An electro-carbon metering and detection method, device and medium based on an electro-carbon coupling model

Abstract

The present invention relates to an electro-carbon metering and detection method, device and medium based on an electro-carbon coupling model. The method includes the following steps: collecting electro-carbon data of a power system and constructing an initial electro-carbon coupling model based on the electro-carbon data of the power system; expanding the initial electro-carbon coupling model, optimizing the expanded electro-carbon coupling model based on a fuzzy logic inference mechanism to obtain an optimized electro-carbon coupling model; generating a simulation scenario based on the optimized electro-carbon coupling model according to the collected electro-carbon data of the power system and preset different operating conditions; performing matching analysis on the simulation scenario and the electro-carbon data of the power system to obtain a comprehensive evaluation parameter, and judging the accuracy of the electro-carbon metering device of the power system based on the comprehensive evaluation parameter.

Description

Technical Field

[0001] The present invention relates to an electro-carbon metering and detection method, device and medium based on an electro-carbon coupling model, and belongs to the technical field of measurement and detection. Background Art

[0002] With the continuous improvement of the global requirements for carbon emission control and efficient energy utilization, the metering of electric energy and the assessment of carbon emissions in the power system have gradually become an important technical requirement. In the actual operation of the power system, the coupling relationship between electric energy and carbon emissions is complex and variable, involving various dynamic parameters such as voltage, current, active power, and reactive power, as well as key carbon emission parameters such as carbon emission factors and carbon emissions per unit of electricity. These parameters not only change dynamically over time but are also affected by factors such as load fluctuations, network topology changes, and external environmental disturbances, resulting in the difficulty of traditional linear metering models to meet the high-precision requirements under complex working conditions.

[0003] Existing electro-carbon metering methods mainly rely on static or linear models, simply coupling power parameters with carbon emission parameters, lacking in-depth characterization of dynamic change characteristics and non-linear relationships. In addition, most metering methods fail to fully consider the impact of multi-dimensional disturbances on metering results. Under complex dynamic working conditions, the system metering error is large and the stability is poor. At the same time, the generation method of simulation scenarios is limited, usually only covering a small number of typical working conditions and unable to comprehensively reflect the complex dynamic conditions in actual operation, which results in insufficient matching between simulation scenarios and actual data, further limiting the comprehensive evaluation ability of existing methods for metering performance.

[0004] However, the above technologies have the following technical problems: under complex dynamic operating conditions, the evaluation of the electro-carbon coupling relationship in electro-carbon detection is inaccurate and the electro-carbon metering and detection are inaccurate. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an electro-carbon metering and detection method, device and medium based on an electro-carbon coupling model.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention provides an electro-carbon metering and detection method based on an electro-carbon coupling model, including the following steps:

[0008] Collect electro-carbon data of the power system and construct an initial electro-carbon coupling model based on the electro-carbon data of the power system;

[0009] Expand the initial electro-carbon coupling model and optimize the expanded electro-carbon coupling model based on a fuzzy logic inference mechanism to obtain an optimized electro-carbon coupling model;

[0010] Generate simulation scenarios based on an optimized electricity-carbon coupling model according to the collected electricity-carbon data of the power system and preset different operating conditions;

[0011] Perform matching analysis on the simulation scenarios and the electricity-carbon data of the power system to obtain comprehensive evaluation parameters, and judge the accuracy of the electricity-carbon metering equipment of the power system based on the comprehensive evaluation parameters.

[0012] As a preferred embodiment of the present invention, the electricity-carbon data of the power system specifically includes the active power, reactive power, current, and carbon emission factor of the corresponding power node.

