CN114357023B - Data-driven direct air cooling unit operation optimization method and system - Google Patents
Data-driven direct air cooling unit operation optimization method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for optimizing the operation of a direct air cooling unit based on data driving, wherein the method comprises the following steps: collecting real-time operation data and preprocessing the real-time operation data; constructing a cold end system related data model according to the real-time operation data, and training the data model to obtain a trained data model; evaluating the correlation among factors influencing the running state of the direct air cooling unit based on the trained data model; and obtaining the optimal back pressure on the basis of the correlation by utilizing an optimization algorithm, and realizing the operation optimization of the direct air cooling unit. The invention can ensure the safe and economic operation of the air cooling unit, realize the comprehensive optimal energy consumption, and guide field operation maintainers to effectively improve and adjust the backpressure control scheme, the air cooling island fan rotating speed control and the like, so that the unit obtains good regulation quality and operation effect.
Description
Technical Field
The invention relates to the technical field of energy conservation and emission reduction, in particular to a method and a system for optimizing the operation of a direct air cooling unit based on data driving.
Background
The power consumption of the fan of the air cooling island of the direct air cooling unit accounts for about 10 percent of the power consumption of the plant and accounts for 1 to 2 percent of the total generated energy. Because the thermal power plant adopts Rankine cycle, the heat loss of a cold end system accounts for the largest proportion, and the heat loss is the part with the largest energy-saving potential. The reduction of the operating pressure (i.e., back pressure) of the condenser increases the work done by the turbine, but also increases the air cooling power consumption of the air cooling island. As shown in fig. 1, in the process of reducing the operating pressure of the condenser, the vacuum degree when the difference between the increase in the generated power of the turbine unit and the increase in the power of the motor driving the air cooling island fan reaches the maximum value is referred to as the optimum vacuum degree, and the back pressure at this time is referred to as the optimum back pressure. The cold end optimization has important significance for improving the cold end performance of the turboset, improving the heat economy of the generator set and saving energy and reducing emission.
Fig. 2 shows a cold end system of a direct air cooling unit, where exhaust steam of a steam turbine is cooled into liquid water in a heat exchange tube bundle of an air cooling island, and the air cooling island has the characteristics of large area of a radiator, numerous fans in a frequency conversion speed regulation mode, strong sensitivity to environmental factors (mainly ambient temperature) of the air cooling system, frequent load change of the unit, complex operation control, and the like. In different seasons, the air cooling controllable backpressure intervals are different, and when the air cooling controllable backpressure intervals exceed the reasonable backpressure intervals, the power consumption increment of an air cooling island of the air cooling controllable backpressure interval may exceed the generating power increment of a unit. When the load of the unit and the ambient temperature are fixed, an optimal running rotating speed of the air cooling fan exists. For the direct air cooling unit which is put into operation, how to increase the output of the unit while reducing the output of the fan as much as possible to obtain the optimal air cooling island fan operation mode of the air cooling unit under different operation loads and different environmental temperatures and improve the economical efficiency of the operation of the direct air cooling unit becomes a problem to be solved urgently.
At present, the optimal operation backpressure of the direct air cooling unit is generally determined by a thermal test. The thermal test needs to be completed by an electric academy or a thermal academy with professional qualifications, the cost is high, the power grid dispatching cannot be accepted in the test process, and the power generation benefit is low. The thermal test can only be performed according to a small number of working conditions and environmental condition working conditions, and real-time operation and operation guidance cannot be achieved. The problems that backpressure economic operation lacks data support, the rotating speed of an air cooling fan lacks real-time recommendation guidance and the like generally exist in an air cooling island of a direct air cooling unit of a thermal power plant. Some scientific research units carry out analytical research on optimization of the air cooling island through theoretical analysis calculation, numerical simulation and other modes, but the existing air cooling island has less landing application and unobvious application effect.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: in the prior art, the thermal test needs to be completed by an electric academy or a thermal academy with professional qualifications, the cost is high, the power grid dispatching cannot be accepted in the test process, the power generation income is low, the thermal test can only carry out the test aiming at a small number of working conditions and environmental condition working conditions, and the real-time operation and operation guidance cannot be realized; the problems that back pressure economic operation lacks data support, the rotating speed of an air cooling fan lacks real-time recommendation guidance and the like commonly exist in an air cooling island of a direct air cooling unit of a thermal power plant; some scientific research units analyze and research the optimization of the air cooling island in modes of theoretical analysis calculation, numerical simulation and the like, but the application on the ground is less at present, and the application effect is not obvious.
