Method, device and equipment for analyzing carbon emission uncertainty of thermal power generating unit and medium

Abstract

The application discloses a thermal power unit carbon emission uncertainty analysis method, device, equipment and medium, relates to the electric power data analysis field, and includes the following steps: constructing a first data set and a second data set based on effective input data; determining the comprehensive uncertainty corresponding to each effective input data according to the first data set and the second data set respectively; determining the correlation coefficient between any two data in the second data set, constructing a corresponding correlation coefficient matrix according to each correlation coefficient, and determining the sensitivity coefficient corresponding to each effective input data according to a preset carbon emission rate measurement model; and performing covariance propagation according to the comprehensive uncertainty, the correlation coefficient matrix and the sensitivity coefficient to complete the uncertainty analysis on the measurement result of the carbon emission of the thermal power unit. The correlation coefficient matrix is constructed by calculating the correlation coefficient of each input quantity in the second data set, so that the combined uncertainty is consistent with the real running state.

Description

Technical Field

[0001] This invention relates to the field of power data analysis, and in particular to methods, apparatus, equipment and media for uncertainty analysis of carbon emissions from thermal power units. Background Technology

[0002] Existing online carbon emission monitoring systems for thermal power units can typically calculate CO2 emission rates continuously. However, when assessing the uncertainty of these emission rates, they often assume that inputs such as CO2 concentration, flue gas flow rate, temperature, pressure, and humidity are independent, or only assess the Type A and Type B uncertainties of each input separately. For actual coal-fired units, load regulation, combustion control, induced draft regulation, desulfurization, denitrification, and wet treatment processes can cause significant correlations between multiple flue gas parameters. If these are continued to be treated as independent inputs, the combined uncertainty will be inconsistent with the actual operating conditions. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for analyzing the uncertainty of carbon emissions from thermal power units. This method can construct a correlation coefficient matrix by calculating the correlation coefficients of each input quantity in a second dataset, ensuring that the combined uncertainty is consistent with the actual operating state. The specific solution is as follows: Firstly, this application provides a method for analyzing the uncertainty of carbon emissions from thermal power units, including: Valid input data of the carbon emission monitoring system of thermal power unit is acquired, and a first dataset and a second dataset are constructed based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operation state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range; The first uncertainty and the second uncertainty corresponding to each of the valid input data are determined according to the first dataset and the second dataset, respectively, and the corresponding comprehensive uncertainty is determined according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the valid input data; Determine the correlation coefficient between any two data points in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data point based on a preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement results; Covariance propagation is performed based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

[0004] Optionally, acquiring valid input data for the carbon emission monitoring system of thermal power units includes: The initial input data of the carbon emission monitoring system of the thermal power unit is collected based on a preset data collection cycle. The initial input data is then processed to remove anomalies in order to obtain the corresponding processed input data. The time scale of each processed input data is unified to obtain the effective input data.

[0005] Optionally, constructing the first dataset and the second dataset based on the valid input data includes: Define a stable operating condition window; wherein, the stable operating condition window represents the time window during which the fluctuation of the operating parameters of the thermal power unit does not exceed a set threshold. First target data located in the stable operating condition window is selected from the valid input data, the first dataset is constructed using the first target data, and the second dataset is constructed directly using the valid input data.

[0006] Optionally, determining the corresponding combined uncertainty based on the first uncertainty and the corresponding second uncertainty includes: The first uncertainty and the corresponding second uncertainty are combined using the root sum of squares method to obtain the comprehensive uncertainty corresponding to each of the valid input data.

[0007] Optionally, determining the correlation coefficient between any two data points in the second dataset includes: Calculate the covariance between any two data points, and determine the first standard deviation corresponding to the first data point and the second standard deviation corresponding to the second data point; wherein, the first standard deviation is a statistic of the dispersion of all data points of the first data type in the second dataset relative to the first data, and the first data type is the data type of the first data; the second standard deviation is a statistic of the dispersion of all data points of the second data type in the second dataset relative to the second data, and the second data type is the data type of the second data. The correlation coefficient between any two data points is determined based on the covariance, the first standard deviation, and the second standard deviation.

[0008] Optionally, the step of performing covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units includes: Covariance propagation is performed based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to obtain the target uncertainty of the preset carbon emission rate measurement model, and the uncertainty of the carbon emission measurement results of thermal power units is quantitatively analyzed based on the target uncertainty.

