A method and system for autonomously identifying the error degradation form of DC measurement equipment

Abstract

The present invention discloses a method and system for autonomously identifying the error degradation form of a DC measuring device. The method comprises: establishing an evaluation standard quantity based on historical data measured by the DC measuring device in a normal operating state after commissioning and in different error degradation forms; establishing a mathematical model of the measurement error degradation form of the DC measuring device based on the measured historical data; extracting a feature sample set of different error forms based on the evaluation standard quantity; establishing and training a DC measuring device error form identification model based on the feature sample set; when the evaluation standard quantity detects that the measurement error state of the DC measuring device is abnormal, using the established mathematical model of the measurement error degradation form of the DC measuring device to perform online detection of the measurement error of the DC measuring device, and applying the trained DC measuring device error form identification model to perform online identification. The present invention can help staff detect abnormal states of DC measuring devices in a timely manner.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and system for autonomously identifying the error degradation form of a direct current measurement device. Background Art

[0002] DC measurement equipment is a vital component of power systems. Its accuracy is crucial for determining and adjusting power system operating conditions, significantly impacting the control, measurement, metering, and protection functions of DC systems. However, in actual operation, subject to varying degrees of environmental influences such as temperature, humidity, and frequency, DC measurement equipment is susceptible to various forms of degradation, resulting in errors that affect the accuracy of energy metering and the reliability of relay protection devices.

[0003] Furthermore, the metering errors of DC measurement equipment exhibit distinct characteristics under different degradation forms. For example, the cumulative impact of periodic error changes on energy metering may be zero. This is because periodic errors cancel each other out within the positive and negative half-cycles and have no substantial impact on energy metering. However, errors in other degradation forms, such as random errors and drift errors, can have varying degrees of cumulative impact on energy metering. Therefore, it is necessary to autonomously detect and identify the metering errors of DC measurement equipment to enable rapid detection and resolution of abnormal conditions.

[0004] Currently, the method for detecting metering errors in DC measuring equipment generally involves calibration by comparison with standard DC measuring equipment. However, due to the strict operating environment requirements of standard DC measuring equipment, this method can only be performed periodically and reflects short-term error states. In power systems, changes in DC measuring equipment metering errors are a long-term, continuous, and cumulative process. This method cannot accurately reflect the long-term operating status of DC measuring equipment. Therefore, to improve the safe operation of power systems, it is necessary to research online detection and autonomous identification methods for the metering errors of DC measuring equipment. These methods aim to accurately perceive changes in the metering error state of DC measuring equipment in real time and precisely identify the degradation of the error state.

[0005] In the prior art, Chinese invention patent application CN109444791A discloses a method and system for online error state monitoring of capacitor voltage transformers. This method collects measurement data from a calibrated and operational three-phase capacitor voltage transformer and uses principal component analysis to perform real-time online evaluation of the capacitor voltage transformer's error state. However, this online detection method only assesses the error state and does not further identify the form of error degradation. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for further identifying the form of error degradation based on online detection of measurement errors of DC measuring equipment in a data-driven manner.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] A method for autonomously identifying the error degradation form of a DC measurement device, comprising:

[0009] S10, establishing an evaluation standard quantity based on historical data measured by the DC measurement equipment under normal operating conditions and different error degradation forms after commissioning;

[0010] S20, establishing a mathematical model of the degradation form of the measurement error of the DC measurement equipment based on the measured historical data;

[0011] S30, extracting feature sample sets of different error forms based on the evaluation standard quantity;

[0012] S40, establishing and training a DC measurement equipment error form identification model based on the feature sample set;

[0013] S50, when the evaluation standard detects that the measurement error state of the DC measurement device is abnormal, the measurement error of the DC measurement device is detected online using the established mathematical model of the degradation form of the measurement error of the DC measurement device, and the trained DC measurement device error form identification model is used to perform online identification.

