A power grid equipment digital twin data credibility evaluation method and system

Abstract

The application discloses a kind of power grid equipment digital twin data credibility evaluation method and system, comprising: obtaining power transmission and transformation equipment data, and the data is preprocessed, feature extraction is carried out based on the data after preprocessing;According to the characteristics of power transmission and transformation equipment data, define evaluation index and the weight corresponding to each index, construct power transmission and transformation equipment input data quality evaluation model to quantify the input data quality of digital twin model;Based on the data after quantitative evaluation, establish power transmission and transformation equipment fault diagnosis model, carry out fault analysis and prediction based on power transmission and transformation equipment fault diagnosis model.The application can effectively detect the state of power grid power transmission and transformation equipment, improve the accuracy of fault prediction, and provide scientific basis for intelligent management and operation of power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grids, and particularly relates to a power grid equipment digital twin data credibility evaluation method and system. BACKGROUND

[0002] Power supply stability is a problem that needs to be solved in modern society construction, and power transmission and transformation equipment, as the key link of power transmission and conversion, has an important influence on the reliability and quality of power supply. In the current development trend of power grid and the process of digitalization of modern society, intelligent management and operation and maintenance have gradually become the core demand, aiming to realize more accurate state detection, fault detection and efficient power grid maintenance and scheduling of power transmission and transformation equipment, so as to ensure the continuous and stable operation of the entire national power system. Digital twin technology, with its characteristics of being able to construct a digital model highly similar to the physical actual in virtual space, provides a new idea and method for power grid power transmission and transformation equipment state monitoring and fault prediction. With the help of digital twin model, the working state of power grid power transmission and transformation equipment can be reflected in real time, and the performance under different conditions can be simulated, thereby providing technical support for the optimized operation and maintenance and fault detection of the equipment.

[0003] However, the effectiveness and reliability of digital twin technology are closely related to the quality of input data of power transmission and transformation equipment. In the actual application scene of power grid, the collection of operation data and environmental parameters faces many challenges. First, the data sources of power transmission and transformation equipment include various sensors and monitoring systems, and the sources are extensive, so it is difficult to ensure consistency in format and accuracy. At the same time, the working environment of power transmission and transformation equipment is complex, and there are many interferences such as electromagnetic interference, which affect the accuracy. In addition, how to ensure the effectiveness and integrity of data and the collaborative credibility of data in the long-term work is also one of the challenges faced by current digital twin technology. At present, the evaluation of input data quality mainly adopts single-dimensional data characteristic analysis, and lacks comprehensive evaluation of data. For the digital twin application scene, how to combine real-time operation data, environmental parameters and historical maintenance records to evaluate the data credibility as a whole to ensure that the digital twin model can accurately reflect the real state of the equipment is still insufficient. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification of the present application in order to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed.

[0006] Therefore, the application provides a power grid equipment digital twin data credibility evaluation method and system to solve the problems mentioned in the background technology.

[0007] To solve the above technical problems, the application provides the following technical solutions.

[0008] In a first aspect, the application provides a power grid equipment digital twin data credibility evaluation method, comprising: obtaining power transmission and transformation equipment data, pre-processing the data, and performing feature extraction based on the pre-processed data.

[0009] According to the characteristics of the power transmission and transformation equipment data, define evaluation indexes and corresponding weights of each index, construct a power transmission and transformation equipment input data quality evaluation model to quantitatively evaluate the input data quality of the digital twin model.

[0010] Based on the quantitatively evaluated data, a power transmission and transformation equipment fault diagnosis model is established, and fault analysis and prediction are performed based on the power transmission and transformation equipment fault diagnosis model.

[0011] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, the data pre-processing includes: using an empirical mode decomposition method to perform signal denoising.

[0012] The intrinsic mode function is represented as:

[0013]

[0014] where N represents the total number of data points, E min (i) represents the upper envelope defined by the local maximum at time point i, E max (i) represents the lower envelope defined by the local minimum at time point i.

