A water vapor quality evaluation model construction method, system, device and medium

Abstract

The application provides a water vapor quality evaluation model construction method, system, device and medium, generates a model index set based on water vapor quality indexes and power plant water vapor abnormality processing procedures, obtains short board coefficients of each index at multiple time points, and generates a short board coefficient set of each index; obtains a dynamic short board effect coefficient of each index based on the maximum value of the short board coefficients of each index at different time points; obtains an evaluation value set of each index based on the expected value and the actual value of each index; establishes a water vapor quality comprehensive evaluation model based on the dynamic short board effect coefficient and the evaluation value set of each index; the application can evaluate the running state of the water vapor system of the power plant in real time, reduces the workload of the offline data analysis of the chemical personnel of the power plant, can fully reflect the water vapor system, can also reflect the single index limit abnormal phenomenon, improves the effectiveness of the safety operation monitoring of the water vapor system of the power plant, prolongs the expected service life of the generator set, and improves the operation safety and economy.

Description

Technical Field

[0001] This invention relates to the field of chemical steam quality evaluation technology in power plants, and in particular to a method, system, equipment and medium for constructing a steam quality evaluation model. Background Technology

[0002] The safe operation of the chemical steam-water system in power plants plays a crucial role in the overall safe operation of the power plant. Establishing a comprehensive evaluation system for the steam-water system is one of the main means of real-time monitoring its safe operation. With the increasing prevalence of smart power plants, the demand for real-time comprehensive evaluation of the steam-water system is also growing to improve the real-time monitoring system. Real-time comprehensive evaluation of the steam-water system reflects its current steam-water state, providing a theoretical basis for formulating medium- and long-term operational strategies.

[0003] However, currently, power plant chemical steam and water systems still rely on manual offline analysis of instrument data. These systems have nearly a hundred related indicators that are interconnected and influence each other. Manually providing steam and water quality status requires highly specialized engineers to perform data analysis, and only provides nodal-level steam and water quality evaluation results. Furthermore, the offline data processing methods used by power plant engineers typically involve single-indicator methods or weighted methods. Single-indicator methods cannot fully reflect the system, while weighted average methods average out anomalies in individual indicators, failing to reflect extreme anomalies. In short, the results of existing power plant steam and water quality evaluation technologies differ significantly from the actual state of the power plant steam and water system, reducing the effectiveness of monitoring the safe operation of the system. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method, system, equipment, and medium for constructing a water and steam quality evaluation model, which can compensate for the shortcomings of comprehensive evaluation and realize accurate and effective monitoring of the water and steam system quality in power plants.

[0005] This invention is achieved through the following technical solution:

[0006] A method for constructing a water vapor quality evaluation model includes the following steps:

[0007] Based on the water vapor quality index and the power plant water vapor anomaly handling procedures, a model index set is generated to obtain the short-board coefficient of each index at multiple time points, and a short-board coefficient set of each index is generated.

[0008] Based on the maximum value of the short board coefficient of each indicator at different time points, the dynamic short board effect coefficient of each indicator is obtained.

[0009] The set of evaluation values ​​for each indicator is obtained based on the expected and actual values ​​of each indicator.

[0010] A comprehensive evaluation model for water vapor quality is established based on the dynamic bottleneck effect coefficients and evaluation value sets of each indicator.

[0011] Furthermore, the water vapor quality indicators include iron-hydrogen content, hydrogen conductivity, dissolved oxygen content, and sodium content.

[0012] Furthermore, the model index set A = (a1, a2, a3, ... a... i .,a n );

[0013] The bottleneck coefficient for each indicator is δ i ,

[0014]

[0015] Among them, a i Let be the short-board coefficient for indicator i. The highest level is when each indicator is in the third level of abnormality; the larger the corresponding short-board coefficient value, the more severe the short-board effect. The set of short-board coefficients for each indicator is generated as δ=(δ1,δ2,δ3,...δ...). i .,δ n ).

[0016] Furthermore, the dynamic bottleneck effect coefficient of each indicator is: δ 总 =max(δ1,δ2,δ3,..δ i .,δ n ).

[0017] Furthermore, the evaluation value in the set of evaluation values ​​is m. i ,

[0018] m i =100-σ i |a i -a i期望值 |,

[0019] Where, σ i For when a i The indicator deviates from the expected value a. i期望值 Deduction coefficient at the time;

[0020] The resulting set of evaluation values ​​is M = (m1, m2, m3, ... m2). i .,m n ).

