CN112462609A - Full-load coordination control method for thermal power generating unit - Google Patents

Full-load coordination control method for thermal power generating unit Download PDF

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CN112462609A
CN112462609A CN202011329291.0A CN202011329291A CN112462609A CN 112462609 A CN112462609 A CN 112462609A CN 202011329291 A CN202011329291 A CN 202011329291A CN 112462609 A CN112462609 A CN 112462609A
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deviation
thermal power
generating unit
load
power generating
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CN112462609B (en
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彭宗贵
刘鑫辉
邹东
张伟
王宏涛
练领先
程世军
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Huaneng Qinbei Power Generation Co Ltd
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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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 coordinate 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

Full-load coordination control method for thermal power generating unit
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 industry, 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 shaving by each high-parameter unit, realize the coordination control among the boilers, and quickly track the load response of a power grid becomes 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 coordination control method of a thermal power generating unit is used for full-load section coordination 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 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:
deviation of main steam pressure
Figure 100002_DEST_PATH_IMAGE002
= pressure set value
Figure 100002_DEST_PATH_IMAGE004
Main steam pressure
Figure 100002_DEST_PATH_IMAGE006
；
Deviation of power
Figure 100002_DEST_PATH_IMAGE008
= load instruction
Figure 100002_DEST_PATH_IMAGE010
-actual power of the hair
Figure 100002_DEST_PATH_IMAGE012
；
Temperature deviation of intermediate point
Figure 100002_DEST_PATH_IMAGE014
= midpoint temperature set value
Figure 100002_DEST_PATH_IMAGE016
Temperature at mid point
Figure 100002_DEST_PATH_IMAGE018
；
Rate of coal consumption
Figure 100002_DEST_PATH_IMAGE020
= coal consumption
Figure 100002_DEST_PATH_IMAGE022
/(correction of calorific value)Positive coefficient
Figure 100002_DEST_PATH_IMAGE024
Figure 100002_DEST_PATH_IMAGE026
Actual power
Figure 535916DEST_PATH_IMAGE012
）；
Rate of steam consumption
Figure 100002_DEST_PATH_IMAGE028
= main steam flow
Figure 100002_DEST_PATH_IMAGE030
Actual power
Figure 249794DEST_PATH_IMAGE012
。
Specifically, the calculation formula of the control performance index and the economic index is as follows:
controlling the performance index:
Figure 100002_DEST_PATH_IMAGE032
；
the economic index is as follows:
Figure 100002_DEST_PATH_IMAGE034
；
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE036
、
Figure 100002_DEST_PATH_IMAGE038
、
Figure 100002_DEST_PATH_IMAGE040
main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients which are used for controlling performance indexes,
Figure 100002_DEST_PATH_IMAGE042
、
Figure 100002_DEST_PATH_IMAGE044
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:
Figure 100002_DEST_PATH_IMAGE046
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE048
is composed of
Figure 421010DEST_PATH_IMAGE030
Dimension optimization variable is as follows
Figure 100002_DEST_PATH_IMAGE050
、
Figure 100002_DEST_PATH_IMAGE052
The two objects are to be achieved by,
Figure 100002_DEST_PATH_IMAGE054
one inequality constraint. Wherein the constraints are:
and (3) target constraint:
Figure 100002_DEST_PATH_IMAGE056
controller parameter constraints:
Figure 100002_DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE060
、
Figure 100002_DEST_PATH_IMAGE062
are respectively the main control of the boiler
Figure 100002_DEST_PATH_IMAGE064
The proportional band and the integration time of (c),
Figure 100002_DEST_PATH_IMAGE066
、
Figure 100002_DEST_PATH_IMAGE068
respectively controlled for intermediate temperature
Figure 100002_DEST_PATH_IMAGE070
The proportional band and the integration time of (c),
Figure 100002_DEST_PATH_IMAGE072
、
Figure 100002_DEST_PATH_IMAGE074
are respectively the main control of the steam turbine
Figure 100002_DEST_PATH_IMAGE076
Proportional 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 conventional coordinated control system for a 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 solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying 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: collecting main steam pressure deviation, power deviation, intermediate point temperature deviation, coal consumption rate and steam consumption rate according to the sampling time default of 1s, wherein the calculation formulas are respectively as follows:
deviation of main steam pressure
Figure 101040DEST_PATH_IMAGE002
= pressure set value
Figure 321937DEST_PATH_IMAGE004
Main steam pressure
Figure 243933DEST_PATH_IMAGE006
；
Deviation of power
Figure 651911DEST_PATH_IMAGE008
= load instruction
Figure 896817DEST_PATH_IMAGE010
-actual power of the hair
Figure 768958DEST_PATH_IMAGE012