In order to solve the technical problems, the invention provides the following technical scheme: collecting real-time operation data and preprocessing the real-time operation data; constructing a cold end system related data model according to the real-time operation data, and training the data model to obtain a trained data model; evaluating the correlation among factors influencing the running state of the direct air cooling unit based on the trained data model; and obtaining the optimal back pressure by utilizing an optimization algorithm on the basis of the correlation, and realizing the operation optimization of the direct air cooling unit.
As a preferable scheme of the data-driven direct air cooling unit operation optimization method, the method comprises the following steps: the real-time operating data includes backpressure, power generation, ambient temperature fan speed, and other necessary parameters associated with the cold end system.
As a preferable scheme of the data-driven direct air cooling unit operation optimization method, the method comprises the following steps: preprocessing the real-time operation data comprises the steps of cleaning the acquired original data in a value domain and a time domain, cleaning according to a reasonable range of parameters, and removing invalid data in the processes of starting and stopping a unit and the like; judging and cleaning data in an abnormal state in the running process of the unit, outliers deviating from a normal working condition and data drift in original measuring points; storing the cleaned product in a database; and continuously removing old data from the database in a dynamic updating mode, adding new data, wherein the data retention period of the database is the latest year.
As a preferable scheme of the data-driven direct air-cooling unit operation optimization method, the method comprises the following steps: the cold end system related data model comprises a power generation power model, an air cooling island heat exchange model and an air cooling island power consumption model; the generated power model is used for evaluating the relation between the back pressure and the generated power under different loads; the air cooling island heat exchange model is used for evaluating the relationship between the power consumption of the air cooling island and the backpressure and the ambient temperature under different power generation powers; the air cooling island power consumption model is used for evaluating the relation between the air cooling island power consumption and the fan rotating speed under different environmental temperatures.
As a preferable scheme of the data-driven direct air cooling unit operation optimization method, the method comprises the following steps: and carrying out timing training on the cold end system related data model by using the continuously updated data in the database, and continuously updating the cold end system related data model.
As a preferable scheme of the data-driven direct air-cooling unit operation optimization method, the method comprises the following steps: obtaining the optimal backpressure based on the interrelationships using an optimization algorithm includes constructing an optimal backpressure algorithm based on the optimization algorithm: under the limitation of adjustability of power and rotating speed of a fan of the air cooling island, the backpressure is searched within the range of 5-30KPa of the operating pressure of the condenser at intervals of 0.1KPa, and the backpressure value under the minimum value of the objective function is used as the optimal backpressure.
As a preferable scheme of the data-driven direct air cooling unit operation optimization method, the method comprises the following steps: the objective function is: generated power-air cooling island power consumption.
As a preferable scheme of the data-driven direct air-cooling unit operation optimization method, the method comprises the following steps: and the cold end system related data model is an AI algorithm model, the input and the output of the model are defined, and the relation is established in a self-learning mode.
In order to solve the above technical problem, the present invention further provides a data-driven direct air cooling unit operation optimization system, including: the data acquisition module is used for acquiring real-time operation data; the cold end system data model building module is connected with the data acquisition module and used for building a cold end system data model, and the data acquired by the data acquisition module is used for training at regular time and updating the cold end system data model; and the optimal parameter recommendation module is connected with the cold end system data model construction module and is used for selecting the optimal back pressure.
The invention has the beneficial effects that: the invention can ensure the safe and economic operation of the air cooling unit, realize the comprehensive optimal energy consumption, and guide field operation maintainers to effectively improve and adjust the backpressure control scheme, the air cooling island fan rotating speed control and the like, so that the unit obtains good regulation quality and operation effect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic diagram illustrating an optimal vacuum level (back pressure) of a method and a system for optimizing operation of a direct air cooling unit based on data driving according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a cold end system of a direct air-cooling unit of a data-driven direct air-cooling unit operation optimization method and system according to an embodiment of the present invention;
fig. 3 is a schematic basic flowchart of a method and a system for optimizing the operation of a direct air-cooling unit based on data driving according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a cold-end system data model construction of a direct air-cooling unit operation optimization method and system based on data driving according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating an energy saving principle and effect of a method and a system for optimizing operation of a direct air-cooling unit based on data driving according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an energy-saving dynamic process of a method and a system for optimizing the operation of a direct air-cooling unit based on data driving according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a basic module of a method and a system for optimizing operation of a direct air-cooling unit based on data driving according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention and that the present invention is not limited by the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not necessarily enlarged to scale, and are merely exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected" and "connected" in the present invention are to be construed broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 6, for an embodiment of the present invention, a method for optimizing operation of a direct air-cooling unit based on data driving is provided, including:
s1: and collecting real-time operation data and preprocessing the real-time operation data.
