CN110794209A - Method and device for identifying and calibrating winding deformation frequency response data errors and storage medium - Google Patents
Method and device for identifying and calibrating winding deformation frequency response data errors and storage medium Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The application relates to the technical field of power equipment, in particular to a method and a device for identifying and calibrating winding deformation frequency response data errors and a storage medium. The application provides a method for identifying and calibrating a winding deformation frequency response data error, which comprises the following steps: firstly, acquiring an on-site frequency response curve of winding deformation in a certain frequency range; decomposing the field frequency response curve into a first frequency response curve, a second frequency response curve and a third frequency response curve according to a frequency range; processing the first frequency response curve, the second frequency response curve and the third frequency response curve by using an EMD method, and establishing a reconstructed frequency response curve after removing noise, wherein the reconstructed frequency response curve comprises a reconstructed first frequency response curve, a reconstructed second frequency response curve and a reconstructed third frequency response curve; and calculating the correction related coefficients of the corresponding frequency ranges of the reconstructed frequency response curve and the factory frequency response curve, and judging the deformation degree of the transformer winding.
Description
Technical Field
The application relates to the technical field of power equipment, in particular to a method and a device for identifying and calibrating winding deformation frequency response data errors and a storage medium.
Background
The power transformer is one of the very important devices in the power system, and the safe operation of the power transformer affects the whole power system. With the increasing capacity of the power grid, the phenomenon of transformer damage caused by short-circuit faults is increased. Winding deformation in an electric power system refers to the phenomenon that when a power transformer receives the action of electrodynamic force or mechanical force, the winding inside the transformer generates irreversible phenomena, such as distortion, displacement, inclination, turn-to-turn short circuit deformation and other fault characteristics. After the winding of the power transformer is deformed, if the power transformer runs for a long time without maintenance, the transformer can be damaged, so that the deformation of the winding of the power transformer needs to be detected, and the transformer is ensured not to be broken down to the maximum extent.
In the implementation of some frequency response methods for testing the deformation of the transformer winding, a sweep frequency signal is applied to one end of the transformer winding, then a response signal at the other end of the winding is collected, the amplitude and the phase of the response signal are used as a function of frequency to draw a frequency response curve, if the winding deforms, the detected frequency response curve can generate some changes, and the deformation degree of the transformer winding is judged by analyzing the correlation of the frequency response curves obtained before and after the fault.
However, because of the system error and the test noise existing in the winding deformation test method, the correlation is inevitably reduced, so that the frequency response is difficult to accurately judge the winding deformation under the influence of the system noise, the test noise and other factors.
Disclosure of Invention
The method, the device and the storage medium for identifying and calibrating the winding deformation frequency response data errors reduce the influence of system noise and test noise by decomposing a frequency response curve by using an EMD (empirical mode decomposition) method, obtain a reconstructed frequency response curve after the noise is reduced, and improve the accuracy of judging the deformation degree of the transformer winding.
The embodiment of the application is realized as follows:
a first aspect of the embodiments of the present application provides a method for identifying and calibrating a winding deformation frequency response data error, including the following steps:
firstly, acquiring an on-site frequency response curve of winding deformation in a certain frequency range;
decomposing the field frequency response curve into a first frequency response curve, a second frequency response curve and a third frequency response curve according to a frequency range;
processing the first frequency response curve, the second frequency response curve and the third frequency response curve by using an EMD method, and establishing a reconstructed frequency response curve after removing noise, wherein the reconstructed frequency response curve comprises a reconstructed first frequency response curve, a reconstructed second frequency response curve and a reconstructed third frequency response curve;
and calculating the correction related coefficients of the corresponding frequency ranges of the reconstructed frequency response curve and the factory frequency response curve, and judging the deformation degree of the transformer winding.
Optionally, the first acquiring a certain frequency range is set to 1-1000 kHz; the frequency range of the first frequency response curve is set to be 1-100 kHz; the frequency range of the second frequency response curve is set to be 101-600 kHz; the frequency range of the third frequency response curve is set to 601 and 1000 kHz.
Optionally, the step of obtaining the reconstructed frequency response curve includes: processing the field frequency response curve by using a cubic spline function interpolation method to obtain an upper envelope line and a lower envelope line of the field frequency response curve; subtracting the average value of the upper envelope line and the lower envelope line from the field frequency response curve to obtain h (t), and further obtaining a plurality of intrinsic mode functions; converting the field frequency response curve into a plurality of intrinsic mode functions with frequencies from high to low and the composition of allowance; removing eigenmode functions of the plurality of eigenmode functions which are considered to be mainly composed of noise; and establishing a reconstructed frequency response curve by using the reserved intrinsic mode functions and the margin.
