CN117311159A - Self-adaptive adjusting method and device of control system, storage medium and electronic equipment - Google Patents
Self-adaptive adjusting method and device of control system, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a self-adaptive adjustment method and device of a control system, a storage medium and electronic equipment. Relates to the field of control systems, the method comprises the following steps: acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; and determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable. By the method and the device, the problem of poor stability of the control system in the related technology is solved.
Description
Technical Field
The present disclosure relates to the field of control systems, and in particular, to a method and apparatus for adaptively adjusting a control system, a storage medium, and an electronic device.
Background
The power production of the thermal power plant is a main controlled process with a speed/slow phase, and the energy between the steam turbine and the boiler is required to achieve dynamic balance while the load demand of the power grid is met. The dynamic acceleration amount is influenced by the load change rate, and if the actual load rate deviates too far from the setting rate, the dynamic acceleration amount generates larger deviation, so that the stability and coordination of the control system are influenced. The dynamic acceleration technique is a technique based on feedforward control. The feedforward control can predict the change trend of the load in advance and make corresponding control actions, but the feedforward control belongs to open loop control, and the accuracy of the feedforward control directly influences the quality and stability of the control.
The optimization strategy of the dynamic acceleration amount is different for different units and different load conditions of the thermal power plant, and the setting of the dynamic acceleration amount is related to the speed of the load instruction. If the actual load rate deviates too far from the tuning rate, a large deviation in the amount of dynamic acceleration is caused, thereby affecting the stability and coordination of the control system. In practical applications, the deviation of the given load change rate and the set time load change rate and the deviation of the given load change rate and the actual load change rate all cause the quality degradation of the regulation of the coordination control system. Therefore, there is a need to develop a more efficient and intelligent control algorithm to overcome the above-mentioned drawbacks of the dynamic acceleration technology and improve the operation efficiency and stability of the thermal power plant.
Aiming at the problem of poor stability of a control system in the related art, no effective solution is proposed at present.
Disclosure of Invention
The main objective of the present application is to provide a method and apparatus for adaptive adjustment of a control system, a storage medium, and an electronic device, so as to solve the problem of poor stability of the control system in the related art.
To achieve the above object, according to one aspect of the present application, there is provided an adaptive adjustment method of a control system. The method comprises the following steps: acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system and at least comprises one of the following: a linear model and a nonlinear model; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; and determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable.
Optionally, determining the state variable predictor of the control system based on the historical input variable, the historical output variable, and the target model comprises: collecting multiple groups of sample data of a control system under the same condition, wherein each group of sample data comprises a historical input variable, a historical output variable and a target model; acquiring state variables corresponding to each group of sample data through preset simulation software, and determining a pre-estimated function between the sample data and the state variables through a regression analysis method; and constructing a state variable predictor of the control system based on the prediction function.
Optionally, after correcting the current output variable according to the correction coefficient to obtain the corrected target output variable, the method further includes: acquiring a stability evaluation value of a target output variable, and determining a stability evaluation value threshold; judging whether the stability evaluation value is smaller than or equal to a stability evaluation value threshold value; and sending out prompt information under the condition that the stability evaluation value is smaller than or equal to the stability evaluation value threshold value, wherein the prompt information is used for prompting the correction coefficient of the adjustment control system.
Optionally, after determining whether the stability evaluation value is equal to or less than the stability evaluation value threshold, the method further comprises: under the condition that the stability evaluation value is larger than the stability evaluation value threshold, adding a state variable predictor to the control system to obtain an updated control system; acquiring an updated performance evaluation value of the control system, and determining an evaluation value threshold; judging whether the performance evaluation value is larger than or equal to a performance evaluation value threshold value; determining the updated control system as a target control system when the performance evaluation value is greater than or equal to the performance evaluation value threshold; and updating the state variable predictor when the performance evaluation value is smaller than the performance evaluation value threshold value, and executing the step of adding the state variable predictor to the control system based on the updated state variable predictor.
Optionally, updating the state variable predictor includes: acquiring a plurality of groups of updated sample data, wherein the number of the plurality of groups of updated sample data is larger than the number of sample data before updating, and the sample data before updating is used for determining the sample data of the state variable predictor before updating; acquiring state variables corresponding to each group of updated sample data through preset simulation software, and determining an updated estimated function between the sample data and the state variables through a regression analysis method; and constructing an updated state variable predictor based on the updated prediction function.
