CN115327910A - Flexibility and deep peak regulation intelligent control method for thermal power generating unit - Google Patents
Flexibility and deep peak regulation intelligent control method for thermal power generating unit Download PDFInfo
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Abstract
The invention discloses an intelligent control method for flexibility and deep peak regulation of a thermal power generating unit, which comprises the following steps: carrying out a disturbance test at a typical working condition point, and establishing a gain scheduling model of the coordination system by methods of fitting, identification and the like; establishing a fuzzy rule base, reasoning a dynamic mathematical model of a coordination system corresponding to the power grid load instruction in real time, and taking the dynamic mathematical model as a prediction model; and the prediction controller predicts the output track according to a prediction equation, estimates the system state by using a Kalman filter and calculates the optimal control quantity according to the performance index. The method is suitable for a thermal power generating unit coordinated control system with frequently changed working conditions, is simple and reliable, is easy to realize, can be implemented in a configuration mode through a power plant DCS system, and has a wide engineering application value.
Description
Technical Field
The invention belongs to the technical field of thermal power engineering and automatic control, and particularly relates to an intelligent control method for flexibility and deep peak shaving of a thermal power generating unit.
Background
In recent years, energy structure adjustment is actively promoted in all countries in the world, new energy power generation technologies such as wind power and photovoltaic power generation are rapidly developed, and 'clean, low-carbon and high-efficiency' is an energy development trend. China clearly points out that the coal power is converted from a base charge type power supply to a regulation type power supply in a clean leading energy conversion production mode. Renewable energy sources have randomness and volatility, the stability of a power grid is poor due to the fact that the renewable energy sources are incorporated into the power grid in a large scale, the difference between the peak and the valley of power supply is aggravated, and in order to absorb clean energy, a thermal power generating unit can be in a normal state when running at low load continuously or in a deep peak regulation mode in the coming years. When the load instruction of the power grid is frequently and greatly changed, the problem of effectively improving the flexibility of the thermal power generating unit needs to be solved urgently.
A Coordinated Control System (CCS) of a unit set is a bridge connecting a generator set and a power grid, and is used for coordinating a boiler-steam turbine to receive a load instruction of a power grid central dispatching station together and respond to a load request in time, so that the unit set has certain peak regulation and frequency regulation capabilities. At present, a thermal power generating unit coordinated control strategy mostly adopts a control structure of proportional feedforward and PID feedback, and the control strategy can obtain a good control effect when the working condition is stable, but the control effect is deteriorated when the working condition changes frequently and the actual working condition deviates from the design working condition for a long time. In addition, some experts and scholars also provide various advanced control algorithms such as predictive control, robust control and fuzzy control to optimize a coordinated control system, and theoretically, the load tracking capability of the unit can be effectively improved, but the advanced control algorithms are relatively complex in structure and large in calculated amount and are difficult to realize in actual engineering.
In view of the fact that no complete, effective and universal thermal power generating unit flexibility and deep peak regulation control scheme exists at present, the intelligent control strategy capable of being applied in the actual thermal engineering process is designed, and the intelligent control strategy has important guiding significance and application value.
Disclosure of Invention
The technical problem is as follows: the invention aims to solve the technical problem of the prior art, and provides an intelligent control method for flexibility and deep peak regulation of a thermal power generating unit, so that the capability of a coordinated control system for quickly tracking the load instruction change of a power grid is improved, and the control quality of key parameters in the regulation transition process is improved.
The technical scheme is as follows: in order to achieve the purpose, the intelligent control method for flexibility and deep peak regulation of the thermal power generating unit comprises the following steps of:
s1, establishing a gain scheduling model: establishing a mathematical model of the controlled object of the coordination system at a reference working condition point based on a dynamic characteristic disturbance test of the controlled object of the coordination control system;
s2, establishing a fuzzy rule base: adopting fuzzy association rule statement to make gain scheduling variable, namely power grid load instruction gain delta N e As a fuzzy rule front piece, converting the gain scheduling model in the form of a transfer function into a state space form and as a fuzzy rule back piece; calculation of power grid load instruction gain deltaN based on Gaussian membership function e Degree of membership mu to each reference operating point i (ii) a Calculating a dynamic mathematical model of a coordination system corresponding to the real-time power grid load instruction through a weighted average defuzzification process;
s3, designing a predictive controller: converting the state space model into an incremental extended state space model, and taking the incremental extended state space model as a prediction model; predicting the future P-step output of the control system based on a prediction model; a Kalman filter is arranged to estimate the state of the control system in real time; and calculating and solving quadratic performance indexes, transmitting a first item delta u (k) of the optimal control quantity to a controlled object at each moment to implement a control action, and calculating the optimal control quantity at the next moment by taking the moment k +1 as a base point to realize rolling optimization.