It should be noted that the real-time operation data includes backpressure, power generation, ambient temperature fan speed, and other necessary parameters related to the cold side system.
Further, preprocessing the real-time operation data comprises:
cleaning the acquired original data in a value domain and a time domain, cleaning according to a reasonable range of parameters, and removing invalid data in the process of starting and stopping a unit;
judging and cleaning data in an abnormal state in the running process of the unit, outliers deviating from a normal working condition and data drift in original measuring points;
storing the cleaned product in a database;
and continuously removing old data from the database in a dynamic updating mode, adding new data, and keeping the database for the latest year.
Specifically, a data server arranged on site is used for communicating with an SIS or DCS real-time database to obtain on-site operation data, and the operation data is collected, cleaned as necessary and stored in the real-time database. The data acquisition measuring points mainly comprise necessary parameters such as back pressure, power generation power, ambient temperature, fan rotating speed and the like related to a cold end system, a dynamic updating mode is adopted for a database for training a data model, the database retains data of the last year, the model is trained at regular time, and the data model is updated.
More specifically, the data acquisition time is: 26/3/2020/11/15/1 # and 2# unit operation data. The heating period has been avoided in the collection of data, because can take out the extraction of certain flow from the steam turbine and be used for the heating in the heating period, and its flow and warm-pressing parameter are constantly undulant, produce great influence to the power that the steam turbine unit sent, are difficult to confirm the backpressure and influence the generating power, and heating period ambient temperature is lower in addition, appears air cooling pipeline icing phenomenon easily, influences data quality.
Required site survey point information: the power of the generator (MW), the ambient temperature (DEG C), the ambient wind speed (m/s), the exhaust pressure (kPa), the main steam pressure of the machine side (MPa), the main steam temperature of the machine side (DEG C), the main steam flow of the boiler (t/h), the temperature of the main exhaust pipe (DEG C), the fan frequency (average fan frequency) (Hz) and the power consumption of the air cooling island (kW).
Further, data acquisition shows:
(1) Neglecting the loss of the exhaust resistance, the exhaust pressure is the operating pressure of the condenser, namely the back pressure.
(2) The air cooling island is defined to have 8 exhaust fans, 7 fans in each row, and the total number of the exhaust fans is 56, and the average fan frequency is obtained by the average frequency of the No. 4 fans in each row.
(3) The air cooling island fan set is powered by 4 air cooling transformers 1A1, 1A2, 1B1 and 1B2, and the total power consumption of the air cooling island fan set is determined by the product of four sections of low-voltage side breaker phase current and 400V bus voltage. Namely:
s2: and constructing a cold end system related data model according to the real-time operation data, and training the data model to obtain a trained data model.
S3: and evaluating the correlation among the factors influencing the running state of the direct air cooling unit based on the trained data model.
As shown in fig. 4, the steps S2 to S3 specifically include:
and performing data modeling on actual equipment in the cold end system by using actual operation data, wherein the related data model of the cold end system comprises a power generation power model, an air cooling island heat exchange model and an air cooling island power consumption model:
the generated power model is used for evaluating the relation between the back pressure and the generated power under different loads;
the air cooling island heat exchange model is used for evaluating the relationship between the power consumption of the air cooling island, the back pressure and the ambient temperature under different power generation powers;
the air cooling island power consumption model is used for evaluating the relation between the air cooling island power consumption and the fan rotating speed under different environmental temperatures.
Specifically, the generating power of the steam turbine set is related to four parameters of main steam pressure, temperature, flow and backpressure of the steam turbine, a generating power model is established by adopting actual operation data, the influence of backpressure change on the generating power under a certain main steam flow is determined, and meanwhile, the influence of backpressure increase or reduction of 1kPa on the generating power can be determined.