Optionally, determining the target model of the control system includes: determining target parameters controlled by a control system, and acquiring system requirements of a user on the control system, wherein the system requirements are used for determining parameter characteristics of the target parameters, and the parameter characteristics comprise linear parameters and nonlinear parameters; under the condition that the system demand characterization determines that the target parameter is a linear parameter, determining that a target model of the control system is a linear model; and under the condition that the system demand characterization determines that the target parameter is a nonlinear parameter, determining that a target model of the control system is a nonlinear model.
Optionally, correcting the current output variable according to the correction coefficient, and obtaining the corrected target output variable includes: and calculating the product of the correction coefficient and the current output variable to obtain a corrected target output variable.
In order to achieve the above object, according to another aspect of the present application, there is provided an adaptive adjustment device of a control system. The device comprises: the system comprises an acquisition unit, a control system and a control system, wherein the acquisition unit is used for acquiring a historical input variable and a historical output variable of the control system and determining a target model of the control system, the target model is a model for describing performance indexes of the control system, and the target model at least comprises one of the following components: a linear model and a nonlinear model; the determining unit is used for determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; the estimating unit is used for estimating the current state variable of the control system through the state variable estimator, the current input variable and the current output variable of the control system; and the correction unit is used for determining the current state variable as a correction coefficient of the control system, correcting the current output variable according to the correction coefficient, and obtaining a corrected target output variable.
Through the application, the following steps are adopted: acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system and at least comprises one of the following: a linear model and a nonlinear model; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; the current state variable is determined as a correction coefficient of the control system, the current output variable is corrected according to the correction coefficient, and a corrected target output variable is obtained, so that the problem of poor stability of the control system in the related technology is solved. The state variable predictor of the control system is determined, the state variable of the control system is predicted based on the state variable predictor, and the state variable is used as a correction coefficient to correct the output signal of the control system, so that the effect of improving the stability of the control system is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a method of adaptive adjustment of a control system provided in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of a dynamic accelerator control system provided in accordance with an embodiment of the present application;
FIG. 3 is a schematic representation of a regression curve provided in accordance with an embodiment of the present application;
FIG. 4 is a schematic diagram of an updated dynamic accelerator control system provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic diagram of a load command rate experiment and simulation performed on a simulation system, according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a control curve of a boiler master after an adaptive tuning method using a control system according to an embodiment of the present application;
FIG. 7 is a schematic diagram of an adaptive modulation device of a control system provided in accordance with an embodiment of the present application;
fig. 8 is a schematic diagram of an electronic device provided according to an embodiment of the present application.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention will be described with reference to preferred implementation steps, and fig. 1 is a flowchart of a method for adaptively adjusting a control system according to an embodiment of the present application, as shown in fig. 1, where the method includes the following steps:
step S101, acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system and at least comprises one of the following steps: linear models and nonlinear models.
Specifically, the control system may be a dynamic accelerator control system for controlling a grid load command rate of a thermal power plant, and fig. 2 is a schematic diagram of the dynamic accelerator control system provided according to an embodiment of the present application, as shown in fig. 2, where the control system includes a first function f 1 (x) Differentiator, second function f 2 (x) And a clipping function. The input variable of the control system is load instruction rate, the output variable is load instruction rate after dynamic acceleration, the state variable is dynamic acceleration weight coefficient, the target model is a mathematical expression which is constructed for quantitatively describing the performance index of the control system and is used for describing the relation between the internal physical quantities of the system, and the nonlinear model is selected according to the structure of the dynamic accelerator and the model type. And analyzing by acquiring the historical input variable and the historical output variable of the control system, thereby determining a state variable predictor of the control system.
F is the same as that of the above 1 (x) The method is used for converting the load instruction after the speed limitation and the boiler main control static relation, and the boiler main control static relation after the boiler main control static relation-inertia link = differential of the boiler main control static relation, namely the calculation formula corresponding to the differentiator is as follows: 1-1/(1+42s) =42s/(1+42s), wherein 1 represents f 1 (x) S is the transfer function of the control system. f (f) 2 (x) The function of (1) is to convert the differentiation of the main control static relationship of the boiler into non-linearity, and table 1 is f provided according to the embodiment 2 (x) Is a value of (a).