Optionally, the removing noise comprises system noise, or test noise.
Optionally, the noise further comprises high frequency eigenmode functions of the signal, which are mainly constituted by sharp components.
Optionally, the retained eigenmode functions are the main low frequency components of the signal.
Optionally, the method further comprises the steps of: and comparing the modified correlation coefficient with the original correlation coefficient, and verifying the effectiveness of the modified correlation coefficient.
Optionally, the original correlation coefficient is a correlation coefficient of a corresponding frequency range of the first frequency response curve, the second frequency response curve, the third frequency response curve, and the factory frequency response curve.
A second aspect of the embodiments of the present application provides an apparatus for error identification and calibration of winding deformation frequency response data, wherein the apparatus includes at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a portion of the computer instructions to implement the operations as set forth in any one of the summary aspects of the embodiments herein.
A third aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when at least part of the computer instructions are executed by a processor, the computer-readable storage medium implements the operations as any one of the contents provided in the first aspect of the embodiments of the present application
The beneficial effects of the embodiment of the application include: the method comprises the steps of dividing a frequency response curve of a field test according to low frequency, medium frequency and high frequency, decomposing the frequency response curves of different frequencies by using an EMD method to obtain intrinsic mode functions and allowance of a plurality of frequencies from high to low, removing the intrinsic mode functions mainly including noise and signals containing sharp components to obtain a reconstructed frequency response curve, and modifying correlation coefficients by using the reconstructed frequency response curve and the outgoing frequency response curve to improve the accuracy of judging the deformation degree of a transformer winding.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 illustrates a factory frequency response curve and an in-situ frequency response curve according to one embodiment of the present application;
FIG. 2 illustrates low frequency partial EMD decomposition results according to one embodiment of the present application;
FIG. 3 illustrates a result of an EMD decomposition of an intermediate frequency portion according to an embodiment of the present application;
FIG. 4 illustrates high frequency partial EMD decomposition results according to an embodiment of the present application;
FIG. 5 illustrates a low band culling section according to an embodiment of the application;
FIG. 6 illustrates a band culling section in accordance with an embodiment of the present application;
FIG. 7 illustrates a high band culling section according to an embodiment of the application;
FIG. 8 illustrates a reconstructed frequency response curve over all frequency bands according to an embodiment of the application;
FIG. 9 illustrates a flow chart of a method of error identification calibration of winding deformation frequency response data according to an embodiment of the present application;
FIG. 10 shows a flow diagram of reconstructed frequency response curve acquisition according to an embodiment of the application.
Detailed Description
Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments are illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the various embodiments of the present application is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present application.
Reference throughout this specification to "embodiments," "some embodiments," "one embodiment," or "an embodiment," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment," or the like, throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the features, structures, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present application.
The transformer winding is a circuit part of the transformer, is formed by winding a copper wire or an aluminum wire with high conductivity, is applied to a power system, and has sufficient insulation strength, mechanical strength and heat resistance.
The frequency response method is to apply a set of sinusoidal voltages with different frequencies to one end of the transformer winding by using a sweep generator, and to directly plot the amplitude or phase signals obtained from the other end of the selected transformer as a function of frequency (frequency response curve). When the structure of the transformer is fixed, the frequency response curve of the transformer is directly drawn. When the structure of the transformer is fixed, the frequency response curve of the transformer is fixed, and when the winding of the transformer is deformed, the frequency response curve of the transformer judges whether the transformer is deformed.
When the winding deforms, the change of the frequency response characteristic curve can be represented by a correlation coefficient. The novel nondestructive transformer oil has a frequency response characteristic, and after a winding is deformed, each point on a frequency response curve can deviate from an original coordinate, so that a novel frequency response curve can appear. And comparing the correlation of the two frequency spectrum curves, and analyzing and evaluating the overall deformation condition of the winding.
Firstly, acquiring a current frequency response curve of winding deformation in a certain frequency range and a factory frequency response curve,
and calculating the correlation of the two curves of each frequency section, and if the correlation does not meet the winding deformation standard requirement (DLT911-2014), improving the correlation after the processing of the method and the device disclosed by the application and meeting the standard requirement, and determining that the transformer winding deformation is still in the usable range.