And establishing an air cooling island heat exchange model by adopting actual operation data, wherein the power consumption of the air cooling island is related to the backpressure, the ambient temperature and the generated power, and the model is used for determining the influence of backpressure change on the power consumption of the air cooling island under a certain working condition.
An air cooling island power consumption model is established by adopting actual operation data, the power consumption of the air cooling island is related to the rotating speed of a fan and the ambient temperature, the air cooling island power consumption model has clear mechanism research, and the model can be obtained through mechanism regression analysis.
Further, the cold end system related data model is an AI algorithm model, the input and the output of the model are defined, the relation is established in a self-learning mode, the cold end system related data model is trained at regular time by using data in a database which is continuously updated, and the cold end system related data model is continuously updated.
Specifically, a turbine power-back pressure model (generated power model):
structural characteristic value: power boost = generated power/main steam flow;
inputting a data model: main steam flow, main steam temperature, main steam pressure and back pressure;
and (3) outputting a data model: the power is increased slightly.
Air cooling island power consumption-backpressure-ambient temperature model (air cooling island heat exchange model):
inputting a model: back pressure, power, ambient temperature;
and (3) outputting a model: and (5) power consumption of the air cooling island.
Air cooling island power consumption-fan frequency-ambient temperature model (air cooling island power consumption model):
inputting a data model: average rotating speed (frequency) of the fan and ambient temperature;
and (3) outputting a data model: and (5) power consumption of the air cooling island.
S4: and obtaining the optimal back pressure by using an optimization algorithm on the basis of the correlation, and realizing the operation optimization of the direct air cooling unit.
It should be noted that the obtaining of the optimal back pressure based on the correlation by using the optimization algorithm includes:
constructing an optimal backpressure algorithm based on an optimization algorithm:
under the limitation of adjustability of power and rotating speed of a fan of the air cooling island, the backpressure is searched within the range of 5-30KPa of the operating pressure of the condenser at intervals of 0.1KPa, and the backpressure value under the minimum value of the objective function is used as the optimal backpressure.
Wherein the objective function is: generated power-air cooling island power consumption.
Further, after the optimal back pressure is obtained, the air cooling island heat exchange characteristic model is called, so that the power consumption of the air cooling island under the back pressure, the ambient temperature and the generated power can be obtained, and after the power consumption of the air cooling island is obtained, the air cooling island power consumption model is called, so that the recommended value of the fan frequency under the ambient temperature can be obtained.
Part of the program codes of the method of the invention are as follows:
according to the invention, the data-mechanism modeling is carried out on the cold end system by utilizing the actual operation data of the direct air cooling unit, the optimal backpressure can be recommended in real time according to the current environmental temperature and the unit load, and further, the parameters such as the operation rotating speed of the air cooling island fan and the like are recommended, the safe and economic operation of the air cooling unit can be ensured, the comprehensive optimal energy consumption can be realized, and the on-site operation maintainers can be guided to effectively improve and adjust the backpressure control scheme, the control of the rotating speed of the air cooling island fan and the like, so that the unit can obtain good regulation quality and operation effect.
In order to verify and explain the technical effects adopted in the method, the method is adopted to carry out verification tests, the real effects of the method are verified by means of scientific demonstration, and the verification results and the energy-saving dynamic process are shown in figures 5 to 6.
As shown in fig. 5, the energy-saving optimization method and system perform incremental changes of the power consumption and the generated power of the air cooling island under the current working condition in real time under different back pressures, and calculate the optimal back pressure; if the operating back pressure is reduced from 10kPa to the optimum back pressure of 7.4kPa, this will result in a net power output increase of 1.877MW, corresponding to a reduction of 0.6% of steam flow, resulting in a change in coal consumption of approximately 1.847 g. Thus, although the power consumption of the air cooling island is increased, the increment of the generated power is larger due to the reduction of the vacuum, and the standard coal consumption of the whole plant is reduced overall. The dynamic process of the overall regulation is shown in fig. 6, the increment of the generated power caused by reducing the back pressure is larger than the increment of the power consumption of the air cooling island, so that the net output power is increased, the net output power cannot be changed due to the fact that the output power of the unit is scheduled by a master regulation signal or an AGC signal, the CCS and the DEH control the main regulating valve of the steam turbine to be closed, so that the main steam flow is reduced, and the fuel quantity is reduced. The effectiveness of the method of the invention can be seen in this way.
