CN116880173A - Particle swarm optimization-based parameter identification control system and method for cogeneration unit - Google Patents
Particle swarm optimization-based parameter identification control system and method for cogeneration unit Download PDFInfo
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Abstract
The application belongs to the technical field of thermal power unit control power generation, and discloses a particle swarm algorithm-based cogeneration unit parameter identification control system and a particle swarm algorithm-based cogeneration unit parameter identification control method; the parameter identification control system of the cogeneration unit is used for inputting the acquired coal supply quantity, the opening of a turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port, carrying out simulation prediction, and outputting simulation prediction values of a turbine load, main steam pressure, a heating load and a heat storage load; the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, and the transfer function is obtained by identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm. The application can improve the load regulation capability of the cogeneration unit, improve the running flexibility of the unit, improve the regulation depth of the thermal power unit and realize large-scale grid connection of the power-assisted renewable energy sources.
Description
Technical Field
The application belongs to the technical field of thermal power unit control power generation, and particularly relates to a particle swarm algorithm-based parameter identification control system and method for a cogeneration unit.
Background
The problems caused by the gradual shortage of fossil energy and the gradual deterioration of climate environment are faced, and the energy conservation and emission reduction and the vigorous development of clean energy are already the basic consensus of various countries. In order to reduce the emission of carbon dioxide and reduce the dependence on fossil energy, clean energy sources such as wind power, photovoltaic power generation and the like are rapidly developed; the new energy has a higher duty ratio in the power grid year by year, and the demand for peak-to-peak power supply is also gradually increased.
Thermal power generating units also have the problem of insufficient flexibility; illustratively, for the safety operation of the unit, the variable load rate of the existing machine-furnace coordination control system generally cannot exceed 5 percent/min of the rated load of the unit; under the dual background that the coal-fired power generating unit occupies the main power supply position and meanwhile large-scale unstable renewable energy is needed to be connected with the grid, the load regulation capability of the thermal power generating unit is needed to be improved.
At present, feasibility analysis is performed on flexibility improvement of a unit by an energy storage system, but a control method for specifically improving the flexibility of the system is not provided; the method is particularly explanatory, in the process of building the heat storage control model in the prior art, the model is too simplified, the influence of the flow rate of the water inlet on the inclined temperature layer in the heat storage is not considered in the control precision, the influence of the inclined temperature layer of the heat storage on the running parameters of the unit is not considered, and the research on the gain aspect brought by the deep peak shaving of the inclined temperature layer heat storage is very few.
Disclosure of Invention
The application aims to provide a particle swarm algorithm-based parameter identification control system and method for a cogeneration unit, which are used for solving one or more of the technical problems. According to the technical scheme provided by the application, the load adjustment capability of the cogeneration unit can be improved, the unit operation flexibility is improved, the adjustment depth of the thermal power unit is improved, and the power-assisted renewable energy source is connected in a large scale.
In order to achieve the above purpose, the application adopts the following technical scheme:
the application provides a particle swarm optimization-based parameter identification control system for a cogeneration unit,
the parameter identification control system of the cogeneration unit is used for inputting the acquired coal supply quantity, the opening of a turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port, carrying out simulation prediction, and outputting simulation prediction values of a turbine load, main steam pressure, a heating load and a heat storage load;
the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, and the transfer function is obtained by identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm.
A further improvement of the present application is that,
the transfer function is structured such that,
wherein W(s) is the transfer function of the controlled object, TS, T 1S 、T 2S Is an inertial time constant, K is a proportionality coefficient, and n is an inertial index.
A further improvement of the present application is that,
the step of identifying and fitting the unknown parameters of the transfer function by adopting the particle swarm optimization comprises the following steps:
obtaining simulation data and carrying out zero initialization processing to obtain an identifiable data range;
based on the distinguishable data range, designing a corresponding transfer function according to the dynamic characteristic curve shape of the key parameters obtained under step disturbance of different input quantities, and identifying unknown parameters in the objective function through a particle swarm algorithm; comparing the identification result with the dynamic characteristic curve, judging whether the precision meets the requirement, if so, completing the parameter identification, and if not, repeating the parameter identification through the particle swarm algorithm.