[0014] In one embodiment of the present invention, establishing an evaluation standard includes:

[0015] S11, based on the measured historical data, construct the data matrix X of the output signal of the DC measurement device 0 ;

[0016] S12, for the data matrix X 0 Perform standardization processing to obtain the standardized matrix X;

[0017] S13, performing dimensionality reduction processing on the standardized matrix X to separate the true value information of the collected DC measurement device data and error information ΔX;

[0018] S14, based on the error information ΔX, calculate the evaluation standard quantity Q that represents the error state, and calculate the limit value Q of the evaluation standard quantity Q c ;

[0019] in, Where ‖·‖2 represents the 2-norm, and the limit Q c is the statistical value when the confidence level is c.

[0020] In one embodiment of the present invention, a mathematical model of the degradation form of the measurement error of the DC measurement equipment is established by the following formula:

[0021] y t =kx t +v x +f t ;

[0022] Where y t It is represented by the real value of the secondary output of the DC measuring device, k is represented by the transformation ratio of the DC measuring device, and x t Expressed as the actual value of the primary side signal amplitude of the DC measuring device, v x and f t They represent random error and systematic error respectively; t represents time;

[0023] Among them, the system error f t include:

[0024] Turns ratio error f1(t): f1(t) = l(t)Δk; where l(t) is the true value and Δk is the difference in turns ratio before and after the DC measuring device experiences a turns ratio error.

[0025] Amplitude fixed error f2(t): Where n ft It is expressed as a step function, t1 represents the start time of the fixed error, and t2 represents the end time of the fixed error;

[0026] Drift error f3: f3 = p(t-t3); where r3 is the time when the drift error starts, and p is a constant.

[0027] In one embodiment of the present invention, a feature sample set is obtained in the following manner:

[0028] S31, based on the time series of the estimated standard quantity {Q i}, first specify an embedding dimension s0, solve the time delay parameter τ, and reconstruct the phase space using the delay coordinates;

[0029] S32, based on the reconstructed phase space, calculate the time series {Q i}'s correlation integral C i (r);

[0030] S33, according to the correlation score C i (r), obtain the correlation dimension D, perform least squares fitting on the logarithmic curve of the correlation dimension D, and obtain the initial correlation dimension estimate D(s0);

[0031] S34, increase the value of the embedding dimension s0 so that the increased embedding dimension s1>s0. As the value of the dimension increases, the initial correlation dimension estimate D(s0) corresponding to the embedding dimension s0 gradually stabilizes within a certain error range. At this time, the correlation dimension is obtained as the time series {Q i}'s correlation dimension;

[0032] S35, calculating the correlation dimension D obtained from the time series as a characteristic quantity of the time series for estimating the standard quantity, and the correlation dimensions under various error degradation forms constitute a characteristic sample set E of different error forms.

[0033] In one embodiment of the present invention, the time delay parameter τ is calculated as follows:

[0034]

[0035] Where R is the autocorrelation function, n-τ is the delay of the nth sampling point corresponding to each time point; the variation curve of the autocorrelation function R with the time delay parameter τ is obtained by calculation, and the time delay parameter τ corresponding to when R is 0 for the first time or drops to 1-1 / e of the initial value is taken as the optimal time delay parameter; where e is a natural constant, i is the i-th sampling point, and - is the mean value of Q;

[0036] The time series {Q i}Perform coordinate delay under the time delay parameter τ to obtain the reconstructed phase space.

[0037] In one embodiment of the present invention, the correlation score C i (r) is obtained by the following formula:

[0038]

[0039] Where θ is represented by the Heaviside unit function, where x=r-|y i -y j ∣,y i 、y j To reconstruct the time series vector in the phase space, r represents the specified distance threshold. When the distance between two vectors is less than r, it can be determined that there is a correlation between the two vectors.

[0040] The correlation dimension D is obtained by the following formula:

[0041]

[0042] Where r represents the specified distance threshold.

[0043] In one embodiment of the present invention, establishing a DC measurement device error form identification model includes:

[0044] S41, the feature sample set E is expressed as E = {a v ,b w}, b w Indicates a v Category, v = 1, 2, ..., n, n is the number of collected samples; a v is the vth input sample; w=1,2,…,m, where m is the number of categories

[0045] S42, establish a mapping function φ(x) to map the points in the feature sample set E to a high-dimensional space: φ(x) = α i j i K(x i ,x); where K(x i ,x) is the kernel function, α i is the Lagrange multiplier, j i Represented as kernel function parameters;

[0046] S43, establishing a DC measurement device error form identification model based on the mapping function φ(x).

