CN114236331A - Transformer insulation state identification method and system based on neural network and fingerprint database - Google Patents
Transformer insulation state identification method and system based on neural network and fingerprint database Download PDFInfo
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Abstract
The invention belongs to the technical field of high voltage and insulation, and particularly relates to a transformer insulation state identification method and system based on a neural network and a fingerprint database. The invention adopts a dielectric response tester to carry out frequency domain dielectric response test on transformer oilpaper so as to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper; calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve; establishing a dielectric response characteristic fingerprint in advance according to the frequency domain dielectric fingerprint parameters, and dividing various different standard insulation states of the transformer oilpaper according to the dielectric response characteristic fingerprint; and identifying the insulation state corresponding to the frequency domain dielectric fingerprint parameter obtained by calculation by adopting a neural network model. According to the method and the device, the neural network is adopted for recognizing the oil paper insulation state, the neural network has the self-learning advantage, and samples in a training sample set are continuously updated, so that the weight value of the established neural network model is continuously optimized, and the recognition precision is improved.
Description
Technical Field
The invention belongs to the technical field of high voltage and insulation, and particularly relates to a transformer insulation state identification method and system based on a neural network and a fingerprint database.
Background
The safe and reliable operation of the electrical equipment is the first line of defense for avoiding major accidents of the power grid, and the oil paper insulation equipment is an important component in the power grid. Oil paper insulation is an important insulation mode and is always applied to power equipment such as large transformers. In the process of oil-paper insulation operation of the transformer, the oil-paper insulation of the transformer is subjected to various external stress effects such as heat, electricity, machinery, chemistry and the like for a long time, so that faults occur and the reliable operation of a power system is influenced. Therefore, the aging degree of the oil paper insulating material is accurately diagnosed, the aging state of the oil paper insulating equipment is mastered in time, a basis can be provided for the insulation state and the whole life cycle management of the oil paper insulating equipment, and the safe operation of a power grid is ensured. At present, the direct and effective method for determining the aging state of the oil paper insulation equipment is to measure the average polymerization degree of the oil paper insulation, but in practical situations, the aging of the oil paper insulation is influenced by other factors, and a pattern recognition algorithm is adopted to determine the insulation state of the oil paper at present, although the method is simple, the error is large, so that the problem of how to improve the recognition accuracy of the insulation state of the oil paper is to be solved urgently.
Disclosure of Invention
In order to solve the problems, the invention provides a transformer insulation state identification method and a system based on a neural network and a fingerprint database, and the specific technical scheme is as follows:
the transformer insulation state identification method based on the neural network and the fingerprint database comprises the following steps:
s1: carrying out frequency domain dielectric response test on the transformer oilpaper by adopting a dielectric response tester to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s2: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve;
s3: establishing a dielectric response characteristic fingerprint in advance according to the frequency domain dielectric fingerprint parameters, and dividing various different standard insulation states of the transformer oilpaper according to the dielectric response characteristic fingerprint;
s4: and identifying the insulation state corresponding to the frequency domain dielectric fingerprint parameter obtained by calculation by adopting a neural network model.
Preferably, the step S2 specifically includes the following steps:
s21: carrying out spectrum analysis on the FDS curve of the frequency domain dielectric spectrum, and respectively extracting frequency bands with frequency domain dielectric response characteristics sensitive to transformer oilpaper aging and moisture states;
s22: and respectively carrying out integral operation on the extracted frequency bands to obtain integral values of FDS curves in different frequency band ranges and obtain two corresponding frequency domain dielectric fingerprint parameters, wherein the two frequency domain dielectric fingerprint parameters respectively represent the aging degree and the moisture degree of the transformer oilpaper.
Preferably, the frequency band 10 ≦ f ≦ 5 × 1 in the FDS curve of the frequency domain dielectric spectrum selected in the step S213Evaluating the aging degree of the transformer oilpaper at 0 Hz; selecting a frequency band 2 x 10 in an FDS curve of a frequency domain dielectric spectrum-4F is less than or equal to 10Hz, and the moisture degree of the transformer oilpaper is evaluated.
Preferably, the dielectric loss integral representing the aging degree of the transformer paper is calculated in the following way:
the moisture degree of the transformer oilpaper is calculated in the following way:
preferably, the training of the neural network model in step S4 specifically includes the following steps:
s41: carrying out frequency domain dielectric response test on the transformer oilpaper with different aging degrees and different moisture degrees to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s42: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve;
s43: establishing frequency domain dielectric fingerprint parameters, corresponding aging degree and moisture degree as mapping, and establishing a test sample set and a training sample set;
s44: establishing a neural network model, and training the neural network model by adopting a training sample set to obtain a trained neural network model;
s45: and testing the trained neural network model by adopting the test sample set, wherein the neural network model meets the requirements when the test precision is greater than or equal to a preset value, and when the test precision is smaller than the preset value, returning to the step S34 to adjust the structure of the neural network model and retraining the established neural network model.
