Boiler heating surface heat deviation judgment method, device and equipment and storage medium

Abstract

The invention discloses a boiler heating surface thermal deviation judgment method, device and equipment and a storage medium, and belongs to the technical field of boilers, the method comprises the steps that a boiler benchmark temperature database is obtained, the boiler benchmark temperature database comprises a plurality of benchmark temperature vectors of a first heating surface, and any benchmark temperature vector corresponds to a load range; the load ranges corresponding to the multiple benchmark temperature vectors are different; acquiring a plurality of temperature data of the first heating surface under the first load; based on the multiple pieces of temperature data, determining a first characteristic temperature vector of the first heating surface under the first load; and based on the Euclidean distance between the benchmark temperature vector corresponding to the first load range where the first load is located and the first characteristic temperature vector, the thermal deviation index of the first heating surface is determined, and the thermal deviation index of the first heating surface is used for conducting thermal deviation judgment on the first heating surface of the boiler. The method can quickly and accurately judge the thermal deviation of the heating surface of the boiler.

Description

Technical Field

[0001] The present disclosure relates to the technical field of boilers, and in particular to a method, device, equipment and storage medium for judging thermal deviation of a heating surface of a boiler. Background Art

[0002] During the deep peak regulation of the boiler, the operating parameters in the furnace change dramatically, which can easily cause uneven flow distribution. Due to the complex combustion environment in the furnace, the combustion flame may be skewed, resulting in a large thermal deviation of the heating surface. Uneven flow distribution and large thermal deviation of the heating surface may cause the water-cooled wall of the boiler to crack and burst due to overheating.

[0003] In the related art, the method for judging the thermal deviation of the boiler heating surface includes: measuring the inlet and outlet temperature and pressure of the working fluid in the heating surface pipeline, and comparing the inlet and outlet enthalpy changes of each pipeline to determine the thermal deviation of the boiler heating surface.

[0004] However, when the boiler load changes rapidly and the boiler load is deeply peaked, the boiler wall temperature changes rapidly. The thermal deviation of the boiler heating surface determined by the changes in the inlet and outlet temperature pressure and the inlet and outlet enthalpy values ​​has strong lag and is not accurate enough. Summary of the invention

[0005] The present disclosure provides a method, device, equipment and storage medium for judging thermal deviation of a boiler heating surface, which can quickly and accurately judge the thermal deviation of a boiler heating surface. The technical solution at least includes the following solutions:

[0006] In a first aspect, a method for judging thermal deviation of a heating surface of a boiler is provided, comprising: obtaining a boiler benchmark temperature database, wherein the boiler benchmark temperature database includes multiple benchmark temperature vectors of a first heating surface, any one of the benchmark temperature vectors corresponds to a load range, and the load ranges corresponding to the multiple benchmark temperature vectors are different; obtaining multiple temperature data of the first heating surface under a first load; determining a first characteristic temperature vector of the first heating surface under the first load based on the multiple temperature data; determining a thermal deviation index of the first heating surface based on a Euclidean distance between a benchmark temperature vector corresponding to a first load range where the first load is located and the first characteristic temperature vector, wherein the thermal deviation index of the first heating surface is used to judge thermal deviation of the first heating surface of the boiler.

[0007] Optionally, determining the thermal deviation index of the first heating surface based on the Euclidean distance between the benchmark temperature vector corresponding to the first load range in which the first load is located and the first characteristic temperature vector includes:

[0008] The following formula is used to determine the thermal deviation index of the first heating surface:

[0009]

[0010] Wherein, ρ is the thermal deviation index of the first heating surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector.

[0011] Optionally, when the first heated surface includes i temperature measuring points, the plurality of temperature data include temperature data of the i temperature measuring points in the first heated surface.

[0012] The determining, based on the plurality of temperature data, a first characteristic temperature vector of the first heating surface under the first load comprises:

[0013] The first temperature characteristic vector is determined using the following formula:

[0014] T s =[T s1 , T s2 , T s3 , ...T si ]

[0015] Among them, T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measuring point, the second temperature measuring point, the third temperature measuring point...the i-th temperature measuring point;

[0016] The kth temperature characteristic component is determined by the following formula:

[0017]

[0018] Where n and k are integers, and the range of n and k is 1 to i, i is a positive integer, T sk is the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point in the plurality of temperature data, T xn It is the temperature data of the nth temperature measurement point among the multiple temperature data.