FIG. 9 is a flow chart illustrating a method for error identification calibration of winding deformation frequency response data according to an embodiment of the present application. In step S1, an in-situ frequency response curve of the transformer winding deformation is first obtained over a frequency range, which in some embodiments is selected to be in the range of 1-1000 kHz.
With continued reference to fig. 9, in step S2, the field frequency response curve is decomposed into a first frequency response curve, a second frequency response curve and a third frequency response curve according to a frequency range.
In some embodiments, the frequency range of the first frequency response is set to 1-100kHz, the low frequency part; the frequency range of the second frequency response curve is set to be 101-600kHz, namely an intermediate frequency part; the frequency range of the third frequency response curve is set to 601 and 1000kHz, namely the high-frequency part.
Continuing to refer to fig. 9, in step S3, processing the first frequency response curve, the second frequency response curve and the third frequency response curve by using an EMD method, and creating a reconstructed frequency response curve after removing noise, where the reconstructed frequency response curve includes reconstructing the first frequency response curve, reconstructing the second frequency response curve and reconstructing the third frequency response curve;
the EMD method is an empirical mode decomposition method, is a novel self-adaptive signal time-frequency processing method, and is particularly suitable for analyzing and processing nonlinear non-stationary signals. Compared with the traditional signal analysis method based on Fourier transform, the EMD not only breaks through the limitation of Fourier transform, but also has no problem of needing preselection of wavelet basis functions like wavelet transform, has good time-frequency resolution and self-adaptability, can perfectly reconstruct the original signal, and has the potential of highlighting the fine geological structure which is possibly ignored in the signal. In the aspect of noise suppression, after the EMD decomposes a noisy signal, the EMD can separate the noise in the signal from an effective signal in different IMFs (intrinsic mode functions), and the aim of removing the noise is achieved by reasonably selecting IMFs to reconstruct the signal.
In this embodiment, the first frequency response curve, the second frequency response curve and the third frequency response curve are decomposed by using an EMD method.
FIG. 10 shows a flow diagram of reconstructed frequency response curve acquisition according to an embodiment of the application.
In step S1, the field frequency response curve is processed by a cubic spline interpolation method to obtain the upper and lower envelope curves of the field frequency response curve.
Listing all local maximum values and local minimum values in the field frequency response curve x (t), and utilizing cubic spline function interpolation to obtain upper and lower envelope lines x of the original datamax(t) and xmin(t)。
With continued reference to fig. 10, in step S2, h (t) is obtained by subtracting the average value of the upper and lower envelope curves from the field frequency response curve x (t), and a plurality of eigenmode functions are further obtained.
For xmax(t) and xmin(t) taking an average value to obtain m (t), wherein the calculation formula is as follows.
Subtracting m (t) from the previous frequency response curve x (t) to obtain new data h (t), wherein the calculation formula is as follows:
h(t)＝x(t)-m(t)
with continued reference to fig. 10, in step S3, the field frequency response curve is converted into a plurality of frequency-from-high-to-low eigenmode functions and a margin composition. Judging h (t) to obtain imf component, repeating the decomposition step until the rest data is monotonic function or constant, and decomposing the field frequency response curve x (t) into multiple orders of eigenmodes imf with frequencies from high to low by EMD method1，imf2，...，imfnAnd finally, selecting a proper component to reconstruct the frequency response curve, wherein the specific steps are as follows.
Judging whether h (t) meets the following two conditions:
① the number of all maxima in the data is equal to or only one different from the number of minima;
② at any point in time, the mean of the upper envelope of the signal, defined by local maxima, and the lower envelope, defined by local minima, must satisfy the following relationship;
wherein x ismax(t) denotes an upper envelope defined by local maxima, xmin(t) represents the lower envelope determined by the local minima.
If the above two conditions are satisfied, h (t) is an intrinsic mode, which is denoted as imf;
otherwise, let x (t) be h (t), repeat the above steps until the condition is satisfied.
Let r be1(t)＝x(t)-imf1Repeating the above steps until rn(t) is a monotonic function or constant stop decomposition, yielding all other eigenmodes imf2，...，imfnAnd a residual component rn(t), then:
wherein r isn(t) represents the average trend or mean of the signal.
It can be seen that x (t) is EMD decomposed to obtain n eigenmode functions imf with frequencies from high to low and at least one margin rn(t)。
With continued reference to fig. 10, in step S4, the eigenmode functions of the plurality of eigenmode functions that are considered to be mainly composed of noise are removed. The high frequency imf, which is generally considered to be a systematic error or sharp component in the signal, is selected from the decomposed eigenmode functions imf.