[0013] As a preferred embodiment of the present invention, the initial electricity-carbon coupling model is specifically shown as the following formula:

[0014] ;

[0015] Where: represents the output of the initial electricity-carbon coupling model at time , , , represent the initial weight coefficients; represents the active power of the power node at time represents the reactive power of the power node at time represents the current of the power node at time represents the carbon emission factor of the power node at time

[0016] As a preferred embodiment of the present invention, expand the initial electricity-carbon coupling model based on the spatio-temporal decomposition method. The specific steps are as follows:

[0017] Decompose the electricity-carbon data of the power system into static reference values, short-term fluctuations, long-term trends, and external disturbances based on the spatio-temporal decomposition method, and construct them into dynamic input variables, as specifically shown in the following formula:

[0018] ;

[0019] Where: represents the dynamic input variable at time represents the static reference value of the electricity-carbon data of the power system; represents the short-term fluctuation of the electricity-carbon data of the power system at time represents the long-term trend of the electricity-carbon data of the power system at time represents External disturbance of the electricity-carbon data of the power system at a certain moment;

[0020] Substitute the dynamic input variables into the initial electricity-carbon coupling model to obtain the extended electricity-carbon coupling model, which is specifically shown as follows:

[0021] ;

[0022] Where: represents the output of the extended electricity-carbon coupling model at a certain moment; represents the th weight coefficient; represents the static reference value of the th type of data sample in the electricity-carbon data of the power system; represents the short-term fluctuation of the th type of data sample in the electricity-carbon data of the power system at a certain moment; represents the long-term trend of the th type of data sample in the electricity-carbon data of the power system at a certain moment; represents the external disturbance of the th type of data sample in the electricity-carbon data of the power system at a certain moment.

[0023] As a preferred embodiment of the present invention, the specific steps for optimizing the extended electricity-carbon coupling model based on the fuzzy logic inference mechanism are as follows:

[0024] For each dynamic input variable, define fuzzy sets, including three fuzzy labels: low, medium, and high, and use an asymmetric Gaussian distribution function to represent the membership degree, which is specifically shown as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] Where: , , respectively represent the fuzzy membership degrees of the input variable for the fuzzy labels low, medium, and high; represents the input variable; , represent the expansion coefficients of the asymmetric Gaussian distribution function; , represent the central values of the asymmetric Gaussian distribution function; represents the natural constant;

[0029] Construct a fuzzy rule base based on the fuzzy membership degrees of each dynamic input variable. The output membership degree of the fuzzy rule is calculated by the following formula:

[0030] ;

[0031] where: represents the output membership degree of the th fuzzy rule; represents the logical result of all dynamic input variables after being mapped by the fuzzy membership degree; represents the th fuzzy rule, and the membership degree of the dynamic input variable in the fuzzy set ; represents the number of dynamic input variables; represents the index of the number of dynamic input variables;

[0032] Construct a defuzzification formula based on the output membership degree of the fuzzy rule and the extended electro-carbon coupling model, as shown in the following formula:

[0033] ;

[0034] where: represents the output result of the defuzzified extended electro-carbon coupling model at time; represents the total number of fuzzy rules; represents the th fuzzy rule's center value; represents the residual correction factor;

[0035] Perform recursive dynamic fusion optimization on the defuzzification output result. The recursive dynamic fusion optimization formula is:

[0036] ;

[0037] where: represents the output of the defuzzified extended electro-carbon coupling model at time after being optimized by the th layer of recursive dynamic fusion; represents the number of recursive layers; represents the th layer of recursive time decay factor; represents the th layer of recursive weight function; represents the th layer of recursive time interval;

[0038] Set the stop condition for recursive dynamic fusion optimization:

[0039] ;

[0040] Wherein: represents the recursive convergence threshold;

[0041] An adaptive bias correction mechanism is introduced to correct the bias of the recursively dynamically fused optimization output, as shown in the following formula:

[0042] ;

[0043] Wherein: represents the bias correction result at time; represents the number of correction factors; represents the th strength of the correction factor; represents the th sensitivity of the correction factor; represents the th correction reference value of the correction factor;

[0044] High-dimensional perturbation optimization is introduced to construct an optimized electro-carbon coupling model, as shown in the following formula:

[0045] ;

[0046] Wherein: represents the output result of the optimized electro-carbon coupling model at time; represents the perturbation amplitude factor; represents the main perturbation frequency; represents the dynamic change value of the external perturbation at time; represents the perturbation smoothing factor.