Example 2
Referring to fig. 7, another embodiment of the present invention is different from the first embodiment in that a data-driven direct air-cooling unit operation optimization system is provided, and the data-driven direct air-cooling unit operation optimization method depends on the system operation, and specifically includes:
the data acquisition module 100 is used for acquiring real-time operation data;
the cold end system data model building module 200 is connected with the data acquisition module 100 and used for building a cold end system data model, and the data acquired by the data acquisition module 100 is used for training at regular time and updating the cold end system data model;
and the optimal parameter recommending module 300 is connected with the cold end system data model constructing module 200 and is used for selecting the optimal back pressure.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (5)
1. A method for optimizing the operation of a direct air cooling unit based on data driving is characterized by comprising the following steps:
real-time operational data is collected and pre-processed, including,
cleaning the acquired original data in a value domain and a time domain, cleaning according to a reasonable range of parameters, and removing invalid data in the starting and stopping process of the unit;
judging and cleaning data in an abnormal state in the running process of the unit, outliers deviating from a normal working condition and data drift in original measuring points;
storing the cleaned product in a database;
continuously removing old data from the database in a dynamic updating mode, adding new data, and keeping the data period of the database in the last year;
constructing a cold end system related data model according to the real-time operation data, wherein the cold end system related data model comprises a power generation power model, an air cooling island heat exchange model and an air cooling island power consumption model;
the cold end system related data model is an AI algorithm model, the input and the output of the model are defined, and the relation is established in a self-learning mode;
the generated power model is used for evaluating the relation between the back pressure and the generated power under different loads;
the air cooling island heat exchange model is used for evaluating the relationship between the power consumption of the air cooling island and the backpressure and the ambient temperature under different power generation powers:
inputting a model: back pressure, power, ambient temperature;
and (3) outputting a model: power consumption of the air cooling island;
the air cooling island power consumption model is used for evaluating the relationship between the air cooling island power consumption and the fan rotating speed at different environmental temperatures:
inputting a data model: average rotating speed and environment temperature of the air conveying machine;
and (3) outputting a data model: power consumption of the air cooling island;
carrying out timing training on the cold end system related data model by using the continuously updated data in the database, and continuously updating the cold end system related data model to obtain a trained data model;
evaluating the correlation among factors influencing the running state of the direct air cooling unit based on the trained data model;
obtaining the optimal back pressure on the basis of the correlation by utilizing an optimization algorithm, and realizing the operation optimization of the direct air cooling unit;
after the optimal back pressure is obtained, calling an air cooling island heat exchange characteristic model to obtain the air cooling island power consumption under the back pressure, the ambient temperature and the generated power, and after the air cooling island power consumption is obtained, calling the air cooling island power consumption model to obtain the fan frequency recommended value under the ambient temperature.
2. The method for optimizing the operation of the direct air-cooling unit based on the data driving as claimed in claim 1, wherein: the real-time operation data comprises backpressure, power generation power and the rotating speed of an ambient temperature fan.
3. The method for optimizing the operation of the direct air cooling unit based on the data driving according to any one of claims 1 and 2, wherein: obtaining an optimal backpressure based on the correlation using an optimization algorithm includes,
constructing an optimal backpressure algorithm based on the optimizing algorithm:
under the adjustable limit of the power and the rotating speed of the air cooling island fan, the backpressure is searched within the range of 5-30KPa of the operating pressure of the condenser by taking 0.1KPa as an interval, and the backpressure value under the minimum value of the objective function is taken as the optimal backpressure.
4. The method for optimizing the operation of the direct air-cooling unit based on the data driving as claimed in claim 3, wherein: the objective function is: generated power-air cooling island power consumption.
5. A data-driven direct air-cooling unit operation optimization system for implementing the data-driven direct air-cooling unit operation optimization method according to claim 1, comprising:
the data acquisition module (100) is used for acquiring real-time operation data;
the cold end system data model building module (200) is connected with the data acquisition module (100) and is used for building a cold end system data model, and the data acquired by the data acquisition module (100) is used for training at regular time and updating the cold end system data model;
and the optimal parameter recommendation module (300) is connected with the cold end system data model construction module (200) and is used for selecting the optimal backpressure.
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