In some embodiments, the removed noise comprises system noise, or test noise.
The system noise is the difference between the average value of the same measured infinite number of measurement results and the measured true value under the repetitive condition, and is mainly characterized by having regularity.
The test noise has certain change rule due to the influence of the measuring principle, the composition structure, the storage and use environment factors of the instrument and the direct action of the operation.
Because the winding deformation tester has system errors, the correlation of a tested frequency response curve is reduced, and the judgment of the winding deformation is influenced.
In some embodiments, the noise also includes high frequency eigenmode functions in the signal that are predominantly sharp components. The filtered component is relatively clean noise, leaving a low frequency imf that is generally considered to be a major component of the signal.
In some embodiments, for the low frequency band, since the low frequency band noise is small, only one term with the minimum amplitude and the highest frequency needs to be removed. And for the intermediate frequency band, removing components with the amplitude not exceeding the maximum range 1/10 of the amplitude of the original frequency response curve. For the high frequency band, the high frequency band has large error and high frequency, components with amplitude values not exceeding 1/10 of the original frequency response curve with the maximum amplitude range need to be removed, and if the amplitudes of the decomposed component curves are all larger than 1/10 of the original frequency response curve with the maximum amplitude range, the components with the minimum amplitude values are removed.
With continued reference to fig. 10, in step S5, a reconstructed frequency response curve is created using the retained eigenmode functions and the margins. And reconstructing the signal without the error by using the reserved part and the margin to obtain a reconstructed frequency response curve y (t), wherein the calculation formula is as follows:
in the formula, rn(t) residual component of signal after n imf is decomposed and screened, imfiIs the main component reserved.
With reference to fig. 9, in step S4, a correction correlation coefficient of the reconstructed frequency response curve and the factory frequency response curve in the corresponding frequency range is calculated, and a determination of the degree of deformation of the transformer winding is made.
And calculating the correlation coefficient R of each frequency band of the factory frequency response curve and the current frequency response curve x (t) and the reconstructed frequency response curve y (t).
The calculation formula is as follows.
In the formula, RXYFor the correlation coefficients found, X, Y are two curves for correlation, LRXYIs a normalized covariance coefficient.
In another embodiment, an in-situ frequency response curve and a factory frequency response curve in the frequency range of 1-1000kHz are used as test data, as shown in FIG. 1.
The correlation coefficients of the two curves at low frequency (1-100kHz), medium frequency (101-600kHz) and high frequency (601-1000kHz) are calculated and recorded as R1, R2 and R3 respectively, and the calculation results are shown in Table 1.
| R1 | R2 | R3 | |
| On-site frequency response curve and delivery frequency response curve | 2.386267 | 1.113308 | 0.722304 |
TABLE 1
Table 1 shows the correlation coefficients of the on-site frequency response curve and the factory frequency response curve.
The low frequency, the intermediate frequency and the high frequency of the field frequency response curve are decomposed by an EMD method respectively to obtain n eigenmode functions from high frequency to low frequency and a margin, as shown in FIGS. 2-4.
High frequencies imf, which are generally considered to be systematic errors or sharp components of the signal, are removed from the decomposed eigenmode functions imf, respectively, the removed portions being shown in fig. 5-7.
The low frequencies imf generally regarded as the main components of the signal are retained, and the retained part and the margin are used to reconstruct the frequency response curve after the error is removed to obtain the reconstructed frequency response curve.
According to the EMD decomposition result, imf is removed from the low-frequency part1Intermediate frequency partial rejection imf1To imf4High frequency partial rejection imf1And imf2Fig. 8 shows the final synthesis result of the reconstructed frequency response curve.
In some embodiments, the method further comprises the step of comparing the modified correlation coefficient with the original correlation coefficient and verifying the effectiveness of the modified correlation coefficient. And the original correlation coefficients are correlation coefficients of corresponding frequency ranges of the first frequency response curve, the second frequency response curve, the third frequency response curve and the factory frequency response curve.
Before and after the elimination, the correlation coefficients of the factory frequency response curve at low frequency, medium frequency and high frequency are respectively calculated, and the calculation results are shown in table 2.
| R1 | R2 | R3 | |
| Before removing | 2.386267 | 1.113308 | 0.722304 |
| After being removed | 2.399358 | 1.206752 | 0.750126 |
TABLE 2
As can be seen from Table 2, the correlation coefficients after the systematic errors and the test noises are removed are increased to different degrees, which indicates the effectiveness of the method.