TABLE 1
| Differentiator output | f2 (x) output (differential gain) |
| -3.0 | -12.0 |
| -0.8 | -8.0 |
| -0.1 | 0 |
| 0.1 | 0 |
| 0.8 | 8.0 |
| 3.0 | 12.0 |
f 2 (x) The piecewise function with linearization is expressed as follows:
the clipping function limits the output of the dynamic accelerator to within + -6%, at f 2 (x) And under the action of the limiting function, the output of the dynamic accelerator presents a rectangular wave form when the load command changes. Output and load instruction of dynamic accelerator with such structureThe speed is irrelevant, and in order to meet the deep peak regulation and AGC-R (normal mode of the generator active self-control system) mode operation conditions, the output of the dynamic accelerator in the form needs to be corrected for the load command speed.
Step S102, determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system.
Specifically, to correct the load command rate for the output of the dynamic accelerator, a state variable predictor is designed based on the target model of the control system, the historical input variables, and the historical output variables. The state variable predictor is used for predicting the state variable of the control system according to the input and output data of the control system.
Step S103, predicting the current state variable of the control system through the state variable predictor, the current input variable and the current output variable of the control system.
Specifically, after the state variable predictor is determined, the state variable predictor is used for predicting and correcting the state variable of the control system in real time. The state variable predictor, the current input variable and the current output variable of the control system are used for predicting the current state variable of the control system, the trend and the rule of the state variable change can be timely found through monitoring, analyzing and predicting the state variable of the control system, and the parameter correction and the control adjustment are carried out on the control system according to the data so as to ensure the running stability and the running efficiency of the control system.
By means of real-time pre-estimation of the state variable of the control system, the output variable is corrected in real time, and the control parameters are adaptively adjusted to better adapt to actual load change, so that control deviation and instability caused by load rate change are avoided. The method realizes the fine control of the complex system and ensures the self-adaptability and the robustness of the control system. The self-adaptive adjusting method and the dynamic accelerating technology based on state variable estimation are control algorithms developed for improving the response speed and stability of a control system of a thermal power plant. The combination of the two can realize a more intelligent, efficient and refined control system of the thermal power plant, thereby meeting the continuously changing power grid load requirement and the efficient utilization requirement on energy.
Step S104, determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable.
Specifically, through real-time monitoring and analysis of the state variables of the control system, the control parameters can be adaptively adjusted under different working conditions so as to meet the control requirements of the system. And multiplying the current state variable serving as a correction coefficient by the output of the control coefficient, so as to correct the current output variable and obtain a corrected target output variable.
According to the self-adaptive adjustment method for the control system, provided by the embodiment of the application, the historical input variable and the historical output variable of the control system are obtained, and the target model of the control system is determined, wherein the target model is a model for describing the performance index of the control system, and at least one of the following is included: a linear model and a nonlinear model; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; the current state variable is determined as a correction coefficient of the control system, the current output variable is corrected according to the correction coefficient, and a corrected target output variable is obtained, so that the problem of poor stability of the control system in the related technology is solved. The state variable predictor of the control system is determined, the state variable of the control system is predicted based on the state variable predictor, and the state variable is used as a correction coefficient to correct the output signal of the control system, so that the effect of improving the stability of the control system is achieved.
In the method for adaptively adjusting a control system provided in the embodiment of the present application, the determining the state variable predictor of the control system based on the historical input variable, the historical output variable and the target model includes: collecting multiple groups of sample data of a control system under the same condition, wherein each group of sample data comprises a historical input variable, a historical output variable and a target model; acquiring state variables corresponding to each group of sample data through preset simulation software, and determining a pre-estimated function between the sample data and the state variables through a regression analysis method; and constructing a state variable predictor of the control system based on the prediction function.
Specifically, by collecting a historical input variable, a historical output variable and a target model under the same condition of a control system as sample data, performing simulation test on preset simulation software such as Matlab according to the sample data of the control system to obtain state variables corresponding to each group of sample data, determining a pre-estimation function between the sample data and the state variables through a regression analysis method, and constructing a state variable pre-estimation device based on the pre-estimation function. In the embodiment, the state variable predictor is constructed to predict the state variable of the control system, so that the output variable of the control system is corrected based on the state variable.