[0019] Optionally, the first heating surface includes i temperature measuring points, and the second load range includes j groups of historical temperature data, the j groups of historical temperature data correspond to different loads respectively, and any group of historical temperature data is an i-dimensional vector, and each dimension of the i-dimensional vector is used to indicate the historical temperature of a temperature measuring point under the load corresponding to the i-dimensional vector. The method also includes: obtaining the benchmark temperature vector component of each group of historical temperature data in the j groups of historical temperature data to obtain j benchmark temperature vector components; averaging the j benchmark temperature vector components to obtain the benchmark temperature vector corresponding to the second load range; wherein the benchmark temperature vector component of the mth group of historical temperature data is obtained in the following manner: determining the average value of the mth group of historical temperature data; subtracting the average value of the mth group of historical temperature data from each dimension in the mth group of historical temperature data to obtain the benchmark temperature vector component of the mth group of historical temperature data, j and m are positive integers, and the value range of m is 1 to j, and the second load range is any load range in the boiler benchmark temperature database.

[0020] In the second aspect, a device for judging thermal deviation of a boiler heating surface is also provided, comprising: a first acquisition module, used to acquire a boiler benchmark temperature database, wherein the boiler benchmark temperature database comprises a plurality of benchmark temperature vectors of a first heating surface, any one of the benchmark temperature vectors corresponds to a load range, and the load ranges corresponding to the plurality of benchmark temperature vectors are different; a second acquisition module, used to acquire a plurality of temperature data of the first heating surface under a first load; a characteristic temperature vector calculation module, used to determine a first characteristic temperature vector of the first heating surface under the first load based on the plurality of temperature data; a thermal deviation index calculation module, used to determine a thermal deviation index of the first heating surface based on a Euclidean distance between a benchmark temperature vector corresponding to a first load range in which the first load is located and the first characteristic temperature vector, wherein the thermal deviation index of the first heating surface is used to make thermal deviation judgment on the first heating surface of the boiler.

[0021] Optionally, the thermal deviation index calculation module is further used to determine the thermal deviation index of the first heating surface using the following formula:

[0022]

[0023] Wherein, ρ is the thermal deviation index of the first heating surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector.

[0024] Optionally, the plurality of temperature data include temperature data of i temperature measurement points within the first heated surface,

[0025] The characteristic temperature vector calculation module is further used to determine the first temperature characteristic vector using the following formula:

[0026] T s =[T s1 , T s2 , T s3 , ...T si ]

[0027] Among them, T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measuring point, the second temperature measuring point, the third temperature measuring point...the i-th temperature measuring point;

[0028] The characteristic temperature vector calculation module is also used to determine the kth temperature characteristic component using the following formula:

[0029]

[0030] Where k is an integer, the value range of k is 1 to i, i is a positive integer, T sk is the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point in the plurality of temperature data, T xn It is the temperature data of the nth temperature measurement point among the multiple temperature data.

[0031] Optionally, the first heating surface includes i temperature measuring points, and the second load range includes j groups of historical temperature data, the j groups of historical temperature data correspond to different loads respectively, and any group of historical temperature data is an i-dimensional vector, and each dimension of the i-dimensional vector is used to indicate the historical temperature of a temperature measuring point under the load corresponding to the i-dimensional vector. The first acquisition module is also used to: obtain the benchmark temperature vector component of each group of historical temperature data in the j groups of historical temperature data to obtain j benchmark temperature vector components; average the j benchmark temperature vector components to obtain the benchmark temperature vector corresponding to the second load range; wherein the benchmark temperature vector component of the mth group of historical temperature data is obtained in the following manner: determine the average value of the mth group of historical temperature data; subtract the average value of the mth group of historical temperature data from each dimension in the mth group of historical temperature data to obtain the benchmark temperature vector component of the mth group of historical temperature data, j and m are positive integers, and the value range of m is 1 to j, and the second load range is any load range in the boiler benchmark temperature database.

[0032] In a third aspect, a computer device is also provided, comprising: a memory and a processor, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor, thereby executing the boiler heating surface thermal deviation judgment method described in the above embodiment.

[0033] In a fourth aspect, a computer-readable storage medium is also provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor, thereby executing the boiler heating surface thermal deviation judgment method described in the above embodiment.

[0034] In a fifth aspect, a computer program product is provided, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the method described in the first aspect is implemented.

[0035] The beneficial effects brought by the technical solution provided by the embodiments of the present disclosure include at least:

[0036] In the disclosed embodiment, the thermal deviation index is calculated by the temperature data of the heating surface, without calculating the enthalpy of the working medium of the heating surface, so that the abnormal thermal deviation condition of the first heating surface can be quickly determined, which can better meet the needs of fast and flexible peak regulation of coal-fired power generation boilers. In addition, by calculating the thermal deviation index based on the Euclidean distance, the influence of the uneven temperature distribution naturally generated during the normal operation of the boiler can be eliminated, so as to improve the accuracy of thermal deviation judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flow chart of a method for determining thermal deviation of a boiler heating surface provided by an exemplary embodiment of the present disclosure is shown;

[0039] Figure 2 A flow chart showing a method for determining thermal deviation of a heating surface of a boiler provided by another exemplary embodiment of the present disclosure is shown;