A further improvement of the present application is that,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
the identification of the load characteristic curve of the steam turbine under the step disturbance of the coal feeding amount is shown in the following formula,
wherein Δp is the turbine load; Δr i Is the coal feeding amount;
under the step disturbance of the coal feeding amount, the identification of the main steam pressure characteristic curve is shown in the following formula,
wherein Δp ms Is the main steam pressure variation;
under the step disturbance of the coal feeding amount, the identification of the heat supply load curve is shown in the following formula,
wherein DeltaP heat Is the heating load variation.
A further improvement of the present application is that,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
under the step disturbance of the turbine regulating valve, the turbine load response particle swarm identification is shown in the following formula,
wherein Deltau ms The opening variation of the regulating valve is regulated; Δp is the turbine load;
under the step disturbance of the turbine regulating valve, the main steam pressure is identified as follows,
wherein Δp ms Is the main steam pressure variation; deltau ms To adjust the valve opening variation.
A further improvement of the present application is that,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
under the step disturbance of the heat supply steam extraction butterfly valve, the load curve of the steam turbine is identified by particle swarm as shown in the following formula,
wherein Δp is the turbine load; deltau heat The variable quantity of the butterfly valve for heating and steam extraction;
under the step disturbance of the heat supply steam extraction butterfly valve, the heat supply load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the heat supply and steam extraction butterfly valve, the heat storage load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP tlk Is a heat storage load; u (u) heat The variable quantity of the butterfly valve for heating and extracting steam.
The application provides a particle swarm optimization-based parameter identification control method for a cogeneration unit, which comprises the following steps:
inputting the obtained coal feeding amount, the opening of a steam turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port into a parameter identification control system of the cogeneration unit for simulation prediction, and outputting simulation predicted values of a steam turbine load, a main steam pressure, a heating load and a heat storage load;
the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, and the transfer function is obtained by identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm.
A further improvement of the present application is that,
the transfer function is structured such that,
wherein W(s) is the transfer function of the controlled object, TS, T 1S 、T 2S Is an inertial time constant, K is a proportionality coefficient, and n is an inertial index.
A further improvement of the present application is that,
the step of identifying and fitting the unknown parameters of the transfer function by adopting the particle swarm optimization comprises the following steps:
obtaining simulation data and carrying out zero initialization processing to obtain an identifiable data range;
based on the distinguishable data range, designing a corresponding transfer function according to the dynamic characteristic curve shape of the key parameters obtained under step disturbance of different input quantities, and identifying unknown parameters in the objective function through a particle swarm algorithm; comparing the identification result with the dynamic characteristic curve, judging whether the precision meets the requirement, if so, completing the parameter identification, and if not, repeating the parameter identification through the particle swarm algorithm.
A further improvement of the present application is that,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
the identification of the load characteristic curve of the steam turbine under the step disturbance of the coal feeding amount is shown in the following formula,
wherein Δp is the turbine load; Δr i Is the coal feeding amount;
under the step disturbance of the coal feeding amount, the identification of the main steam pressure characteristic curve is shown in the following formula,
wherein Δp ms Is the main steam pressure variation;
under the step disturbance of the coal feeding amount, the identification of the heat supply load curve is shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the turbine regulating valve, the turbine load response particle swarm identification is shown in the following formula,
wherein Deltau ms The opening variation of the regulating valve is regulated; Δp is the turbine load;
under the step disturbance of the turbine regulating valve, the main steam pressure is identified as follows,
wherein Δp ms Is the main steam pressure variation; deltau ms The opening variation of the regulating valve is regulated;
under the step disturbance of the heat supply steam extraction butterfly valve, the load curve of the steam turbine is identified by particle swarm as shown in the following formula,
wherein Δp is the turbine load; deltau heat The variable quantity of the butterfly valve for heating and steam extraction;
under the step disturbance of the heat supply steam extraction butterfly valve, the heat supply load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the heat supply and steam extraction butterfly valve, the heat storage load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP tlk Is a heat storage load; u (u) heat The variable quantity of the butterfly valve for heating and extracting steam.