[0009] Optionally, the covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient includes: The independent variance propagation term for each effective input data is determined based on the comprehensive uncertainty and the corresponding sensitivity coefficient; the independent variance propagation term is the component of the comprehensive uncertainty of a single effective input data that propagates independently to the carbon emission rate measurement result. The covariance propagation term between any two valid input data is determined based on the correlation coefficient matrix, the comprehensive uncertainty, and the corresponding sensitivity coefficient. The independent variance propagation term and the covariance propagation term are then integrated to obtain the target uncertainty. The covariance propagation term is the coupled component of the standard uncertainty of any two valid input data propagating to the carbon emission rate measurement result due to their correlation.

[0010] Secondly, this application provides a device for analyzing the uncertainty of carbon emissions from thermal power units, comprising: The dataset construction module is used to acquire valid input data from the carbon emission monitoring system of thermal power units, and construct a first dataset and a second dataset based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operating state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range; The comprehensive uncertainty determination module is used to determine the first uncertainty and the second uncertainty corresponding to each of the effective input data according to the first dataset and the second dataset respectively, and to determine the corresponding comprehensive uncertainty according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the effective input data; The sensitivity coefficient determination module is used to determine the correlation coefficient between any two data points in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data point based on a preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement result; The covariance propagation module is used to propagate covariance based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

[0011] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for analyzing the uncertainty of carbon emissions from thermal power units.

[0012] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned method for analyzing the uncertainty of carbon emissions from thermal power units.

[0013] This application first acquires valid input data from a carbon emission monitoring system for thermal power units, and constructs a first dataset and a second dataset based on the valid input data. The first dataset corresponds to input data under stable operating conditions, and the second dataset covers input data across the unit's operating range. Then, a first uncertainty and a second uncertainty are determined for each valid input data point based on the first and second datasets, respectively. A corresponding comprehensive uncertainty is then determined based on the first uncertainty and the corresponding second uncertainty. The first uncertainty represents the uncertainty component assessed by the dispersion of repeated measurements under stable operating conditions, and the second uncertainty represents the uncertainty component determined by the measuring instruments corresponding to each valid input data point. Next, the correlation coefficient between any two data points in the second dataset is determined, and a corresponding correlation coefficient matrix is ​​constructed based on each correlation coefficient. A sensitivity coefficient is then determined based on a preset carbon emission rate measurement model. The sensitivity coefficient represents the degree of influence of changes in the valid input data on the carbon emission rate measurement results. Finally, covariance propagation is performed based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of the thermal power units. Therefore, this application constructs a correlation coefficient matrix by calculating the correlation coefficients of each input quantity in the second dataset, and performs covariance propagation by combining the comprehensive uncertainty and sensitivity coefficient of each input quantity. This incorporates the covariance term between the input quantities into the combined uncertainty of the carbon emission rate measurement model, solving the problem that the traditional method assumes that each input quantity is independent, leading to inconsistencies between the evaluation results and the actual operating status of the unit. Finally, the uncertainty analysis of the carbon emission measurement results is completed through covariance propagation, providing a basis for quantitatively assessing the reliability of the measurement results. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the process for analyzing the uncertainty of carbon emissions from thermal power units disclosed in this application. Figure 2 This is a schematic diagram of a carbon emission uncertainty analysis system for thermal power units disclosed in this application; Figure 3 This application discloses a flowchart for the uncertainty analysis of carbon emissions from thermal power units. Figure 4 This application discloses a flowchart for covariance propagation and contribution diagnosis. Figure 5 This is a schematic diagram of the structure of a carbon emission uncertainty analysis device for thermal power units disclosed in this application; Figure 6 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0017] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for uncertainty analysis of carbon emissions from thermal power units, including: Step S11: Obtain valid input data from the carbon emission monitoring system of the thermal power unit, and construct a first dataset and a second dataset based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operating state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range.

[0018] This invention does not simply add a statistical report to an existing CEMS (Continuous Emission Monitoring System), but rather embeds the correlation of input quantities as a component of the uncertainty propagation model into the online evaluation process. The system first receives multi-source input data from the CO2-CEMS; secondly, it generates usable data through quality control; thirdly, it constructs stable operating condition datasets and effective operating datasets; then, it uses the stable operating condition datasets to evaluate the Type A uncertainty of each input quantity, and combines this with instrument technical specifications, existing uncertainty evaluation results, or field-available data to evaluate the Type B uncertainty; simultaneously, it uses the effective operating datasets to calculate the input quantity correlation coefficient matrix R; finally, it introduces a covariance term based on the sensitivity coefficient of the CO2 emission rate measurement model to obtain the combined standard uncertainty and expanded uncertainty considering the correlation of input quantities.

