CN114114907A - Fuzzy self-adaptive based coordination control strategy - Google Patents
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Abstract
The invention discloses a fuzzy self-adaptive coordination control strategy, which comprises the following steps: and establishing an offline model of the coordination control system according to field test data, introducing a fuzzy rule to perform online correction on model parameters, and performing optimal control on the coordination control system based on a predictive control algorithm. The control strategy overcomes the difficulty brought by the large inertia, nonlinearity and time-varying property of the unit to a certain extent to the coordinated control, can effectively ensure the control effect of the unit when the load is changed rapidly in a large range, meets the actual engineering requirement, and is suitable for the thermal power unit coordinated control system with frequently changed working conditions.
Description
Technical Field
The invention relates to the field of thermal power engineering and automatic control, in particular to a fuzzy self-adaptive coordination control strategy.
Background
Under the large background of the vigorous development of new energy power generation technologies such as photovoltaic and wind power and a double-carbon target in China, a thermal power generating unit needs to frequently undertake the task of deep peak shaving of a power grid, the load regulation range of the unit is wider, the speed is higher, and the nonlinearity and the time-varying property of a controlled object of a turbine boiler coordination control system are more obvious. At present, a thermal power generating unit coordinated control system is mostly designed based on a traditional PID control strategy, and a regulation scheme of load instruction feedforward and PID feedback is adopted. However, under the conditions of variable coal types, inaccurate unit control and measurement equipment and frequent great deviation between operation parameters and design parameters, the control quality is difficult to ensure by conventional control means, and the control effect is obviously poor. Therefore, a coordination control strategy capable of adapting to the large-range variable load operation state of the unit needs to be designed.
Disclosure of Invention
In order to solve the problems, the invention discloses a fuzzy self-adaptive coordination control strategy, which improves the self-adaptive capacity of a coordination control system, improves the flexibility of a unit and optimizes the control quality of the coordination system.
In order to achieve the purpose, the invention adopts the technical scheme that: a fuzzy adaptive based coordination control strategy comprising the steps of:
step S1: establishing an offline model of the coordination control system according to the field test data;
step S2: introducing a fuzzy rule to carry out online correction on the model parameters;
step S3: and performing optimization control on the coordination control system based on a predictive control algorithm.
Further, the control quantity of the offline model of the coordinated control system in the step S1 is the fuel quantity DfWater feed amount DwOpening degree mu of regulating valve of steam turbineTThe controlled quantity is the actual power P of the uniteMain steam pressure pTMiddle point temperature TmFitting a coordination system model at 30%, 50%, 70%, 85% and 100% load operating points according to field test data, specifically expressed as:
where u is the input variable matrix of the system, x is the state variable matrix of the system, y is the output variable matrix of the system, and A, B, C is the system matrix.
Further, the fuzzy rule introduced in step S2 is an associated inference rule in the form of IF-THEN, and the model parameter is corrected online by a membership function according to a precondition-inference back-part parameter of the fuzzy rule, where the fuzzy rule is specifically expressed as:
Rulei:Ify1is y1；and y2is y2iandy3is y3i
wherein r is the fuzzy rule number.
Furthermore, the online correction of the model parameters by the membership function is specifically expressed as:
wherein the content of the first and second substances,
μiis the degree of membership.
Further, the predictive control algorithm in the step S3 adopts a modified generalized predictive control state space algorithm.
Furthermore, the output of the coordination system is predicted based on an output equation of the prediction model, which is specifically expressed as:
wherein, N is the prediction step length,
furthermore, the non-measurable state parameters are estimated in real time by using a kalman filter, which is specifically represented as:
xp(k)＝xp(k)+Kk·xe(k)
wherein x ispRepresenting the estimated value of the posterior state, xeRepresenting the prior state estimate, KkRepresenting the Kalman gain matrix, PkRepresenting the estimation error covariance matrix, QkRepresenting the excitation noise covariance matrix, RkRepresenting the measurement noise covariance matrix.
Furthermore, a quadratic performance index is adopted to solve the optimal control quantity, which is specifically expressed as:
wherein, KyRepresenting the output error weighting coefficient matrix, KuAnd Z represents a control increment change quantity weighting coefficient matrix and a reference track.
By adopting the technical scheme, the invention has the following beneficial effects:
the method can effectively improve the self-adaptive capacity of the thermal power generating unit under frequent and large-amplitude variable working conditions, can solve the problem of limited optimization of control action rate and amplitude in a thermal power generating unit coordination control system, and has the advantages of quick and accurate control, excellent control performance and stable and reliable unit operation.
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FIG. 1 is a schematic view of the principle of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
The embodiment discloses a fuzzy adaptive coordination control strategy, as shown in fig. 1, which includes the following steps:
step S1: establishing an offline model of the coordination control system according to the field test data;
the control quantity of the offline model of the coordinated control system in the step S1 is the fuel quantity DfWater supply amountDwOpening degree mu of regulating valve of steam turbineTThe controlled quantity is the actual power P of the uniteMain steam pressure pTMiddle point temperature TmFitting a coordination system model at 30%, 50%, 70%, 85% and 100% load operating points according to field test data, specifically expressed as:
where u is the input variable matrix of the system, x is the state variable matrix of the system, y is the output variable matrix of the system, and A, B, C is the system matrix.
Step S2: introducing a fuzzy rule to carry out online correction on the model parameters;
the fuzzy rule introduced in the step S2 is an associated inference rule in the form of IF-THEN, and the model parameter is corrected on line by a membership function according to the posterior part parameter of the fuzzy rule inferred according to the precondition, wherein the fuzzy rule is specifically expressed as:
Rulei：If y1is y1i and y2is y2iand y3is y3i
wherein r is the fuzzy rule number.