It should be noted that, performing regression analysis on the sample data and the corresponding state variables includes: first, the load command rate and corresponding state variables are taken as data points. Data points may be represented as { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) X, where x i Representing load command rate, y i And representing a state variable corresponding to the load instruction rate, wherein i is more than or equal to 1 and n is more than or equal to n. For example, by using a nonlinear regression method, a nonlinear pure quadratic equation can be obtained based on the above data points: y=a 0 +a 1 x+a 2 x 2 Wherein a is 0 、a 1 And a 2 And respectively representing each weight coefficient of the regression equation.
For example, when the load command rate is 2% pe/min, the state variable (dynamic acceleration weight coefficient) is set to 1.0, and a plurality of sets of sample data of the same pressure deviation of the control system are collected, wherein the load command rate is 2% pe/min, and the history of each set of sample data is used as a reference in regression analysisThe input variables are respectively: 0.5% Pe/min, 1.0% Pe/min, 1.5% Pe/min, 2.0% Pe/min, 2.5% Pe/min and 3.0% Pe/min. The load instructions carried out by the Matlab simulation system are respectively as follows: and (3) carrying out load disturbance tests of 0.5%/min Pe, 1.0% Pe/min, 1.5% Pe/min, 2.0% Pe/min and 3.0% Pe/min, setting 2.0% Pe/min as a reference load instruction rate, and obtaining state variables corresponding to each group of sample data under the condition of basically equalizing pressure deviation conditions before a machine. Table 2 is the values of the data points sampled during the regression analysis provided according to the present embodiment. FIG. 3 is a schematic diagram of a regression curve provided according to an embodiment of the present application, as shown in FIG. 3, the regression equation is y=0.266667+0.73333X 1 /(X 2 *P e 100.0), wherein y is a state variable, X 1 For the actual load command rate, the unit is MW/min, X 2 For the reference load command rate, the unit is% e /min，P e For rated load, the unit is MW.
TABLE 2
After correcting the output variable of the control system, the stability of the control system needs to be evaluated, optionally, in the adaptive adjustment method of the control system provided in the embodiment of the present application, after correcting the current output variable according to the correction coefficient, the method further includes: acquiring a stability evaluation value of a target output variable, and determining a stability evaluation value threshold; judging whether the stability evaluation value is smaller than or equal to a stability evaluation value threshold value; and sending out prompt information under the condition that the stability evaluation value is smaller than or equal to the stability evaluation value threshold value, wherein the prompt information is used for prompting the correction coefficient of the adjustment control system.
Specifically, after the adaptive adjustment method provided by the embodiment is adopted to correct the output variable of the control system, the performance evaluation and optimization of the control system to which the adaptive technique adjustment method is applied also need to be performed in time. And the control performance and the adaptability of the control system are evaluated through means of experiments, simulation and the like, and the scheme is adjusted and improved according to the evaluation result. For example, the simulation software determines the correction duration of the output variable of the control system when the correction is performed, the ratio of the correction duration to the manually preset standard correction duration is used as a stability evaluation value, the stability evaluation value is compared with a manually set stability evaluation value threshold, if the stability evaluation value is smaller than or equal to the stability evaluation value threshold, the stability of the target output variable is poor, that is, the stability of the control system is poor, and the self-adaptive adjustment method provided by the embodiment has weak adjustment effect on the control system and needs to readjust the correction coefficient of the control system.
In addition to evaluating the stability of the control system, the performance of the control system needs to be evaluated, and optionally, in the adaptive adjustment method of the control system provided in the embodiment of the present application, after determining whether the stability evaluation value is less than or equal to the stability evaluation value threshold, the method further includes: under the condition that the stability evaluation value is larger than the stability evaluation value threshold, adding a state variable predictor to the control system to obtain an updated control system; acquiring an updated performance evaluation value of the control system, and determining an evaluation value threshold; judging whether the performance evaluation value is larger than or equal to a performance evaluation value threshold value; determining the updated control system as a target control system when the performance evaluation value is greater than or equal to the performance evaluation value threshold; and updating the state variable predictor when the performance evaluation value is smaller than the performance evaluation value threshold value, and executing the step of adding the state variable predictor to the control system based on the updated state variable predictor.