[0015] Let the original signal be x(t), determine the maximum and minimum values using cubic spline interpolation, obtain the upper and lower envelope lines, and calculate the envelope line average, denoted as m1. Remove the envelope line average from the original signal, represented as:

[0016] h1=x(t)-m1

[0017] If the difference h1 satisfies the intrinsic mode function, then h1=c1, if not, then h1=x(t), repeat the calculation to obtain m 11 , and calculate h 11 =h1-m 11 .

[0018] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, it further includes: if the intrinsic mode function is not satisfied, repeat the iteration for k times until the requirement is met, and record the obtained signal as c1=h 1kwhere h 1k = h 1(k-1) -m 1k ;

[0019] Separate c1 from x(t) to obtain r1=x(t)-c n Take r1(t) as the signal to be decomposed, and repeat the iteration process to obtain a second component c2 that meets the requirements of the eigenmode function, and after n iterations, n components are obtained;By n eigenmode components and one r n Obtain:

[0020]

[0021] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, wherein: the feature extraction based on the preprocessed data comprises: decomposing the signal based on a wavelet basis function to obtain decomposition coefficients;

[0022] Reconstructing the decomposition coefficients to extract signal details in a single frequency band and determining the frequency band in the signal set, and performing power spectrum analysis on the signal to extract features.

[0023] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, wherein: according to the data characteristics of the power transmission and transformation equipment, the evaluation indexes and the corresponding weights of each index are defined, and the input data quality evaluation model of the power transmission and transformation equipment is constructed to quantify the input data quality of the digital twin model, comprising: according to the data characteristics of the power transmission and transformation equipment, selecting integrity, accuracy, correctness and effectiveness as evaluation indexes, determining the weight according to expert scoring, and calculating as follows:

[0024]

[0025] Where, l max Indicates the maximum eigenvalue in the judgment matrix, and n indicates the number of evaluation indexes;

[0026] The consistency judgment result is P, if P=0, the evaluation setting is reasonable, the weight is effective, if P<0.1, the expected consistency level is reached, if P>0.1, the weight is invalid, and needs to be redivided.

[0027] After defining each index and its corresponding weight, data mining technology is introduced to calculate and construct the input data quality evaluation model of the power transmission and transformation equipment.

[0028] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, wherein: further comprising: when performing multi-dimensional data mining, outlier mining is performed in combination with the relationship of each index, and if the result calculated through the formula is inconsistent, it is regarded as abnormal data; during calculation, assuming that the dependent variable index input is y, and the independent variable index is x, wherein x is a group of power transmission and transformation equipment data, represented as x={x1, x2, x3, …, x n}, and combining a prediction method, the residual error is calculated:

[0029]

[0030] Wherein, represents the predicted value;

[0031] Solving the deviation degree A sequence:

[0032]

[0033] Combining the deviation tolerance to detect the deviation degree sequence, setting the tolerance parameter T, when T=0, the power transmission and transformation equipment input data quality evaluation model is used to detect whether the measured value is consistent with the predicted value, to judge the quality of the power grid data; when T≠0, the model is used to detect the deviation between the actual value and the user acceptable predicted value.

[0034] As a preferred scheme of the power grid equipment digital twin data credibility evaluation method, wherein: based on the quantitatively evaluated data, a power transmission and transformation equipment fault diagnosis model is established, and based on the power transmission and transformation equipment fault diagnosis model, fault analysis and prediction are performed, including:

[0035] Selecting sample data and characteristic variables, standardizing the sample data, and dividing it into a pre-training set, an optimization set and a test set according to a fixed ratio;

[0036] Encoding the fault type and state of the power transmission and transformation equipment;

[0037] Establishing a transformer fault diagnosis model based on a deep belief network, and initializing the model parameters to random values; using the unlabeled samples in the pre-training set to pre-train the shallow layers of the model layer by layer;

[0038] Using the labeled samples in the optimization set to optimize the entire network through a back propagation algorithm, so that the network performance approaches the global optimum; saving the trained network, and testing its diagnosis performance with the data samples in the test set;

[0039] According to the calculation capability index, the system running time index and the system accuracy index of the system running equipment, the fault analysis model is optimized to realize fault analysis and prediction.