Furthermore, the online correction of the model parameters by the membership function is specifically expressed as:
wherein the content of the first and second substances,
μ i is the degree of membership.
Step S3: performing optimization control on the coordination control system based on a predictive control algorithm; the predictive control algorithm adopts an improved generalized predictive control state space algorithm.
Furthermore, the output of the coordination system is predicted based on an output equation of the prediction model, which is specifically expressed as:
wherein, N is the prediction step length,
furthermore, the non-measurable state parameters are estimated in real time by using a kalman filter, which is specifically represented as:
xp(k)＝xp(k)+Kk·xe(k)
wherein x ispRepresenting the estimated value of the posterior state, xeRepresenting the prior state estimate, KkRepresenting the Kalman gain matrix, PkRepresenting the estimation error covariance matrix, QkRepresenting the excitation noise covariance matrix, RkRepresenting the measurement noise covariance matrix.
Furthermore, a quadratic performance index is adopted to solve the optimal control quantity, which is specifically expressed as:
wherein, KyRepresenting the output error weighting coefficient matrix, KuAnd Z represents a control increment change quantity weighting coefficient matrix and a reference track.
The following takes a 660MW supercritical unit coordination system of a certain power plant as an example, and adopts the coordination control strategy of the invention to describe the content of the invention in detail. Selecting five load working condition points of 200MW (30%), 330MW (50%), 460MW (70%), 560MW (85%) and 660MW (100%) as typical working conditions, respectively establishing five offline state space models and dividing corresponding fuzzy sets, calculating membership degree on-line correction coordination system models of the fuzzy sets according to fuzzy rules, and using the corrected models as prediction models to implement prediction control. The predictive controller parameter settings are shown in table 1.
TABLE 1 predictive controller parameter settings
Wherein I is an identity matrix.
The above examples show that: the fuzzy self-adaptive coordination control strategy can effectively improve the control quality of a large thermal power generating unit coordination control system in a large working condition range, and the control system has the advantages of high response speed, good stability, high accuracy, good robustness and certain self-adaptive capacity.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (6)
1. A fuzzy adaptive based coordination control strategy, comprising the steps of:
step S1: establishing an offline model of the coordination control system according to the field test data;
step S2: introducing a fuzzy rule to carry out online correction on the model parameters;
step S3: and performing optimization control on the coordination control system based on a predictive control algorithm.
2. The fuzzy adaptive-based cooperative control strategy of claim 1, wherein: the control quantity of the offline model of the coordinated control system in the step S1 is the fuel quantity DfWater feed amount DwOpening degree mu of regulating valve of steam turbineTThe controlled quantity is the actual power P of the uniteMain steam pressure pTMiddle point temperature TmFitting a coordination system model at 30%, 50%, 70%, 85% and 100% load operating points according to field test data, specifically expressed as:
where u is the input variable matrix of the system, x is the state variable matrix of the system, y is the output variable matrix of the system, and A, B, C is the system matrix.
3. The fuzzy adaptive-based cooperative control strategy of claim 1, wherein: the fuzzy rule introduced in the step S2 is an associated inference rule in the form of IF-THEN, and the model parameter is corrected on line by a membership function according to the posterior part parameter of the fuzzy rule inferred according to the precondition, wherein the fuzzy rule is specifically expressed as:
Rulei:If y1 is y1i and y2 is y2i and y3 is y3i
wherein r is the fuzzy rule number.
4. The fuzzy adaptive-based coordination control strategy of claim 3, wherein: the parameters of the online correction model through the membership function are specifically expressed as follows:
wherein the content of the first and second substances,
μiis the degree of membership.
5. The fuzzy adaptive-based cooperative control strategy of claim 1, wherein:
the predictive control algorithm in the step S3 adopts an improved generalized predictive control state space algorithm, predicts the output of the coordination system based on the output equation of the predictive model, estimates the unmeasured state parameters in real time by using the kalman filter, and solves the optimal control quantity by using the quadratic performance index.
6. The fuzzy adaptive-based coordination control strategy of claim 5, wherein: predicting the output of the coordination system based on an output equation of the prediction model, wherein the output equation is specifically expressed as:
wherein, N is the prediction step length,
the method comprises the following steps of utilizing a Kalman filter to carry out real-time estimation on an unmeasured state parameter, and specifically comprising the following steps:
xp(k)＝xp(k)+Kk·xe(k)
wherein x ispRepresenting the estimated value of the posterior state, xeRepresenting the prior state estimate, KkRepresenting the Kalman gain matrix, PkRepresenting the estimation error covariance matrix, QkRepresenting the excitation noise covariance matrix, RkRepresenting a measurement noise covariance matrix;
solving the optimal control quantity by using quadratic performance indexes, which is specifically expressed as follows:
wherein, KyRepresenting the output error weighting coefficient matrix, KuAnd Z represents a control increment change quantity weighting coefficient matrix and a reference track.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112147891A (en) * | 2020-09-07 | 2020-12-29 | 东南大学 | Thermal power generating unit coordination system global nonlinear optimization control method |
| CN113448248A (en) * | 2021-06-23 | 2021-09-28 | 南京英纳维特自动化科技有限公司 | Intelligent control method for flexibility and deep peak regulation of thermal power generating unit |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112147891A (en) * | 2020-09-07 | 2020-12-29 | 东南大学 | Thermal power generating unit coordination system global nonlinear optimization control method |
| CN113448248A (en) * | 2021-06-23 | 2021-09-28 | 南京英纳维特自动化科技有限公司 | Intelligent control method for flexibility and deep peak regulation of thermal power generating unit |
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