[0047] As a preferred embodiment of the present invention, the steps for generating the simulation scenario are as follows:

[0048] Based on the collected electro-carbon data of the power system, a simulation scenario generation formula is constructed, as shown in the following formula:

[0049] ;

[0050] Wherein: represents the simulation data corresponding to the simulation scenario generated based on the electro-carbon data of the power system at time; represents the preset fluctuation amplitude of the current simulation scenario; represents the preset fluctuation frequency of the current simulation scenario; represents the preset random perturbation intensity of the current simulation scenario; represents the random perturbation.

[0051] As a preferred embodiment of the present invention, the simulation data corresponding to the generated simulation scenario is input into the optimized electricity-carbon coupling model to obtain a simulation output, as specifically shown in the following formula:

[0052] ;

[0053] Where: represents the simulation output at time

[0054] The electricity-carbon data of the power system collected is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output;

[0055] A multi-dimensional comparison and matching analysis algorithm is introduced to perform a comparison and matching analysis on the simulation output and the actual output to generate a comprehensive evaluation parameter.

[0056] As a preferred embodiment of the present invention, the specific calculation steps of the comprehensive evaluation parameter are as follows:

[0057] Construct a recursive dynamic mapping formula to adjust the simulation output. The recursive dynamic mapping formula is as shown in the following formula:

[0058] ;

[0059] Where: represents the mapping value of the simulation output at time after layers of recursion; represents the number of recursion layers of the recursive dynamic mapping; represents the th layer of recursive dynamic weight; represents the actual output at time

[0060] Based on the output result of the recursive dynamic mapping, calculate the multi-dimensional matching error index between the simulation output and the actual output, as specifically shown in the following formula:

[0061] ;

[0062] Where: represents the multi-dimensional matching error index between the simulation output and the actual output at time represents the total number of input dimensions; represents the component of the th input dimension in the simulation output mapping value at time represents the component of the th input dimension in the actual output at time represents the Standard deviation of the input dimension;

[0063] Based on the multi-dimensional matching error index, the comprehensive evaluation parameter is calculated as follows:

[0064] ;

[0065] Where: Represents the comprehensive evaluation parameter; Represents the evaluation time range; Represents The dynamic matching weight based on the simulation output mapping value at the moment.

[0066] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0067] On yet another aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0068] The present invention has the following beneficial effects:

[0069] 1. By introducing the dynamic fuzzy recursive fusion algorithm, the present invention makes full use of the real-time collected electro-carbon data to construct an accurate electro-carbon coupling model. The optimized electro-carbon coupling model can dynamically adapt to the spatio-temporal changes of input parameters, including the combined effects of short-term fluctuations, long-term trends, and external disturbances, significantly improving the accuracy and adaptability of the model;

[0070] 2. The present invention adopts a fuzzy logic inference mechanism to handle the uncertainty and non-linear characteristics of input variables; by designing Gaussian membership functions and a fuzzy rule base, the non-linear coupling relationship between power parameters and carbon emission parameters is efficiently analyzed, effectively reducing the influence of measurement errors under complex working conditions;

[0071] 3. Through the multi-parameter simulation scenario generation technology and combined with the recursive dynamic mapping algorithm, the present invention realizes the layer-by-layer matching of simulation data and actual data. The diversity and dynamic optimization ability of the simulation scenario enhance the applicability of measurement detection under complex operating conditions and can effectively quantify the performance of the system under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 Is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0075] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless otherwise clearly specified in the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0076] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0077] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0078] Embodiment 1:

[0079] See Figure 1 , an electro-carbon metering and detection method based on an electro-carbon coupling model, comprising the following steps:

[0080] Collect electro-carbon data of the power system and construct an initial electro-carbon coupling model based on the electro-carbon data of the power system;

[0081] Expand the initial electro-carbon coupling model and optimize the expanded electro-carbon coupling model based on the fuzzy logic inference mechanism to obtain an optimized electro-carbon coupling model;

[0082] Generate a simulation scenario based on the optimized electro-carbon coupling model according to the collected electro-carbon data of the power system and preset different operating conditions;

[0083] Perform matching analysis on the simulation scenario and the electro-carbon data of the power system to obtain a comprehensive evaluation parameter, and judge the accuracy of the electro-carbon metering device of the power system based on the comprehensive evaluation parameter.