Specifically, if the stability evaluation value is greater than the stability evaluation value threshold, which indicates that the stability of the target output variable is better, the adaptive adjustment method provided in this embodiment is effective for adjusting the control system, and the adaptive adjustment method may be applied to the control system, that is, the state variable predictor is added to the control system FIG. 4, for example, is a schematic diagram of an updated dynamic accelerator control system provided in accordance with an embodiment of the present application, as shown in FIG. 4, wherein f 3 (x) Is a state variable predictor based on load command rate. After the state variable predictor is added, the output variable of the control system is automatically corrected based on the state variable. And evaluating the system performance of the updated dynamic accelerator control system, acquiring the updated dynamic accelerator control system performance evaluation value through simulation software, comparing the updated dynamic accelerator control system performance evaluation value with a manually set performance evaluation value threshold, if the performance evaluation value is greater than or equal to the performance evaluation value threshold, indicating that the system performance of the updated dynamic accelerator control system is better, and if the performance evaluation value is smaller than the performance evaluation value threshold, indicating that the system performance of the updated dynamic accelerator control system is poorer, and updating and adjusting the state variable predictor are needed. The embodiment ensures that the updated control system can control the variable more stably by evaluating the performance of the control system.
For example, fig. 5 is a schematic diagram of a load command rate experiment and simulation performed on a simulation system according to an embodiment of the present application, where as shown in fig. 5, on a 600MW subcritical group, the load command is increased from 300WM to 600MW in a small step manner with a 10MW amplitude, and then is decreased from 600MW to 300MW in a step manner with a 300MW amplitude; the rates of load command during the test were 2.0% pe/min, 1.0% pe/min and 0.5% pe/min, respectively. The maximum deviation between the machine side pressure and the set value is 0.32Mpa (prescribed in the acceptance test rule of the analog quantity control system of the DLT 657-2015 thermal power plant), and the maximum deviation between the machine side pressure and the set value is less than 0.5Mpa under the condition that the speed of a given load instruction is 2.0%Pe/min, so as to obtain the simulation effect meeting the requirements.
Fig. 6 is a schematic diagram of a control curve of a boiler master control after a self-adaptive adjustment method of a control system is adopted, as shown in fig. 6, on a 660MW supercritical unit, the actual load speed is randomly changed between 0.5% pe/min and 2.0% pe/min after repeated start and stop of a grinding set, the deviation between the machine side pressure and a set value is only 0.47MPa, and in the 2 nd part of the technical guideline of an optimal control system of a DL (T) 1492.2-2016 thermal power plant: in the acceptance test of the coordination and steam temperature optimization control system, the maximum deviation between the machine side pressure and the set value is less than 0.6Mpa under the conditions that the speed of a given load instruction is 2.0% Pe/min and no grinding set start-stop is provided.
Therefore, after the self-adaptive technology based on state variable estimation is adopted, the main control curve of the boiler is very stable in performance, and the random change of repeated start-stop of the grinding set and the actual load speed within a certain range can be effectively treated. The deviation between the machine side pressure and the set value meets the requirements of the technical guidelines of the optimized control system of the DL (T) 1492.2-2016 thermal power plant. Therefore, the self-adaptive adjusting method of the control system has good application prospect and can provide effective technical support for the coordination control of the thermal power plant. The self-adaptive adjusting method based on state variable estimation has better stability and accuracy in practical application, and can effectively reduce the running cost and maintenance cost of the system; the method is suitable for various power systems and industrial control systems, and has wide application prospect and market value.
Optionally, in the adaptive adjustment method of a control system provided in the embodiment of the present application, updating the state variable predictor includes: acquiring a plurality of groups of updated sample data, wherein the number of the plurality of groups of updated sample data is larger than the number of sample data before updating, and the sample data before updating is used for determining the sample data of the state variable predictor before updating; acquiring state variables corresponding to each group of updated sample data through preset simulation software, and determining an updated estimated function between the sample data and the state variables through a regression analysis method; and constructing an updated state variable predictor based on the updated prediction function.