Preferably, the test accuracy is calculated as follows:
and (4) identifying the correct number of samples/number of samples in the test sample set by the trained neural network model.
Preferably, the method further comprises normalizing the domain dielectric fingerprint parameters in the test sample set and the training sample set, specifically as follows:
in the formula, Tij' is the transformed fingerprint characteristic parameter; t isijThe fingerprint characteristic parameters before change; max { T }kjIs the maximum characteristic parameter of the whole set, min { T }kjAnd is the minimum feature parameter of the complete set.
Preferably, a genetic algorithm is used to optimize the initial weight values of the neural network model.
The transformer insulation state identification system based on the neural network and the fingerprint database comprises a data acquisition module, a data calculation module, a data processing module, a neural network training module, a standard insulation state database, an identification module and an output display module; the data acquisition module, the data calculation module and the data processing module are sequentially connected; the data processing module is respectively connected with the neural network training module and the recognition module; the identification module is connected with the output display module;
the data acquisition module is used for acquiring an FDS curve of a frequency domain dielectric spectrum obtained by a frequency domain dielectric response test of the dielectric response tester on the transformer oilpaper;
the data calculation module is used for calculating frequency domain dielectric fingerprint parameters of the dielectric loss integral factor according to the FDS curve obtained by the data acquisition module;
the data processing module is used for normalizing the frequency domain dielectric fingerprint parameters of the dielectric loss integral factors obtained through calculation;
the neural network training module is used for training the neural network model to obtain a trained neural network model;
the standard insulation state database is used for storing the standard insulation state of the transformer oil paper;
the identification module is used for identifying the insulation state of the transformer oilpaper corresponding to the acquired FDS curve by adopting a trained neural network model;
and the output display module is used for outputting the identified insulation state of the transformer oilpaper and displaying the corresponding state.
The invention has the beneficial effects that: the invention adopts a dielectric response tester to carry out frequency domain dielectric response test on transformer oilpaper so as to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper; calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve; establishing a dielectric response characteristic fingerprint in advance according to the frequency domain dielectric fingerprint parameters, and dividing various different standard insulation states of the transformer oilpaper according to the dielectric response characteristic fingerprint; and identifying the insulation state corresponding to the frequency domain dielectric fingerprint parameter obtained by calculation by adopting a neural network model. Compared with the mode recognition algorithm adopted in the prior art for judging the insulation degree of the oil paper, the neural network is adopted for recognition, so that the recognition accuracy is improved. Although the mode recognition algorithm is simple in judging the insulation degree of the oil paper, a large measurement error exists, the neural network is adopted for recognizing the insulation state of the oil paper, the neural network has the advantage of self-learning, samples in a training sample set are continuously updated, so that the weight value of the built neural network model is continuously optimized, the initial weight value of the neural network model is optimized by adopting the genetic algorithm, the neural network is prevented from falling into a local minimum value during training, and the convergence speed is accelerated.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow diagram of the process of the present invention;
fig. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the embodiment of the present invention provides a transformer insulation state identification method based on a neural network and a fingerprint database, including the following steps:
s1: carrying out frequency domain dielectric response test on the transformer oilpaper by adopting a dielectric response tester to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s2: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve; the method specifically comprises the following steps:
s21: carrying out spectrum analysis on the FDS curve of the frequency domain dielectric spectrum, and respectively extracting frequency bands with frequency domain dielectric response characteristics sensitive to transformer oilpaper aging and moisture states; selecting a frequency band f of not less than 10 and not more than 5 multiplied by 10 in an FDS curve of the frequency domain dielectric spectrum3Hz evaluating the aging degree of the transformer oilpaper; selecting a frequency band 2 x 10 in an FDS curve of a frequency domain dielectric spectrum-4F is less than or equal to 10Hz, and the moisture degree of the transformer oilpaper is evaluated.
S22: and respectively carrying out integral operation on the extracted frequency bands to obtain integral values of FDS curves in different frequency band ranges and obtain two corresponding frequency domain dielectric fingerprint parameters, wherein the two frequency domain dielectric fingerprint parameters respectively represent the aging degree and the moisture degree of the transformer oilpaper. The dielectric loss integral representing the aging degree of the transformer oilpaper is calculated in the following way:
the moisture degree of the transformer oilpaper is calculated in the following way:
for a particular aged, moisture content oiled paper insulation sample, it corresponds to its fixed Stanδ(m·c)(f) And Stanδ(DP)(f) In that respect If the influence of aging or water content on dielectric loss integral factors is singly considered, the insulating sample paperboard is kept at the same water content, the dielectric loss integral factors are different under different aging degrees, and each different dielectric loss integral factor can be used as a dielectric fingerprint parameter for evaluating the insulating state of the transformer oil paper insulating sample.