[0040] Figure 3 It is the temperature distribution diagram of the high temperature reheater at a certain moment from working condition 1 to working condition 3;

[0041] Figure 4 A schematic structural diagram of a device for judging thermal deviation of a heating surface of a boiler provided by an exemplary embodiment of the present disclosure is shown;

[0042] Figure 5 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] Unless otherwise defined, the technical or scientific terms used herein shall have the usual meanings understood by persons of ordinary skill in the field to which the present disclosure belongs. The words "first", "second", "third" and similar words used in the patent application specification and claims of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate a quantitative limitation, but rather indicate the presence of at least one. Words such as "include" or "comprise" and similar words mean that the elements or objects appearing before "include" or "comprises" cover the elements or objects listed after "include" or "comprises" and their equivalents, and do not exclude other elements or objects.

[0044] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0045] Figure 1 A flow chart of a method for determining thermal deviation of a boiler heating surface provided by an exemplary embodiment of the present disclosure is shown. The method can be executed by a computer device. Figure 1 , the method comprising:

[0046] In step 101, a boiler benchmark temperature database is obtained.

[0047] The boiler benchmark temperature database includes multiple benchmark temperature vectors of the first heating surface, any benchmark temperature vector corresponds to a load range, and the load ranges corresponding to the multiple benchmark temperature vectors are different.

[0048] Optionally, the load range is determined based on the peak load range of the boiler unit. For example, each p% in the peak load range of the boiler unit is a load range. The value range of p is 1 to 6, for example, 1, 2, 4, 5, 6, etc.

[0049] In step 102, a plurality of temperature data of a first heating surface under a first load is obtained.

[0050] Here, the first load may be the real-time load of the boiler, and the plurality of temperature data of the first heating surface under the first load is the real-time temperature data of the first heating surface under the first load. The first heating surface is the heating surface of the boiler that needs to be judged for thermal deviation.

[0051] In step 103, based on a plurality of temperature data, a first characteristic temperature vector of the first heating surface under a first load is determined.

[0052] In step 104, a thermal deviation index of the first heating surface is determined based on a Euclidean distance between a benchmark temperature vector and a first characteristic temperature vector corresponding to a first load range in which the first load is located.

[0053] The thermal deviation index of the first heating surface is used to judge the thermal deviation of the first heating surface of the boiler. In the disclosed embodiment, the smaller the thermal deviation index of the first heating surface is, the smaller the thermal deviation of the first heating surface is, indicating that the first heating surface is relatively normal; the larger the thermal deviation index of the first heating surface is, the larger the thermal deviation of the first heating surface is, indicating that the first heating surface is abnormal and requires manual intervention by staff.

[0054] In the disclosed embodiment, the thermal deviation index is calculated by the temperature data of the heating surface, without calculating the enthalpy of the working medium of the heating surface, so that the abnormal thermal deviation condition of the first heating surface can be quickly determined, which can better meet the needs of fast and flexible peak regulation of coal-fired power generation boilers. In addition, by calculating the thermal deviation index based on the Euclidean distance, the influence of the uneven temperature distribution naturally generated during the normal operation of the boiler can be eliminated, so as to improve the accuracy of thermal deviation judgment.

[0055] Figure 2 A flow chart of a method for determining thermal deviation of a boiler heating surface provided by another exemplary embodiment of the present disclosure is shown. The method can be executed by a computer device. Figure 2 , the method comprising:

[0056] In step 201, a boiler benchmark temperature database is obtained.

[0057] The boiler benchmark temperature database includes multiple benchmark temperature vectors of the first heating surface, any benchmark temperature vector corresponds to a load range, and the load ranges corresponding to the multiple benchmark temperature vectors are different.

[0058] For the relevant contents of the load range, please refer to the aforementioned step 101, and the detailed description is omitted here.

[0059] Optionally, the first heating surface includes i temperature measurement points, and the second load range includes j groups of historical temperature data of the first heating surface, where j is a positive integer. The j groups of historical temperature data correspond to different loads within the second load range, respectively. Any group of historical temperature data is an i-dimensional vector, and each dimension of the i-dimensional vector is used to indicate the historical temperature of a temperature measurement point under the load corresponding to the i-dimensional vector. The second load range is any load range in the boiler benchmark temperature database. In this case, the benchmark temperature vector corresponding to the second load range is obtained using the following steps ab.

[0060] Step a: obtaining a benchmark temperature vector component of each set of historical temperature data in j sets of historical temperature data within a second load range, and obtaining j benchmark temperature vector components.

[0061] Before executing step a, it is necessary to first obtain j groups of historical temperature data of the first heating surface.

[0062] Optionally, the benchmark temperature vector component of the mth group of historical temperature data within the second load range is acquired by the following two steps, wherein m is a positive integer and the value range of m is 1 to j.

[0063] The first step is to determine the average value of the mth group of historical temperature data.