Specifically, the state variable predictor is updated by acquiring more sample data and adopting more diversified sample data to carry out regression analysis, so that an updated prediction function is determined, and the updated state variable predictor can more accurately predict the state variable. The embodiment ensures that the estimated state variable is more accurate by updating the state variable predictor.
Optionally, in the adaptive adjustment method of a control system provided in the embodiment of the present application, determining the target model of the control system includes: determining target parameters controlled by a control system, and acquiring system requirements of a user on the control system, wherein the system requirements are used for determining parameter characteristics of the target parameters, and the parameter characteristics comprise linear parameters and nonlinear parameters; under the condition that the system demand characterization determines that the target parameter is a linear parameter, determining that a target model of the control system is a linear model; and under the condition that the system demand characterization determines that the target parameter is a nonlinear parameter, determining that a target model of the control system is a nonlinear model.
Specifically, the parameter characteristics controlled by different control systems are different, the target model is determined based on the parameter characteristics of the target parameter controlled by the control system, if the parameter characteristics of the target parameter are linear parameters, the target model is determined to be a linear model, and if the parameter characteristics of the target parameter are nonlinear parameters, the target model is determined to be a nonlinear model.
Optionally, in the adaptive adjustment method of a control system provided in the embodiment of the present application, correcting the current output variable according to the correction coefficient, and obtaining the corrected target output variable includes: and calculating the product of the correction coefficient and the current output variable to obtain a corrected target output variable.
Specifically, after the current state variable of the control system is estimated by the state variable estimator, the current state variable is used as a correction coefficient to multiply the current output variable, so that the corrected target output variable can be obtained.
It should be noted that, the adaptive adjustment method of the control system provided in this embodiment may implement control quality assurance under different load command rate changes. Aiming at the wide variable load demand of AGC (Automatic Generation Control, active self-control system of the generator), the method can realize the control quality assurance of the load instruction under the speed change of 0.8-2.5% Pe/min. Through real-time monitoring and adjustment of control quality, the system can be ensured to operate efficiently and stably under different working conditions. The self-adaptive adjustment can be performed according to the real-time data, excessive prior setting of the control system is not needed, and the method has better universality and adaptability. In addition, the method can be applied to control systems in other fields to realize fine control and self-adaptive adjustment.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application also provides an adaptive adjustment device of the control system, and it should be noted that the adaptive adjustment device of the control system of the embodiment of the application can be used for executing the adaptive adjustment method for the control system provided by the embodiment of the application. The following describes an adaptive adjustment device of a control system provided in an embodiment of the present application.
Fig. 7 is a schematic diagram of an adaptive adjustment device of a control system provided according to an embodiment of the present application. As shown in fig. 7, the apparatus includes:
a first obtaining unit 10, configured to obtain a historical input variable and a historical output variable of the control system, and determine a target model of the control system, where the target model is a model describing a performance index of the control system, and the target model includes at least one of the following: a linear model and a nonlinear model;
a first determining unit 20, configured to determine a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, where the state variable predictor is configured to predict a state variable of the control system;
A prediction unit 30, configured to predict a current state variable of the control system through the state variable predictor, a current input variable and a current output variable of the control system;
and the correction unit 40 is configured to determine the current state variable as a correction coefficient of the control system, and correct the current output variable according to the correction coefficient to obtain a corrected target output variable.
According to the self-adaptive adjusting device of the control system, the first obtaining unit 10 is used for obtaining the historical input variable and the historical output variable of the control system and determining the target model of the control system, wherein the target model is a model for describing the performance index of the control system, and at least one of the following is included: a linear model and a nonlinear model; a first determining unit 20 for determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; the estimating unit 30 estimates the current state variable of the control system through the state variable estimator, the current input variable and the current output variable of the control system; the correction unit 40 determines the current state variable as a correction coefficient of the control system, corrects the current output variable according to the correction coefficient to obtain a corrected target output variable, solves the problem of poor stability of the control system in the related art, predicts the state variable of the control system based on the state variable predictor by determining the state variable predictor of the control system, and corrects the output signal of the control system by taking the state variable as the correction coefficient, thereby achieving the effect of improving the stability of the control system.