[0019] The structure of the carbon emission uncertainty analysis system for thermal power units in this embodiment is as follows: Figure 2 As shown, the system can be divided into a flue gas measurement layer, a data acquisition and quality control layer, and a data processing and diagnostic layer. The flue gas measurement layer outputs... Input quantities include H; the data acquisition and quality control layer is responsible for time synchronization, unit unification, anomaly removal, stable operating condition identification, and effective operation dataset construction; the data processing and diagnosis layer is responsible for Type A / Type B uncertainty assessment, correlation coefficient matrix calculation, covariance propagation, expanded uncertainty output, and covariance contribution diagnosis. For carbon dioxide concentration, t represents the flue gas flow rate, and t represents the flue gas temperature. Here, H represents the absolute pressure of the flue gas, and H represents the humidity of the flue gas. The specific system composition is shown in Table 1. Table 1

[0020] In addition, the method flow in this embodiment is as follows: Figure 3 As shown, the process includes steps such as data quality control, building a stable operating condition database, and evaluating Type A and Type B uncertainties, as detailed below: S1, Collect input data. The system acquires data according to a preset sampling period. And H, and record the data source, timestamp, operating status flag and instrument status flag.

[0021] S2, perform data quality control. Remove missing values, invalid values, and data exceeding the valid or physically reasonable range of the data source; identify significant abrupt changes, data from maintenance / calibration periods, and data with inconsistent timestamps; synchronize data from different instruments to the same time scale. The aforementioned valid range and judgment limits can be configured by the field system or obtained from historical data statistics, and do not need to be limited to fixed values ​​in this invention.

[0022] S3, Calculate CO2 emission rate Calculate the data that has passed quality control. and will It is stored together with each input quantity as a basis for evaluation.

[0023] S4, Construct a stable operating condition dataset (i.e., the first dataset). Based on unit load, , or The stability window is selected based on the fluctuation conditions; the window can be determined manually, by system preset rules, or by statistical adaptive rules. In this embodiment, 15 sets of stable data are used for Type A uncertainty assessment.

[0024] S5, assess the Type A uncertainty (i.e., the first uncertainty) and Type B uncertainty (i.e., the second uncertainty) of each input quantity. The Type A uncertainty is obtained from repeated measurements under steady-state conditions; the Type B uncertainty can be calculated from instrument specifications, resolution, standard material information, existing assessment results, or data available on-site. In one specific implementation, the Type B uncertainty can be measured by measuring the data in the second dataset using a measuring instrument.

[0025] S6, Building an Effective Running Dataset (i.e., the second dataset). It is not required that all points be in the same stable window, but the data must pass quality control and cover the typical operating range of the unit; in the example, D_R contains 10435 valid operating points.

[0026] S7, calculate the correlation coefficient matrix R of the input quantities. Any two input quantities and Calculate the Pearson correlation coefficient This forms a 5×5 correlation coefficient matrix.

[0027] S8, Calculate the sensitivity coefficient. Calculate the partial derivatives of Equation 1 with respect to each input quantity, or use numerical differentiation to calculate ci, to obtain the standard uncertainty of the input quantity. The weight of propagation.

[0028] S9, perform covariance propagation. Incorporate both independent variance terms and pairwise covariance terms into the calculation of the combined standard uncertainty.

[0029] S10, Output results and diagnostics. Calculate the expanded uncertainty. and relative expanded uncertainty The dominant offsetting term and dominant increasing term are output according to the covariance contribution rate.

[0030] Where U represents the carbon dioxide emission rate The expanded uncertainty, unit and Same; k represents the coverage factor, which is used to extend the combined standard uncertainty to the uncertainty interval under the preset inclusion probability. In one implementation, k can be taken as 2. This represents the combined standard uncertainty after taking into account the correlation of input quantities, with units of 1 / 2π. same. This represents the relative expanded uncertainty.

[0031] In this embodiment, obtaining valid input data for the carbon emission monitoring system of thermal power units includes: collecting initial input data of the carbon emission monitoring system of thermal power units based on a preset data acquisition cycle; performing anomaly removal processing on the initial input data to obtain corresponding processed input data; and unifying the time scale of each processed input data to obtain valid input data.

[0032] In different CO2-CEMS or carbon emission accounting scenarios, the inputs may further include oxygen content, flue gas moisture content as a substitute parameter, standard state dry flue gas flow rate, fuel lower heating value, fuel carbon content, purchased electricity or other corrections.

[0033] It should be noted that this embodiment treats data quality control as a separate step because the correlation of input quantities is highly sensitive to outliers. Data quality control includes at least the following: timestamp alignment, unit conversion, range checking, physical reasonable range checking, instrument status flag checking, data removal during maintenance and calibration, missing value removal, abnormal mutation removal, and identification of unit start-up / shutdown or abnormal operating conditions. Quality control rules can be implemented in the system in the form of threshold tables, configuration files, or database rule tables.