[0040] In a second aspect, the present application provides a power grid equipment digital twin data credibility evaluation system, comprising:

[0041] A data acquisition processing module is configured to acquire power transmission and transformation equipment data, pre-process the data, and perform feature extraction based on the pre-processed data.

[0042] An evaluation module is configured to define evaluation indexes and corresponding weights of each index according to the features of the power transmission and transformation equipment data, and construct an evaluation model to quantify the input data quality of the digital twin model.

[0043] A fault prediction module is configured to establish a power transmission and transformation equipment fault diagnosis model based on the quantitatively evaluated data, and perform fault analysis and prediction based on the power transmission and transformation equipment fault diagnosis model.

[0044] In a third aspect, the present application provides an electronic device, comprising:

[0045] A memory and a processor.

[0046] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which implement the steps of the power grid equipment digital twin data credibility evaluation method when executed by the processor.

[0047] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which implement the steps of the power grid equipment digital twin data credibility evaluation method when executed by the processor.

[0048] Compared with the prior art, the present application has the following advantages: the present application collects equipment operation data and environmental parameters in real time, combines historical maintenance records, and uses big data mining technology to comprehensively analyze the consistency, accuracy and integrity of the data; further introduces a data quality scoring system to quantify the credibility of the input data, ensuring the reliability of the digital twin model; can effectively detect the state of power transmission and transformation equipment, improve the accuracy of fault prediction, and provide a scientific basis for intelligent management and operation of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor. Among them:

[0050] Figure 1 The method flowchart of the power grid equipment digital twin data credibility evaluation method and system according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the above objectives, features and advantages of the present application more clear and comprehensible, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the protection scope of the present application.

[0052] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present application. Accordingly, the present application is not limited to the embodiments described herein.

[0053] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments.

[0054] The present application is described in detail with reference to the accompanying drawings. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is partially enlarged without the general proportion for the convenience of description, and the schematic view is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0055] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0056] Unless otherwise specifically defined and limited, the terms "mounting, connecting, connection" in the present application should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0057] Example 1

[0058] Referring to Figure 1 For an embodiment of the present application, the embodiment provides a power grid equipment digital twin data credibility evaluation method, comprising:

[0059] S100: Obtain power transmission and transformation equipment data, and pre-process the data, and perform feature extraction based on the pre-processed data;

[0060] In the embodiment of the present application, the obtained power transmission and transformation equipment data can include electrical quantities, temperatures, and vibration signals.

[0061] In the embodiment of the present application, the data pre-processing includes: using an empirical mode decomposition method to perform signal denoising;

[0062] The intrinsic mode function is expressed as:

[0063]

[0064] Wherein, N represents the total number of data points, E min (i) represents the upper envelope defined by the local maximum at time point i, E max (i) represents the lower envelope defined by the local minimum at time point i;

[0065] Let the original signal be x(t), determine the maximum and minimum values using cubic spline interpolation, obtain the upper and lower envelope lines, and calculate the envelope line average value, denoted as m1, and remove the envelope line average value from the original signal, expressed as:

[0066] h1=x(t)-m1

[0067] If the difference h1 satisfies the intrinsic mode function, then h1=c1, if not, then h1=x(t), repeat the calculation to obtain m 11 , and calculate h 11 =h1-m 11 .

[0068] In the embodiment of the present application, if the intrinsic mode function is not satisfied, repeat the iteration k times until the requirement is met, and record the obtained signal as c1=h 1k , wherein h 1k =h 1(k-1) -m 1k ;

[0069] Separate c1 from x(t) to obtain r1=x(t)-c n , take r1(t) as the signal to be decomposed, and repeat the iteration process to obtain a second component c2 that satisfies the intrinsic mode function requirement, and after n iterations, n components are obtained; from the n intrinsic mode components and one r n :

[0070]

[0071] It should be noted that the power transmission equipment data is pre-processed to improve the signal quality through amplification, filtering, conversion and other operations. Among them, filtering is crucial because it involves removing noise from the signal. In this case, the empirical mode decomposition method can be used for signal denoising.

