CN113240151A - Method and device for predicting performance parameters of condenser of direct air cooling unit - Google Patents
Method and device for predicting performance parameters of condenser of direct air cooling unit Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting performance parameters of a condenser of a direct air cooling unit, which are used for solving the problem of inaccuracy in predicting the performance parameters of the condenser of the direct air cooling unit. This scheme includes: the method comprises the steps of obtaining a plurality of measurable parameters of a condenser of the direct air cooling unit and historical data of the measurable parameters; determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit; generating a thermal resistance model of the condenser comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter; and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter. The condenser thermal resistance model generated by the scheme can accurately represent the incidence relation between the target measurable parameter and the condenser thermal resistance, and further the accuracy of predicting the performance parameters of the condenser of the direct air cooling unit is improved.
Description
Technical Field
The invention relates to the technical field of steam turbines, in particular to a method and a device for predicting performance parameters of a condenser of a direct air cooling unit.
Background
The air-cooled generator set utilizes forced flowing air as a carrier of a heat source to achieve the purpose of radiating heat of equipment. The specific implementation is that a plurality of square grooves are reserved at the joint of a stator core and a shell of the generator, strong wind formed when axial flow fan blades on a rotor of the generator rotate flows through the square grooves of the stator core and the shell, and heat is discharged out of the generator body by a coil, so that the purpose of heat dissipation is achieved.
The direct air cooling unit comprises a direct air cooling condenser which directly utilizes cold air to cool the steam turbine exhaust, has the advantages of freezing prevention, water saving, small occupied area and the like, and is widely applied to water-deficient or high-cold areas. In the operation process of a power station, the performance state of the condenser has important influence on the normal operation of the whole unit. The condenser is mainly used for heat exchange in the direct air cooling unit, and along with the long-time and high-load operation of the direct air cooling unit, the condenser can have the phenomena of scaling, dust deposition and the like, so that the related parameters of heat exchange are changed, and the whole heat exchange efficiency of the direct air cooling unit is influenced.
In practical application, the practical conditions of scaling and dust deposition of the condenser are difficult to accurately obtain. Moreover, factors causing the thermal resistance change of the condenser are many, and the thermal resistance of the condenser is influenced by the change of atmospheric conditions and working conditions. If the performance parameters of the condenser of the direct air cooling unit are predicted only according to the initial thermal resistance of the condenser, large deviation often occurs.
How to improve the accuracy of predicting the performance parameters of the condenser of the direct air cooling unit is the technical problem to be solved by the application.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for predicting performance parameters of a condenser of a direct air cooling unit, which are used for solving the problem of inaccuracy in predicting the performance parameters of the condenser of the direct air cooling unit.
In a first aspect, a method for predicting performance parameters of a condenser of a direct air cooling unit is provided, which includes:
acquiring a plurality of measurable parameters of a condenser of a direct air cooling unit and historical data of the measurable parameters;
determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit;
generating a condenser thermal resistance model comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, wherein the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance;
and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
In a second aspect, a device for predicting condenser performance parameters of a direct air cooling unit is provided, which includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of measurable parameters of a condenser of the direct air cooling unit and historical data of the measurable parameters;
the determining module is used for determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit;
the generating module is used for generating a condenser thermal resistance model comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, and the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance;
and the prediction module is used for predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
In a third aspect, an electronic device is provided, the electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method as in the first aspect.
In the embodiment of the application, a plurality of measurable parameters of a condenser of a direct air cooling unit and historical data of the measurable parameters are obtained; determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit; generating a condenser thermal resistance model comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, wherein the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance; and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter. The condenser thermal resistance model generated by the scheme can accurately represent the incidence relation between the target measurable parameter and the condenser thermal resistance, and further the accuracy of predicting the performance parameters of the condenser of the direct air cooling unit is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a method for predicting performance parameters of a condenser of a direct air-cooling unit according to an embodiment of the present invention.
Fig. 2 is a second schematic flow chart of the method for predicting the condenser performance parameters of the direct air-cooling unit according to the embodiment of the invention.
Fig. 3 is a third schematic flow chart of a method for predicting performance parameters of a condenser of a direct air-cooling unit according to an embodiment of the present invention.
Fig. 4 is a fourth schematic flowchart of a method for predicting performance parameters of a condenser of a direct air-cooling unit according to an embodiment of the present invention.