In step S1, based on a dynamic characteristic disturbance test of the controlled object of the coordination control system, establishing a mathematical model of the controlled object of the coordination control system at the reference operating point, where the mathematical model includes:
s1.1, aiming at an ultra (super) critical direct current unit of a thermal power plant, selecting N reference working condition points, and respectively carrying out fuel quantity r B Feed water flow rate D W Opening u of valve of steam turbine T The disturbance test comprises the steps of fitting real power P of each disturbance to the unit through identification according to test data e Main steam pressure p st And a separator temperature outlet T sep The transfer function model of (2);
s1.2, aiming at a subcritical steam drum furnace unit of a thermal power plant, selecting N reference working condition points, and respectively carrying out fuel quantity r B Feed water flow rate D W Opening u of steam turbine valve T The disturbance test comprises the steps of fitting real power P of each disturbance to the unit through identification according to test data e Main steam pressure p st And drum level H qb The transfer function model of (2).
The step S2 includes the steps of:
s2.1, adopting IF-THEN association rule statement to schedule a gain scheduling variable, namely power grid load instruction gain delta N e As a fuzzy rule front piece, converting the gain scheduling transfer function model into a state space form as a fuzzy rule back piece, specifically expressed as:
k represents any sampling moment, k +1 represents the next sampling moment of k, N represents the number of reference operating points, u represents a control quantity, y represents system output, x represents a system state, and A, B and C represent a system matrix;
s2.2, calculating power grid load instruction gain delta N based on Gaussian membership function e Degree of membership mu to each reference operating point i Specifically, it is represented as:
wherein σ 2 Represents a variance;
s2.3, calculating a dynamic mathematical model of the coordination system corresponding to the real-time power grid load instruction through defuzzification processing by a weighted average method, wherein the dynamic mathematical model is specifically represented as follows:
wherein, the first and the second end of the pipe are connected with each other,
the step S3 includes the steps of:
s3.1, converting the state space model into an incremental extended state space model, and taking the incremental extended state space model as a prediction model, wherein the model is specifically expressed as follows:
wherein θ represents a zero matrix, and I represents an identity matrix;
s3.2, predicting the future P step output of the control system based on the prediction model, wherein the prediction equation is specifically expressed as follows:
wherein the content of the first and second substances,
s3.3, a Kalman filter is arranged to estimate the state of the control system in real time, and the estimation is specifically represented as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the posterior state estimate, x e (k) Representing the estimated value of the prior state, K k Representing the Kalman gain matrix, P k Representing the estimation error covariance matrix, Q k Representing the excitation noise covariance matrix, R k Representing a measurement noise covariance matrix;
s3.4, calculating and solving a quadratic performance index, transmitting a first item delta u (k) of the optimal control quantity to a controlled object at each moment to implement a control action, and calculating the optimal control quantity at the next moment by taking the moment k +1 as a base point to realize rolling optimization, wherein the performance index of the control system is specifically expressed by adopting a quadratic function:
wherein, Λ y Representing the output error weighting coefficient matrix, Λ u Weight coefficient matrix, Y, representing the amount of change of control increments r A reference trajectory is indicated.
The optimal control quantity is solved according to the least square rule as follows:
wherein the content of the first and second substances,
has the advantages that:
1. the fuzzy intelligent control is realized by establishing a gain scheduling model and a fuzzy rule base, the capability of a coordination control system for tracking the change of the power grid load instruction is improved, the method is attached to the background of frequent and large-amplitude change of the power grid load instruction, and the flexibility of the thermal power generating unit is improved;
2. the control performance of a coordination system is improved by utilizing predictive control, and the problem of poor control effect of the existing PID control strategy in low-load operation and deep peak regulation of a thermal power generating unit is solved;
3. the control algorithm has simple structure and small on-line calculation amount, can be used as an expanded decentralized processing unit to be integrated into the whole DCS, and has implementability.