[0021] Furthermore, based on the characteristics of various water vapor quality indicators, when a i When the index is set to the "the bigger the better" type:

[0022]

[0023] when a iWhen the index is of the intermediate good type:

[0024]

[0025] when a i When the index is set to the smallest possible value,

[0026]

[0027] Furthermore, the comprehensive evaluation model for water vapor quality is as follows:

[0028]

[0029] Where β is the weight value of each indicator in the model;

[0030] When δ 总 When δ = 0, none of the indicators of the water vapor system fall within the range of tertiary treatment, and the evaluation model does not consider the dynamic bottleneck effect coefficient; 总 When β ≠ 0, the evaluation model enters the penalty phase based on the dynamic weakest link effect coefficient. -1 M and m i The larger the difference in input, the more severe the penalty to the final result of the evaluation model.

[0031] A system for constructing a water vapor quality evaluation model includes:

[0032] The index coefficient generation module is used to generate a model index set based on water vapor quality index and power plant water vapor anomaly handling procedures, obtain the short board coefficient of each index at multiple time points, and generate a set of short board coefficients for each index.

[0033] The dynamic short board effect coefficient generation module is used to obtain the dynamic short board effect coefficient of each indicator based on the maximum value of the short board coefficient of each indicator at different time points.

[0034] The evaluation value set generation module is used to obtain the evaluation value set of each indicator based on the expected value and the actual value of each indicator;

[0035] The water vapor quality comprehensive evaluation model generation module is used to establish a water vapor quality comprehensive evaluation model based on the dynamic short-board effect coefficient and evaluation value set of each indicator.

[0036] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a step of the method for constructing a water vapor quality evaluation model.

[0037] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for constructing a water vapor quality evaluation model.

[0038] Compared with the prior art, the present invention has the following beneficial technical effects:

[0039] This invention provides a method, system, equipment, and medium for constructing a water and steam quality evaluation model. Based on water and steam quality indicators combined with power plant water and steam anomaly handling procedures, a model indicator set is generated. The system obtains the bottleneck coefficients of each indicator at multiple time points and generates a set of bottleneck coefficients for each indicator. Based on the maximum values ​​of the bottleneck coefficients of each indicator at different time points, the dynamic bottleneck effect coefficient of each indicator is obtained. Based on the expected and actual values ​​of each indicator, a set of evaluation values ​​for each indicator is obtained. Based on the dynamic bottleneck effect coefficients and the set of evaluation values ​​for each indicator, a comprehensive water and steam quality evaluation model is established. This application can perform real-time evaluation of the operating status of the power plant water and steam system, reducing the workload of offline data analysis for power plant chemical personnel. It can comprehensively reflect the water and steam system and also reflect the extreme anomalies of single indicators, with minimal difference from the actual state of the power plant water and steam system, thus improving the effectiveness of monitoring the safe operation of the power plant water and steam system. By analyzing the correlation relationships of a certain number of representative power plant water and steam systems, combined with the actual operating procedures of the power plant, the quality of the water and steam system can be judged, guiding the adjustment of boiler water and steam quality to the optimal state, which can extend the expected service life of generator units and improve their operational safety and economy. Attached Figure Description

[0040] Figure 1 This is a flowchart of a method for constructing a water vapor quality evaluation model according to the present invention. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0042] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0044] This invention provides a method for constructing a water vapor quality evaluation model, such as... Figure 1 As shown, it includes the following steps:

[0045] S101: Based on the water vapor quality index and the power plant water vapor anomaly handling procedure, generate a model index set, obtain the short board coefficient of each index at multiple time points, and generate a short board coefficient set for each index.

[0046] S102: Based on the maximum value of the short board coefficient of each indicator at different time points, the dynamic short board effect coefficient of each indicator is obtained;

[0047] S103: Obtain the set of evaluation values ​​for each indicator based on the expected and actual values ​​of each indicator;

[0048] S104: Based on the dynamic bottleneck effect coefficients and evaluation value sets of each indicator, establish a comprehensive evaluation model for water vapor quality.

[0049] Specifically, this application employs the core idea of ​​the "barrel theory" to evaluate water vapor quality. The core idea of ​​the barrel theory is that the shortest stave in a barrel plays a crucial role in its water capacity. In application scenarios, the barrel model is interpreted as a representation of the true state of a system, depending on finding the worst-performing element within that system; the quality of the water vapor is reflected in this worst-performing element.

[0050] Preferably, the water vapor quality indicators include iron-hydrogen content, hydrogen conductivity, dissolved oxygen content, and sodium content.

[0051] Preferably, the model index set A = (a1, a2, a3, ... a... i .,a n );

[0052] The bottleneck coefficient for each indicator is δ i ,

[0053]

[0054] Among them, a i Let be the short-board coefficient for indicator i. The highest level is when each indicator is in the third level of abnormality; the larger the corresponding short-board coefficient value, the more severe the short-board effect. The set of short-board coefficients for each indicator is generated as δ=(δ1,δ2,δ3,...δ...). i .,δ n ).

