A method for calculating mutual inductor error based on singular value decomposition and outlier identification

Abstract

The present invention discloses a method for calculating the error of a mutual inductor based on singular value decomposition and outlier identification, comprising the following steps: collecting the secondary side operating data of the mutual inductor, and preprocessing the secondary side operating data of the mutual inductor to determine the principal component space and error space of the secondary side operating data of the mutual inductor; performing error analysis on the mutual inductor according to the error space of the secondary side operating data of the mutual inductor, respectively calculating the error information corresponding to the operating data of each channel mutual inductor, determining the mutual inductor in an out-of-tolerance state, and calculating the virtual standard value of the mutual inductor according to the mutual inductor in a non-out-of-tolerance state; calculating the error of each channel mutual inductor based on the virtual standard value of the mutual inductor. The present invention solves the technical problem of how to reflect the dynamic change of the error of the mutual inductor under actual working conditions in real time and realize the quantitative analysis of the operating error of the mutual inductor.

Description

Technical Field

[0001] The present invention relates to the technical field of mutual inductors, and in particular to a mutual inductor error calculation method based on singular value decomposition and outlier recognition. Background Art

[0002] Capacitor Voltage Transformer (CVT) is widely used in high voltage application scenarios due to its good insulation properties. Compared with traditional electromagnetic voltage transformers, capacitor voltage transformers have a more complex structure and are more prone to out-of-tolerance phenomena during operation, thus affecting the fairness of electricity settlement trade. According to the metrological verification regulations, the existing verification method is power-off offline verification. The same voltage signal is applied to the transformer to be verified and the high-precision standard at the same time. The difference between the output value of the transformer to be verified and the output value of the standard is the static error of the transformer. However, this method cannot reflect the dynamic change process of the error of the transformer under actual working conditions in real time, and it is difficult to shut down and overhaul high-voltage transmission and transformation lines. In addition, the maintenance time for standard verification is 4 years. This one-size-fits-all maintenance method does not fully consider the health status of each transformer, and the maintenance efficiency is low. The patent document with application number CN202211371496.4 discloses a voltage transformer error evaluation method and device, which obtains the first electrical property characteristic data in the offline detection process to construct an offline training set; trains the MLP model based on the offline training set to obtain the same model CVT error state model; constructs a first working condition-based same model CVT error state prediction model based on the same model CVT error state model; performs working condition adaptive training based on the first working condition-based same model CVT error state prediction model, and outputs a second working condition-based same model CVT error prediction model; inputs the online monitoring data of the voltage transformer to be tested into the second working condition-based same model CVT error state prediction model, and outputs the error state of the voltage transformer to be tested. Therefore, it is urgent to propose a transformer error calculation method based on singular value decomposition and outlier recognition to solve the technical problem of how to reflect the dynamic change of the transformer error under actual working conditions in real time and realize quantitative analysis of the transformer operation error. Summary of the invention

[0003] The main purpose of the present invention is to propose a transformer error calculation method based on singular value decomposition and outlier identification, aiming to solve the technical problem of how to reflect the dynamic change of transformer error under actual working conditions in real time and realize quantitative analysis of transformer operation error.

[0004] To achieve the above object, the present invention provides a method for calculating a mutual inductor error based on singular value decomposition and outlier identification, wherein the method for calculating a mutual inductor error based on singular value decomposition and outlier identification comprises the following steps:

[0005] S1. Collecting the secondary side operating data of the transformer, and preprocessing the secondary side operating data of the transformer to determine the principal component space and error space of the secondary side operating data of the transformer;

[0006] S2. Perform error analysis on the mutual inductor according to the error space of the secondary side operating data of the mutual inductor, calculate the error information corresponding to the operating data of each channel mutual inductor respectively, determine the mutual inductor in an out-of-tolerance state, and calculate the virtual standard value of the mutual inductor according to the mutual inductor in a non-out-of-tolerance state;

[0007] S3. Calculate the error of the mutual inductor of each channel based on the virtual standard value of the mutual inductor.

[0008] In one of the preferred solutions, the operating data of the secondary side of the transformer is:

[0009]

[0010] in, It is a two-dimensional array of the secondary side operating data of the transformer with M rows and N columns, where M is the number of sampling points and N is the number of channels of the transformer.

