CN112462609B - Full-load coordination control method for thermal power generating unit - Google Patents
Full-load coordination control method for thermal power generating unit Download PDFInfo
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Abstract
The invention relates to a thermal power generating unit full load coordination control method, which is used for the full load section coordination control of a thermal power plant. According to the method, on the basis of not changing the original control logic, an optimal scheme is decided by using a multi-target particle swarm algorithm and is output to be used for coordinating the parameters of the controller, the parameters of the controller are adjusted according to the data calculated by the method, the control precision can be effectively improved, the operation stability of the unit is ensured, the coal consumption and the steam consumption of the unit are reduced in the adjusting process, and the economic benefit is high.
Description
Technical Field
The invention belongs to the technical field of automatic control of a heat energy system, and particularly relates to a full-load coordination control method for a thermal power generating unit.
Background
With the continuous development of new energy industries, the proportion of clean energy is continuously increased, and because new energy such as wind power, solar energy and the like has instability of power generation, how to frequently and efficiently solve the problem of frequency modulation and peak regulation by each high-parameter unit, realize the coordination control among the furnaces, and quickly track the load response of a power grid to become the main task of each power plant.
The invention provides a thermal power generating unit full-load coordination control method which can improve control precision, ensure unit operation stability and has high economic benefit, and aims to further improve load regulation quality of a thermal power generating unit in a wider load section and ensure unit operation stability without changing the existing control logic shown in figure 1.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a full-load coordination control method for a thermal power generating unit, which can improve the control precision, ensure the operation stability of the unit and has high economic benefit.
The technical scheme adopted by the invention is as follows: a full-load coordinated control method of a thermal power generating unit is used for the full-load section coordinated control of a thermal power plant and comprises the following steps:
the method comprises the following steps: collecting main steam pressure deviation, power deviation, intermediate point temperature deviation, coal consumption rate and steam consumption rate according to sampling time;
step two: calculating a control performance index and an economic index according to the acquired data;
step three: and obtaining controller parameters of an optimal scheme by using a multi-target particle swarm algorithm according to the control performance indexes and the economic indexes of different load sections, and coordinating the controller according to the parameters.
Specifically, the calculation formula of the main steam pressure deviation, the power deviation, the midpoint temperature deviation, the coal consumption rate and the steam consumption rate is as follows:
Temperature deviation of intermediate point= midpoint temperature set valueTemperature at the midpoint;
Specifically, the calculation formula of the control performance index and the economic index is as follows:
wherein, the first and the second end of the pipe are connected with each other,、、main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients for controlling performance indexes,、respectively the coal consumption rate and the steam consumption rate coefficient in the economic index.
Specifically, the method for calculating the parameters of the coordination controller by the multi-target particle swarm algorithm comprises the following steps:
multi-target particle swarm calculation model:
in the formula (I), the compound is shown in the specification,is composed ofDimension optimization variables of、The two objects are to be presented,one inequality constraint. Wherein the constraints are:
wherein, the first and the second end of the pipe are connected with each other,、are respectively the main control of the boilerThe proportional band and the integration time of (c),、respectively controlled for intermediate temperatureThe proportional band of (a) and the integration time,、are respectively the main control of the steam turbineProportional band and integration time.
Specifically, in step one, the sampling time is 1 s.
The invention has the beneficial effects that: on the basis of not changing the original control logic, the method acquires various data of the thermal power generating unit, calculates the control performance index and the economic index, calculates and obtains a plurality of schemes by using a multi-target particle swarm algorithm according to the weight of the two indexes, decides the optimal scheme and outputs the optimal scheme for coordinating the parameters of the controller, and regulates the parameters of the controller according to the data calculated by the method, thereby effectively improving the control precision, ensuring the operation stability of the unit, reducing the coal consumption and the steam consumption of the unit in the regulation process and having high economic benefit.
Drawings
Fig. 1 is a flow chart of a coordinated control system of a conventional high-parameter thermal power generating unit;
FIG. 2 is a flow chart of parameter setting of the multi-target particle swarm optimization algorithm of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments of the present invention, belong to the protection scope of the present invention, and are specifically described below with reference to the embodiments.