[0034] By eliminating invalid data through quality control and constructing two separate datasets, Type A uncertainty can reflect the repeatability under stable operating conditions, and the correlation coefficient matrix can reflect the coupling relationship of the actual input quantities within the effective operating range of the unit. Therefore, the parameters used in subsequent calculations have clear statistical meanings, preventing the misinterpretation of load changes as instrument repeatability and avoiding the use of excessively small samples to represent long-term correlations.

[0035] In addition, the construction of the first dataset and the second dataset based on the effective input data includes: defining a stable operating condition window; wherein, the stable operating condition window represents the time window in which the fluctuation of the operating parameters of the thermal power unit does not exceed a set threshold; filtering out the first target data located in the stable operating condition window from the effective input data, constructing the first dataset using the first target data, and directly constructing the second dataset using the effective input data.

[0036] It should be noted that, Fixed historical period, sliding time window, grouped window by load interval, or grouped window by operating condition category can be used; Stability test data can be manually specified, or the system can automatically identify the stable segment.

[0037] One of the key aspects of this invention is the construction of two datasets with different purposes. The first dataset... Used to assess Type A uncertainty, it requires small fluctuations in the input quantity within the same operating condition, reflecting the discreteness of repeated measurements; the second dataset It is used to identify the correlation of input quantities, and is required to cover the typical operating range of the unit and reflect the actual operating coupling relationship. and There can be overlap, but they don't have to be exactly the same. The reason for this is that Type A uncertainty emphasizes repeatability under stable conditions, while correlation identification emphasizes the synchronous changes between inputs under changes in operating state; the two have different statistical purposes.

[0038] The aforementioned dual-dataset processing method is a key technical feature that distinguishes this invention from conventional online uncertainty assessment schemes. If only a small sample of stable operating conditions is used to calculate the correlation coefficient, the correlation matrix is ​​easily unstable due to sample size limitations; if full-condition operating data is directly used to assess Type A repeatability, fluctuations caused by unit load changes and operational adjustments will be mistakenly included in the repeatability measurement discreteness. This invention addresses this issue by… and The separation of functions allows Type A uncertainty assessment and input correlation identification to correspond to appropriate statistical objects, thereby improving the interpretability and engineering reliability of online assessment results.

[0039] The construction rules for the two datasets mentioned above are shown in Table 2: Table 2

[0040] Step S12: Determine the first uncertainty and the second uncertainty corresponding to each of the valid input data according to the first dataset and the second dataset respectively, and determine the corresponding comprehensive uncertainty according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the valid input data.

[0041] This embodiment first provides a brief description of the basic content of the present invention: Coal combustion in thermal power units produces CO2-containing flue gas. Online monitoring of carbon emissions from stationary pollution sources typically involves continuously measuring the CO2 concentration in the flue gas using a CO2-CEMS (Continuous Electrochemical Analyzer) and calculating the CO2 mass emission rate by combining this data with parameters such as flue gas flow rate, temperature, pressure, and humidity. Such systems generally include sampling probes, preprocessing units, non-dispersive infrared (NDIR) or other CO2 analyzers, flue gas velocity / flow rate measurement devices, temperature, pressure, and humidity sensors, data acquisition and processing units, and host computer software.

[0042] In the direct flue gas measurement method, CO2 emission rate It can be determined by CO2 concentration Actual flue gas flow rate Absolute pressure The temperature t and humidity H of the flue gas are converted. The following formula is used as the online measurement model in this embodiment: (1) In the formula, CO2 mass emission rate, in t / h; This represents the volume fraction of CO2 in flue gas, expressed as %vol. This refers to the flue gas flow rate, expressed in m³ / h. t represents the absolute pressure of the flue gas, in Pa; t represents the temperature of the flue gas, in degrees Celsius; and H represents the humidity of the flue gas, in %. The conventional practice is to obtain... Then, the uncertainty of each input quantity is evaluated according to the measurement uncertainty evaluation method, and then the results are combined using the sensitivity coefficients.

[0043] The traditional uncertainty propagation model can be written as follows when the inputs are uncorrelated: (2) in, This represents the carbon dioxide emission rate under the assumption that all inputs are independent. The square of the combined standard uncertainty is the combined variance. This represents the combined standard uncertainty under the independent input model; n represents the number of inputs involved in the uncertainty synthesis. This represents the i-th input; This represents the standard uncertainty of the i-th input quantity; Indicates output quantity For the i-th input quantity The sensitivity coefficient can be expressed as The model is easy to calculate, but it is based on the premise that there is no statistical correlation between the input quantities.