[0064] Optionally, the mth group of historical temperature data is represented by T m =[T m1 , T m2 , T m3 , ...T mi ] is an i-dimensional vector. In this case, the first step is to calculate using formula (1).

[0065]

[0066] In formula (1), n ​​is an integer ranging from 1 to i, S m is the average value of the mth group of historical temperature data, T mn It is the nth dimension in the mth group of historical temperature data, indicating the temperature of the nth temperature measurement point in the mth group of historical temperature data.

[0067] In the second step, the average value of the mth group of historical temperature data is subtracted from each dimension in the mth group of historical temperature data to obtain the benchmark temperature vector component of the mth group of historical temperature data.

[0068] Optionally, the benchmark temperature vector component of the mth group of historical temperature data is expressed using formula (2).

[0069] T m -S m =[(T m1 -S m ), (T m2 -S m ), (T m3 -S m ),...(T mi -S m )] (2)

[0070] In formula (2), T m -S m is the benchmark temperature vector component of the mth group of historical temperature data. The meanings of other parameters in formula (2) are the same as those in formula (1), and detailed description is omitted here.

[0071] For the other groups of historical temperature data except the mth group of historical temperature data among the j groups of historical temperature data within the second load range, the first step to the second step can also be used to obtain the benchmark temperature vector components.

[0072] Step b: averaging j benchmark temperature vector components to obtain a benchmark temperature vector corresponding to the second load range.

[0073] Optionally, step b is expressed using formula (3).

[0074]

[0075] In formula (3), is the benchmark temperature vector corresponding to the second load range, and the benchmark temperature vector is also an i-dimensional vector. They are the first component, the second component, the third component...the i-th component of the benchmark temperature vector, respectively, representing the benchmark temperature of the first temperature measuring point, the benchmark temperature of the second temperature measuring point, the benchmark temperature of the third temperature measuring point...the benchmark temperature of the i-th temperature measuring point of the first heated surface within the second load range. The meanings of other parameters in formula (3) are the same as those in formula (1) and formula (2), and detailed description is omitted here.

[0076] When considering the thermal deviation of the boiler, the load fluctuation of the boiler will also cause the temperature of the boiler heating surface to change, but this change is a normal phenomenon and not a thermal deviation. The thermal deviation judgment method in the related art is prone to judge the change in the temperature of the boiler heating surface caused by the load fluctuation of the boiler as a thermal deviation. In the embodiment of the present disclosure, by taking the average value in the above steps ab to obtain the benchmark temperature vector, it is possible to eliminate the influence of the temperature change of the heating surface caused by the boiler load fluctuation in the historical temperature of the boiler on the subsequent judgment of the thermal deviation.

[0077] Optionally, when acquiring any set of historical temperature data, if a certain temperature measurement point includes at least two temperature sensors, an average temperature value of the at least two temperature sensors is used as the historical temperature data of the temperature measurement point.

[0078] Here, since the temperature of the boiler is high when it is running, the temperature difference changes greatly, which makes the temperature sensor easy to be damaged. If only one temperature sensor is used in a certain temperature measurement point, it is impossible to obtain accurate temperature data in the temperature sensor after the temperature sensor is damaged. Therefore, any temperature measurement point in the related art usually includes at least two temperature sensors for obtaining the temperature of the temperature measurement point.

[0079] In step 202, a plurality of temperature data of a first heating surface under a first load is obtained.

[0080] Here, the first load may be the real-time load of the boiler, and the plurality of temperature data of the first heating surface under the first load is the real-time temperature data of the first heating surface under the first load. The first heating surface is the heating surface of the boiler that needs to be judged for thermal deviation.

[0081] The multiple temperature data are real-time temperature data of multiple temperature measuring points in the first heated surface. The real-time temperature data of any temperature measuring point can be processed in the same manner as the historical temperature data, that is, if a temperature measuring point includes at least two temperature sensors, the real-time temperature average value of the at least two temperature sensors is used as the real-time temperature data of the temperature measuring point.

[0082] Optionally, the historical temperature data and real-time temperature data of the first heating surface involved in steps 201-202 may be acquired through a DCS (Distributed Control System) system of the boiler.

[0083] Optionally, after obtaining the historical temperature data and the real-time temperature data, the historical temperature data and the real-time temperature data may be preprocessed, including but not limited to outlier removal, missing value interpolation, etc. There are many implementation methods for preprocessing in the related art, and detailed description is omitted here.

[0084] In step 203, based on a plurality of temperature data, a first characteristic temperature vector of the first heating surface under a first load is determined.

[0085] Optionally, when the first heated surface includes i temperature measuring points, the multiple temperature data in step 202 include temperature data of the i temperature measuring points in the first heated surface.