Compared with the prior art, the application has the following beneficial effects:
according to the technical scheme provided by the application, step disturbance of parameters such as coal feeding quantity, opening of a steam turbine regulating valve, opening of a heat supply butterfly valve and the like is carried out through a mechanism model of the thermal power unit, so that the variable quantities of parameters such as the load of the steam turbine, the pressure of main steam, the heat supply load and the heat storage load are obtained; according to the structure of the transfer function, unknown parameters in the transfer function are identified and fitted through a particle swarm algorithm, so that the transfer function of the system control model is obtained, and the combination of thermal power and new energy power generation can be realized. In conclusion, the control model after parameter identification has higher precision, can improve the load adjustment capability of the cogeneration unit, improve the unit operation flexibility, improve the adjustment depth of the thermal power unit, assist renewable energy sources for large-scale grid connection, and can provide operation reference for the operation of an actual unit.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description of the embodiments or the drawings used in the description of the prior art will make a brief description; it will be apparent to those of ordinary skill in the art that the drawings in the following description are of some embodiments of the application and that other drawings may be derived from them without undue effort.
FIG. 1 is a schematic diagram of a control model of a heat-storage-based cogeneration unit in an embodiment of the application;
FIG. 2 is a schematic diagram of a control model parameter identification process according to an embodiment of the application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which 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 those skilled in the art based on the embodiments of the present application 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 the 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 such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise 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 application is described in further detail below with reference to the attached drawing figures:
referring to fig. 1, the parameter identification control system of the cogeneration unit provided by the embodiment of the application is used for inputting the obtained coal feeding amount, the opening of a turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port, performing simulation prediction, and outputting the load of the turbine, the pressure of main steam, the heating load and the heat storage load; wherein, the liquid crystal display device comprises a liquid crystal display device,
the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, wherein a transfer function is obtained by identifying and fitting unknown parameters in the transfer function according to a transfer function structure and by combining a particle swarm algorithm.
In the technical scheme provided by the embodiment of the application, the aim of improving the load regulation capability of the cogeneration unit, improving the running flexibility of the unit, improving the regulation depth of the thermal power unit and realizing large-scale grid connection of the power-assisted renewable energy sources is achieved; in order to obtain a control model of the heat and power cogeneration unit based on heat storage, the application carries out step disturbance on parameters such as coal feeding quantity, opening of a steam turbine regulating valve, opening of a heat supply butterfly valve and the like through a thermal power unit mechanism model to obtain variable quantities of parameters such as steam turbine load, main steam pressure, heat supply load, heat storage load and the like; according to the structure of the transfer function, unknown parameters in the transfer function are identified and fitted through a Particle Swarm (PSO) algorithm, so that a control model transfer function of the system is obtained, and a foundation can be provided for the subsequent research of a control method.
The embodiment of the application is specifically explanatory, the control model refers to a reference model based on which the parameters of the controller are adjusted, and compared with the mechanism model, the control model has a simple structure and is mainly used for researching the control method of the thermal power unit; the control model is simpler in construction process than the mechanism model, and the control model can be exemplarily divided into 4 steps, including:
(1) Determining model inputs and outputs;
(2) Determining the structure of the model and the number of transfer functions;
(3) Carrying out parameter identification on the transfer function by adopting a modern mathematical method;
(4) And verifying the accuracy of the model.
In the embodiment of the application, the control model is of a 4-in 4-out structure; the input of the model is the coal feeding amount, the opening of the steam regulating valve, the opening of the heat supply butterfly valve and the flow of the heat storage hot water port, the output of the model is the load of the steam turbine, the main steam pressure, the heat load and the heat storage load, 8 transfer functions are all arranged in the control model, and the final control model is obtained after parameter identification is carried out on the 8 transfer functions.