The method is used for the full-load section coordination control of the thermal power plant and comprises the following steps:
the method comprises the following steps: according to the default sampling time of 1s, acquiring main steam pressure deviation, power deviation, intermediate point temperature deviation, coal consumption rate and steam consumption rate, wherein the calculation formulas are respectively as follows:
Wherein, the coal consumption is the output value of the coal quantity instruction in fig. 1, and the main steam flow is the output value of the steam turbine flow instruction.
Step two: and calculating a control performance index and an economic index according to the acquired data, wherein the calculation formula is as follows:
wherein the content of the first and second substances,、、main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients which are used for controlling performance indexes,、respectively the coal consumption rate and the steam consumption rate coefficient in the economic index.
Step three: and obtaining controller parameters of an optimal scheme by using a multi-target particle swarm algorithm according to the control performance indexes and the economic performance indexes of different load sections, and coordinating the controller according to the parameters. The method for calculating the parameters of the coordination controller by the multi-target particle swarm algorithm comprises the following steps:
multi-target particle swarm calculation model:
in the formula (I), the compound is shown in the specification,is composed ofDimension optimization variables of、The two objects are to be achieved by,a number of inequalities constrain. Wherein the constraints are:
wherein, the first and the second end of the pipe are connected with each other,、are respectively the main control of the boilerThe proportional band and the integration time of (c),、respectively controlled for intermediate temperatureThe proportional band of (a) and the integration time,、are respectively the main control of the steam turbineProportional band and integration time.
FIG. 2 is a flow chart of parameter setting of a multi-objective particle swarm algorithm, wherein the multi-objective particle swarm algorithm obtains an optimal solution by utilizing a domination relationship among particles and collects a solution without domination relationship with a non-inferior solution set, and has better global search capability by continuously adjusting search strategies through optimal historical memory and current optimal, and the specific implementation process is as follows:
(1) initializing a particle swarm
For a population of N particles, each particle gi has D dimensions, i.e. D optimization variables, then each particle should be in particle spaceIn (1) satisfy
Then the initial time is in spaceIn which the position vector of the particle is randomly definedAnd velocity vectorAre respectively as
Simultaneously initializing inertial weights for particle movement,When the global optimizing ability is larger, the global optimizing ability is stronger, and when the local optimizing ability is smaller, the local optimizing ability is stronger, generally, the selection is carried out。
Particles optimized for historyLearning factor ofGlobally optimal particleLearning factor ofSatisfy the following requirements。
(2) Calculating particle fitness value
The particle fitness is a standard for evaluating the quality of particles, different fitness functions can enable the particles to have different degrees of quality, the fitness function of each particle in the multi-target problem is a target function with constraints, and if the constraints are not met, the fitness of the particle is directly set to be infinite, so that the particle can not be used as an optimal solution. And (4) separating out the optimal particles according to the fitness function obtained by calculation, taking the optimal particles as global optimal particles, and recording the historical optimal position of each particle.
The solution set obtained by satisfying the constraint condition is called a feasible solution set, but solutions which are mutually dominant exist in the feasible solution, the feasible solution is not a final Pareto non-inferior solution set, and the solutions in the non-inferior solution set are also called Pareto dominant solutions, namely satisfying the requirement of Pareto dominant solutions
For non-inferior solutions, because of the existence of multiple objectives, two solutions of a non-inferior solution set cannot be compared with each other because a certain objective function is superior, and other objective functions are not dominant, so that a subsequent-line scheme is required for decision making.
(3) Updating historical optimal solution sets
When entering the next moment, all the particles need to be updated, and the update of the speed is determined based on the historical optimal position and the global optimal position, that is, each particle moves towards the optimal position with a certain trend, as shown in the following formula
Wherein the content of the first and second substances,,is a random number within (0, 1),is the weight of the speed at the last time. And carrying out iterative updating through the above formula until the optimal solution set is updated, and obtaining a plurality of schemes in the solution set.