[0086] In this case, step 203 includes:

[0087] The first temperature eigenvector is determined using formula (4):

[0088] T s =[T s1 , T s2 , T s3 , ...T si ] (4)

[0089] In formula (4), T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measurement point, the second temperature measurement point, the third temperature measurement point...the i-th temperature measurement point.

[0090] Optionally, the kth temperature characteristic component is determined using formula (5).

[0091]

[0092] In formula (5), n and k are integers, and the range of n and k is 1 to i, i is a positive integer, T sk is the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point among multiple temperature data, T xn It is the temperature data of the nth temperature measurement point among multiple temperature data.

[0093] When considering the thermal deviation of the boiler, the load fluctuation of the boiler will also cause the temperature of the boiler heating surface to change, but this change is a normal phenomenon and not a thermal deviation. The thermal deviation judgment method in the related art is prone to judge the change in the temperature of the boiler heating surface caused by the load fluctuation of the boiler as a thermal deviation. In the embodiment of the present disclosure, by obtaining the first temperature characteristic vector by taking the average value in the above step 203, the influence of the change in the temperature of the heating surface caused by the boiler load fluctuation in the real-time temperature of the boiler on the subsequent judgment of the thermal deviation can be eliminated.

[0094] In step 204, a thermal deviation index of the first heating surface is determined based on the Euclidean distance between the benchmark temperature vector and the first characteristic temperature vector corresponding to the first load range in which the first load is located.

[0095] The thermal deviation index of the first heating surface is used to judge the thermal deviation of the first heating surface of the boiler.

[0096] Optionally, formula (6) is used to determine the thermal deviation index of the first heated surface.

[0097]

[0098] In formula (6), ρ is the thermal deviation index of the first heating surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector. The meanings of other parameters in formula (6) are the same as those in formula (4) and formula (5), and detailed description is omitted here.

[0099] Here, the load range in the boiler benchmark temperature database where the first load is located is the first load range, and the benchmark temperature vector corresponding to the first range in the boiler benchmark temperature database is denoted by To express, That is, the nth component of the benchmark temperature vector.

[0100] The multiple temperature data of the first heating surface under the first load can also be displayed in the form of a heating surface temperature distribution diagram, so that the staff can intuitively determine the severity of the thermal deviation of the first heating surface based on the thermal deviation index of the first heating surface.

[0101] Taking a 660MW high-efficiency supercritical coal-fired steam turbine generator set as an example, the two boilers are high-efficiency ultra-supercritical parameter transformer-operated DC furnaces, single furnace, single reheat, balanced ventilation, solid slag discharge, all-steel frame, and fully suspended structure; the boiler adopts a tight-fitting, closed, Π-shaped layout boiler.

[0102] Exemplarily, the first heating surface is a high temperature reheater of the boiler, and there are 96 temperature measuring points on the high temperature reheater, that is, i is equal to 96. Each temperature measuring point includes 10 temperature sensors, and a total of 30,000 sets of historical temperature data are obtained. Based on these 30,000 sets of historical temperature data, the boiler benchmark temperature database of the boiler is constructed using the method in step 201.

[0103] Then, multiple temperature data of the high temperature reheater of the boiler under the first load are collected, that is, the temperature data of 96 temperature measuring points of the high temperature reheater under the first load. Since each temperature measuring point includes 10 temperature sensors, a total of 960 temperature data are obtained, and the 10 data of any temperature measuring point are averaged to obtain the temperature data of the 96 temperature measuring points of the high temperature reheater under the first load.

[0104] In this embodiment, the first load takes three values ​​respectively, and obtains three sets of temperature data of working conditions 1 to 3 as shown in Tables 1 to 3. Here, the three sets of temperature data of working conditions 1 to 3 shown in Tables 1 to 3 are actually a 96-dimensional vector, which is represented as a 16-by-6 matrix for ease of display.

[0105] Table 1: Temperature data of 96 temperature measuring points of the high temperature reheater when the first load is 661.4115 MW (operating condition 1).

[0106] 554.94000 569.45001 574.37000 584.02000 595.24004 602.42002 596.26000 609.55001 614.85003 613.28002 612.07002 615.07004 617.95001 616.73003 611.71003 609.96005 609.48002 565.16002 606.99003 606.91002 598.38002 607.39002 602.08002 602.32003 604.68005 608.56002 607.75000 603.25001 606.68003 602.62003 582.71000 599.93000 607.94002 612.39005 609.60004 608.85005 612.99001 612.04004 612.65003 614.31001 617.59001 567.45002 610.87001 613.65000 614.63001 619.15001 616.65002 618.55002 619.33003 613.91003 612.28001 612.81004 612.23003 613.91003 615.92002 611.23003 610.02003 615.98002 613.46003 608.87003 607.65006 612.05001 609.13003 607.72003 610.18001 607.01001 604.25001 579.71001 609.99003 613.78002 607.70005 600.11004 601.37004 605.14003 605.28003 607.30003 605.96005 607.40005 588.84002 608.59002 611.50006 610.31002 608.31002 614.22001 609.40002 610.27001 610.78002 610.29001 609.44002 603.75002 600.74002 596.79004 590.01002 584.16000 577.70003 566.71005

[0107] Table 2: Temperature data of 96 temperature measuring points of the high temperature reheater when the first load is 620.1071MW (operating condition 2).