[0011] One of the preferred solutions is that in step S1, the secondary side operation data of the transformer is preprocessed, specifically:

[0012] Normalizing the secondary side operating data of the transformer to obtain a first data matrix;

[0013] The first data matrix is:

[0014]

[0015] in, is the first data matrix, is a two-dimensional array of transformer secondary side operating data with M rows and N columns, For running data The mean of For running data The standard deviation of

[0016] Perform singular value decomposition on the first data matrix; specifically:

[0017]

[0018] in, is a unitary matrix, a unitary matrix for The corresponding eigenvector, unitary matrix for The corresponding matrix of eigenvectors, is a diagonal matrix, a diagonal matrix All elements except the main diagonal are zero, and the diagonal matrix Each element on the main diagonal of is a singular value, which is , and satisfies .

[0019] One of the preferred solutions is that the principal component space and error space of the transformer secondary side operation data are determined in step S1, specifically:

[0020] Set the contribution threshold, determine the principal component space and error space according to the contribution threshold, and calculate the contribution of each principal component in the principal component space in turn. ; Determine the number of eigenvectors in the principal component space by the contribution threshold ;

[0021] Take the unitary matrix Before eigenvectors, i.e., principal component space , the remaining eigenvectors are the error space .

[0022] In one of the preferred solutions, the contribution degree is:

[0023]

[0024] in, is the contribution of each principal component in the principal component space, is a diagonal matrix The elements on the main diagonal of .

[0025] In one preferred solution, the error information corresponding to the operating data of each channel mutual inductor in step S2 is:

[0026]

[0027] in, is the error information corresponding to the operating data of each channel transformer, is the error space.

[0028] One of the preferred solutions, the step S2 determines the transformer in the out-of-tolerance state, and calculates the virtual standard value of the transformer according to the transformer in the non-out-of-tolerance state, specifically:

[0029] According to the error information of each channel, calculate the mean of the error information of each channel and take the largest one. The channels are marked as the channels of the transformers in the out-of-tolerance state;

[0030] The mean value of the transformer operating data in a non-out-of-tolerance state is calculated to obtain the virtual standard value of the transformer.

[0031] In one preferred solution, the virtual standard value is:

[0032]

[0033] in, is the virtual standard value, N is the number of channels of the transformer, is the number of channels of the transformer in the out-of-tolerance state, It is the operating data of the transformer of the Nmth channel in the non-out-of-tolerance state.

[0034] In one of the preferred solutions, step S3 is specifically:

[0035] Based on the transformer secondary side operating data and the virtual standard value, the mean value of the transformer secondary side operating data and the virtual standard value are calculated in sequence by a sliding window method; the mean value of the transformer secondary side operating data is: ;

[0036] The error of each channel mutual inductor is calculated based on the mean value and virtual standard value of the secondary side operating data of the mutual inductor.

[0037] In one of the preferred solutions, the error of each channel mutual inductor is:

[0038]

[0039] in, For Channel The error of the transformer, For Channel The mean value of the operating data of the secondary side of the transformer, is a virtual standard value, , is the channel number of the transformer.

[0040] In the above technical scheme of the present invention, the transformer error calculation method based on singular value decomposition and outlier identification includes the following steps: collecting transformer secondary side operation data, and preprocessing the transformer secondary side operation data to determine the principal component space and error space of the transformer secondary side operation data; performing error analysis on the transformer according to the error space of the transformer secondary side operation data, respectively calculating the error information corresponding to the operation data of each channel transformer, determining the transformer in an out-of-tolerance state, and calculating the virtual standard value of the transformer according to the transformer in a non-out-of-tolerance state; calculating the error of each channel transformer based on the virtual standard value of the transformer. The present invention solves the technical problem of how to reflect the dynamic change of the error of the transformer under actual working conditions in real time and realize the quantitative analysis of the transformer operation error.

[0041] In the present invention, a singular value decomposition method is adopted to establish a projection matrix that characterizes the error information of the mutual inductor. Through the method of outlier recognition, the channel with out-of-tolerance error is identified, so as to calculate the virtual standard value of the mutual inductor according to the channel height in the non-out-of-tolerance state, that is, the channel with normal error state, thereby realizing quantitative analysis and calculation of the operating errors of multi-channel mutual inductors, and reflecting the dynamic changes of the errors of the mutual inductors under actual working conditions in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0043] Figure 1 A schematic diagram of a method for calculating a mutual inductor error based on singular value decomposition and outlier identification according to an embodiment of the present invention.