Fig. 5 is a fifth schematic flow chart of the method for predicting the condenser performance parameters of the direct air-cooling unit according to the embodiment of the invention.
Fig. 6 is a sixth schematic flow chart of a method for predicting the performance parameters of the condenser of the direct air-cooling unit according to an embodiment of the present invention.
FIG. 7 is a graphical illustration of the relationship between fan speed versus rated speed versus time for one embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating the relationship between the backpressure of the unit and the time according to an embodiment of the present invention;
FIG. 9 is a schematic diagram illustrating the correlation between the temperature of the cold air at the inlet of the condenser and the time according to an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating the relationship between the inflow rate of the condenser and the time according to an embodiment of the present invention;
FIG. 11 is a schematic diagram showing the correlation between the actual value of the product of the heat exchange system of the condenser and the effective heat exchange area and the time according to one embodiment of the present invention;
FIG. 12 is a schematic diagram showing the correlation between the fitting value of the product of the heat exchange system of the condenser and the effective heat exchange area and the time according to one embodiment of the present invention;
fig. 13 is a schematic structural view of a device for predicting condenser performance parameters of a direct air-cooling unit according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. The reference numbers in the present application are only used for distinguishing the steps in the scheme and are not used for limiting the execution sequence of the steps, and the specific execution sequence is described in the specification.
In order to solve the problems existing in the prior art, an embodiment of the present application provides a method for predicting performance parameters of a condenser of a direct air cooling unit, as shown in fig. 1, including:
s11: the method comprises the steps of obtaining a plurality of measurable parameters of a condenser of the direct air cooling unit and historical data of the measurable parameters.
Optionally, the measurable parameter includes at least one of:
the system comprises a direct air cooling unit load parameter, a condenser steam inlet flow parameter, a direct air cooling unit backpressure parameter, a direct air cooling unit steam exhaust parameter, a direct air cooling unit turbine outlet pressure parameter, a condenser air inlet temperature parameter, a direct air cooling unit cooling air flow and a direct air cooling unit axial flow fan rotating speed parameter.
In practical application, a plurality of measurable parameters of the direct air cooling unit can be periodically monitored and recorded through a power plant control system. In this step, the plurality of measurable parameters of the condenser of the direct air-cooling unit may specifically include parameters directly acquired by the power plant control system, or may also include parameters calculated according to the acquired parameters and the mechanism of the direct air-cooling unit.
For example, the measurable parameter may include a rotational speed parameter of an axial flow fan of the direct air cooling unit, and the parameter may be directly measured by a control system of the power plant. The measurable parameters can also comprise the cooling air flow of the direct air cooling unit, and the cooling air flow is difficult to directly and accurately measure by a measuring instrument, but can be calculated according to the measured rotating speed parameters of the axial flow fan.
After a plurality of measurable parameters of the condenser of the direct air cooling unit are obtained, historical data of the measurable parameters can be inquired and called through a power plant control system. Optionally, historical data within a preset time period is obtained, for example, historical data within the last 24 hours is obtained.
S12: and determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit.
The correlation between the measurable parameters and the thermal resistance of the condenser of the direct air cooling unit is influenced by various factors. In this step, the target measurable parameter may be determined from the result of the correlation analysis. In practical application, the types of correlation analysis methods are more, and a suitable correlation analysis method can be selected according to actual requirements and the characteristics of historical data. For example, the correlation analysis method may include graph correlation analysis, covariance and covariance matrix correlation analysis, correlation coefficient analysis, univariate regression and multivariate regression, information entropy and mutual information, and the like.
The correlation between each measurable parameter and the thermal resistance of the condenser of the direct air cooling unit can be represented by the result obtained by the correlation analysis method. Where thermal resistance is a comprehensive quantity reflecting the ability to prevent heat transfer. The condenser is mainly used for heat exchange in a direct air cooling unit, and the thermal resistance is a main factor influencing the heat exchange capacity of the condenser. The step determines a target measurable parameter closely related to the thermal resistance from the plurality of measurable parameters through correlation analysis. When the target measurable parameter is determined, measurable parameters larger than the preset correlation parameter can be determined as the target measurable parameters, or a plurality of measurable parameters can be sorted based on the correlation parameter, and a preset number of measurable parameters with high correlation parameters can be determined as the target measurable parameters.