[0108] 545.33003 560.25003 563.76000 570.52000 577.65000 583.98003 593.44002 585.46004 586.66004 584.31001 582.95003 587.89001 589.83006 590.23003 587.54002 589.12001 590.10001 549.75000 588.69004 591.23002 584.29001 595.39003 592.24002 594.68002 597.53002 601.61003 602.14001 601.62001 608.42001 605.28002 587.86002 606.12001 615.16002 621.12002 618.70000 620.07003 62509002 62501004 62641000 62883001 63154003 58097001 626.49002 630.63000 631.81001 636.83001 634.56000 635.70002 637.56003 631.84004 629.42001 629.92004 628.81003 630.56002 631.86002 627.22002 624.95003 630.84002 627.01002 621.62002 618.75000 623.08000 618.67003 615.60006 617.06001 615.19005 610.97004 580.77003 610.27003 612.50000 606.46002 596.38002 596.99005 599.78001 599.46001 600.46004 599.19003 600.71004 582.42001 600.36006 602.47005 601.66003 599.35003 604.05005 602.78003 600.83005 602.29003 602.51000 601.22004 595.18007 590.87005 588.15003 582.25002 578.66002 570.76005 558.71003

[0109] Table 3: Temperature data of 96 temperature measuring points of the high temperature reheater when the first load is 367.6739MW (operating condition 3).

[0110] 603.41005 604.18001 596.41003 605.56001 600.57002 601.01001 601.69001 604.94000 603.53004 601.62003 605.61003 600.59001 582.06002 600.47000 604.86003 609.90002 607.17002 608.09003 610.23003 609.77000 609.60003 612.24000 613.51003 565.62002 610.13001 612.17002 613.16001 616.93001 615.00002 615.84002 615.12003 610.69004 609.47002 609.87003 609.05003 611.56001 611.83002 608.71001 606.95002 611.32001 608.64004 605.54001 604.46005 606.99000 604.87001 604.82003 607.97002 607.89005 605.72001 576.28004 608.42002 610.02005 606.22006 600.71002 602.26003 604.78001 605.34003 606.22002 605.70003 607.75005 590.06004 608.52006 610.14002 609.54005 608.45004 613.07002 613.63006 608.46000 608.35006 607.86005 606.52003 602.10005 599.07001 596.54003 591.33002 587.31001 579.25005 565.85002

[0111] Steps 203 - 204 are respectively executed for the three groups of temperature data of working conditions 1 to 3 shown in Tables 1 to 3, and the thermal deviation indicators corresponding to working conditions 1 to 3 are obtained as ρ1 = 0.3113, ρ2 = 1.1236, and ρ3 = 0.7505, respectively.

[0112] Figure 3 This is the temperature distribution diagram of the high-temperature reheater at a certain moment from operating condition 1 to operating condition 3. Figure 3 Part (a) is the temperature distribution diagram of the high temperature reheater at a certain moment when the first load is 661.4115MW; Figure 3 Part (b) is the temperature distribution diagram of the high temperature reheater at a certain moment when the first load is 620.1071MW; Figure 3 Part (c) is the temperature distribution diagram of the high temperature reheater at a certain moment when the first load is 367.6739MW. Figure 3 In the figure, the blue line represents the temperature distribution of the high temperature reheater from condition 1 to condition 3 at a certain moment, and the orange line represents the benchmark temperature corresponding to a certain condition.

[0113] pass Figure 3 From parts (a) to (c), it can be seen that the smaller the thermal deviation index is, the closer the temperature is to the benchmark temperature corresponding to the working condition. For example, the thermal deviation index of working condition 1 is the smallest, so Figure 3 In the temperature distribution diagram of part (a), the temperature distribution of working condition 1 is closest to the benchmark temperature corresponding to working condition 1. Similarly, the larger the thermal deviation index of the working condition, the greater the deviation between its temperature and the benchmark temperature corresponding to the working condition. For example, the thermal deviation index of working condition 2 is the highest, so Figure 3 In the temperature distribution diagram in part (b), the temperature distribution of working condition 2 has the largest deviation from the benchmark temperature corresponding to working condition 2.

[0114] The following are device embodiments of the present application. For details not described in detail in the device embodiments, reference may be made to the above method embodiments.

[0115] Figure 4 FIG. 1 is a schematic diagram showing a structure of a boiler heating surface thermal deviation judgment device provided by an exemplary embodiment of the present disclosure. Figure 4 The boiler heating surface thermal deviation judgment device 400 includes: a first acquisition module 401, a second acquisition module 402, a characteristic temperature vector calculation module 403 and a thermal deviation index calculation module 404.