[0084] As a preferred implementation manner of this embodiment, the electro-carbon data of the power system specifically includes the active power, reactive power, current, and carbon emission factor of the corresponding power node.

[0085] As a preferred embodiment of this embodiment, the initial electro-carbon coupling model is specifically shown as follows:

[0086] ;

[0087] Where: represents the output of the initial electro-carbon coupling model at time , , , represent the initial weight coefficients; represents the active power of the power node at time represents the reactive power of the power node at time represents the current of the power node at time represents the carbon emission factor of the power node at time

[0088] As a preferred embodiment of this embodiment, the initial electro-carbon coupling model is extended based on the spatio-temporal decomposition method. The specific steps are as follows:

[0089] Based on the spatio-temporal decomposition method, the electro-carbon data of the power system is decomposed into static reference values, short-term fluctuations, long-term trends, and external disturbances, and constructed into dynamic input variables, specifically shown as follows:

[0090] ;

[0091] Where: represents the dynamic input variable at time represents the static reference value of the electro-carbon data of the power system; represents the short-term fluctuation of the electro-carbon data of the power system at time represents the long-term trend of the electro-carbon data of the power system at time represents the change caused by external disturbances of the electro-carbon data of the power system at time

[0092] Substitute the dynamic input variables into the initial electro-carbon coupling model to obtain the extended electro-carbon coupling model, specifically shown as follows:

[0093] ;

[0094] Where: represents the output of the extended electro-carbon coupling model at time denotes the th weight coefficient; denotes the static reference value of the th type of data sample in the power system's electricity-carbon data; denotes the short-term fluctuation of the th type of data sample in the power system's electricity-carbon data at the moment; denotes the long-term trend of the th type of data sample in the power system's electricity-carbon data at the moment; denotes the external disturbance of the th type of data sample in the power system's electricity-carbon data at the moment.

[0095] As a preferred implementation manner of this embodiment, the specific steps for optimizing the extended electricity-carbon coupling model based on the fuzzy logic inference mechanism are as follows:

[0096] For each dynamic input variable, define fuzzy sets, including three fuzzy labels: low, medium, and high, and use an asymmetric Gaussian distribution function to represent the membership degree, as shown in the following formula:

[0097] ;

[0098] ;

[0099] ;

[0100] Where: , , respectively represent the fuzzy membership degrees of the input variable for the fuzzy labels low, medium, and high; represents the input variable; , represent the expansion coefficients of the asymmetric Gaussian distribution function; , represent the central values of the asymmetric Gaussian distribution function; represents the natural constant;

[0101] Based on the fuzzy membership degrees of each dynamic input variable, construct a fuzzy rule base (for example, if is high and is high, then the electricity-carbon coupling is strong; if is medium and is low, then the electricity-carbon coupling is weak), and the output membership degree of the fuzzy rule is calculated by the following formula:

[0102] ;

[0103] Where: Denote the output membership degree of the th fuzzy rule; Generated by fuzzy logic rule reasoning, representing the logical result after the fuzzy membership mapping of all dynamic input variables; Denote the th fuzzy rule, the membership degree of the dynamic input variable in the fuzzy set ; Denote the number of dynamic input variables; Denote the index of the number of dynamic input variables;

[0104] Construct a defuzzification formula based on the output membership degree of the fuzzy rule and the extended electro-carbon coupling model, as shown in the following formula:

[0105] ;

[0106] Where: Denote the output result of the defuzzified extended electro-carbon coupling model at time; Denote the total number of fuzzy rules; Denote the center value of the th fuzzy rule; Denote the residual correction factor, used to enhance the adaptability of fuzzy logic, determined by the expert experience method;

[0107] Perform recursive dynamic fusion optimization on the defuzzified output result, and the recursive dynamic fusion optimization formula is:

[0108] ;