[0072] In the embodiments of the present application, the feature extraction based on the pre-processed data includes: decomposing the signal based on the wavelet basis function to obtain the decomposition coefficients;

[0073] The decomposition coefficients are reconstructed to extract the signal details in a single frequency band and determine the frequency bands in the signal set, and the power spectrum analysis of the signal is performed for feature extraction.

[0074] S200: According to the power transmission equipment data feature, define the evaluation index and the corresponding weight of each index, and construct the power transmission equipment input data quality evaluation model to quantify the input data quality of the digital twin model;

[0075] In the embodiments of the present application, according to the power transmission equipment data feature, define the evaluation index and the corresponding weight of each index, and construct the power transmission equipment input data quality evaluation model to quantify the input data quality of the digital twin model includes: according to the characteristics of the power transmission equipment data, select integrity, accuracy, correctness and effectiveness as evaluation indexes, determine the weight according to the expert score, and calculate as follows:

[0076]

[0077] Wherein, l max represents the maximum eigenvalue in the judgment matrix, and n represents the number of evaluation indexes;

[0078] The consistency judgment result is P, if P=0, the evaluation setting is reasonable, the weight is effective, if P<0.1, the expected consistency level is reached, if P>0.1, the weight is invalid, and needs to be re-divided.

[0079] After defining each index and its corresponding weight, data mining technology is introduced to calculate and construct the power transmission equipment input data quality evaluation model.

[0080] It should be noted that when mining abnormal data, if the data volume is very small, manual detection can be used; if it is a large amount of data, or a large number of states, machine intelligence means need to be used to realize.

[0081] In the embodiment of the present application, when multi-dimensional data mining is performed, outlier mining is performed in combination with the relationships of the indexes, and if the results calculated by the formula are inconsistent, the data is regarded as abnormal data; when calculating, it is assumed that the dependent variable index input is y and the independent variable index is x, where x is a group of power transmission and transformation equipment data, represented as x={x1, x2, x3,..., x n}, and the residual error is calculated in combination with the prediction method:

[0082]

[0083] wherein, represents the predicted value;

[0084] The deviation degree sequence A is solved:

[0085]

[0086] The deviation degree sequence is detected in combination with the deviation tolerance, a tolerance parameter T is set, when T=0, the power transmission and transformation equipment input data quality evaluation model is used to detect whether the measured value is consistent with the predicted value, to judge the quality of the power grid data; when T≠0, the model is used to detect the deviation between the actual value and the user-acceptable predicted value.

[0087] It should be noted that the deviation degree sequence is detected in combination with the deviation tolerance. By setting the tolerance parameter, the flexibility of the evaluation model in calculation is ensured.

[0088] S300: Based on the quantitatively evaluated data, a power transmission and transformation equipment fault diagnosis model is established, and fault analysis and prediction are performed based on the power transmission and transformation equipment fault diagnosis model;

[0089] In the embodiment of the present application, based on the quantitatively evaluated data, a power transmission and transformation equipment fault diagnosis model is established, and fault analysis and prediction are performed based on the power transmission and transformation equipment fault diagnosis model, which includes:

[0090] Select sample data and characteristic variables, standardize the sample data, and divide it into a pre-training set, an optimization set, and a test set according to a fixed ratio;

[0091] Encode the power transmission and transformation equipment fault type and state;

[0092] A transformer fault diagnosis model based on a deep belief network is established, and the model parameters are initialized as random values; the unlabeled samples in the pre-training set are used to pre-train the shallow layers of the model layer by layer;

[0093] The labeled samples in the optimization set are used to optimize the entire network through a back propagation algorithm, so that the network performance approaches the global optimum; the trained network is saved, and the data samples in the test set are used to test its diagnosis performance;

[0094] The fault analysis model is optimized according to the computing power index of the system running device, the system running time index and the system precision index, so as to realize fault analysis and prediction.