[0116] The first acquisition module 401 is used to acquire a boiler benchmark temperature database, which includes multiple benchmark temperature vectors of the first heating surface. Any benchmark temperature vector corresponds to a load range, and multiple benchmark temperature vectors correspond to different load ranges.

[0117] The second acquisition module 402 is used to acquire a plurality of temperature data of the first heating surface under the first load.

[0118] The characteristic temperature vector calculation module 403 is used to determine a first characteristic temperature vector of the first heating surface under a first load based on a plurality of temperature data.

[0119] The thermal deviation index calculation module 404 is used to determine the thermal deviation index of the first heating surface based on the Euclidean distance between the benchmark temperature vector and the first characteristic temperature vector corresponding to the first load range of the first load. The thermal deviation index of the first heating surface is used to make thermal deviation judgment on the first heating surface of the boiler.

[0120] Optionally, the thermal deviation index calculation module 404 is further configured to determine the thermal deviation index of the first heating surface using the following formula:

[0121]

[0122] Where ρ is the thermal deviation index of the first heated surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector.

[0123] Optionally, the plurality of temperature data include temperature data of i temperature measurement points within the first heated surface,

[0124] The characteristic temperature vector calculation module 403 is further used to determine the first temperature characteristic vector using the following formula:

[0125] T s =[T s1 , T s2 , T s3 , ...T si ]

[0126] Among them, T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measurement point, the second temperature measurement point, the third temperature measurement point...the i-th temperature measurement point;

[0127] The characteristic temperature vector calculation module 403 is also used to determine the kth temperature characteristic component using the following formula:

[0128]

[0129] Where k is an integer, the value range of k is 1 to i, i is a positive integer, T skis the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point among multiple temperature data, T xn It is the temperature data of the nth temperature measurement point among multiple temperature data.

[0130] Optionally, the first heating surface includes i temperature measuring points, and the second load range includes j groups of historical temperature data, the j groups of historical temperature data correspond to different loads respectively, and any group of historical temperature data is an i-dimensional vector, and each dimension of the i-dimensional vector is used to indicate the historical temperature of a temperature measuring point under the load corresponding to the i-dimensional vector. The first acquisition module 401 is also used to: obtain the benchmark temperature vector component of each group of historical temperature data in the j groups of historical temperature data, and obtain j benchmark temperature vector components; calculate the average value of the j benchmark temperature vector components to obtain the benchmark temperature vector corresponding to the second load range; wherein, the benchmark temperature vector component of the mth group of historical temperature data is obtained in the following manner: determine the average value of the mth group of historical temperature data; subtract the average value of the mth group of historical temperature data from each dimension in the mth group of historical temperature data to obtain the benchmark temperature vector component of the mth group of historical temperature data, j and m are positive integers, the value range of m is 1 to j, and the second load range is any load range in the boiler benchmark temperature database.

[0131] It should be noted that: when the boiler heating surface thermal deviation judgment device provided in the above embodiment performs the boiler heating surface thermal deviation judgment, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the boiler heating surface thermal deviation judgment device provided in the above embodiment and the boiler heating surface thermal deviation judgment method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0132] The division of modules in the embodiments of the present disclosure is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present disclosure may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0133] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a terminal device (which can be a personal computer, a mobile phone, or a communication device, etc.) or a processor (processor) to perform all or part of the steps of the method of each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0134] Figure 5 Schematic diagram of the structure of the computer device provided by the embodiment of the present disclosure. Figure 5 As shown, the computer device 500 includes: a processor 501 and a memory 502 .

[0135] The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 501 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 501 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0136] The memory 502 may include one or more computer-readable storage media, which may be non-transitory. The memory 502 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 502 is used to store at least one instruction, which is used to be executed by the processor 501 to implement the boiler heating surface thermal deviation judgment method provided in the embodiment of the present disclosure.

[0137] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the computer device 500, and the computer device 500 may include more or less components than those shown in the figure, or combine some components, or adopt a different arrangement of components.

[0138] The embodiment of the present disclosure also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of a computer device, the computer device can execute the boiler heating surface thermal deviation judgment method provided in the embodiment of the present disclosure.

[0139] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for determining thermal deviation of a heating surface of a boiler provided in the embodiment of the present disclosure.