Optionally, in the adaptive adjustment device of a control system provided in the embodiment of the present application, the first determining unit 20 includes: the acquisition module is used for acquiring a plurality of groups of sample data of the control system under the same condition, wherein each group of sample data comprises a historical input variable, a historical output variable and a target model; the first acquisition module is used for acquiring state variables corresponding to each group of sample data through preset simulation software, and determining a pre-estimated function between the sample data and the state variables through a regression analysis method; the first construction module is used for constructing a state variable predictor of the control system based on the prediction function.
Optionally, in the adaptive adjustment device of a control system provided in the embodiment of the present application, the device further includes: a second acquisition unit configured to acquire a stability evaluation value of the target output variable and determine a stability evaluation value threshold; a first judging unit for judging whether the stability evaluation value is less than or equal to the stability evaluation value threshold; and the prompting unit is used for sending prompting information under the condition that the stability evaluation value is smaller than or equal to the stability evaluation value threshold value, wherein the prompting information is used for prompting the correction coefficient of the adjustment control system.
Optionally, in the adaptive adjustment device of a control system provided in the embodiment of the present application, the device further includes: the adding unit is used for adding the state variable predictor to the control system to obtain an updated control system under the condition that the stability evaluation value is larger than the stability evaluation value threshold value; a third acquisition unit for acquiring the updated performance evaluation value of the control system and determining an evaluation value threshold; a second judging unit for judging whether the performance evaluation value is greater than or equal to the performance evaluation value threshold; a second determining unit configured to determine the updated control system as a target control system in a case where the performance evaluation value is equal to or greater than the performance evaluation value threshold; and the updating unit is used for updating the state variable predictor when the performance evaluation value is smaller than the performance evaluation value threshold value, and executing the step of adding the state variable predictor to the control system based on the updated state variable predictor.
Optionally, in the adaptive adjustment device of a control system provided in the embodiment of the present application, the update unit includes: the second acquisition module is used for acquiring a plurality of groups of updated sample data, wherein the number of the plurality of groups of updated sample data is larger than that of sample data before updating, and the sample data before updating is used for determining the sample data of the state variable predictor before updating; the third acquisition module is used for acquiring state variables corresponding to each group of updated sample data through preset simulation software, and determining an updated estimated function between the sample data and the state variables through a regression analysis method; and the second construction module is used for constructing an updated state variable predictor based on the updated prediction function.
Optionally, in the adaptive adjustment device of a control system provided in the embodiment of the present application, the first obtaining unit 10 includes: the first determining module is used for determining target parameters controlled by the control system and obtaining system requirements of a user on the control system, wherein the system requirements are used for determining parameter characteristics of the target parameters, and the parameter characteristics comprise linear parameters and nonlinear parameters; the second determining module is used for determining that the target model of the control system is a linear model under the condition that the system demand characterization determines that the target parameter is a linear parameter; and the third determining module is used for determining that the target model of the control system is a nonlinear model under the condition that the system demand characterization determines that the target parameter is the nonlinear parameter.
Optionally, in the adaptive adjustment device of the control system provided in the embodiment of the present application, the correction unit 40 includes: and the calculation module is used for calculating the product of the correction coefficient and the current output variable to obtain a corrected target output variable.
The adaptive adjusting device of the control system includes a processor and a memory, the first acquiring unit 10, the first determining unit 20, the estimating unit 30, the correcting unit 40, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more, and the stability of the control system is improved by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the invention provides a computer readable storage medium, on which a program is stored, which when executed by a processor, implements a method for adaptively adjusting a control system.
The embodiment of the invention provides a processor, which is used for running a program, wherein the self-adaptive adjustment method of a control system is executed when the program runs.
Fig. 8 is a schematic diagram of an electronic device provided according to an embodiment of the present application. As shown in fig. 8, the electronic device 801 includes a processor, a memory, and a program stored on the memory and executable on the processor, and the processor implements the following steps when executing the program: acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system and at least comprises one of the following: a linear model and a nonlinear model; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; and determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable. The device herein may be a server, PC, PAD, cell phone, etc.
The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system and at least comprises one of the following: a linear model and a nonlinear model; determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system; predicting a current state variable of the control system through a state variable predictor, a current input variable and a current output variable of the control system; and determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable.