[0044] In this embodiment, determining the corresponding comprehensive uncertainty based on the first uncertainty and the corresponding second uncertainty includes: using the root sum of squares method to synthesize the first uncertainty and the corresponding second uncertainty to obtain the comprehensive uncertainty corresponding to each valid input data.

[0045] Specifically, for each input quantity The system obtained Type A standard uncertainty respectively. Type B standard uncertainty When the two are independent, the standard uncertainty of the input quantity is... (i.e., the overall uncertainty) can be synthesized using the following formula: (3) In the formula: This represents the Type A standard uncertainty of the input quantity. This represents the Type B standard uncertainty of the input quantity.

[0046] The mean and uncertainty of the input quantities are shown in Table 2. This table can be used as the raw parameter table for the data. Since this invention pertains to method and system improvement, the specific models, ranges, accuracy classes, or certificate numbers of the CO2 analyzers, flow meters, temperature, pressure, and humidity meters can be recorded as information during implementation and are not essential technical features of this invention. When the above information is not yet available, the already evaluated Type A and Type B standard uncertainty values ​​can be directly used to describe the embodiments.

[0047] Table 2

[0048] Step S13: Determine the correlation coefficient between any two data points in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data point based on the preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement results.

[0049] In this embodiment, determining the correlation coefficient between any two data points in the second dataset includes: calculating the covariance between any two data points, and determining the first standard deviation corresponding to the first data point and the second standard deviation corresponding to the second data point; wherein, the first standard deviation is a statistic of the dispersion of all data points of the first data type in the second dataset relative to the first data, and the first data type is the data type of the first data; the second standard deviation is a statistic of the dispersion of all data points of the second data type in the second dataset relative to the second data, and the second data type is the data type of the second data; and determining the correlation coefficient between any two data points based on the covariance, the first standard deviation, and the second standard deviation.

[0050] Specifically, for effectively running datasets Any two input quantities and The system calculates the Pearson correlation coefficient. .like A value greater than 0 indicates that the two input quantities have the same trend of change in the valid running samples; if A value less than 0 indicates that the two input quantities have an inverse trend. The diagonal elements of the correlation coefficient matrix R are 1s, and the off-diagonal elements are the correlation coefficients between the corresponding input quantities.

[0051] (4)

[0052] In the formula: Input quantity and The Pearson correlation coefficient between them; Let the covariance of the two be the variances. , These are the sample standard deviations for the two, respectively.

[0053] When using N valid running data points for calculation, the above formula can be further written in the following implementable form: (5) In the formula: and The input quantities in the m-th valid running data point are respectively and The value; and These correspond to the input quantities in the valid running dataset. The mean of the data points; N is the number of valid running data points.

[0054] Arrange the pairwise correlation coefficients of each input quantity in order of input quantity to obtain the correlation coefficient matrix R: (6) In the formula: R is the correlation coefficient matrix; in this embodiment, n=5, and the input quantities are as follows: And H.

[0055] In uncertainty propagation, the sensitivity coefficient represents the degree to which the CO2 emission rate is sensitive to changes in various input quantities, and it is defined as: (7) In the formula: The sensitivity coefficient represents... The degree of sensitivity to changes in the corresponding input.

[0056] For the product-type direct flue gas measurement model shown in the above equation, the analytical sensitivity coefficients of each main input quantity can be written as: (8) For any two distinct inputs and Its covariance propagation term can be expressed as: (9) In the formula: For covariance propagation terms; This indicates the offsetting of the composite variance. This indicates an increase in the composite variance.

[0057] When taking into account the correlation of input quantities, the combined standard uncertainty of the CO2 emission rate Gh is calculated using the following formula: (10) In the formula: The combined standard uncertainty of CO2 emission rates takes into account the correlation of input quantities; the first term is the independent variance propagation term for each input quantity; the second term is the covariance propagation term caused by the pairwise correlation of input quantities.

[0058] After obtaining the combined standard uncertainty, the expanded uncertainty and the relative expanded uncertainty are calculated using the following formulas: (11) In the formula: U is the expanded uncertainty; k is the coverage factor; This represents the relative expanded uncertainty.

[0059] To output correlation diagnostic results, the system can also calculate the contribution rate of each covariance term: (12) In the formula: The contribution rate of the covariance term is negative, indicating a negation of the contribution, and positive, indicating an increase in the contribution.

[0060] For the product-type measurement model shown in equation (1), the sensitivity coefficient can be obtained analytically using equations (7) and (8), or it can be automatically calculated by the system using numerical differentiation. To avoid difficulties in formula adjustment due to different units, wet / dry basis, or standard state conversions at different engineering sites, the system can simultaneously retain both the analytical sensitivity coefficient module and the numerical sensitivity coefficient module; when the formula configuration changes, the numerical sensitivity coefficient module can still be executed.