S13: and generating a condenser thermal resistance model comprising the at least one target measurable parameter according to the historical data of the at least one target measurable parameter, wherein the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance.
The at least one target measurable parameter is a parameter with high correlation with the thermal resistance of the condenser, and the thermal resistance model generated according to the at least one target measurable parameter can express the characteristics of the thermal resistance by using the parameter with high correlation, so that the consistency of the characteristics expressed by the thermal resistance model and the actual thermal resistance is improved. The historical data of the at least one target measurable parameter can reflect the change characteristics of each target measurable parameter in a historical time period, and the change characteristics can indirectly reflect the characteristics of the thermal resistance of the condenser. For example, the heat exchange performance parameters of the condenser can be estimated according to the flow and the temperature of cold air at the air inlet of the condenser and the flow and the temperature of hot air at the air outlet of the condenser, and the heat exchange performance parameters are directly related to the thermal resistance of the condenser. Therefore, the thermal resistance model of the condenser generated according to the at least one target measurable parameter and the historical data can represent the characteristics of the thermal resistance of the condenser by using high-correlation parameters, and can represent the relation between each target measurable parameter and the thermal resistance more accurately.
S14: and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
The relation between each target measurable parameter and the thermal resistance of the condenser can be accurately represented by the thermal resistance model of the condenser generated in the step, and real-time data corresponding to the at least one target measurable parameter can be input into the thermal resistance model of the condenser in the step so as to preset performance parameters of the condenser of the direct air cooling unit in a future time period. The performance parameters of the condenser can include thermal resistance or related parameters representing the thermal resistance and heat exchange performance of the condenser.
The real-time data corresponding to the at least one target measurable parameter can be directly measured by the power plant control system, and can be obtained through real-time measurement of the detection instrument.
According to the technical scheme, at least one target measurable parameter is selected according to the correlation of the parameters, and a thermal resistance model of the condenser is generated by combining historical data so as to predict the performance parameters of the condenser in the future time period. Since the target measurable parameter is determined based on the correlation with the thermal resistance, and the finally generated model is determined based on historical data, the change caused by the aging of the condenser can be accurately reflected. After real-time data are input into the model, performance parameters of the condenser can be determined according to the output thermal resistance value, and heat exchange efficiency can be determined based on the mechanism of the condenser generally, so that the model is used for determining unit load, temperature, capacity and the like.
As the thermal resistance of the condenser is changed due to scaling, dust deposition and the like in the actual use process, the relation between at least one target measurable parameter and the thermal resistance of the condenser can be determined through the scheme provided by the embodiment of the application, and the thermal resistance or other performance parameters of the condenser in a future period of time can be predicted based on the determined relation and measured real-time data. The scheme can be widely applied to direct air cooling units of different structures and scales, the prediction accuracy is high, the unit can be further monitored based on the predicted performance parameters, and the stability and the reliability of the unit are improved. In addition, the scheme provided by the embodiment of the application does not need to consume too much manpower, and can be automatically realized.
Based on the solution provided by the foregoing embodiment, optionally, as shown in fig. 2, the foregoing step S12 includes:
s21: and determining a correlation index of each measurable parameter in the plurality of measurable parameters and the thermal resistance of the condenser by a principal component analysis method.
In this step, an index of correlation between each measurable parameter and the thermal resistance of the condenser was determined by Principal Component Analysis (PCA). The principal component analysis is also called principal component analysis, and aims to convert multiple indexes into a few comprehensive indexes (namely principal components) by using the idea of dimension reduction. Wherein each principal component can reflect most information of the original variable, and the contained information is not repeated. The method can lead the complex factors to be classified into a plurality of main components while introducing multi-aspect variables, simplify the problem and obtain more scientific and effective data information. In practical problem research, in order to analyze problems comprehensively and systematically, a plurality of influencing factors must be considered. These involved factors are generally referred to as indicators, and also as variables in multivariate statistical analysis. Because each variable reflects some information about the problem under study to a different degree, and the indicators have some correlation with each other, the resulting statistics reflect some degree of overlap. The main methods include eigenvalue decomposition, SVD, NMF, etc.
The correlation index of each measurable parameter and the thermal resistance of the condenser can be determined through the step, and the correlation index can visually reflect the correlation of the corresponding measurable parameter and the thermal resistance of the condenser.
S22: and determining at least one measurable parameter corresponding to the correlation index meeting the preset correlation standard as the target measurable parameter.