Referring to fig. 2, when parameter identification is performed on 8 transfer functions, parameter identification is performed based on a PSO algorithm according to an exemplary embodiment of the present application; firstly, carrying out zero initialization processing on simulation data, and observing to obtain an identifiable data range; then, designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities; and then identifying unknown parameters in the objective function through a particle swarm algorithm, comparing an identification result with a dynamic characteristic curve, judging whether the accuracy meets the requirement, if so, completing the parameter identification, and if not, repeating the parameter identification.
The particle swarm algorithm adopts a population global search strategy and a simple speed displacement mode. A prey flock (random solution) is regarded as a system, a bait (optimal solution) exists somewhere in the area, each member in the flock has a respective flying speed and direction, and the distance (adaptability) between the member and the bait is known, but the direction of the bait is not known, so that in order to find the bait, a bird closest to the bait is first found, then the bird is searched in the area nearby the bird, and after multiple searches, the bait is finally found, namely the system obtains the optimal solution. The high-order system has self-balancing capability and belongs to a high-order inertia link. When the pulverized coal flow is subjected to step change, dynamic characteristic curves of a turbine load, main steam pressure, a heating load, a heat storage load and the like are highly similar to the shape of a high-order inertial transfer function.
In the embodiment of the application, the dynamic characteristic curves of the turbine load, the main steam pressure, the heat supply load, the heat storage load and the like are highly similar to the shape of the high-order inertia transfer function when the pulverized coal flow rate is subjected to step change. Therefore, the transfer function of the controlled object has a structure as shown in formula (1) or formula (2):
wherein W(s) is a transfer function, TS, T 1S 、T 2S Is an inertial time constant, K is a proportionality coefficient, and n is an inertial index.
In the embodiment of the application, under the step disturbance of the coal feeding amount, the variation of the load of the steam turbine, the main steam pressure and the heat supply load are respectively obtained, and the transfer function is respectively deduced as follows:
under the step disturbance of the coal feeding amount, the identification of the load characteristic curve of the steam turbine is shown in a formula (3):
wherein Δp is turbine load, kW; Δr i The coal feeding amount is t/h;
under the step disturbance of the coal feeding amount, the identification of the main steam pressure characteristic curve is shown as a formula (4):
wherein Δp ms Is the main steam pressure variation, MPa;
under the step disturbance of the coal feeding amount, the heat supply load curve identification is shown as a formula (5):
wherein DeltaP heat For the heating load variation, kW.
In the embodiment of the application, under the step disturbance of the turbine regulating valve, the turbine load and the main steam pressure variation are respectively obtained, and the transfer function is respectively deduced as follows:
under the step disturbance of the turbine regulating valve, the turbine load response particle swarm identification is shown as a formula (6):
wherein Deltau ms For adjusting the valve opening variation,%;
under the step disturbance of the turbine regulating valve, the main steam pressure identification result is shown as a formula (7):
wherein Δp ms Is the main steam pressure variation, MPa; deltau ms For adjusting the valve opening variation,%.
In the embodiment of the application, under the condition that the heat supply and steam extraction butterfly valve generates step disturbance, the obtained turbine load, heat supply load and heat storage load variation are deduced as follows based on a PSO algorithm:
under the condition that the heat supply and steam extraction butterfly valve is subjected to step disturbance, a turbine load curve is identified by particle swarm as shown in (8):
under the step disturbance of the heat supply and steam extraction butterfly valve, the heat supply load curve is identified by particle swarm as shown in formula (9):
wherein Deltau heat For the variable quantity of the heating steam extraction butterfly valve,%.
Under the step disturbance of the heat supply and steam extraction butterfly valve, the heat storage load delta P tlk The curve is identified by the particle swarm as shown in formula (10):
compared with the traditional mechanical modeling, the control model of the embodiment of the application has a simple structure, is mainly used for researching a thermal power unit control method, combines a PSO algorithm to perform parameter identification of the model, improves the load adjustment capacity of the cogeneration unit, improves the unit operation flexibility and the adjustment depth of the thermal power unit, and aims at large-scale grid connection of the power-assisted renewable energy.