[0140] The above description is only an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for judging thermal deviation of a boiler heating surface, characterized in that: The method comprises: Acquire a boiler benchmark temperature database, wherein the boiler benchmark temperature database includes a plurality of benchmark temperature vectors of a first heating surface, any one of the benchmark temperature vectors corresponds to a load range, and the load ranges corresponding to the plurality of benchmark temperature vectors are different; Acquire a plurality of temperature data of the first heating surface under a first load; Based on the plurality of temperature data, determining a first characteristic temperature vector of the first heating surface under the first load; Based on the Euclidean distance between the benchmark temperature vector corresponding to the first load range in which the first load is located and the first characteristic temperature vector, a thermal deviation index of the first heating surface is determined, and the thermal deviation index of the first heating surface is used to judge the thermal deviation of the first heating surface of the boiler.

2. The method according to claim 1, characterized in that: The determining of the thermal deviation index of the first heating surface based on the Euclidean distance between the benchmark temperature vector corresponding to the first load range in which the first load is located and the first characteristic temperature vector comprises: The following formula is used to determine the thermal deviation index of the first heating surface: Wherein, ρ is the thermal deviation index of the first heating surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector.

3. The method according to claim 1 or 2, characterized in that: In the case where the first heating surface includes i temperature measuring points, the plurality of temperature data include temperature data of the i temperature measuring points in the first heating surface. The determining, based on the plurality of temperature data, a first characteristic temperature vector of the first heating surface under the first load comprises: The first temperature characteristic vector is determined using the following formula: T s =[T s1 T s2 ,T s3 ,…T si ] Among them, T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measuring point, the second temperature measuring point, the third temperature measuring point...the i-th temperature measuring point; The kth temperature characteristic component is determined by the following formula: Where n and k are integers, and the range of n and k is 1 to i, i is a positive integer, T sk is the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point in the plurality of temperature data, T xn It is the temperature data of the nth temperature measurement point among the multiple temperature data.

4. The method according to claim 1 or 2, characterized in that: The first heating surface includes i temperature measurement points, and the second load range includes j groups of historical temperature data, the j groups of historical temperature data correspond to different loads respectively, and any group of historical temperature data is an i-dimensional vector, and each dimension of the i-dimensional vector is used to indicate the historical temperature of a temperature measurement point under the load corresponding to the i-dimensional vector. The method further comprises: Obtaining a benchmark temperature vector component of each group of historical temperature data in the j groups of historical temperature data to obtain j benchmark temperature vector components; averaging the j benchmark temperature vector components to obtain a benchmark temperature vector corresponding to the second load range; Among them, the benchmark temperature vector component of the historical temperature data of the mth group is obtained in the following way: Determine the average value of the mth group of historical temperature data; Subtract the average value of the mth group of historical temperature data from each dimension in the mth group of historical temperature data to obtain the benchmark temperature vector component of the mth group of historical temperature data, where j and m are positive integers, the value range of m is 1 to j, and the second load range is any load range in the boiler benchmark temperature database.

5. A device for judging thermal deviation of a boiler heating surface, characterized in that: The device comprises: A first acquisition module is used to acquire a boiler benchmark temperature database, wherein the boiler benchmark temperature database includes a plurality of benchmark temperature vectors of a first heating surface, any one of the benchmark temperature vectors corresponds to a load range, and the load ranges corresponding to the plurality of benchmark temperature vectors are different; A second acquisition module, used for acquiring a plurality of temperature data of the first heating surface under a first load; a characteristic temperature vector calculation module, configured to determine a first characteristic temperature vector of the first heating surface under the first load based on the plurality of temperature data; A thermal deviation index calculation module is used to determine the thermal deviation index of the first heating surface based on the Euclidean distance between the benchmark temperature vector corresponding to the first load range in which the first load is located and the first characteristic temperature vector. The thermal deviation index of the first heating surface is used to make a thermal deviation judgment on the first heating surface of the boiler.

6. The device according to claim 5, characterized in that The thermal deviation index calculation module is also used to determine the thermal deviation index of the first heating surface using the following formula: Wherein, ρ is the thermal deviation index of the first heating surface, is the benchmark temperature vector corresponding to the first load range where the first load is located, T s is the first temperature eigenvector.

7. The device according to claim 5, characterized in that The plurality of temperature data include temperature data of i temperature measurement points in the first heated surface, The characteristic temperature vector calculation module is further used to determine the first temperature characteristic vector using the following formula: T s =[T s1 ,T s2 ,T s3 ,...T si ] Among them, T s is the first temperature eigenvector, T s1 , T s2 , T s3 , ...T si is the temperature characteristic component of the first temperature characteristic vector at the first temperature measuring point, the second temperature measuring point, the third temperature measuring point...the i-th temperature measuring point; The characteristic temperature vector calculation module is also used to determine the kth temperature characteristic component using the following formula: Where k is an integer, the value range of k is from 1 to i, i is a positive integer, T sk is the kth temperature characteristic component, T xk is the temperature data of the kth temperature measurement point in the plurality of temperature data, T xn It is the temperature data of the nth temperature measurement point among the multiple temperature data.

8. A computer device, characterized in that: The computer device comprises: a memory and a processor, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the method according to any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 4.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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