[0047] In one embodiment of the present invention, the DC measurement device error form identification model f(x) is as follows:

[0048]

[0049] In the formula, d represents the bias term of the function, α i j i K(x i ,x) is expressed as a nonlinear mapping function.

[0050] In one embodiment of the present invention, the evaluation standard detects whether the measurement error state of the DC measurement device is abnormal by:

[0051] If Q<Q c , then the DC measuring equipment is in normal operation;

[0052] If Q>Q c , the DC measuring equipment is in an abnormal state at this time, and abnormal measurement error occurs.

[0053] Among them, Q represents the evaluation standard quantity, Q c Expressed as the limit value of the evaluation standard quantity Q.

[0054] The present invention also provides an autonomous identification system for the error degradation form of a DC measurement device, comprising:

[0055] An evaluation standard module is used to establish evaluation standard quantities based on historical data measured by DC measurement equipment under normal operating conditions and different error degradation forms after commissioning;

[0056] An error detection module is used to establish a mathematical model of the degradation form of the DC measurement error of the DC measurement equipment based on the measured historical data;

[0057] The sample extraction module is used to extract feature sample sets of different error forms based on the evaluation standard quantity;

[0058] Identification model training module, used to establish and train the error form identification model of DC measurement equipment based on the feature sample set;

[0059] The online detection module is used to perform online detection of the measurement error of the DC measurement equipment when the evaluation standard detects that the measurement error state of the DC measurement equipment is abnormal, using the established mathematical model of the degradation form of the measurement error of the DC measurement equipment, and applying the trained DC measurement equipment error form identification model to perform online identification.

[0060] Compared with the prior art, the present invention has the following advantages: based on the online detection of the measurement error of DC measuring equipment, the present invention further identifies the form of error degradation, reconstructs the phase space using the embedding dimension and the time delay parameter, and obtains the correlation dimension as the characteristic parameter of the error identification model. The error identification model is further constructed by establishing a mapping function and a decision function, thereby realizing the online identification of the error degradation form. The present invention does not require the establishment of a knowledge base of different types of DC measuring equipment. Based on a data-driven approach, the error identification model is established, and the error characteristic vector is calculated using the online sampling data of different devices to further identify the specific error degradation form. The identification result is more accurate and has a wider range of applications.

[0061] The present invention analyzes the collected measurement data of DC measuring equipment to construct an evaluation standard quantity Q that characterizes the error state, recorded as a statistic, and realizes online detection of the error state of DC measuring equipment. By establishing a mathematical model of the measurement error degradation form, calculating the correlation dimension of the statistical time series, analyzing the differences in the correlation dimension under different error degradation forms, and constructing the correlation dimension into a characteristic quantity, the present invention realizes online identification of the error degradation form of DC measuring equipment. This can help personnel detect abnormal conditions of DC measuring equipment in a timely manner, facilitate the development of more differentiated DC measuring equipment maintenance and repair strategies, thereby improving the reliability and stability of DC measuring equipment and ensuring the safe, economical and stable operation of the power grid.

[0062] Extracting the correlation dimension of the statistics representing the error state of DC measurement equipment as its identification feature parameter can fully reflect the differences in the error state degradation form. Using the correlation dimension of the statistical time series as the input feature, an error identification model is established to achieve autonomous identification of the error form of DC measurement equipment.

[0063] The present invention collects operating data of DC measuring equipment in normal operating states and different error degradation forms after commissioning, performs error detection and identification in an autonomous identification system for error degradation forms, and feeds back the results to on-site staff. This allows real-time detection of whether the error state of the DC measuring equipment is abnormal and identification of the error form, which helps to formulate more differentiated maintenance and inspection strategies for DC measuring equipment, thereby improving the reliability and stability of the DC measuring equipment and ensuring the safe, economical and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a method for autonomously identifying the error degradation form of a DC measurement device according to an embodiment of the present invention.