[0055] Preferably, the dynamic bottleneck effect coefficients of each indicator are:

[0056] δ 总 =max(δ1,δ2,δ3,..δ i .,δ n ).

[0057] Preferably, in the method for constructing a water vapor quality evaluation model, the evaluation value in the evaluation value set is m. i ,

[0058] m i =100-σ i |a i -a i期望值 |,

[0059] Where, σ i For when a i The indicator deviates from the expected value a. i期望值 Deduction coefficient at the time;

[0060] The resulting set of evaluation values ​​is M = (m1, m2, m3, ... m2). i .,m n ).

[0061] Preferably, the step of establishing a set M based on the characteristics of various water vapor quality indicators involves different m values. i The evaluation value is calculated as follows: when a i When the index is set to the "the bigger the better" type:

[0062]

[0063] when a i When the index is of intermediate good type:

[0064]

[0065] when a i When the index is set to be as small as possible,

[0066]

[0067] Preferably, the comprehensive evaluation model for water vapor quality is as follows:

[0068]

[0069] Where β is the weight value of each indicator in the model;

[0070] When δ 总 When δ = 0, none of the indicators of the water vapor system fall within the range of tertiary treatment, and the evaluation model does not consider the dynamic bottleneck effect coefficient; 总 When β ≠ 0, the evaluation model enters the penalty phase based on the dynamic weakest link effect coefficient. -1 M and m i The larger the difference in input, the more severe the penalty to the final result of the evaluation model;

[0071] Specifically, the evaluation values ​​of the comprehensive evaluation model W in this application are 100-95, 94-90, 89-80, and 79-0, respectively, and their corresponding evaluation levels are: excellent, good, qualified, and abnormal.

[0072] This invention provides a system for constructing a water vapor quality evaluation model, comprising:

[0073] The index coefficient generation module is used to generate a model index set based on water vapor quality index and power plant water vapor anomaly handling procedures, obtain the short board coefficient of each index at multiple time points, and generate a set of short board coefficients for each index.

[0074] The dynamic short-board effect coefficient generation module is used to obtain the dynamic short-board effect coefficient of each indicator based on the maximum value of the short-board coefficient of each indicator at different time points.

[0075] The evaluation value set generation module is used to obtain the evaluation value set of each indicator based on the expected value and the actual value of each indicator;

[0076] The water vapor quality comprehensive evaluation model generation module is used to establish a water vapor quality comprehensive evaluation model based on the dynamic short-board effect coefficient and evaluation value set of each indicator.

[0077] The present invention provides an embodiment as follows:

[0078] Taking the condensate steam system of a power plant as an example, a long series of data on indicators such as iron and hydrogen conductivity, sodium and dissolved oxygen at the condensate pump outlet are read from the power plant server database to generate a set of steam system indicators. To demonstrate the specific implementation process, only the data from time t1 to t44 in the long series data are used as a matrix.

[0079]

[0080] Step 2: Construct the bottleneck coefficients for each indicator. Any indicator in the water-vapor system can become a bottleneck, causing abnormal water-vapor quality. Therefore, based on the power plant's anomaly handling procedures, the bottleneck coefficients for each indicator are determined as follows:

[0081]

[0082] The set of bottleneck coefficients for each indicator corresponding to time points t1-t44 is as follows:

[0083] Step 3: Confirm the dynamic bottleneck effect coefficient. The dynamic bottleneck effect coefficient is determined by the maximum value of the bottleneck coefficients of each indicator. The larger the value of the bottleneck coefficient, the more obvious the bottleneck effect. Therefore, the dynamic bottleneck effect coefficient at time t1 is δ. 总1 =max(δ1,δ2,δ3,δ4)=0; the dynamic bottleneck effect coefficient at time t2 is, δ 总2 =max(δ1,δ2,δ3,δ4)=max(0,2,0,0)=2; Dynamic bottleneck effect coefficients δ at times t2 and t3 总3 and δ 总4 They are 1 and 0 respectively.

[0084] Step 4: Confirm the calculation method for the evaluation values ​​of each indicator. Indicators a1, a2, and a3 are "the smaller the better" indicators, based on... Among them, the deduction coefficients σ1, σ2, and σ3 are determined to be 10, 20, and 5 respectively, based on power industry standards and experience from long-term power plant operation data; a 1期望值 a 2期望值 and a 3期望值 The values ​​are determined to be 1, 0.15, and 1 respectively, based on the water and steam operation procedures of each power plant.