[0061] Because this invention introduces a covariance term into the combined standard uncertainty, when there are inverse or unidirectional correlations between input quantities, the system can automatically calculate the offsetting or increasing effects of these correlations on the combined variance. Therefore, compared with the independent input model, the uncertainty obtained by this invention is more consistent with the actual operating conditions. In the embodiment, the relative expanded uncertainty of the independent model is 4.423%, which is 1.578% after taking correlation into account, a reduction of approximately 64.3%, indicating that ignoring correlation significantly affects the evaluation results.

[0062] In addition to Pearson correlation coefficient, Spearman rank correlation, Kendall correlation, robust correlation coefficient or piecewise linear correlation coefficient can also be used in this embodiment; when the nonlinear coupling is strong, mutual information or correlation identification methods based on regression residuals can also be used.

[0063] Step S14: Perform covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

[0064] The covariance propagation and contribution diagnosis process in this embodiment is as follows: Figure 4 As shown, the system first obtains the standard uncertainty of each input quantity. Sensitivity coefficient and correlation coefficient Next, calculate the covariance term. Then add it to the independent variance term to obtain Finally, based on Output the positive and negative signs and absolute values ​​to diagnose contributions. When it is negative, its function is to cancel out the combined variance; When it is positive, its function is to increase the composite variance.

[0065] Accordingly, in this embodiment, covariance propagation is performed based on each comprehensive uncertainty, correlation coefficient matrix, and sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units. This includes: performing covariance propagation based on each comprehensive uncertainty, correlation coefficient matrix, and sensitivity coefficient to obtain the target uncertainty of the preset carbon emission rate measurement model, and performing quantitative analysis of the uncertainty of the carbon emission measurement results of thermal power units based on the target uncertainty.

[0066] Furthermore, the process of covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient may specifically include: determining the independent variance propagation term for each valid input data based on the comprehensive uncertainty and the corresponding sensitivity coefficient; the independent variance propagation term is the component of the comprehensive uncertainty of a single valid input data that propagates independently to the carbon emission rate measurement result; determining the covariance propagation term between any two valid input data based on the correlation coefficient matrix, the comprehensive uncertainty, and the corresponding sensitivity coefficient, and integrating the independent variance propagation term and the covariance propagation term to obtain the target uncertainty; the covariance propagation term is the coupled component of the standard uncertainty of any two valid input data that propagates jointly to the carbon emission rate measurement result due to correlation.

[0067] The calculation results of this embodiment are briefly described below: When evaluated using the traditional independent input model, the combined standard uncertainty of the CO2 emission rate is 1.6593 t / h, and the expanded relative uncertainty is 4.423%. According to the present invention, after considering input correlation, the combined standard uncertainty is 0.5918 t / h, and the expanded relative uncertainty is 1.578%. The average CO2 emission rate Gh in the example is 75.0257 t / h. Compared with the independent model, the expanded relative uncertainty after considering correlation is reduced by approximately 64.3%.

[0068] Table 3 shows the relevant combinations of the main input quantities and the direction of covariance contribution in the embodiments. This table helps the agent understand that this invention differs from schemes that only output the final uncertainty value: this invention can explain which combinations of input quantities cause cancellation and which combinations of input quantities cause increase.

[0069] Table 3

[0070] Furthermore, the online output of this invention can include three categories: real-time output, periodic output, and retrospective output. Real-time output is used to display the current or most recent window. , The output includes U and relative expanded uncertainty; periodic output is used to generate daily, monthly, quarterly, or accounting cycle reports; retrospective output is used to derive the correlation coefficient matrix, data removal ratio, main covariance contribution items, and assessment parameter versions by time range. Specific output content is shown in Table 4. Table 4

[0071] Furthermore, this embodiment can also employ the Monte Carlo method to input the marginal distributions and correlation matrices of each input quantity into random simulation to obtain the Gh distribution and expanded uncertainty, thereby replacing the original uncertainty propagation method.

[0072] The system in this embodiment can be deployed on a CEMS industrial control computer, an environmental data acquisition instrument, a plant-level SIS / DCS server, a cloud-based carbon management platform, or an edge computing gateway; each module can be deployed centrally or in a distributed manner. Furthermore, this embodiment can output Word / PDF reports, database records, web dashboards, API interfaces, monitoring platform data packets, or alarm events, without specific limitations.