[0065] Figure 2 This is a flowchart of the specific steps for autonomously identifying the error degradation form of a DC measurement device according to an embodiment of the present invention.

[0066] Figure 3 This is a block diagram of a system for autonomously identifying the error degradation form of a DC measurement device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] To facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described with reference to the accompanying drawings.

[0068] The terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0069] See also Figure 1 and Figure 2 As shown, the present invention discloses a method for autonomously identifying the error degradation form of a DC measurement device, comprising:

[0070] S10, establishing an evaluation standard quantity based on historical data measured by the DC measurement equipment under normal operating conditions and different error degradation forms after commissioning.

[0071] In one embodiment of the present invention, establishing an evaluation standard includes:

[0072] S11, based on the measured historical data, construct the data matrix X of the output signal of the DC measurement device 0 Specifically, construct the data matrix X of the output signal of the DC measurement device 0 ∈R m×n , n is the number of samples collected, m is the number of output signals collected, X 0 for:

[0073]

[0074] Where: X nm Indicates the nth sampling point of the mth output signal collected.

[0075] S12, for the data matrix X 0 The standardized matrix X is obtained by standardization. The processed data matrix is:

[0076] X=(X 0 -Ib T )∑ -1 ;

[0077] Where: I = [1, 1, ..., 1] T ∈R n×1 , T represents the transpose. b=(X 0 ) T I / n is the mean vector of the data, Σ=diag(σ1 2 ,...,σ m 2 ) is the variance matrix of the data, σ m is the variance of the mth column.

[0078] S13, performing dimensionality reduction processing on the standardized matrix X to separate the main space and the residual space, that is, separating the true value information of the collected DC measurement device data and error information ΔX;

[0079]

[0080] Where, It represents the true value information of the collected DC measurement device data, and ΔX represents the error information of the collected DC measurement device data.

[0081] S14, based on the error information ΔX, calculate the evaluation standard quantity Q that represents the error state, and calculate the evaluation standard quantity Q and its limit value Q by the following formula c : Q c is the statistical value when the confidence level is c, that is, when the confidence level is 99%, 99% of the calibration samples can be found below this statistical value.

[0082] S20, establishing a mathematical model of the degradation form of the measurement error of the DC measurement equipment based on the measured historical data.

[0083] In one embodiment of the present invention, a mathematical model of the degradation form of the measurement error of a DC measurement device is established, including:

[0084] S21, establish a mathematical model of the output of the DC measurement device:

[0085] y t =kx t +v x +f t ;

[0086] Where y t is the true value of the secondary output of the DC measuring device, k represents the transformation ratio of the DC measuring device, and x t is the actual value of the primary side signal amplitude of the DC measuring device, v x and f t represent random error and systematic error respectively, and t represents time.

[0087] It should be noted that random errors primarily arise from noise interference and the randomness of the measurement process itself, and are difficult to eliminate through calibration. Systematic errors, on the other hand, primarily arise from design flaws in the DC measurement equipment itself or variations in the operating environment, which can lead to measurement accuracy deviations. These errors can be eliminated or reduced through calibration or component replacement.

[0088] S22, based on the mathematical model of the DC measurement device output, uses different error functions to represent various system errors of the DC measurement device.

[0089] The mathematical model based on the output of the DC measuring device in step S22 uses different error functions to represent various system errors of the DC measuring device, specifically:

[0090] S221, specifically, when the transformation ratio of the DC measuring device changes due to temperature or other environmental influences, a transformation ratio error occurs. The error function is expressed as: f1(t) = l(t)Δk, where Δk is the difference in the transformation ratio of the DC measuring device before and after the transformation ratio error occurs, and l(t) is the true value.

[0091] S222, when the DC measurement device error remains unchanged in phase and a fixed value is added to the amplitude, it is a fixed amplitude error, and the error function is expressed as: Where n ft It is a step function, t1 is the starting time of the fixed error, t2 is the ending time of the fixed error, and it approaches infinity.

[0092] S223, when DC measuring equipment ages due to environmental factors such as electromagnetic fields, temperature and humidity, its measurement value will drift over time. The mathematical model of the drift error is: f3 = p(t-t3), where t3 is the time when the drift error starts and p is a constant.