[0109] Where: Denote the output of the defuzzified extended electro-carbon coupling model at time after the th layer of recursive dynamic fusion optimization; Denote the number of recursive layers; Denote the th layer of recursive time decay factor, , Denote the time decay coefficient, Denote the time point referred to in the th layer of recursive optimization process, ; Denote the th layer of recursive weight function, used to balance the data contributions of different historical layers, , Denote the frequency factor of the dynamic weight function, Denote the phase shift of the dynamic weight function, Represents the dynamic weight perturbation term; Represents the Time interval of the

[0110] Set the stop condition for recursive dynamic fusion optimization:

[0111] ;

[0112] Where: Represents the recursive convergence threshold, determined by the expert experience method;

[0113] Introduce an adaptive bias correction mechanism to correct the bias of the output of recursive dynamic fusion optimization, as shown in the following formula:

[0114] ;

[0115] Where: Represents the Bias correction result at time Represents the number of correction factors (i.e., the total number of key states to be corrected, external perturbation dimensions, input variable categories, or time period quantities); Represents the Strength of the Represents the Sensitivity of the Represents the Correction reference value of the

[0116] Introduce high-dimensional perturbation optimization to construct an optimized electro-carbon coupling model, as shown in the following formula:

[0117] ;

[0118] Where: Represents the output result of the optimized electro-carbon coupling model at Time; Represents the perturbation amplitude factor, used to adjust the sensitivity of perturbation optimization; Represents the main perturbation frequency, reflecting the dominant change frequency of external perturbations; Represents the Dynamic change value of external perturbations at time, obtained through sensors; Represents the perturbation smoothing factor, controlling the smoothness of the perturbation response.

[0119] As a preferred implementation manner of this embodiment, the steps for generating the simulation scenario are:

[0120] Based on the collected electro-carbon data of the power system, construct a simulation scenario generation formula, as shown in the following formula:

[0121] ;

[0122] Wherein: represents the simulation data corresponding to the simulation scenario generated based on the electricity-carbon data of the power system at time; represents the preset fluctuation amplitude of the current simulation scenario; represents the preset fluctuation frequency of the current simulation scenario; represents the preset random disturbance intensity of the current simulation scenario; represents random disturbance.

[0123] As a preferred implementation manner of this embodiment, the simulation data corresponding to the generated simulation scenario is input into the optimized electricity-carbon coupling model to obtain a simulation output, as shown in the following formula:

[0124] ;

[0125] Wherein: represents the simulation output at

[0126] The electricity-carbon data of the power system collected is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output;

[0127] A multi-dimensional comparison and matching analysis algorithm is introduced to perform comparison and matching analysis on the simulation output and the actual output to generate a comprehensive evaluation parameter.

[0128] As a preferred implementation manner of this embodiment, the specific calculation steps of the comprehensive evaluation parameter are as follows:

[0129] Construct a recursive dynamic mapping formula to adjust the simulation output to approximate the actual data in each dimension. The recursive dynamic mapping formula is as shown in the following formula:

[0130] ;

[0131] Wherein: represents the mapped value of the simulation output at time after layers of recursion; represents the number of recursion layers of the recursive dynamic mapping; represents the dynamic weight of the th layer of recursion, , represents the weight condition factor; represents the actual output at

[0132] Calculate the multi-dimensional matching error index between the simulation output and the actual output based on the recursive dynamic mapping output result, as shown in the following formula:

[0133] ;

[0134] Wherein: represents the multi-dimensional matching error index between the simulation output and the actual output at a moment, reflecting the matching degree between the mapped value and the actual value in the multi-dimensional space; represents the total number of input dimensions; represents the component of the th input dimension in the simulation output mapped value at a moment; represents the component of the th input dimension in the actual output at a moment; represents the th standard deviation of the input dimension;

[0135] Based on the multi-dimensional matching error index, the comprehensive evaluation parameter is calculated as follows:

[0136] ;