(4) Plan decision
The decision making method adopts a linear weighting method as shown in the following formula:
in the formula (I), the compound is shown in the specification,is a firstParticles of, thereforeAll particles shareA scheme is due toAndthe unit of (2) is different, the schemes of all the particles cannot be compared, and the optimal particle cannot be obtained, so the two indexes are normalized by substituting the formula, and each particle is respectively normalizedObtained byAndbrought into the above-mentioned formulaObtaining weightedAndfinally, the performance index of each particle is obtained:
For dimensionless numbers, the comparison can be made directly, after each iteration, for all particlesSequencing is carried out, the smaller the value is, the better the satisfaction degree of the scheme is, the comprehensive of the control performance weighted value and the economic performance weighted value is the minimum, and the final schemeCan be selected from the following:
can be derived from the above formulaCorresponding to the minimum value ofBy usingThe controller parameters are adjusted by each item of data corresponding to the minimum value of (1), namely the schemeIn (1)Andare respectively the main control of the boilerThe proportional band and the control parameters of the integration time,andrespectively controlled for intermediate temperatureThe proportional band and the control parameters of the integration time,、are respectively the main control of the steam turbineThe control parameters of the proportional band and the integral time are obtained by the multi-target particle swarm algorithm, the controller is adjusted according to the parameters obtained by the multi-target particle swarm algorithm, the control precision can be improved, the running stability of the unit is ensured, and meanwhile, the coal consumption and the steam consumption of the unit are reducedAnd the economic benefit is improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (3)
1. A full-load coordination control method of a thermal power generating unit is characterized by comprising the following steps: the method is used for the coordination control of the full load section of the thermal power plant and comprises the following steps:
the method comprises the following steps: collecting main steam pressure deviation, power deviation, intermediate point temperature deviation, coal consumption rate and steam consumption rate according to sampling time, wherein the calculation formulas of the main steam pressure deviation, the power deviation, the intermediate point temperature deviation, the coal consumption rate and the steam consumption rate are as follows:
main steam pressure deviation delta P is equal to pressure set value P set -the main steam pressure P is,
power deviation Δ MW ═ load command MW set -a real power MW of the power to be delivered,
the intermediate point temperature deviation Δ T is equal to the intermediate point temperature set value T set -a temperature T at an intermediate point,
the coal consumption rate B is equal to the coal consumption B/(the heat value correction coefficient k is multiplied by the actual power MW),
the steam consumption rate D is equal to the main steam flow D/actual power MW;
step two: calculating a control performance index and an economic index according to the acquired data, wherein the calculation formula of the control performance index and the economic index is as follows:
controlling the performance index: K-K 1 ΔP 2 +k 2 ΔMW 2 +k 3 ΔT 2 ,
The economic index is as follows: E-E 1 b+e 2 d,
Wherein k is 1 、k 2 、k 3 Main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients, e, which are control performance indexes respectively 1 、e 2 Respectively the coal consumption rate and the steam consumption rate coefficient in the economic index;
step three: and obtaining controller parameters of an optimal scheme by using a multi-target particle swarm algorithm according to the control performance indexes and the economic indexes of different load sections, and coordinating the controller according to the parameters.
2. The thermal power generating unit full-load coordination control method according to claim 1, characterized in that: in the third step, the method for calculating the parameters of the coordination controller by the multi-target particle swarm algorithm comprises the following steps:
multi-target particle swarm calculation model:
minF(x)=(K(x),E(x))
st.g i (x)≤0,i=1,2,…,p
x=[x 1 ,x 2 ,…,x d ,…,x D ]
in the formula, x d For D-dimension optimization variables, two targets of K (x) and E (x) are provided, and p inequality constraints are provided;
wherein the constraints are:
wherein x is 1 、x 2 Proportional band and integration time, x, respectively, for boiler master control PID1 3 、x 4 Proportional band and integration time, x, of PID2, respectively, for mid-point temperature control 5 、x 6 Proportional band and integration time of the steam turbine master control PID3, respectively.
3. The thermal power generating unit full load coordinated control method according to claim 1, characterized in that: in step one, the sampling time is 1 s.
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