[0073] Therefore, this application constructs a correlation coefficient matrix by calculating the correlation coefficients of each input quantity in the second dataset, and performs covariance propagation by combining the comprehensive uncertainty and sensitivity coefficient of each input quantity. This incorporates the covariance term between the input quantities into the combined uncertainty of the carbon emission rate measurement model, solving the problem that the traditional method assumes that each input quantity is independent, leading to inconsistencies between the evaluation results and the actual operating status of the unit. Finally, the uncertainty analysis of the carbon emission measurement results is completed through covariance propagation, providing a basis for quantitatively assessing the reliability of the measurement results.

[0074] See Figure 5 As shown in the figure, an embodiment of the present invention discloses a device for analyzing the uncertainty of carbon emissions from thermal power units, comprising: The dataset construction module 11 is used to acquire valid input data from the carbon emission monitoring system of thermal power units, and construct a first dataset and a second dataset based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operating state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range; The comprehensive uncertainty determination module 12 is used to determine the first uncertainty and the second uncertainty corresponding to each of the effective input data according to the first dataset and the second dataset respectively, and to determine the corresponding comprehensive uncertainty according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the effective input data; The sensitivity coefficient determination module 13 is used to determine the correlation coefficient between any two data in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data according to a preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement result; The covariance propagation module 14 is used to propagate the covariance based on the comprehensive uncertainty, the correlation coefficient matrix and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

[0075] In some specific embodiments, the dataset construction module 11 may specifically include: The data acquisition unit is used to acquire the initial input data of the carbon emission monitoring system of the thermal power unit based on a preset data acquisition cycle, and to perform anomaly removal processing on the initial input data to obtain the corresponding processed input data. A data unification unit is used to unify the time scale of each processed input data in order to obtain the effective input data.

[0076] In some specific embodiments, the dataset construction module 11 may specifically include: A window definition unit is used to define a stable operating condition window; wherein, the stable operating condition window represents the time window during which the fluctuation of the operating parameters of the thermal power unit does not exceed a set threshold. The dataset construction unit is used to filter out the first target data located in the stable operating condition window from the valid input data, construct the first dataset using the first target data, and directly construct the second dataset using the valid input data.

[0077] In some specific embodiments, the comprehensive uncertainty determination module 12 may specifically include: The comprehensive uncertainty determination unit is used to synthesize the first uncertainty and the corresponding second uncertainty using the root sum of squares method to obtain the comprehensive uncertainty corresponding to each of the valid input data.

[0078] In some specific embodiments, the sensitivity coefficient determination module 13 may specifically include: The covariance calculation unit is used to calculate the covariance between any two data points and to determine the first standard deviation of the first data point and the second standard deviation of the second data point; wherein, the first standard deviation is a statistic of the dispersion of all data points of the first data type in the second dataset relative to the first data, and the first data type is the data type of the first data; the second standard deviation is a statistic of the dispersion of all data points of the second data type in the second dataset relative to the second data, and the second data type is the data type of the second data. The correlation coefficient determination unit is used to determine the correlation coefficient between any two data points based on the covariance, the first standard deviation, and the second standard deviation.

[0079] In some specific embodiments, the covariance propagation module 14 may specifically include: The uncertainty analysis unit is used to perform covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to obtain the target uncertainty of the preset carbon emission rate measurement model, and to perform quantitative analysis on the uncertainty of the carbon emission measurement results of thermal power units based on the target uncertainty.

[0080] In some specific embodiments, the covariance propagation module 14 may specifically include: The variance propagation term determination unit is used to determine the independent variance propagation term of each effective input data based on the comprehensive uncertainty and the corresponding sensitivity coefficient; the independent variance propagation term is the component of the comprehensive uncertainty of a single effective input data that propagates independently to the carbon emission rate measurement result. The variance propagation term integration unit is used to determine the covariance propagation term between any two valid input data based on the correlation coefficient matrix, each of the comprehensive uncertainties and the corresponding sensitivity coefficients, and to integrate each of the independent variance propagation terms and each of the covariance propagation terms to obtain the target uncertainty; the covariance propagation term is the coupled component of the standard uncertainty of any two valid input data propagating together to the carbon emission rate measurement result due to correlation.