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-usable 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 described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
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-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method for adaptively adjusting a control system, comprising:
acquiring a historical input variable and a historical output variable of a control system, and determining a target model of the control system, wherein the target model is a model describing performance indexes of the control system, and at least comprises one of the following: a linear model and a nonlinear model;
determining a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, wherein the state variable predictor is used for predicting the state variable of the control system;
estimating a current state variable of the control system through the state variable estimator, a current input variable and a current output variable of the control system;
and determining the current state variable as a correction coefficient of the control system, and correcting the current output variable according to the correction coefficient to obtain a corrected target output variable.
2. The method of claim 1, wherein determining a state variable predictor of the control system based on the historical input variable, the historical output variable, and the target model comprises:
Collecting multiple groups of sample data of a control system under the same condition, wherein each group of sample data comprises a historical input variable, a historical output variable and a target model;
acquiring state variables corresponding to each group of sample data through preset simulation software, and determining a pre-estimated function between the sample data and the state variables through a regression analysis method;
and constructing a state variable predictor of the control system based on the pre-estimated function.
3. The method of claim 1, wherein after correcting the current output variable according to the correction coefficient to obtain a corrected target output variable, the method further comprises:
acquiring a stability evaluation value of the target output variable, and determining a stability evaluation value threshold;
judging whether the stability evaluation value is smaller than or equal to the stability evaluation value threshold value;
and sending prompt information under the condition that the stability evaluation value is smaller than or equal to the stability evaluation value threshold, wherein the prompt information is used for prompting and adjusting the correction coefficient of the control system.
4. The method according to claim 3, wherein after determining whether the stability evaluation value is equal to or less than the stability evaluation value threshold, the method further comprises:
Adding the state variable predictor to the control system under the condition that the stability evaluation value is larger than the stability evaluation value threshold value to obtain an updated control system;
acquiring a performance evaluation value of the updated control system, and determining an evaluation value threshold;
judging whether the performance evaluation value is larger than or equal to the performance evaluation value threshold value;
determining the updated control system as a target control system when the performance evaluation value is greater than or equal to the performance evaluation value threshold;
and updating the state variable predictor when the performance evaluation value is smaller than the performance evaluation value threshold value, and executing the step of adding the state variable predictor to the control system based on the updated state variable predictor.
5. The method of claim 4, wherein updating the state variable predictor comprises:
acquiring a plurality of groups of updated sample data, wherein the number of the plurality of groups of updated sample data is larger than the number of sample data before updating, and the sample data before updating is used for determining the sample data of the state variable predictor before updating;
Acquiring state variables corresponding to each group of updated sample data through preset simulation software, and determining an updated estimated function between the sample data and the state variables through a regression analysis method;
and constructing an updated state variable predictor based on the updated predictor function.
6. The method of claim 1, wherein determining a target model of the control system comprises:
determining a target parameter controlled by the control system, and acquiring a system requirement of a user on the control system, wherein the system requirement is used for determining a parameter characteristic of the target parameter, and the parameter characteristic comprises a linear parameter and a nonlinear parameter;
under the condition that the system demand characterization determines that the target parameter is a linear parameter, determining that a target model of the control system is a linear model;
and under the condition that the system demand characterization determines that the target parameter is a nonlinear parameter, determining that a target model of the control system is a nonlinear model.
7. The method of claim 1, wherein correcting the current output variable based on the correction factor to obtain a corrected target output variable comprises:
And calculating the product of the correction coefficient and the current output variable to obtain a corrected target output variable.
8. An adaptive adjustment device for a control system, comprising:
an obtaining unit, configured to obtain a historical input variable and a historical output variable of a control system, and determine a target model of the control system, where the target model is a model describing a performance index of the control system, and the target model includes at least one of: a linear model and a nonlinear model;
a determining unit, configured to determine a state variable predictor of the control system based on the historical input variable, the historical output variable and the target model, where the state variable predictor is configured to predict a state variable of the control system;
the estimating unit is used for estimating the current state variable of the control system through the state variable estimator, the current input variable and the current output variable of the control system;
and the correction unit is used for determining the current state variable as a correction coefficient of the control system, correcting the current output variable according to the correction coefficient, and obtaining a corrected target output variable.
9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of adaptive adjustment of a control system according to any one of claims 1 to 7.
10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of adaptive tuning of a control system of any of claims 1-7.
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