[0095] It should be noted that the present application overcomes the incomplete problem caused by single by adopting multi-dimensional input data. During data preprocessing, on the basis of conventional amplification, filtering and conversion operations, the empirical mode decomposition method is used for noise reduction, which more effectively improves the signal-to-noise ratio of the signal. After noise reduction, the wavelet transform is used for feature extraction. The signal is decomposed by selecting a suitable wavelet basis function, the coefficients are reconstructed to determine the frequency band and perform power spectrum analysis, and the features are accurately extracted. The input data is quantified by using the credibility score, and the integrity, accuracy, correctness and effectiveness of the four evaluation indexes are combined to build an evaluation model, which more comprehensively evaluates the data credibility and further improves the fault detection accuracy. A comprehensive data analysis framework is constructed and a data quality scoring system is introduced. By real-time acquisition of equipment operation data and environmental parameters, combined with historical maintenance records, the data consistency, accuracy and integrity are comprehensively analyzed by using big data mining technology, the data credibility is quantified, the digital twin model is ensured to be reliable, the equipment state is effectively detected, and the fault prediction accuracy is improved, which provides strong support for intelligent management and operation of power grid.

[0096] Embodiment 2

[0097] The above embodiment is a schematic scheme of a power grid equipment digital twin data credibility evaluation method. It should be noted that the technical scheme of the power grid equipment digital twin data credibility evaluation system belongs to the same concept as the technical scheme of the power grid equipment digital twin data credibility evaluation method described above. The technical scheme of the power grid equipment digital twin data credibility evaluation system in this embodiment is not described in detail, and can be referred to the description of the technical scheme of the power grid equipment digital twin data credibility evaluation method.

[0098] The power grid equipment digital twin data credibility evaluation system in this embodiment comprises:

[0099] The data acquisition and processing module is configured to acquire power transmission and transformation equipment data, pre-process the data, and extract features based on the pre-processed data.

[0100] The evaluation module is configured to define evaluation indexes and corresponding weights of each index according to the characteristics of the power transmission and transformation equipment data, and construct an evaluation model to quantify the input data quality of the digital twin model.

[0101] The fault prediction module is configured to establish a power transmission and transformation equipment fault diagnosis model based on the quantitatively evaluated data, and perform fault analysis and prediction based on the power transmission and transformation equipment fault diagnosis model.

[0102] The embodiment also provides an electronic device suitable for the power grid equipment digital twin data credibility evaluation method, and the electronic device comprises:

[0103] The memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the power grid equipment digital twin data credibility evaluation method proposed in the above embodiment.

[0104] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the power grid equipment digital twin data credibility evaluation method proposed in the above embodiment.

[0105] The storage medium proposed in the embodiment and the power grid equipment digital twin data credibility evaluation method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A power grid equipment digital twin data trustworthiness evaluation method, characterized in that, Comprise: Obtaining power transmission and transformation equipment data, and preprocessing the data, and performing feature extraction based on the preprocessed data; According to the characteristics of the power transmission and transformation equipment data, select integrity, accuracy, correctness and effectiveness as evaluation indexes, determine the weight according to the expert score, and calculate as follows: ； wherein, denotes the largest eigenvalue in the judgment matrix, and n denotes the number of evaluation indexes. The consistency judgment result is P, if P=0, the evaluation setting is reasonable, and the weight is effective, if P<0.1, the expected consistency level is reached, if P>0.1, the weight is invalid, and needs to be redivided; After defining each index and its corresponding weight, data mining technology is introduced to calculate and construct the input data quality evaluation model of the power transmission and transformation equipment; Based on the quantitatively evaluated data, a power transmission and transformation equipment fault diagnosis model is established, and based on the power transmission and transformation equipment fault diagnosis model, fault analysis and prediction are carried out, specifically: select sample data and characteristic variables, standardize the sample data, and divide them into pre-training set, optimization set and test set according to fixed proportion; The fault types and states of the power transmission and transformation equipment are coded; A transformer fault diagnosis model based on deep belief network is established, and the model parameters are initialized as random values; The unlabeled samples in the pre-training set are used to pre-train the shallow restricted Boltzmann machine layer of the model layer by layer; Using labeled samples in the optimization set, the entire network is optimized by back propagation algorithm, so that the network performance is close to the global optimum; Save the trained network, and test its diagnostic performance with data samples in the test set; According to the calculation capability index, system running time index and system precision index of the system running equipment, the fault analysis model is optimized to realize fault analysis and prediction.