The embodiment of the application provides a particle swarm algorithm-based parameter identification control method for a cogeneration unit, which comprises the following steps:
inputting the obtained coal feeding amount, the opening of a steam turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port into a parameter identification control system of the cogeneration unit for simulation prediction, and outputting simulation predicted values of a steam turbine load, a main steam pressure, a heating load and a heat storage load; the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, wherein a transfer function is used for identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm, and a time constant of the transfer function in the simulation model of the cogeneration unit is identified. The obtained simulation model is used for simulating the actual running condition of the unit and providing guidance opinion for the running of the unit. Therefore, the provided simulation model has the following operation adjustment modes for the unit:
(1) When the load of the turbine needs to be increased, the load of the turbine is increased by increasing the coal feeding amount of the turbine, the opening of a turbine regulating valve and the opening of a heating butterfly valve; conversely, when the turbine load needs to be reduced, the adjustment mode is opposite.
(2) Main steam pressure, when the main steam pressure needs to be increased, the main steam pressure is increased by increasing the coal feeding amount of the steam turbine and reducing the opening of the regulating valve of the steam turbine; conversely, when the main steam pressure needs to be reduced, the adjustment mode is opposite.
(3) When the heating load needs to be increased, the heating load is increased by increasing the coal supply amount of the steam turbine and reducing the opening of a heating butterfly valve; conversely, when the heating load needs to be reduced, the adjustment mode is opposite.
(4) The heat storage load is increased by reducing the flow of the hot water port and the opening of the heat supply butterfly valve when the heat storage load needs to be increased; conversely, when the heat storage load needs to be reduced, the adjustment mode is opposite.
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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (10)
1. A particle swarm optimization-based parameter identification control system for a cogeneration unit is characterized in that,
the parameter identification control system of the cogeneration unit is used for inputting the acquired coal supply quantity, the opening of a turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port, carrying out simulation prediction, and outputting simulation prediction values of a turbine load, main steam pressure, a heating load and a heat storage load;
the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, and the transfer function is obtained by identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm.
2. A particle swarm optimization-based cogeneration unit parameter identification control system according to claim 1, wherein,
the transfer function is structured such that,
wherein W(s) is the transfer function of the controlled object, TS, T 1S 、T 2S Is an inertial time constant, K is a proportionality coefficient, and n is an inertial index.
3. A particle swarm optimization-based cogeneration unit parameter identification control system according to claim 2, wherein,
the step of identifying and fitting the unknown parameters of the transfer function by adopting the particle swarm optimization comprises the following steps:
obtaining simulation data and carrying out zero initialization processing to obtain an identifiable data range;
based on the distinguishable data range, designing a corresponding transfer function according to the dynamic characteristic curve shape of the key parameters obtained under step disturbance of different input quantities, and identifying unknown parameters in the objective function through a particle swarm algorithm; comparing the identification result with the dynamic characteristic curve, judging whether the precision meets the requirement, if so, completing the parameter identification, and if not, repeating the parameter identification through the particle swarm algorithm.
4. A particle swarm optimization-based cogeneration unit parameter identification control system according to claim 3, wherein,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
the identification of the load characteristic curve of the steam turbine under the step disturbance of the coal feeding amount is shown in the following formula,
wherein Δp is the turbine load; Δr i Is the coal feeding amount;
under the step disturbance of the coal feeding amount, the identification of the main steam pressure characteristic curve is shown in the following formula,
wherein Δp ms Is the main steam pressure variation;
under the step disturbance of the coal feeding amount, the identification of the heat supply load curve is shown in the following formula,
wherein DeltaP heat Is the heating load variation.
5. A particle swarm optimization-based cogeneration unit parameter identification control system according to claim 3, wherein,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
under the step disturbance of the turbine regulating valve, the turbine load response particle swarm identification is shown in the following formula,
wherein Deltau ms The opening variation of the regulating valve is regulated; Δp is the turbine load;
under the step disturbance of the turbine regulating valve, the main steam pressure is identified as follows,
wherein Δp ms Is the main steam pressure variation; deltau ms To adjust the valve opening variation.