[0093] S30, extracting feature sample sets of different error forms based on the evaluation standard quantity.

[0094] In one embodiment of the present invention, a feature sample set is obtained in the following manner:

[0095] S31, based on the time series of the estimated standard quantity {Q i}, first specify an embedding dimension s0, solve the time delay parameter τ, and reconstruct the phase space using the delay coordinates.

[0096] In one embodiment of the present invention, based on the time series {Q i}, first specify a smaller embedding dimension s0, solve the time delay parameter τ, and reconstruct the phase space using the delay coordinates. Specifically, the time delay parameter τ is calculated as follows:

[0097] For the time series {Q i}, its autocorrelation function R can be expressed as:

[0098]

[0099] In the formula, R represents the autocorrelation function, n-τ represents the delay of the nth sampling point corresponding to each time point. The variation curve of the autocorrelation function R with the time delay τ is obtained by calculation, and the τ corresponding to when R is 0 for the first time or drops to 1-1 / e of the initial value is taken as the optimal time delay parameter. Among them, e represents a natural constant. i represents the i-th sampling point, - represents the mean value of Q. After obtaining the time delay parameter τ, the time series {Q i}Perform coordinate delay under the time delay parameter τ to obtain the reconstructed phase space.

[0100] S32, based on the reconstructed phase space, calculate the time series {Q i}'s correlation integral C i (r).

[0101] In one embodiment of the present invention, the correlation score C i (r) is obtained by the following formula:

[0102]

[0103] Where θ is represented by the Heaviside unit function, where x=r-|y i -y j ∣,y i 、y j To reconstruct the time series vector in the phase space, r represents the specified distance threshold. When the distance between two vectors is less than r, it can be determined that there is a correlation between the two vectors.

[0104] S33, according to the correlation score C i (r), obtain the correlation dimension D, perform least squares fitting on the logarithmic curve of the correlation dimension D, and obtain the initial correlation dimension estimate D(s0).

[0105] In one embodiment of the present invention, the correlation dimension D is obtained by the following formula:

[0106]

[0107] Where r represents the specified distance threshold.

[0108] S34, increase the value of the embedding dimension s0 so that the increased embedding dimension s1>s0. As the value of the dimension increases, the initial correlation dimension estimate D(s0) corresponding to the embedding dimension s0 gradually stabilizes within a certain error range. At this time, the correlation dimension is obtained as the time series {Q i}'s correlation dimension;

[0109] S35, calculating the correlation dimension D obtained from the time series as a characteristic quantity of the time series for estimating the standard quantity, and the correlation dimensions under various error degradation forms constitute a characteristic sample set E of different error forms.

[0110] S40, based on the feature sample set, establishing a DC measurement equipment error form identification model and performing training.

[0111] In one embodiment of the present invention, establishing a DC measurement device error form identification model includes:

[0112] S41, the feature sample set E is expressed as E = {a v ,b w}, v = 1, 2, ..., n, n is the number of collected samples; a v is the vth input sample; w=1,2,…,m, m is the number of categories, b w Indicates a v Category.

[0113] S42, establish a mapping function φ(x) to map the points in the feature sample set E to a high-dimensional space: φ(x) = α i j i K(x i ,x); where K(x i,x) is the kernel function, and you can choose kernel functions such as linear kernel, polynomial kernel, radial basis kernel, etc. i is the Lagrange multiplier, j i Represented as kernel function parameters.

[0114] S43, establishing a DC measurement device error form identification model based on the mapping function φ(x).

[0115] In this embodiment, the DC measurement device error form identification model f(x) is expressed as:

[0116]

[0117] Among them, α i j i K(x i ,x) is a nonlinear mapping function, and d is the bias term of the function.

[0118] S50: When the DC measurement device metering error state is abnormal, the DC measurement device metering error is detected online using the established mathematical model for the DC measurement device metering error degradation form, and the trained DC measurement device error form identification model is used to perform online identification.

[0119] In one embodiment of the present invention, the evaluation standard detects whether the measurement error state of the DC measurement device is abnormal by:

[0120] If Q<Q c , then the DC measuring equipment is in normal operation;

[0121] If Q>Q c , the DC measuring equipment is in an abnormal state at this time, and abnormal measurement error occurs.