S3: and establishing a dielectric response characteristic fingerprint in advance according to the frequency domain dielectric fingerprint parameters, and dividing various different standard insulation states of the transformer oilpaper according to the dielectric response characteristic fingerprint.
The dielectric loss curve integrals in three different frequency bands are selected as dielectric characteristic fingerprint information for representing paperboards in different insulation states. The integral extraction formula is shown below.
In summary, the phenomenon of slight deviation between the actual insulation state and the preset state of the sample caused by experiment, test and external interference is solved. The integral of the tan delta curve for the samples under different insulation conditions was extracted at 45 ℃ using equation (3). To make S1＇-S3' remaining in the same dimension, requiring appropriate data processing, i.e. S1＝S1＇×103，S2＝S2' X102 and S3＝S3X 101. In addition, the invention also selects Si(i-1-3) as a dielectric response characteristic parameter. After the correction is performed by the steps, the corresponding relations between the oil-impregnated insulating paper board samples with different insulating states, polymerization degrees, water contents and standard insulating states of the oil-impregnated insulating paper board samples prepared in a laboratory are described in table 1.
As shown in the following Table 1, the dielectric fingerprint database method for evaluating the insulation aging and moisture state, which is established based on the dielectric loss integral factor, has higher accuracy.
TABLE 1 Standard insulation State diversity Table
S4: and identifying the insulation state corresponding to the frequency domain dielectric fingerprint parameter obtained by calculation by adopting a neural network model.
The training of the neural network model in step S4 specifically includes the following steps:
s41: carrying out frequency domain dielectric response test on the transformer oilpaper with different aging degrees and different moisture degrees to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s42: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve;
s43: establishing frequency domain dielectric fingerprint parameters, corresponding aging degree and moisture degree as mapping, and establishing a test sample set and a training sample set; normalizing the domain dielectric fingerprint parameters in the test sample set and the training sample set, which is specifically as follows:
in the formula, Tij' is the transformed fingerprint characteristic parameter; t isijThe fingerprint characteristic parameters before change; max { T }kjIs the maximum characteristic parameter of the whole set, min { T }kjAnd is the minimum feature parameter of the complete set.
S44: establishing a neural network model, training the neural network model by adopting a training sample set, and optimizing an initial weight value of the neural network model by adopting a genetic algorithm to obtain the trained neural network model;
s45: and testing the trained neural network model by adopting the test sample set, wherein the neural network model meets the requirements when the test precision is greater than or equal to a preset value, and when the test precision is smaller than the preset value, returning to the step S34 to adjust the structure of the neural network model and retraining the established neural network model. The test accuracy is calculated as follows:
and (4) identifying the correct number of samples/number of samples in the test sample set by the trained neural network model.
And the initial weight value of the neural network model is optimized by adopting a genetic algorithm, so that the neural network training is prevented from falling into a local minimum value, and the convergence speed is accelerated.
As shown in fig. 2, the present embodiment provides a transformer insulation state identification system based on a neural network and a fingerprint database, which includes a data acquisition module, a data calculation module, a data processing module, a neural network training module, a standard insulation state database, an identification module, and an output display module; the data acquisition module, the data calculation module and the data processing module are sequentially connected; the data processing module is respectively connected with the neural network training module and the recognition module; the identification module is connected with the output display module;
the data acquisition module is used for acquiring an FDS curve of a frequency domain dielectric spectrum obtained by a frequency domain dielectric response test of the dielectric response tester on the transformer oilpaper;
the data calculation module is used for calculating frequency domain dielectric fingerprint parameters of the dielectric loss integral factor according to the FDS curve obtained by the data acquisition module;
the data processing module is used for normalizing the frequency domain dielectric fingerprint parameters of the dielectric loss integral factors obtained through calculation;
the neural network training module is used for training the neural network model to obtain a trained neural network model;
the standard insulation state database is used for storing the standard insulation state of the transformer oil paper;
the identification module is used for identifying the insulation state of the transformer oilpaper corresponding to the acquired FDS curve by adopting a trained neural network model;
and the output display module is used for outputting the identified insulation state of the transformer oilpaper and displaying the corresponding state.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. The transformer insulation state identification method based on the neural network and the fingerprint database is characterized by comprising the following steps of: the method comprises the following steps:
s1: carrying out frequency domain dielectric response test on the transformer oilpaper by adopting a dielectric response tester to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s2: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve;
s3: establishing a dielectric response characteristic fingerprint in advance according to the frequency domain dielectric fingerprint parameters, and dividing various different standard insulation states of the transformer oilpaper according to the dielectric response characteristic fingerprint;
s4: and identifying the insulation state corresponding to the frequency domain dielectric fingerprint parameter obtained by calculation by adopting a neural network model.