；
Temperature deviation of intermediate point
Figure 607994DEST_PATH_IMAGE014
= midpoint temperature set value
Figure 549274DEST_PATH_IMAGE016
Temperature at mid point
Figure 20707DEST_PATH_IMAGE018
；
Rate of coal consumption
Figure 920923DEST_PATH_IMAGE020
= coal consumption
Figure 960554DEST_PATH_IMAGE022
/(calorific value correction factor)
Figure 248185DEST_PATH_IMAGE024
Figure 585626DEST_PATH_IMAGE026
Actual power
Figure 42146DEST_PATH_IMAGE012
）；
Rate of steam consumption
Figure 174050DEST_PATH_IMAGE028
= main steam flow
Figure 810961DEST_PATH_IMAGE030
Actual power
Figure 765142DEST_PATH_IMAGE012
。
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:
controlling the performance index:
Figure 371441DEST_PATH_IMAGE032
；
the economic index is as follows:
Figure 549613DEST_PATH_IMAGE034
；
wherein the content of the first and second substances,
Figure 623222DEST_PATH_IMAGE036
、
Figure 381094DEST_PATH_IMAGE038
、
Figure 186107DEST_PATH_IMAGE040
main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients which are used for controlling performance indexes,
Figure 239907DEST_PATH_IMAGE042
、
Figure 209000DEST_PATH_IMAGE044
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. 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:
Figure 613306DEST_PATH_IMAGE046
in the formula (I), the compound is shown in the specification,
Figure 695662DEST_PATH_IMAGE048
is composed of
Figure 340270DEST_PATH_IMAGE030
Dimension optimization variable is as follows
Figure 642332DEST_PATH_IMAGE050
、
Figure 742006DEST_PATH_IMAGE052
The two objects are to be achieved by,
Figure 177405DEST_PATH_IMAGE054
one inequality constraint. Wherein the constraints are:
and (3) target constraint:
Figure 337122DEST_PATH_IMAGE056
controller parameter constraints:
Figure 736266DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 967528DEST_PATH_IMAGE060
、
Figure 54170DEST_PATH_IMAGE062
are respectively the main control of the boiler
Figure 306160DEST_PATH_IMAGE064
The proportional band and the integration time of (c),
Figure 753453DEST_PATH_IMAGE066
、
Figure 950255DEST_PATH_IMAGE068
respectively controlled for intermediate temperature
Figure 596131DEST_PATH_IMAGE070
The proportional band and the integration time of (c),
Figure 189661DEST_PATH_IMAGE072
、
Figure 186567DEST_PATH_IMAGE074
are respectively the main control of the steam turbine
Figure 933200DEST_PATH_IMAGE076
Proportional band and integration time.
Fig. 2 is a flow chart of parameter setting of a multi-target particle swarm algorithm, the multi-target particle swarm algorithm obtains an optimal solution by using a dominance relationship between particles, collects a solution without a dominance relationship between the particles into a non-inferior solution set, and continuously adjusts a search strategy through optimal historical memory and current optimal memory, so that the multi-target particle swarm algorithm has better global search capability, 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 space
Figure DEST_PATH_IMAGE078
In (1), satisfy
Figure DEST_PATH_IMAGE080
Then the initial time is in space
Figure 682850DEST_PATH_IMAGE078
In the random definition of the position vector of the particle
Figure DEST_PATH_IMAGE082
And velocity vector
Figure DEST_PATH_IMAGE084
Are respectively as
Figure DEST_PATH_IMAGE086
Figure DEST_PATH_IMAGE088
Simultaneously initializing inertial weights for particle movement
Figure DEST_PATH_IMAGE090
，
Figure 171250DEST_PATH_IMAGE090
When the total optimizing capacity is larger, the overall optimizing capacity is stronger, and the total optimizing capacity is smallerThe local optimization ability is stronger, and the selection is general
Figure DEST_PATH_IMAGE092
。
Particles optimized for history
Figure DEST_PATH_IMAGE094
Learning factor of
Figure DEST_PATH_IMAGE096
Globally optimal particle
Figure DEST_PATH_IMAGE098
Learning factor of
Figure DEST_PATH_IMAGE100
Satisfy the following requirements
Figure DEST_PATH_IMAGE102
。
(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
Figure DEST_PATH_IMAGE104
Figure DEST_PATH_IMAGE106
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 the next time is reached, all the particles need to be updated, and the speed update 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
Figure DEST_PATH_IMAGE108
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE110
，
Figure DEST_PATH_IMAGE112
is a random number within (0, 1),
Figure DEST_PATH_IMAGE114
is the weight of the speed at the last time. And carrying out iterative updating through the 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:
Figure DEST_PATH_IMAGE116
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE118
is as follows
Figure 136012DEST_PATH_IMAGE118
Particles of, therefore
Figure DEST_PATH_IMAGE120
All particles share
Figure 827281DEST_PATH_IMAGE120
A scheme is due to
Figure DEST_PATH_IMAGE122
And
Figure DEST_PATH_IMAGE124
the 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 normalized
Figure 572383DEST_PATH_IMAGE118
Obtained by
Figure DEST_PATH_IMAGE126
And
Figure DEST_PATH_IMAGE128
brought into the above-mentioned formula
Figure DEST_PATH_IMAGE130
Obtaining weighted
Figure DEST_PATH_IMAGE132
And
Figure DEST_PATH_IMAGE134