[0044] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In addition, in the present invention, the descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0047] Furthermore, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0048] See also Figure 1According to one aspect of the present invention, the present invention provides a method for calculating a mutual inductor error based on singular value decomposition and outlier identification, wherein the method for calculating a mutual inductor error based on singular value decomposition and outlier identification comprises the following steps:

[0049] S1. Collecting the secondary side operating data of the transformer, and preprocessing the secondary side operating data of the transformer to determine the principal component space and error space of the secondary side operating data of the transformer;

[0050] S2. Perform error analysis on the mutual inductor according to the error space of the secondary side operating data of the mutual inductor, calculate the error information corresponding to the operating data of each channel mutual inductor respectively, determine the mutual inductor in an out-of-tolerance state, and calculate the virtual standard value of the mutual inductor according to the mutual inductor in a non-out-of-tolerance state;

[0051] S3. Calculate the error of the mutual inductor of each channel based on the virtual standard value of the mutual inductor.

[0052] Specifically, in this embodiment, the operating data of the secondary side of the transformer is collected, and the operating data of the secondary side of the transformer is:

[0053]

[0054] in, It is a two-dimensional array of transformer secondary side operating data with M rows and N columns, where M is the number of sampling points and N is the number of channels of the transformer.

[0055] Specifically, in this embodiment, the preprocessing of the transformer secondary side operation data in step S1 is as follows:

[0056] Normalizing the secondary side operating data of the transformer to obtain a first data matrix;

[0057] The first data matrix is:

[0058]

[0059] in, is the first data matrix, is a two-dimensional array of transformer secondary side operating data with M rows and N columns, For running data The mean of For running data The standard deviation is to subtract the mean of each channel from the initial data and then divide it by the standard deviation of the channel. The mean of the data after normalization preprocessing is zero and the variance is 1;

[0060] Perform singular value decomposition on the first data matrix; specifically:

[0061]

[0062] in, is a unitary matrix, a unitary matrix for The corresponding eigenvector, unitary matrix for The corresponding matrix of eigenvectors, is a diagonal matrix, a diagonal matrix All elements except the main diagonal are zero, and the diagonal matrix Each element on the main diagonal of is a singular value, which is , and satisfies .

[0063] Specifically, in this implementation, the principal component space and error space of the transformer secondary side operating data are determined in step S1, specifically:

[0064] Set the contribution threshold, determine the principal component space and error space according to the contribution threshold, and calculate the contribution of each principal component in the principal component space in turn. ; Determine the number of eigenvectors in the principal component space by the contribution threshold , , is a constant set in the present invention. , the present invention does not make any specific limitation, and can be set according to needs;

[0065] Take the unitary matrix Before eigenvectors, i.e., principal component space , the remaining eigenvectors are the error space The projection of the real-time monitoring data of the transformer in the principal component space represents the primary side voltage information of the transformer, and the projection of the real-time monitoring data of the transformer in the error space represents the error information of the transformer.

[0066] Specifically, in this embodiment, the contribution degree is:

[0067]

[0068] in, is the contribution of each principal component in the principal component space, is a diagonal matrix The elements on the main diagonal of .

[0069] Specifically, in this embodiment, the error information corresponding to the operating data of each channel mutual inductor in step S2 is:

[0070]

[0071] in, is the error information corresponding to the operating data of each channel transformer, is the projection matrix of the error space.

[0072] Specifically, in this embodiment, the step S2 determines the transformer in the out-of-tolerance state, and calculates the virtual standard value of the transformer according to the transformer in the non-out-of-tolerance state, specifically:

[0073] According to the error information of each channel , calculate the mean of the error information of each channel, and take the largest one The channels are marked as the channels of the transformers in the out-of-tolerance state; the transformer status monitoring is based on the group idea, and the number of channels with out-of-tolerance errors is small. Based on this, the channels with larger errors are marked as the channels of the transformers in the out-of-tolerance state. A channel is identified as a channel with an error exceeding the tolerance. In the present invention, when hour, Take 4, the present invention is not specifically limited, and can be set according to needs;

[0074] Calculate the mean value of the transformer operating data in the non-out-of-tolerance state to obtain the virtual standard value of the transformer; , Take this as an example to illustrate, if the channel number of the identified transformer out of tolerance is , then the channel number of the transformer in the non-out-of-tolerance state, that is, the transformer in the normal state, is , calculate the mean of the operating data of the transformer in normal state, and then get the virtual standard value of the transformer.

[0075] Specifically, in this embodiment, the virtual standard value is:

[0076]

[0077] in, is the virtual standard value, N is the number of channels of the transformer, is the number of channels of the transformer in the out-of-tolerance state, It is the operating data of the transformer of the Nmth channel in the non-out-of-tolerance state.