[0122] In one embodiment of the present invention, during online detection:

[0123] Collect online operating data of DC measurement equipment and build a data matrix of output signal amplitude;

[0124] Based on the data matrix, calculate the Q, Q c , and make comparison to determine whether the error state of the online operation data of the DC measuring equipment is abnormal;

[0125] If an exception occurs:

[0126] Inputting online operation data into a mathematical model of the degradation form of the DC measurement equipment measurement error, and performing online detection of the DC measurement equipment measurement error;

[0127] At the same time, the correlation dimension of the abnormal time series is calculated to extract the characteristics of the error form;

[0128] The characteristics are identified using a DC measurement equipment error form identification model to obtain an autonomous identification result of the error degradation form.

[0129] See also Figures 1 to 3 As shown, the present invention also provides a system for autonomously identifying the degradation form of a DC measurement device error, which applies the above-mentioned method for autonomously identifying the degradation form of a DC measurement device error, including:

[0130] An evaluation standard module is used to establish evaluation standard quantities based on historical data measured by DC measurement equipment under normal operating conditions and different error degradation forms after commissioning;

[0131] The error detection module is used to establish a mathematical model of the degradation form of the measurement error of the DC measurement equipment based on the measured historical data.

[0132] The sample extraction module is used to extract feature sample sets of different error forms based on the evaluation standard quantity.

[0133] The identification model training module is used to establish and train the error form identification model of DC measurement equipment based on the feature sample set.

[0134] The online detection module is used to perform online detection of the measurement error of the DC measurement equipment when the evaluation standard detects that the measurement error state of the DC measurement equipment is abnormal, using the established mathematical model of the degradation form of the measurement error of the DC measurement equipment, and applying the trained DC measurement equipment error form identification model to perform online identification.

[0135] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced herein, and any reference signs in the claims should not be construed as limiting the claims to which they relate.

[0136] The above-mentioned embodiments merely represent the implementation methods of the invention. The protection scope of the present invention is not limited to the above-mentioned embodiments. For those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention, which all fall within the protection scope of the present invention.

Claims

1. A method for autonomously identifying the error degradation form of a DC measurement device, characterized in that: include: S10: Based on the historical data measured by the DC measurement equipment under normal operation and different error degradation forms after commissioning, establish evaluation standard quantities, including: S11, based on the measured historical data, construct a data matrix of the output signal of the DC measurement device ; S12, for the data matrix Perform standardization to obtain a standardized matrix ; S13, normalized matrix Perform dimensionality reduction processing to separate the true value information of the collected DC measurement equipment data and error information ; S14, according to the error information , calculate the evaluation standard quantity that characterizes the error state Q , and calculate the evaluation standard quantity Q Limit Q c ; in, ², where ² represents the 2-norm, the limit Q c The confidence level is c Statistical value at time; S20, based on the measured historical data, establish a mathematical model of the degradation form of the DC measurement equipment measurement error, the formula is: ； Where, Expressed as the true value of the secondary output of a DC measuring device, Expressed as the transformation ratio of the DC measuring device, It is expressed as the actual value of the primary side signal amplitude of the DC measuring device. and They are expressed as random error and systematic error respectively; Expressed as time; Among them, the systematic error include: Ratio error : Where, Represents the true value, It is expressed as the difference in the ratio before and after the DC measuring device has a ratio error; Amplitude fixed error : Where, Expressed as a step function, It represents the starting time of fixed error, It represents the end time of fixed error; Drift error : Where, It represents the time when the drift error starts. Expressed as a constant; S30, based on the evaluation standard, extracts feature sample sets of different error forms, including: S31, based on the time series of the estimated standard quantity { Q i }, first specify an embedding dimension s 0, solve for time delay parameters ， Reconstruct phase space using delayed coordinates; S32, based on the reconstructed phase space, calculate the time series { Q i }'s correlation integral C i ( r ); S33, according to the correlation score C i ( r ), get the correlation dimension , for the correlation dimension The logarithmic curve of the least squares method is fitted to obtain the initial correlation dimension estimate D ( s 0); S34, increase the embedding dimension s The value of 0 makes the embedding dimension increased s 1> s 0, when the dimension value increases, the embedding dimension s The initial correlation dimension estimate corresponding to 0 D ( s 0) Then it gradually stabilizes within a certain error range, and the correlation dimension is obtained as the time series { Q i }'s correlation dimension; S35, calculate the time series to obtain the correlation dimension D As the characteristic quantity of the time series of the estimation standard quantity, the correlation dimension under various error degradation forms constitutes the characteristic sample set of different error forms E ; Among them, solving the time delay parameter , by: ， i =0，1，…， n ； Where, Expressed as the autocorrelation function, Represents the first The delay of the sampling points; the autocorrelation function is obtained by calculation R With time delay parameters The change curve of R When it is 0 for the first time or drops to 1-1 / of the initial value e The time delay parameter corresponding to is the optimal time delay parameter; where, e Expressed as a natural constant, Expressed as sampling points, Expressed as Q The mean of The time series { Q i }In the time delay parameter Perform coordinate delay to obtain the reconstructed phase space; S40, establishing and training a DC measurement equipment error form identification model based on the feature sample set; S50, when the evaluation standard detects that the measurement error state of the DC measurement device is abnormal, the measurement error of the DC measurement device is detected online using the established mathematical model of the degradation form of the measurement error of the DC measurement device, and the trained DC measurement device error form identification model is used to perform online identification.