The preset correlation standard may be preset according to actual requirements, for example, each measurable parameter is sorted from large to small according to the corresponding correlation index, and the preset two measurable parameters with the largest value are determined as the target measurable parameters.
Optionally, as shown in fig. 3, step S22 includes:
s31: and determining the target measurable parameter according to the measurable parameter corresponding to the correlation index which is larger than the preset correlation parameter.
The preset correlation parameter may be, for example, 0.8, and in this step, a measurable parameter corresponding to a correlation index greater than 0.8 is determined as a target measurable parameter, and is used to generate a thermal resistance model of the condenser in a subsequent step. In practical applications, the specific value of the preset correlation parameter may be changed according to actual requirements.
Through the scheme provided by the embodiment of the application, at least one target measurable parameter related to high thermal resistance of the condenser can be selected from a plurality of measurable parameters. The number of parameters for generating the thermal resistance model of the condenser is reduced, the dimension reduction of data is realized, meanwhile, the correlation between the parameters for generating the thermal resistance model of the condenser and the thermal resistance of the condenser is improved, and the accuracy of the predicted thermal resistance of the condenser is improved.
Based on the solution provided by the foregoing embodiment, optionally, as shown in fig. 4, the foregoing step S13 includes:
s41: and generating a historical data curve according to historical data of the at least one target measurable parameter, wherein the historical data curve represents the correlation relationship between the reciprocal of the thermal resistance and the time.
In this step, a historical data curve is generated based on historical data of at least one target measurable parameter. Specifically, a parameter curve representing the correlation between the value of the target measurable parameter and time may be generated for the historical data of each target measurable parameter. The historical data curve is then generated based on the parametric curve for each target measurable parameter. The abscissa of the history data curve may be time, and the ordinate may be the inverse of the thermal resistance.
S42: and fitting a curve to the historical data according to the at least one target measurable parameter to generate a thermal resistance model of the condenser comprising the at least one target measurable parameter.
In this step, a basic curve may be first constructed based on the at least one target measurable parameter, and a curve fitting method may be used to perform curve fitting on the historical data, so as to generate a thermal resistance model of the condenser including the at least one target measurable parameter.
Based on the solution provided by the foregoing embodiment, optionally, as shown in fig. 5, the foregoing step S31 includes:
s51: and determining the measurable parameter corresponding to the correlation index which is greater than the preset correlation parameter as the first measurable parameter.
S52: respectively determining condenser mechanism analysis results of the first measurable parameters according to the condensers of the direct air cooling unit;
s53: and determining at least one first measurable parameter as the target measurable parameter according to the condenser mechanism analysis result.
In the embodiment of the application, the first measurable parameter determined in the step is a measurable parameter with high correlation with the thermal resistance of the condenser. And then respectively determining the mechanical analysis result of the condenser of each first measurable parameter, wherein the mechanical analysis result can be determined according to the structure, the characteristics or other factors of the condenser. For example, in the step S52, the correlation between each first measurable parameter and each operating condition of the condenser may be determined, and then the correlation between each first measurable parameter and the thermal resistance of the condenser under different operating conditions may be determined, so as to generate the condenser mechanism analysis result. Wherein, various operating modes of condenser operation can be confirmed according to the structure, the characteristic or other factors of condenser.
According to the technical scheme provided by the embodiment of the application, the correlation between the determined target measurable parameter and the thermal resistance of the condenser can be improved by combining with the analysis result of the condenser mechanism, and the condition that the correlation between the target measurable parameter and the thermal resistance of the condenser is reduced due to the change of working conditions or other factors is avoided.
Based on the scheme provided by any one of the above embodiments, optionally, the target measurable parameters include a load parameter Pe of the direct air cooling unit and an outlet pressure parameter P of a steam turbine of the direct air cooling unitzTemperature parameter T of air inlet of condensergiDirect air cooling unit axial flow fan rotating speed parameter Ng；
As shown in fig. 6, step S13 includes:
s61: determining a steam inlet flow parameter G of the condenser according to the load parameter Pe of the direct air cooling units；
S62: according to the outlet pressure parameter P of the steam turbine of the direct air cooling unitzDetermining backpressure parameter P of direct air cooling units；
S63: generating a steam inlet flow parameter G comprising a condenser according to historical data of the at least one target measurable parametersBackpressure parameter P of direct air cooling unitsTemperature parameter T of air inlet of condensergiAnd the rotating speed parameter N of the axial flow fan of the direct air cooling unitgThe thermal resistance model of the condenser.