The beneficial effects of the embodiment of the application include: the method comprises the steps of dividing a frequency response curve of a field test according to low frequency, medium frequency and high frequency, decomposing the frequency response curves of different frequencies by using an EMD method to obtain intrinsic mode functions and allowance of a plurality of frequencies from high to low, removing the intrinsic mode functions mainly including noise and signals containing sharp components to obtain a reconstructed frequency response curve, and modifying correlation coefficients by using the reconstructed frequency response curve and the outgoing frequency response curve to improve the accuracy of judging the deformation degree of a transformer winding.
It should be appreciated that the present application provides an apparatus for error identification and calibration of winding deformation frequency response data, the apparatus comprising at least one processor and at least one memory. In some embodiments, the apparatus may be implemented by hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The electronic device of the present application may be implemented not only by a hardware circuit such as a very large scale integrated circuit or a gate array, a semiconductor such as a logic chip, a transistor, or the like, or a programmable hardware device such as a field programmable gate array, a programmable logic device, or the like, but also by software executed by various types of processors, for example, and by a combination of the above hardware circuit and software (for example, firmware).
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (10)
1. A method for identifying and calibrating winding deformation frequency response data errors is characterized by comprising the following steps:
firstly, acquiring an on-site frequency response curve of winding deformation in a certain frequency range;
decomposing the field frequency response curve into a first frequency response curve, a second frequency response curve and a third frequency response curve according to a frequency range;
processing the first frequency response curve, the second frequency response curve and the third frequency response curve by using an EMD method, and establishing a reconstructed frequency response curve after removing noise, wherein the reconstructed frequency response curve comprises a reconstructed first frequency response curve, a reconstructed second frequency response curve and a reconstructed third frequency response curve;
and calculating the correction related coefficients of the corresponding frequency ranges of the reconstructed frequency response curve and the factory frequency response curve, and judging the deformation degree of the transformer winding.
2. The method of claim 1, wherein the method comprises the steps of,
firstly, acquiring a certain frequency range and setting the frequency range to be 1-1000 kHz;
the frequency range of the first frequency response curve is set to be 1-100 kHz;
the frequency range of the second frequency response curve is set to be 101-600 kHz;
the frequency range of the third frequency response curve is set to 601 and 1000 kHz.
3. The method for error identification and calibration of winding deformation frequency response data according to claim 2, wherein the step of obtaining the reconstructed frequency response curve comprises:
processing the field frequency response curve by using a cubic spline function interpolation method to obtain an upper envelope line and a lower envelope line of the field frequency response curve;
subtracting the average value of the upper envelope line and the lower envelope line from the field frequency response curve to obtain h (t), and further obtaining a plurality of intrinsic mode functions;
converting the field frequency response curve into a plurality of intrinsic mode functions with frequencies from high to low and the composition of allowance;
removing eigenmode functions of the plurality of eigenmode functions which are considered to be mainly composed of noise;
and establishing a reconstructed frequency response curve by using the reserved intrinsic mode functions and the margin.
4. The method of claim 3, wherein the removing noise comprises systematic noise or test noise.
5. The method of claim 4, wherein the noise further comprises high frequency eigenmode functions of the signal, the high frequency eigenmode functions being mainly sharp components.
6. The method of claim 5, wherein the retained eigenmode functions are the main low frequency components of the signal.
7. The method for error identification and calibration of winding deformation frequency response data according to claim 1, further comprising the steps of:
and comparing the modified correlation coefficient with the original correlation coefficient, and verifying the effectiveness of the modified correlation coefficient.
8. The method according to claim 7, wherein the original correlation coefficients are correlation coefficients of corresponding frequency ranges of the first frequency response curve, the second frequency response curve, the third frequency response curve and the factory frequency response curve.
9. An apparatus for error identification and calibration of winding deformation frequency response data, the apparatus comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least some of the computer instructions to implement the operations of any of claims 1-8.
10. A computer-readable storage medium having stored thereon computer instructions, at least some of which, when executed by a processor, perform operations according to any one of claims 1 to 8.
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| CN114675118A (en) * | 2022-05-30 | 2022-06-28 | 广东电网有限责任公司佛山供电局 | Transformer winding abnormality detection method, device, equipment and storage medium |
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