Drawings
FIG. 1 is a schematic diagram of a control structure according to the present invention;
FIG. 2 is a graph of membership function distribution in a specific embodiment;
FIG. 3 is a response curve of system output for a particular embodiment; wherein, (a) is a response curve of the actual power response curve of the unit, (b) is a response curve of the main steam pressure, and (c) is a response curve of the temperature at the outlet of the separator.
FIG. 4 is a graph of the variation of the control amount of the system in a specific embodiment; wherein, (a) is a fuel quantity change curve, (b) is a water supply quantity change curve, and (c) is a steam turbine regulating valve opening change curve.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following detailed description is made with reference to the accompanying drawings and specific embodiments.
The invention discloses an intelligent control method for flexibility and deep peak regulation of a thermal power generating unit, wherein a structural schematic diagram of a coordinated control system is shown as an attached figure 1, wherein a gain scheduling model is a coordinated system transfer function model which is established by performing a dynamic characteristic disturbance test on a reference working condition point through methods of fitting, identification and the like; the fuzzy rule base adopts IF-THEN association rule statements to schedule the gain of variable (power grid load instruction gain delta N) e ) As a fuzzy rule front piece, converting the gain scheduling transfer function model into a state space form as a fuzzy rule back piece, and reasoning coordination corresponding to the power grid load instruction in real timeAdjusting a dynamic mathematical model of the system, and taking the dynamic mathematical model as a prediction model; and the prediction controller predicts the output track according to a prediction equation, estimates the system state by using a Kalman filter and calculates the optimal control quantity according to the performance index.
The technical scheme of the invention is specifically described by taking a 660MW supercritical direct current unit of a certain power plant as an example. An intelligent control method for flexibility and deep peak shaving of a thermal power generating unit comprises the following steps:
s1, establishing a gain scheduling model;
s2, establishing a fuzzy rule base;
and S3, designing a predictive controller.
Further, said step S1 of establishing a gain scheduling model is at 30% -100% e Selecting 7 reference working condition points in the working condition range to respectively carry out fuel quantity r B Feed water flow rate D W Opening u of steam turbine valve T The disturbance test comprises the steps of fitting real power P of each disturbance to the unit through identification according to test data e Main steam pressure p st And separator outlet temperature T sep As shown in table 1:
TABLE 1 model of transfer function of reference operating point coordination system
Further, the step S2 includes the following steps:
s2.1, adopting IF-THEN association rule statement to schedule variable (power grid load instruction gain delta N) e ) As a fuzzy rule front-part, converting the gain scheduling transfer function model into a state space form as a fuzzy rule back-part, specifically expressed as:
in this example, the reference operating point number is 7, and the control amount u = [ D = w u T r B ] T Output y = [ P ] e p st T sep ] T System state x = [ r ] B p sep h sep ] T ，p sep Denotes the separator outlet pressure, h sep Representing separator outlet enthalpy, a, B, C representing the system matrix;
s2.2, calculating power grid load instruction gain delta N based on Gaussian membership function e Degree of membership mu to each reference operating point i Specifically, it is represented as:
in this example σ 2 =3, membership function profile as shown in figure 2;
s2.3, calculating a dynamic mathematical model of the coordination system corresponding to the real-time power grid load instruction through defuzzification processing by a weighted average method, wherein the dynamic mathematical model is specifically represented as follows:
in the present example, the number of the first and second,
further, the step S3 includes the following steps:
s3.1, converting the state space model into an incremental extended state space model, and taking the incremental extended state space model as a prediction model, wherein the model is specifically expressed as:
in this example, θ represents a zero matrix, and I represents an identity matrix;
s3.2, predicting the future P-step output of the control system based on the prediction model, wherein the prediction equation is specifically expressed as:
in the present example, the number of the first and second,
s3.3, a Kalman filter is arranged to estimate the state of the control system in real time, and the estimation is specifically represented as follows:
in this example, P is initialized k 、Q k 、R k Is a unit diagonal matrix;
s3.4, calculating and solving a quadratic performance index, transmitting a first item delta u (k) of the optimal control quantity to a controlled object at each moment to implement a control action, calculating the optimal control quantity at the next moment by taking the moment k +1 as a base point to realize rolling optimization, and specifically expressing as follows by taking a quadratic function as the performance index of the control system:
in this example,. Lambda y Representing the output error weighting coefficient matrix, Λ u Weight coefficient matrix representing control delta variation, Y r Represents a reference trajectory;
preferably, the following optimal control quantities are solved according to the least square rule:
in the present example, the number of the first and second,
further, the predictive controller parameter settings in this example are shown in table 2:
TABLE 2 predictive controller parameters
| Parameter(s) | (symbol) | Value of | |
| Sampling period | Ts | 5s | |
| Predicting step size | P | 120 | |
| Step | M | 20 | |
| Matrix of output error weighting coefficients | Λ y | 50I | |
| Control delta change weighting coefficient matrix | Λ u | I |
In order to verify the feasibility of the scheme, a Control system simulation test is carried out, and a simulation test process simulation unit operates in an AGC (Automatic Generation Control) mode.
Assuming an initial operating point of 30% e (200 MW), the control quantity of the coordinated control system is the water supply flow D W =660.1 (t/h), steam turbine valve opening u T =82.2% and fuel quantity r B =108.5 (t/h), and the output quantity is set actual power P e =200 (MW), main steam pressure p st =12.6 (MPa) and separator outlet temperature T sep ＝368.2(℃)。
The specific simulation process is as follows:
no change of load command in the time period of 0-388 s, i.e. 30% P e (200 MW) steady state operation at a work point; giving a load-up instruction to the system in a 389-994 s time period, and increasing the load of the unit to 300MW along a ramp signal of 2%/min (namely 13.2 MW/min); not changing the load command during the 995-2195 s period, i.e. at 45% e (300 MW) steady state operation at a working point; giving a load reduction instruction to the system in a period of 2196-2800 s, and reducing the load of the unit to 200MW along a ramp signal of 2%/min (namely 13.2 MW/min) of the power instruction; not changing the load command during the time period 2801-4000 s, i.e. 30% e (200 MW) steady state operation at the operating point.
Fig. 3 is a response curve of the system output in the embodiment, and it can be seen from the graph that the output power of the unit rapidly changes along with the grid load instruction in the processes of increasing and decreasing the load. The output power overshoot of the unit is only 2MW, the maximum dynamic deviation of the main steam pressure is about 0.7MPa, and the dynamic deviation of the temperature at the outlet of the separator is within 2 ℃, and is strictly controlled within an allowable range.
Fig. 4 is a change curve of the system control amount in the specific embodiment, and it can be seen from the graph that the fuel amount changes in time, so that the response speed of the boiler side in the boiler-turbine coordination system is effectively improved, and the adjustment transition process time of the whole boiler-turbine coordination control system is further shortened. In addition, the change of each control quantity is relatively smooth under the constraint condition of meeting the control action, and the stability of a control system is facilitated.
The above embodiments are only preferred embodiments of the present invention for more clearly illustrating the method of the present invention, and not for limiting the present invention in any other way, it should be noted that any obvious modifications made to the method by those skilled in the art without departing from the principle and spirit of the present invention should be included in the protection scope of the claims of the present invention.