The following description illustrates the present solution by way of example, and according to the heat transfer theory and the working principle of the direct air-cooled condenser, it can be seen that the heat exchange process of the condenser is mainly divided into three parts, namely, the convection heat exchange between the cold air in the condenser tube and the tube wall, the heat conduction of the tube wall, and the convection heat exchange between the steam outside the condenser tube and the tube wall. By analyzing the three heat exchange processes, the steam inlet flow (unit load), the unit back pressure (exhaust enthalpy), the cooling air inlet temperature, the cooling air flow (axial flow fan rotating speed), unchanged performance parameters of the condenser under variable working conditions and the like are main influence factors on the thermal resistance R. Wherein, the load Pe data of the unit is measurable, and the steam inlet flow G of the condensersCalculating the steam inlet flow rate through measurable load data by utilizing the relation between the load and the steam inlet quantity, wherein the corresponding function is as the following formula (1):
Gs＝0.475*Pe+17.185 (1)
backpressure P of unitsHas direct relation with the exhaust enthalpy value, and the monitoring quantity of the pressure at the outlet of the steam turbine is vacuum degree PzThe back pressure is the sum of the vacuum degree and the local atmospheric pressure, and is shown in the following formula (2):
Ps＝(Pz+77.4)*1000 (2)
the inlet temperature of the cooling air is directly measured by a temperature sensor, the flow rate of the cooling air is usually difficult to directly measure, but can be determined by the rotating speed of the axial flow fan, and the higher the rotating speed is, the larger the flow rate is. To fit these measurable parameters to their relationship to thermal resistance, an actual thermal resistance value is first required. Therefore, it is necessary to perform a test for obtaining heat under a rated operation conditionResistance, heat transfer Q of the condenser and logarithmic mean temperature difference DeltatmAs shown in the following formula (3):
wherein G iscThe cold air flow is obtained through experiments; c. CpIs the constant pressure specific heat of air; delta t is the temperature difference of the cold air inlet and the cold air outlet; t is tsThe condensing temperature of steam in the condenser; t is ts1、ts2The temperature of the cold air inlet and outlet is shown. Therefore, it is not only easy to use
This equation can be used to calculate the actual thermal resistance.
However, in a condenser in actual operation, it is generally difficult to determine the cold air flow rate. In the modeling process of the condenser, the heat exchange quantity, the outlet air temperature and the like need to be calculated by using the thermal resistance. Therefore, the thermal resistance needs to be calculated by using measurable parameters for modeling control and unit operation adjustment. And taking historical data as a target value, and taking the steam inlet flow of the condenser, the back pressure of the unit, the inlet temperature of cooling air and the rotating speed of the axial flow fan as model inputs to generate a thermal resistance model of the condenser.
According to the technical scheme, measurable data are utilized to conduct parameter identification on the direct air-cooling condenser, a condenser thermal resistance model is generated, and the method can be used for predicting performance parameters of the condenser within a period of time in the future. And further, the modeling control of the condenser system, the operation adjustment of the unit and the monitoring of the operation state of the unit by using measurable parameters are facilitated.
According to the technical scheme, the condenser performance parameters can be accurately estimated in real time, the running state of the condenser can be monitored by using measurable parameters, the efficiency of the unit and the sensitivity and precision of running adjustment can be further improved, and the running reliability of the unit is improved. The deviation of calculating the performance parameters in the states of dust deposition, scaling, variable working conditions and the like of the condenser by using a given empirical formula is eliminated, and the problems of time consumption and labor consumption in determining the performance parameters through tests are solved.
The present solution is further explained below with reference to specific parameters. In the operation process of the direct air-cooled generator set of the power plant, the working medium is cooled in the air cooling tower by utilizing ambient cold air, so that although the water resource consumption and the energy consumption of a cold end system are reduced, larger energy loss exists, such as excessive consumption of electric energy of a large axial flow fan group of the air-cooled generator set. Therefore, to reduce the energy consumption of the system, optimization of the cold-side system operation is required. In the process, the dynamic characteristics of the unit need to be researched to find the operation state with the lowest energy consumption. However, in order to ensure the stability of the power generation quality and the safety of the unit operation, it is difficult to directly conduct relevant experimental research. Therefore, it is necessary to model the cold-end system of the direct air-cooling unit and study the dynamic characteristics of the system by a model method.