[0137] Wherein: represents the comprehensive evaluation parameter, representing the performance score of the electro-carbon metering device, which is an important index for quantifying the performance of the electro-carbon metering detection system. Its value range is between [0, 1]. The closer the value is to 1, the higher the matching accuracy and the better the metering performance; the closer the value is to 0, the greater the error and the worse the dynamic adaptation ability; represents the evaluation time range; represents the dynamic matching weight based on the simulation output mapped value at a moment;

[0138] According to the value range of the comprehensive evaluation parameter , the electro-carbon metering detection results are divided into the following five performance levels: 1) Excellent performance, : having extremely high metering accuracy, extremely small matching error between simulation data and actual data, stable dynamic weight distribution, suitable for complex working conditions; 2) Good performance, , indicating that the overall performance is relatively high, with only slight error fluctuations under specific working conditions, suitable for general dynamic conditions; 3) Medium performance, , indicating that the metering accuracy is relatively average, with obvious error fluctuations and limited adaptability to dynamic changes; 4) Poor performance, , indicating that the matching accuracy is relatively low, with large errors, suitable for static working conditions; 5) Failed performance , indicating that the metering error is too large and the dynamic adaptation ability is extremely poor, and it needs to be recalibrated or replaced.

[0139] Example 2:

[0140] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0141] Embodiment Three:

[0142] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0143] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0144] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0145] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0146] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0147] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An electric-carbon measurement detection method based on an electric-carbon coupling model, characterized in that: The following steps are involved: Collect the electricity-carbon data of the power system and build an initial electricity-carbon coupling model based on the electricity-carbon data of the power system; The initial electric-carbon coupling model is expanded, and the expanded electric-carbon coupling model is optimized based on the fuzzy logic reasoning mechanism to obtain the optimized electric-carbon coupling model. The specific steps are as follows: For each dynamic input variable, a fuzzy set is defined, including three fuzzy labels: low, medium, and high, and the membership is represented by an asymmetric Gaussian distribution function, as shown in the following formula: ； ； ； in: , , Respectively represent the fuzzy membership of the input variable to the fuzzy label low, medium, and high; represents input variables; , represents the expansion coefficient of the asymmetric Gaussian distribution function; , Represents the central value of the asymmetric Gaussian distribution function; represents a natural constant; A fuzzy rule base is constructed based on the fuzzy membership of each dynamic input variable. The output membership of the fuzzy rule is calculated by the following formula: ； in: Indicates The output membership of the fuzzy rules; It represents the logical result of all dynamic input variables after fuzzy membership mapping; Indicates In the fuzzy rules, the dynamic input variable In the fuzzy set The degree of membership in ; Indicates the number of dynamic input variables; Indicates the dynamic input variable quantity index; The defuzzification formula is constructed based on the output membership of fuzzy rules and the extended electric-carbon coupling model, as shown in the following formula: ； in: The extended electric-carbon coupling model after defuzzification is Output results at the moment; represents the total number of fuzzy rules; Indicates The central value of the fuzzy rules; represents the residual correction factor; express Always expand the output of the electric-carbon coupling model; The defuzzified output results are recursively dynamically fused and optimized. The recursive dynamic fusion optimization formula is: ； in: The extended electric-carbon coupling model after defuzzification is The output result at time Output after layer-by-layer recursive dynamic fusion optimization; Indicates the number of recursive layers; Indicates Time decay factor for layer recursion; Indicates Layer recursive weight function; Indicates The time interval between layer recursions; Set the stopping condition for recursive dynamic fusion optimization: ； in: represents the recursive convergence threshold; An adaptive deviation correction mechanism is introduced to correct the deviation of the recursive dynamic fusion optimization output, as shown in the following formula: ； in: express The deviation correction result at the moment; Indicates the number of correction factors; Indicates The strength of the correction factor; Indicates The sensitivity of the correction factor; Indicates The calibration reference value of the calibration factor; High-dimensional perturbation optimization is introduced to construct an optimized electric-carbon coupling model, as shown in the following formula: ； in: Indicates that the optimized electric-carbon coupling model is Output results at the moment; represents the disturbance amplitude factor; represents the main frequency of disturbance; express The dynamic change value of external disturbance at every moment; represents the disturbance smoothing factor; Generate simulation scenarios based on the optimized electricity-carbon coupling model according to the collected electricity-carbon data of the power system and different preset operating conditions; The simulation scenarios and the electric carbon data of the power system are matched and analyzed to obtain comprehensive evaluation parameters, and the accuracy of the electric carbon metering equipment of the power system is judged based on the comprehensive evaluation parameters.

2. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 1 is characterized in that: The electric power system electric carbon data specifically includes the active power, reactive power, current and carbon emission factor of the corresponding power node.

3. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 2 is characterized in that: The initial electric-carbon coupling model is specifically shown in the following formula: ； in: express Output of the initial electric-carbon coupling model at time instant; , , , represents the initial weight coefficient; Represents a power node Active power at the moment; Represents a power node Reactive power at the moment; Represents a power node The current at the moment; Represents a power node Carbon emission factor at the time.

4. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 3 is characterized in that: The initial electric-carbon coupling model is expanded based on the time-space decomposition method. The specific steps are as follows: Based on the spatiotemporal decomposition method, the electric carbon data of the power system is decomposed into static baseline values, short-term fluctuations, long-term trends and external disturbance forms, and constructed as dynamic input variables, as shown in the following formula: ； in: express Dynamic input variables at the moment; Represents the static benchmark value of the electric carbon data of the power system; express The short-term fluctuation of electricity carbon data in the power system at the moment; express Long-term trend of electricity and carbon data in the power system at the moment; express External disturbances in the electricity and carbon data of the power system at the moment; Substituting the dynamic input variables into the initial electric-carbon coupling model, the extended electric-carbon coupling model is obtained, as shown in the following formula: ； in: Indicates Weight coefficients; Indicates the carbon data of the power system Static benchmark value of class data samples; Indicates the carbon data of the power system Class data samples are Short-term fluctuations at different times; Indicates the carbon data of the power system Class data samples are Long-term trend at the moment; Indicates the carbon data of the power system Class data samples are External disturbances at any time.

5. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 4 is characterized in that: The steps to generate the simulation scenario are: Based on the collected electric carbon data of the power system, a simulation scenario generation formula is constructed, as shown in the following formula: ； in: Indicates based on Simulation data corresponding to the simulation scenario generated by the electric carbon data of the power system at the moment; Indicates the fluctuation range of the current simulation scenario preset; Indicates the preset fluctuation frequency of the current simulation scene; Indicates the random disturbance intensity preset in the current simulation scenario; represents a random disturbance.

6. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 5 is characterized in that: The simulation data corresponding to the generated simulation scenario is input into the optimized electric-carbon coupling model to obtain the simulation output, as shown in the following formula: ； in: express Simulation output at the moment; The collected electricity-carbon data of the power system is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output; A multi-dimensional comparison and matching analysis algorithm is introduced to perform comparison and matching analysis on the simulation output and the actual output to generate comprehensive evaluation parameters.

7. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 6 is characterized in that: The specific calculation steps of the comprehensive evaluation parameters are: A recursive dynamic mapping formula is constructed to adjust the simulation output. The recursive dynamic mapping formula is shown as follows: ； in: Indicates passing After layer recursion Simulate the output mapping value at each moment; Indicates the number of recursive levels of the recursive dynamic mapping; Indicates Dynamic weights of layer recursion; express The actual output at the moment; The multi-dimensional matching error index between the simulation output and the actual output is calculated based on the recursive dynamic mapping output results, as shown in the following formula: ； in: express Multi-dimensional matching error index between simulation output and actual output at each moment; Indicates the total number of input dimensions; express The simulation output mapping value at time The components of the input dimensions; express The actual output at the moment The components of the input dimensions; Indicates The standard deviation of the input dimensions; The comprehensive evaluation parameters are calculated based on the multi-dimensional matching error index, as shown in the following formula: ； in: represents comprehensive evaluation parameters; Indicates the time frame of the assessment; express Dynamic matching weights based on simulation output mapping values ​​at all times.

8. An electronic 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 program, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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