[0078] Specifically, in this embodiment, step S3 is as follows:

[0079] Based on the transformer secondary side operating data and the virtual standard value, the transformer secondary side operating data With virtual standard value The data dimensions are consistent, because the operating data of the secondary side of the transformer It fluctuates in real time. When actually calculating the transformer error, a sliding window method is used. The window can be set according to the sampling frequency. The window length is set to L. Within the window length, the mean value and virtual standard value of the transformer secondary side operating data are calculated in turn; the mean value of the transformer secondary side operating data is: ;

[0080] The error of each channel mutual inductor is calculated based on the mean value and virtual standard value of the secondary side operating data of the mutual inductor.

[0081] Specifically, in this embodiment, the error of each channel mutual inductor is:

[0082]

[0083] in, For Channel The error of the transformer, For Channel The mean value of the operating data of the secondary side of the transformer, is a virtual standard value, , is the channel number of the transformer.

[0084] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A method for calculating mutual inductor error based on singular value decomposition and outlier identification, characterized in that: The following steps are involved: S1. Collecting the secondary side operating data of the transformer, and preprocessing the secondary side operating data of the transformer to determine the principal component space and error space of the secondary side operating data of the transformer; The operating data of the secondary side of the transformer is: ； in, is a two-dimensional array of secondary side operating data of the transformer with M rows and N columns, where M is the number of sampling points and N is the number of channels of the transformer; In step S1, the secondary side operation data of the transformer is preprocessed, specifically: Normalizing the secondary side operating data of the transformer to obtain a first data matrix; The first data matrix is: ； in, is the first data matrix, is a two-dimensional array of transformer secondary side operating data with M rows and N columns, For running data The mean of For running data The standard deviation of Perform singular value decomposition on the first data matrix; specifically: ； in, is a unitary matrix, a unitary matrix for The corresponding eigenvector, unitary matrix for The corresponding matrix of eigenvectors, is a diagonal matrix, a diagonal matrix All elements except the main diagonal are zero, and the diagonal matrix Each element on the main diagonal of is a singular value, which is , and satisfies ; S2. Perform error analysis on the mutual inductor according to the error space of the secondary side operating data of the mutual inductor, calculate the error information corresponding to the operating data of each channel mutual inductor respectively, determine the mutual inductor in an out-of-tolerance state, and calculate the virtual standard value of the mutual inductor according to the mutual inductor in a non-out-of-tolerance state; The error information corresponding to the operating data of each channel mutual inductor is: ； in, is the error information corresponding to the operating data of each channel transformer, is the error space; The step S2 determines the transformer in the out-of-tolerance state, and calculates the virtual standard value of the transformer according to the transformer in the non-out-of-tolerance state, specifically: According to the error information of each channel, calculate the mean of the error information of each channel and take the largest one. The channels are marked as the channels of the transformers in the out-of-tolerance state; The mean value of the transformer operation data in a non-out-of-tolerance state is calculated to obtain a virtual standard value of the transformer; the virtual standard value is: ； in, is the virtual standard value, N is the number of channels of the transformer, is the number of channels of the transformer in the out-of-tolerance state, The operating data of the transformer of the Nmth channel in a non-out-of-tolerance state; S3. Calculate the error of each channel mutual inductor based on the virtual standard value of the mutual inductor; specifically: Based on the transformer secondary side operating data and the virtual standard value, the mean value of the transformer secondary side operating data and the virtual standard value are calculated in sequence by a sliding window method; the mean value of the transformer secondary side operating data is: ; The error of each channel mutual inductor is calculated based on the mean value and virtual standard value of the secondary side operating data of the mutual inductor.

2. The method for calculating mutual inductor error based on singular value decomposition and outlier identification according to claim 1, characterized in that: In step S1, the principal component space and error space of the secondary side operating data of the transformer are determined, specifically: Set the contribution threshold, determine the principal component space and error space according to the contribution threshold, and calculate the contribution of each principal component in the principal component space in turn. ; Determine the number of eigenvectors in the principal component space by the contribution threshold ; Take the unitary matrix Before eigenvectors, i.e., principal component space , the remaining eigenvectors are the error space .

3. The method for calculating mutual inductor error based on singular value decomposition and outlier identification according to claim 2, characterized in that: The contribution is: ； in, is the contribution of each principal component in the principal component space, is a diagonal matrix The elements on the main diagonal of .

4. The method for calculating mutual inductor error based on singular value decomposition and outlier identification according to claim 1, characterized in that: The error of each channel mutual inductor is: ； in, For Channel The error of the transformer, For Channel The mean value of the operating data of the secondary side of the transformer, is a virtual standard value, , is the channel number of the transformer.

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