As an important component of a cold end system, the condenser is a circulating cold source device in the thermodynamic cycle process of the steam turbine set, and can condense exhaust steam into water and maintain the vacuum degree of an exhaust port. In the modeling process of the condenser module, the product of the effective heat exchange area and the total heat exchange coefficient of the unit in actual operation is a key parameter. However, this parameter is influenced by various factors and needs to be determined according to the actual state of the unit. Therefore, the scheme provided by the invention can be used for determining the thermal resistance of the condenser by using measurable parameters and is used for modeling and subsequent characteristic research of the condenser.
The method is characterized in that the actual operation data of a 600MW supercritical direct air cooling unit of a certain power plant is taken as the basis, and the thermal resistance value of a condenser of the unit is subjected to parameter identification. In this embodiment, the plurality of measurable parameters include steam inlet flow of the condenser, back pressure of the unit, inlet temperature of the cooling air, rotational speed of the axial flow fan, and main technical parameters of the air cooling system. The determined target measurable parameters comprise 4 parameters including unit backpressure, cooling air inlet temperature and condenser steam inlet flow, and data corresponding to the parameters in the historical period are shown in figures 7-10.
The historical data curves determined from fig. 7-10 above are shown in fig. 11. In this embodiment, fitting is performed by a least square fitting method, a fitting value is shown in fig. 12, it can be seen that deviation between fig. 11 and fig. 12 is small, several target measurable parameters having the largest correlation with the performance parameter are selected as model inputs, and a mathematical fitting relation of the thermal resistance of the direct air-cooled condenser of the unit is obtained as shown in the following formula (4):
the corresponding relation among the abbreviations of the parameters, the parameters and the corresponding units is as follows:
r-thermal resistance of condenser, DEG C/W;
Gs-inlet steam flow m of condenser3/s；
Ps-turbine back pressure, Pa;
Tgi-the temperature of the condenser inlet cold air is at DEG C;
Ng-fan speed,%.
The thermal resistance of the direct air-cooling condenser mainly considers unit load and backpressure when the working condition is changed, the two items determine the heat dissipation load of the condenser, and the inlet cold air temperature and the rotating speed (flow rate) of the axial flow fan reflect the cooling condition and the running mode of the axial flow fan. Therefore, the variable working condition influence factors of the condenser are considered comprehensively by the fitting model, and the model can be used for calculating and analyzing the performance parameters of the condenser in real time under the variable working condition.
In order to solve the problems existing in the prior art, an embodiment of the present application further provides a device 130 for predicting performance parameters of a condenser of a direct air-cooling unit, as shown in fig. 13, including:
the acquisition module 131 is used for acquiring a plurality of measurable parameters of the condenser of the direct air cooling unit and historical data of the measurable parameters;
a determining module 132, configured to determine at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit;
the generating module 133 is configured to generate a condenser thermal resistance model including the at least one target measurable parameter according to the historical data of the at least one target measurable parameter, where the condenser thermal resistance model represents an association relationship between the at least one target measurable parameter and the condenser thermal resistance;
and the prediction module 134 is used for predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
According to the device provided by the embodiment of the application, a plurality of measurable parameters of the condenser of the direct air cooling unit and historical data of the measurable parameters are obtained; determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit; generating a thermal resistance model of the condenser comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter; and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter. The condenser thermal resistance model generated by the scheme can accurately represent the incidence relation between the target measurable parameter and the condenser thermal resistance, and further the accuracy of predicting the performance parameters of the condenser of the direct air cooling unit is improved.
Preferably, an embodiment of the present invention further provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the computer program, when executed by the processor, implements each process of the above-mentioned embodiment of the method for predicting performance parameters of a condenser of a direct air-cooling unit, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes each process of the embodiment of the method for predicting the performance parameters of the condenser of the direct air cooling unit, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for predicting performance parameters of a condenser of a direct air cooling unit is characterized by comprising the following steps:
acquiring a plurality of measurable parameters of a condenser of a direct air cooling unit and historical data of the measurable parameters;
determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit;
generating a condenser thermal resistance model comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, wherein the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance;
and predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
2. The method of claim 1, wherein the measurable parameters of the direct air cooling unit condenser comprise at least one of:
the system comprises a direct air cooling unit load parameter, a condenser steam inlet flow parameter, a direct air cooling unit backpressure parameter, a direct air cooling unit steam exhaust parameter, a direct air cooling unit turbine outlet pressure parameter, a condenser air inlet temperature parameter, a direct air cooling unit cooling air flow and a direct air cooling unit axial flow fan rotating speed parameter.