Claims (5)
1. An intelligent control method for flexibility and deep peak shaving of a thermal power generating unit is characterized by comprising the following steps:
s1, establishing a gain scheduling model: establishing a mathematical model of the controlled object of the coordination system at a reference working condition point based on a dynamic characteristic disturbance test of the controlled object of the coordination control system;
s2, establishing a fuzzy rule base: adopting fuzzy association rule statement to make gain scheduling variable, namely power grid load instruction gain delta N e As a fuzzy rule front piece, converting the gain scheduling model in the form of a transfer function into a state space form and as a fuzzy rule back piece; calculation of power grid load instruction gain deltaN based on Gaussian membership function e Degree of membership mu to each reference operating point i (ii) a Calculating a dynamic mathematical model of a coordination system corresponding to the real-time power grid load instruction through a weighted average defuzzification process;
s3, designing a predictive controller: converting the state space model into an incremental extended state space model, and taking the incremental extended state space model as a prediction model; predicting the future P-step output of the control system based on a prediction model; a Kalman filter is arranged to estimate the state of the control system in real time; and calculating and solving quadratic performance indexes, transmitting a first item delta u (k) of the optimal control quantity to a controlled object at each moment to implement a control action, and calculating the optimal control quantity at the next moment by taking the moment k +1 as a base point to realize rolling optimization.
2. The intelligent control method for thermal power generating unit flexibility and deep peak shaving according to claim 1, wherein the step S1 comprises:
s1.1 aiming at supercritical of thermal power plantSelecting N reference working condition points, and respectively carrying out fuel quantity r on the N reference working condition points B Feed water flow rate D W Opening u of valve of steam turbine T The disturbance test of (2) is to set actual power P by identifying and fitting each disturbance according to the test data e Main steam pressure p st And a separator temperature outlet T sep The transfer function model of (2);
s1.2, aiming at a subcritical steam drum furnace unit of a thermal power plant, selecting N reference working condition points, and respectively carrying out fuel quantity r B Feed water flow rate D W Opening u of steam turbine valve T The disturbance test of (2) is to set actual power P by identifying and fitting each disturbance according to the test data e Main steam pressure p st And drum level H qb The transfer function model of (2).
3. The intelligent control method for flexibility and deep peak shaving of the thermal power generating unit according to claim 1, wherein the step S2 comprises the following steps:
s2.1, adopting a fuzzy association rule statement to schedule a gain scheduling variable, namely a power grid load instruction gain delta N e As a fuzzy rule front piece, converting the gain scheduling transfer function model into a state space form as a fuzzy rule back piece, specifically expressed as:
wherein k represents any sampling time, k +1 represents the next sampling time of k, N represents the number of reference operating points, u represents a control quantity, y represents system output, x represents a system state, and A, B and C represent a system matrix;
s2.2, calculating power grid load instruction gain delta N based on Gaussian membership function e Degree of membership mu to each reference operating point i Specifically, it is represented as:
wherein σ 2 Represents the variance;
s2.3, calculating a dynamic mathematical model of the coordination system corresponding to the real-time power grid load instruction through defuzzification processing by a weighted average method, wherein the dynamic mathematical model is specifically represented as follows:
wherein, the first and the second end of the pipe are connected with each other,
4. the intelligent control method for flexibility and deep peak shaving of the thermal power generating unit according to claim 1, wherein the step S3 comprises the following steps:
s3.1, converting the state space model into an incremental extended state space model, and taking the incremental extended state space model as a prediction model, wherein the model is specifically expressed as:
wherein θ represents a zero matrix, and I represents an identity matrix;
s3.2, predicting the future P step output of the control system based on the prediction model, wherein the prediction equation is specifically expressed as follows:
wherein the content of the first and second substances,
s3.3, a Kalman filter is arranged to estimate the state of the control system in real time, and the estimation is specifically represented as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the estimated value of the posterior state, x e (k) Representing the prior state estimate, K k Representing the Kalman gain matrix, P k Representing the estimation error covariance matrix, Q k Representing the excitation noise covariance matrix, R k Representing a measurement noise covariance matrix;
s3.4, calculating and solving a quadratic performance index, transmitting a first item delta u (k) of the optimal control quantity to a controlled object at each moment to implement a control action, and calculating the optimal control quantity at the next moment by taking the moment k +1 as a base point to realize rolling optimization, wherein the performance index of the control system is specifically expressed by adopting a quadratic function:
wherein, Λ y Representing the output error weighting coefficient matrix, Λ u Weight coefficient matrix representing control delta variation, Y r A reference trajectory is indicated.
5. The thermal power generating unit flexibility and depth peak regulation intelligent control method according to claim 4, wherein the optimal control quantity is solved according to a least square rule as follows:
wherein, the first and the second end of the pipe are connected with each other,
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