[0081] Furthermore, embodiments of this application also disclose an electronic device, Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0082] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the carbon emission uncertainty analysis method for thermal power units disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0083] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0084] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0085] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the carbon emission uncertainty analysis method for thermal power units executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0086] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for uncertainty analysis of carbon emissions from thermal power units. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0087] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0088] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0090] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0091] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for uncertainty analysis of carbon emissions from thermal power units, characterized in that, include: Valid input data of the carbon emission monitoring system of thermal power unit is acquired, and a first dataset and a second dataset are constructed based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operation state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range; The first uncertainty and the second uncertainty corresponding to each of the valid input data are determined according to the first dataset and the second dataset, respectively, and the corresponding comprehensive uncertainty is determined according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the valid input data; Determine the correlation coefficient between any two data points in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data point based on a preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement results; Covariance propagation is performed based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

2. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 1, characterized in that, The acquisition of valid input data for the carbon emission monitoring system of thermal power units includes: The initial input data of the carbon emission monitoring system of the thermal power unit is collected based on a preset data collection cycle. The initial input data is then processed to remove anomalies in order to obtain the corresponding processed input data. The time scale of each processed input data is unified to obtain the effective input data.

3. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 1, characterized in that, The construction of the first dataset and the second dataset based on the valid input data includes: Define a stable operating condition window; wherein, the stable operating condition window represents the time window during which the fluctuation of the operating parameters of the thermal power unit does not exceed a set threshold. First target data located in the stable operating condition window is selected from the valid input data, the first dataset is constructed using the first target data, and the second dataset is constructed directly using the valid input data.

4. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 1, characterized in that, The determination of the corresponding combined uncertainty based on the first uncertainty and the corresponding second uncertainty includes: The first uncertainty and the corresponding second uncertainty are combined using the root sum of squares method to obtain the comprehensive uncertainty corresponding to each of the valid input data.

5. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 1, characterized in that, Determining the correlation coefficient between any two data points in the second dataset includes: Calculate the covariance between any two data points, and determine the first standard deviation corresponding to the first data point and the second standard deviation corresponding to the second data point; wherein, the first standard deviation is a statistic of the dispersion of all data points of the first data type in the second dataset relative to the first data, and the first data type is the data type of the first data; the second standard deviation is a statistic of the dispersion of all data points of the second data type in the second dataset relative to the second data, and the second data type is the data type of the second data. The correlation coefficient between any two data points is determined based on the covariance, the first standard deviation, and the second standard deviation.

6. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 1, characterized in that, The step of performing covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units includes: Covariance propagation is performed based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to obtain the target uncertainty of the preset carbon emission rate measurement model, and the uncertainty of the carbon emission measurement results of thermal power units is quantitatively analyzed based on the target uncertainty.

7. The method for uncertainty analysis of carbon emissions from thermal power units according to claim 6, characterized in that, The covariance propagation based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient includes: The independent variance propagation term for each effective input data is determined based on the comprehensive uncertainty and the corresponding sensitivity coefficient; the independent variance propagation term is the component of the comprehensive uncertainty of a single effective input data that propagates independently to the carbon emission rate measurement result. The covariance propagation term between any two valid input data is determined based on the correlation coefficient matrix, the comprehensive uncertainty, and the corresponding sensitivity coefficient. The independent variance propagation term and the covariance propagation term are then integrated to obtain the target uncertainty. The covariance propagation term is the coupled component of the standard uncertainty of any two valid input data propagating to the carbon emission rate measurement result due to their correlation.

8. A device for analyzing the uncertainty of carbon emissions from thermal power units, characterized in that, include: The dataset construction module is used to acquire valid input data from the carbon emission monitoring system of thermal power units, and construct a first dataset and a second dataset based on the valid input data; wherein, the first dataset is a dataset corresponding to the input data under the stable operating state of the unit, and the second dataset is a dataset covering the input data of the unit's operating range; The comprehensive uncertainty determination module is used to determine the first uncertainty and the second uncertainty corresponding to each of the effective input data according to the first dataset and the second dataset respectively, and to determine the corresponding comprehensive uncertainty according to the first uncertainty and the corresponding second uncertainty; wherein, the first uncertainty characterizes the uncertainty component evaluated by the dispersion of repeated measurement data under the stable operation state of the unit, and the second uncertainty characterizes the uncertainty component determined according to the measuring instrument corresponding to each of the effective input data; The sensitivity coefficient determination module is used to determine the correlation coefficient between any two data points in the second dataset, construct a corresponding correlation coefficient matrix based on each correlation coefficient, and determine the sensitivity coefficient corresponding to each effective input data point based on a preset carbon emission rate measurement model; wherein, the sensitivity coefficient characterizes the degree of influence of changes in the effective input data on the carbon emission rate measurement result; The covariance propagation module is used to propagate covariance based on the comprehensive uncertainty, the correlation coefficient matrix, and the sensitivity coefficient to complete the uncertainty analysis of the carbon emission measurement results of thermal power units.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method for uncertainty analysis of carbon emissions from thermal power units as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the method for uncertainty analysis of carbon emissions from thermal power units as described in any one of claims 1 to 7.

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