3. The method of claim 1, wherein determining at least one target measurable parameter based on the correlation of the plurality of measurable parameters to the thermal resistance of the direct air cooling unit condenser comprises:
determining a correlation index between each measurable parameter in the plurality of measurable parameters and the thermal resistance of the condenser by a principal component analysis method;
and determining at least one measurable parameter corresponding to the correlation index meeting the preset correlation standard as the target measurable parameter.
4. The method of claim 1, wherein determining the measurable parameter corresponding to at least one correlation index meeting a predetermined correlation criterion as the target measurable parameter comprises:
and determining the target measurable parameter according to the measurable parameter corresponding to the correlation index which is larger than the preset correlation parameter.
5. The method of claim 4, wherein generating a thermal resistance model of the condenser including the at least one target measurable parameter from historical data of the at least one target measurable parameter comprises:
generating a historical data curve according to historical data of the at least one target measurable parameter, wherein the historical data curve represents the correlation between the reciprocal of the thermal resistance and time;
and fitting a curve to the historical data according to the at least one target measurable parameter to generate a thermal resistance model of the condenser comprising the at least one target measurable parameter.
6. The method of claim 4, wherein determining the target measurable parameter from measurable parameters corresponding to a correlation index greater than a preset correlation parameter comprises:
determining measurable parameters corresponding to the correlation indexes larger than preset correlation parameters as first measurable parameters;
respectively determining condenser mechanism analysis results of the first measurable parameters according to the condensers of the direct air cooling unit;
and determining at least one first measurable parameter as the target measurable parameter according to the condenser mechanism analysis result.
7. The method according to any one of claims 1 to 6, wherein the target measurable parameters include a load parameter Pe of the direct air cooling unit, a turbine outlet pressure parameter P of the direct air cooling unitzTemperature parameter T of air inlet of condensergiDirect air cooling unit axial flow fan rotating speed parameter Ng；
Generating a thermal resistance model of the condenser comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, wherein the thermal resistance model comprises:
determining a steam inlet flow parameter G of the condenser according to the load parameter Pe of the direct air cooling units；
According to the outlet pressure parameter P of the steam turbine of the direct air cooling unitzDetermining backpressure parameter P of direct air cooling units；
Generating a steam inlet flow parameter G comprising a condenser according to historical data of the at least one target measurable parametersBackpressure parameter P of direct air cooling unitsTemperature parameter T of air inlet of condensergiAnd the rotating speed parameter N of the axial flow fan of the direct air cooling unitgThe thermal resistance model of the condenser.
8. The utility model provides a prediction unit of direct air cooling unit condenser performance parameter which characterized in that includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of measurable parameters of a condenser of the direct air cooling unit and historical data of the measurable parameters;
the determining module is used for determining at least one target measurable parameter according to the correlation between the plurality of measurable parameters and the thermal resistance of the condenser of the direct air cooling unit;
the generating module is used for generating a condenser thermal resistance model comprising the at least one target measurable parameter according to historical data of the at least one target measurable parameter, and the condenser thermal resistance model represents the association relation between the at least one target measurable parameter and the condenser thermal resistance;
and the prediction module is used for predicting the performance parameters of the condenser of the direct air cooling unit according to the thermal resistance model of the condenser and the real-time data of the at least one target measurable parameter.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN113624025A (en) * | 2021-08-18 | 2021-11-09 | 浙江大学 | Condenser vacuum low-jump machine symptom capturing method based on correlation of operation parameters |
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| US20050109032A1 (en) * | 2003-11-07 | 2005-05-26 | Harpster Joseph W. | Condensers and their monitoring |
| CN111401686A (en) * | 2020-02-14 | 2020-07-10 | 东南大学 | Method and device for monitoring dust and dirt condition of air cooling radiating fin |
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| US20050109032A1 (en) * | 2003-11-07 | 2005-05-26 | Harpster Joseph W. | Condensers and their monitoring |
| CN111401686A (en) * | 2020-02-14 | 2020-07-10 | 东南大学 | Method and device for monitoring dust and dirt condition of air cooling radiating fin |
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