2. The method for identifying the insulation state of the transformer based on the neural network and the fingerprint database according to claim 1, wherein: the step S2 specifically includes the following steps:
s21: carrying out spectrum analysis on the FDS curve of the frequency domain dielectric spectrum, and respectively extracting frequency bands with frequency domain dielectric response characteristics sensitive to transformer oilpaper aging and moisture states;
s22: and respectively carrying out integral operation on the extracted frequency bands to obtain integral values of FDS curves in different frequency band ranges and obtain two corresponding frequency domain dielectric fingerprint parameters, wherein the two frequency domain dielectric fingerprint parameters respectively represent the aging degree and the moisture degree of the transformer oilpaper.
3. The method for identifying the insulation state of the transformer based on the neural network and the fingerprint database according to claim 2, wherein: in step S21, f in the FDS curve of the frequency domain dielectric spectrum is selected to be not less than 10 and not more than 5 × 103Hz evaluating the aging degree of the transformer oilpaper; selecting a frequency band 2 x 10 in an FDS curve of a frequency domain dielectric spectrum-4F is less than or equal to 10Hz, and the moisture degree of the transformer oilpaper is evaluated.
4. The method for identifying the insulation state of the transformer based on the neural network and the fingerprint database according to claim 3, wherein: the dielectric loss integral calculation mode for representing the aging degree of the transformer oilpaper is as follows:
the moisture degree of the transformer oilpaper is calculated in the following way:
5. the transformer insulation state recognition method based on the neural network and the fingerprint database according to claim 4, wherein: the training of the neural network model in step S4 specifically includes the following steps:
s41: carrying out frequency domain dielectric response test on the transformer oilpaper with different aging degrees and different moisture degrees to obtain an FDS curve of a frequency domain dielectric spectrum of the transformer oilpaper;
s42: calculating a frequency domain dielectric fingerprint parameter of the dielectric loss integral factor according to the FDS curve;
s43: establishing frequency domain dielectric fingerprint parameters, corresponding aging degree and moisture degree as mapping, and establishing a test sample set and a training sample set;
s44: establishing a neural network model, and training the neural network model by adopting a training sample set to obtain a trained neural network model;
s45: and testing the trained neural network model by adopting the test sample set, wherein the neural network model meets the requirements when the test precision is greater than or equal to a preset value, and when the test precision is smaller than the preset value, returning to the step S34 to adjust the structure of the neural network model and retraining the established neural network model.
6. The transformer insulation state recognition method based on the neural network and the fingerprint database according to claim 5, wherein: the test accuracy is calculated as follows:
and (4) identifying the correct number of samples/number of samples in the test sample set by the trained neural network model.
7. The transformer insulation state recognition method based on the neural network and the fingerprint database according to claim 5, wherein: the method further comprises the following step of normalizing the domain dielectric fingerprint parameters in the test sample set and the training sample set, wherein the normalization step comprises the following specific steps:
in the formula, Tij' is the transformed fingerprint characteristic parameter; t isijThe fingerprint characteristic parameters before change; max { T }kjIs the maximum characteristic parameter of the whole set, min { T }kjAnd is the minimum feature parameter of the complete set.
8. The transformer insulation state recognition method based on the neural network and the fingerprint database according to claim 5, wherein: and optimizing the initial weight value of the neural network model by adopting a genetic algorithm.
9. Transformer insulation state identification system based on neural network and fingerprint storehouse, its characterized in that: the device comprises a data acquisition module, a data calculation module, a data processing module, a neural network training module, a standard insulation state database, an identification module and an output display module; the data acquisition module, the data calculation module and the data processing module are sequentially connected; the data processing module is respectively connected with the neural network training module and the recognition module; the identification module is connected with the output display module;
the data acquisition module is used for acquiring an FDS curve of a frequency domain dielectric spectrum obtained by a frequency domain dielectric response test of the dielectric response tester on the transformer oilpaper;
the data calculation module is used for calculating frequency domain dielectric fingerprint parameters of the dielectric loss integral factor according to the FDS curve obtained by the data acquisition module;
the data processing module is used for normalizing the frequency domain dielectric fingerprint parameters of the dielectric loss integral factors obtained through calculation;
the neural network training module is used for training the neural network model to obtain a trained neural network model;
the standard insulation state database is used for storing the standard insulation state of the transformer oil paper;
the identification module is used for identifying the insulation state of the transformer oilpaper corresponding to the acquired FDS curve by adopting a trained neural network model;
and the output display module is used for outputting the identified insulation state of the transformer oilpaper and displaying the corresponding state.
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