[0085] A4 is a good-to-medium standard indicator.

[0086] Among them, the deduction coefficient σ4 is 12; a 4期望值1 and a 4期望值2 Based on the water and steam operation procedures of each power plant, they are divided into 10 and 20 respectively.

[0087] Step 5: Based on the evaluation calculation methods of each indicator, generate the M-set corresponding to time points t1-t44.

[0088] Step 6: Establish a comprehensive evaluation model for water vapor quality. Based on power industry standards and experience from long-term power plant operation data, the weight set of each indicator in the model is confirmed as: β=(β1,β2,β3,β4)=(0.2,0.4,0.3,0.1). Based on the dynamic bottleneck effect coefficient, the comprehensive evaluation model for water vapor quality is established as follows:

[0089]

[0090] in:

[0091]

[0092] According to step 3, δ at time t1 and time t4 is... 总 =0, W=Mβ -1 The values ​​are 99.7 and 98.8 respectively. At time t2 and time t3, the individual indicators enter the abnormal handling range, W2 = 100 - 2.5(80.3 - 66) = 64.25, W3 = 100 - 1.5(87.9 - 73) = 77.65.

[0093] Step 7: According to the evaluation set, the water vapor system quality status at times t1 to t4 is excellent, abnormal, abnormal, and excellent, respectively.

[0094] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a water vapor quality evaluation model construction method.

[0095] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the water vapor quality evaluation model construction method in the above embodiments.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a water vapor quality evaluation model, characterized in that, Includes the following steps: Based on the water vapor quality index and the power plant water vapor anomaly handling procedures, a model index set is generated to obtain the short-board coefficient of each index at multiple time points, and a short-board coefficient set of each index is generated. Based on the maximum value of the short board coefficient of each indicator at different time points, the dynamic short board effect coefficient of each indicator is obtained. The set of evaluation values ​​for each indicator is obtained based on the expected and actual values ​​of each indicator. Based on the dynamic bottleneck effect coefficients and evaluation value sets of each indicator, a comprehensive evaluation model for water vapor quality is established. The comprehensive evaluation model for water vapor quality is as follows: ； Where β is the weight value of each indicator in the model; when When the value is 0, none of the indicators of the water vapor system have entered the tertiary treatment range, and the evaluation model does not consider the dynamic bottleneck effect coefficient. When the value is not equal to 0, the evaluation model enters the penalty phase based on the dynamic weakest link effect coefficient. and The larger the difference, the more severe the penalty to the final result of the evaluation model.

2. The method for constructing a water vapor quality evaluation model according to claim 1, characterized in that, The water vapor quality indicators include iron-hydrogen content, hydrogen conductivity, dissolved oxygen content, and sodium content.

3. The method for constructing a water vapor quality evaluation model according to claim 1, characterized in that, The model index set ; The weakness coefficients of each indicator are , ， in, Let be the short-board coefficient of indicator i; each indicator is at the highest level when it is in the third level of abnormality range. The larger the corresponding short-board coefficient value, the more severe the short-board effect. The set of short-board coefficients for each indicator is generated as follows: .

4. The method for constructing a water vapor quality evaluation model according to claim 3, characterized in that, The dynamic bottleneck effect coefficients of each indicator are: .

5. The method for constructing a water vapor quality evaluation model according to claim 3, characterized in that, The evaluation values ​​in the evaluation value set are , ， in, For when The indicator deviates from the expected value of the indicator. Deduction coefficient at the time; The resulting set of evaluation values ​​is .

6. The method for constructing a water vapor quality evaluation model according to claim 5, characterized in that, Based on the characteristics of various water vapor quality indicators, when When the index is set to the "the bigger the better" type: ； when When the index is of intermediate good type: ； when When the index is set to be as small as possible, 。 7. A system for constructing a water vapor quality evaluation model, characterized in that, A method for constructing a water vapor quality evaluation model based on any one of claims 1-6, comprising: The index coefficient generation module is used to generate a model index set based on water vapor quality index and power plant water vapor anomaly handling procedures, obtain the short board coefficient of each index at multiple time points, and generate a set of short board coefficients for each index. The dynamic short board effect coefficient generation module is used to obtain the dynamic short board effect coefficient of each indicator based on the maximum value of the short board coefficient of each indicator at different time points. The evaluation value set generation module is used to obtain the evaluation value set of each indicator based on the expected value and the actual value of each indicator; The water vapor quality comprehensive evaluation model generation module is used to establish a water vapor quality comprehensive evaluation model based on the dynamic short-board effect coefficient and evaluation value set of each indicator.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the water vapor quality evaluation model construction method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the water vapor quality evaluation model construction method as described in any one of claims 1-6.

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