6. A particle swarm optimization-based cogeneration unit parameter identification control system according to claim 3, wherein,
the step of identifying unknown parameters in the objective function by a particle swarm algorithm is carried out by designing corresponding transfer functions according to the dynamic characteristic curve shapes of key parameters obtained under step disturbance of different input quantities based on the distinguishable data range,
under the step disturbance of the heat supply steam extraction butterfly valve, the load curve of the steam turbine is identified by particle swarm as shown in the following formula,
wherein Δp is the turbine load; deltau heat The variable quantity of the butterfly valve for heating and steam extraction;
under the step disturbance of the heat supply steam extraction butterfly valve, the heat supply load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the heat supply and steam extraction butterfly valve, the heat storage load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP tlk Is a heat storage load; u (u) heat The variable quantity of the butterfly valve for heating and extracting steam.
7. The particle swarm optimization-based parameter identification control method for the cogeneration unit is characterized by comprising the following steps of:
inputting the obtained coal feeding amount, the opening of a steam turbine regulating valve, the opening of a heating butterfly valve and the flow of a hot water port into a parameter identification control system of the cogeneration unit for simulation prediction, and outputting simulation predicted values of a steam turbine load, a main steam pressure, a heating load and a heat storage load;
the parameter identification control system of the cogeneration unit adopts a thermal power unit mechanism model, and the transfer function is obtained by identifying and fitting unknown parameters of the transfer function according to a transfer function structure and by adopting a particle swarm algorithm.
8. The method for controlling the parameter identification of a cogeneration unit based on a particle swarm optimization according to claim 7,
the transfer function is structured such that,
wherein W(s) is the transfer function of the controlled object, TS, T 1S 、T 2S Is an inertial time constant, K is a proportionality coefficient, and n is an inertial index.
9. The method for controlling the parameter identification of the cogeneration unit based on the particle swarm optimization according to claim 8, wherein,
the step of identifying and fitting the unknown parameters of the transfer function by adopting the particle swarm optimization comprises the following steps:
obtaining simulation data and carrying out zero initialization processing to obtain an identifiable data range;
based on the distinguishable data range, designing a corresponding transfer function according to the dynamic characteristic curve shape of the key parameters obtained under step disturbance of different input quantities, and identifying unknown parameters in the objective function through a particle swarm algorithm; comparing the identification result with the dynamic characteristic curve, judging whether the precision meets the requirement, if so, completing the parameter identification, and if not, repeating the parameter identification through the particle swarm algorithm.
10. The method for controlling the parameter identification of the cogeneration unit based on the particle swarm optimization according to claim 9, wherein,
the identification of the load characteristic curve of the turbine under the step disturbance of the coal feeding amount is shown in the following formula in the step of identifying the unknown parameters in the objective function through the particle swarm algorithm,
wherein Δp is the turbine load; Δr i Is the coal feeding amount;
under the step disturbance of the coal feeding amount, the identification of the main steam pressure characteristic curve is shown in the following formula,
wherein Δp ms Is the main steam pressure variation;
under the step disturbance of the coal feeding amount, the identification of the heat supply load curve is shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the turbine regulating valve, the turbine load response particle swarm identification is shown in the following formula,
wherein Deltau ms The opening variation of the regulating valve is regulated; Δp is the turbine load;
under the step disturbance of the turbine regulating valve, the main steam pressure is identified as follows,
wherein Δp ms Is the main steam pressure variation; deltau ms The opening variation of the regulating valve is regulated;
under the step disturbance of the heat supply steam extraction butterfly valve, the load curve of the steam turbine is identified by particle swarm as shown in the following formula,
wherein Δp is the turbine load; deltau heat The variable quantity of the butterfly valve for heating and steam extraction;
under the step disturbance of the heat supply steam extraction butterfly valve, the heat supply load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP heat Is the heating load variation;
under the step disturbance of the heat supply and steam extraction butterfly valve, the heat storage load curve is identified by particle swarm as shown in the following formula,
wherein DeltaP tlk Is a heat storage load; u (u) heat The variable quantity of the butterfly valve for heating and extracting steam.
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