finally, the performance index of each particle is obtained
Figure DEST_PATH_IMAGE136
：
Figure DEST_PATH_IMAGE138
，
Figure 635279DEST_PATH_IMAGE136
For dimensionless numbers, the comparison can be made directly, after each iteration, for all particlesSeed of Japanese apricot
Figure 810040DEST_PATH_IMAGE136
Sequencing 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 scheme
Figure DEST_PATH_IMAGE140
Can be selected from the following:
Figure DEST_PATH_IMAGE142
。
can be obtained by the above formula
Figure 629966DEST_PATH_IMAGE136
Corresponding to the minimum value of
Figure DEST_PATH_IMAGE144
By using
Figure 901679DEST_PATH_IMAGE136
The controller parameters are adjusted by the data corresponding to the minimum value of (1), namely the scheme
Figure 243798DEST_PATH_IMAGE140
In (1)
Figure 155122DEST_PATH_IMAGE060
And
Figure 984931DEST_PATH_IMAGE062
are respectively the main control of the boiler
Figure 439047DEST_PATH_IMAGE064
The proportional band and the control parameters of the integration time,
Figure 811122DEST_PATH_IMAGE066
and
Figure 960475DEST_PATH_IMAGE068
respectively controlled for intermediate temperature
Figure 341778DEST_PATH_IMAGE070
The proportional band and the control parameters of the integration time,
Figure 758721DEST_PATH_IMAGE072
、
Figure 442644DEST_PATH_IMAGE074
are respectively the main control of the steam turbine
Figure 797401DEST_PATH_IMAGE076
The control parameters of the proportional band and the integral time are adjusted by the controller through the parameters obtained by the multi-target particle swarm algorithm, so that the control precision can be improved, the operation stability of the unit is ensured, the coal consumption and the steam consumption of the unit are reduced, and 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 (6)

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;
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.
2. The thermal power generating unit full-load coordination control method according to claim 1, characterized in that: in the first step, the calculation formulas of the main steam pressure deviation, the power deviation, the middle point temperature deviation, the coal consumption rate and the steam consumption rate are as follows:
deviation of main steam pressure
Figure DEST_PATH_IMAGE002
= pressure set value
Figure DEST_PATH_IMAGE004
Main steam pressure
Figure DEST_PATH_IMAGE006
；
Deviation of power
Figure DEST_PATH_IMAGE008
= load instruction
Figure DEST_PATH_IMAGE010
-actual power of the hair
Figure DEST_PATH_IMAGE012
；
Temperature deviation of intermediate point
Figure DEST_PATH_IMAGE014
= midpoint temperature set value
Figure DEST_PATH_IMAGE016
Temperature at mid point
Figure DEST_PATH_IMAGE018
；
Rate of coal consumption
Figure DEST_PATH_IMAGE020
= coal consumption
Figure DEST_PATH_IMAGE022
/(calorific value correction factor)
Figure DEST_PATH_IMAGE024
Figure DEST_PATH_IMAGE026
Actual power
Figure 657932DEST_PATH_IMAGE012
）；
Rate of steam consumption
Figure DEST_PATH_IMAGE028
= main steam flow
Figure DEST_PATH_IMAGE030
Actual power
Figure 994629DEST_PATH_IMAGE012
。
3. The thermal power generating unit full-load coordination control method according to claim 1, characterized in that: in step two, the calculation formula of the control performance index and the economic index is as follows:
controlling the performance index:
Figure DEST_PATH_IMAGE032
；
the economic index is as follows:
Figure DEST_PATH_IMAGE034
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE036
、
Figure DEST_PATH_IMAGE038
、
Figure DEST_PATH_IMAGE040
main steam pressure deviation, power deviation and intermediate point temperature deviation coefficients which are used for controlling performance indexes,
Figure DEST_PATH_IMAGE042
、
Figure DEST_PATH_IMAGE044
respectively the coal consumption rate and the steam consumption rate coefficient in the economic index.
4. 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:
Figure DEST_PATH_IMAGE046
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE048
is composed of
Figure 554180DEST_PATH_IMAGE030
Dimension optimization variable is as follows
Figure DEST_PATH_IMAGE050
、
Figure DEST_PATH_IMAGE052
The two objects are to be achieved by,
Figure DEST_PATH_IMAGE054
one inequality constraint.
5. Wherein the constraints are:
and (3) target constraint:
Figure DEST_PATH_IMAGE056
controller parameter constraints:
Figure DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE060
、
Figure DEST_PATH_IMAGE062
are respectively the main control of the boiler
Figure DEST_PATH_IMAGE064
The proportional band and the integration time of (c),
Figure DEST_PATH_IMAGE066
、
Figure DEST_PATH_IMAGE068
respectively controlled for intermediate temperature
Figure DEST_PATH_IMAGE070
The proportional band and the integration time of (c),
Figure DEST_PATH_IMAGE072
、
Figure DEST_PATH_IMAGE074
are respectively the main control of the steam turbine
Figure DEST_PATH_IMAGE076
Proportional band and integration time.
6. The thermal power generating unit full-load coordination control method according to claim 1, characterized in that: in step one, the sampling time is 1 s.
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