2. The autonomous identification method of the error degradation form of the DC measurement equipment according to claim 1, characterized in that: Correlation Points Obtained by the following formula: )； Where, is expressed as the Heaviside unit function, where , , 、 , Represents the specified distance threshold. When the distance between two vectors is less than When , it can be determined that there is a correlation between the two vectors; Correlation dimension , obtained by the following formula: ； Where, Represents the specified distance threshold.

3. The autonomous identification method of the error degradation form of a DC measurement device according to claim 1, characterized in that: Establish a DC measurement equipment error form identification model, including: S41, the feature sample set E Expressed as E= { a v , b w }, b w express a v Category, v =1,2,…, n , n is the number of samples collected; a v For the v input samples; w =1,2,…, m , m is the number of categories S42, establish mapping function , the feature sample set E The points in are mapped into high-dimensional space: = ;in, K ( x i , x ) is the kernel function, α i is the Lagrange multiplier, Represented as function parameters; S43, according to the mapping function , establish the error form identification model of DC measurement equipment.

4. The autonomous identification method of the error degradation form of a DC measurement device according to claim 3, characterized in that: Error Form Identification Model for DC Measurement Equipment , which is the following formula: ； Where, The bias term expressed as a function.

5. The autonomous identification method of the error degradation form of a DC measurement device according to claim 1, characterized in that: The evaluation standard detects whether the DC measurement error status is abnormal by the following methods: like Q < Q c , then the DC measuring equipment is in normal operation; like Q > Q c , then the DC measuring device is in an abnormal state and abnormal measurement error occurs; in, Q Expressed as the evaluation standard quantity, Q c Expressed as evaluation standard Q limit.

6. An autonomous identification system for the error degradation form of a DC measurement device, characterized in that: The method for autonomously identifying the error degradation form of a DC measurement device according to any one of claims 1 to 5 comprises: An evaluation standard module is used to establish evaluation standard quantities based on historical data measured by DC measurement equipment under normal operating conditions and different error degradation forms after commissioning; An error detection module is used to establish a mathematical model of the degradation form of the DC measurement error of the DC measurement equipment based on the measured historical data; The sample extraction module is used to extract feature sample sets of different error forms based on the evaluation standard quantity; Identification model training module, used to establish and train the error form identification model of DC measurement equipment based on the feature sample set; The online detection module is used to perform online detection of the measurement error of the DC measurement equipment when the evaluation standard detects that the measurement error state of the DC measurement equipment is abnormal, using the established mathematical model of the degradation form of the measurement error of the DC measurement equipment, and applying the trained DC measurement equipment error form identification model to perform online identification.

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