchical control in Supervisory Predictive Control | |
| Zhang et al. | Combining predictive functional control and PID for liquid level of coking furnace | |
| Ma et al. | The application of model free adaptive control | |
| Yu et al. | Model reference PID control and tuning for steam temperature in thermal power plant |
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 |