2. The power grid equipment digital twin data trust assessment method of claim 1, wherein, The data preprocessing includes: using empirical mode decomposition method for signal denoising; The intrinsic mode function is expressed as: ； where N denotes the total number of data points, denotes the upper envelope defined by local maxima at time points i, denotes the lower envelope defined by local minima at time points i. Let the original signal be The maximum and minimum values are determined by cubic spline interpolation, and the upper and lower envelope lines are obtained. The average value of the envelope lines is calculated and denoted as The envelope line average value is removed from the original signal and represented as ； If the difference satisfies the eigenmode function, then , if not, then , repeat the calculation to obtain , and calculate .

3. The power grid equipment digital twin data trustworthiness evaluation method of claim 2, wherein, Also include: If the eigenmode function is not satisfied, repeat the iteration k times until the requirement is met, and the obtained signal is recorded as wherein ; From separate , get , take as the signal to be decomposed, repeat the iteration process to obtain the second component that meets the eigenfunction requirements , after n iterations, n components will be obtained; from n eigenmode components and one results in: 。 4. The power grid equipment digital twin data trustworthiness evaluation method of claim 3, wherein, Based on the preprocessed data, feature extraction includes: based on wavelet basis function, the signal is decomposed to obtain decomposition coefficients; Reconstruct the decomposition coefficients to extract the signal details in a single frequency band and determine the frequency band in the signal set, and perform power spectrum analysis on the signal to extract features.

5. The power grid equipment digital twin data trustworthiness evaluation method of claim 4, wherein, Also include: When performing multi-dimensional data mining, outlier mining is performed in combination with the relationship between the indicators. If the results calculated by the formula are inconsistent, it is considered to be abnormal data. When calculating, it is assumed that the input dependent variable indicator is y, and the independent variable indicator is x, where x is a set of power transmission equipment data, represented as At the same time, in combination with the prediction method, the residual error is calculated: ； wherein represents a predicted value; Solve the deviation degree A sequence: ； Combine the deviation tolerance to detect the deviation degree sequence, set the tolerance parameter T, when T=0, the input data quality evaluation model of the power transmission and transformation equipment is used to detect whether the measured value is consistent with the predicted value, to judge the quality of the power grid data; When T≠0, the model is used to detect the deviation between the actual value and the user acceptable predicted value.

6. A power grid equipment digital twin data trust evaluation system, applied to the method of any one of claims 1-5, characterized in that, Comprise: Data acquisition and processing module, for acquiring power transmission and transformation equipment data, and preprocessing the data, and performing feature extraction based on the preprocessed data; Evaluation module, for defining evaluation indexes and corresponding weights of each index according to the characteristics of power transmission and transformation equipment data, and constructing evaluation model to quantitatively evaluate the input data quality of digital twin model; A fault prediction module is configured to establish a fault diagnosis model of the power transmission and transformation equipment based on the quantized and evaluated data, and perform fault analysis and prediction based on the fault diagnosis model of the power transmission and transformation equipment. 7.An electronic device, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the power grid equipment digital twin data trust evaluation method in any one of claims 1 to 5. 8.A computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the power grid equipment digital twin data trust evaluation method in any one of claims 1 to 5.

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