CN112485609B - Raman spectrum diagnosis method for insulation aging of transformer oil paper - Google Patents
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- 230000032683 aging Effects 0.000 title claims abstract description 168
- 238000009413 insulation Methods 0.000 title claims abstract description 104
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- 238000001237 Raman spectrum Methods 0.000 title claims abstract description 75
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- 238000013528 artificial neural network Methods 0.000 description 14
- 239000000126 substance Substances 0.000 description 12
- 238000011160 research Methods 0.000 description 7
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 description 6
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- HYBBIBNJHNGZAN-UHFFFAOYSA-N furfural Chemical compound O=CC1=CC=CO1 HYBBIBNJHNGZAN-UHFFFAOYSA-N 0.000 description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
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- 238000010276 construction Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000003878 thermal aging Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
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Abstract
A Raman spectrum diagnosis method for aging of oil paper insulation of a transformer realizes aging diagnosis of the oil paper insulation based on a Raman spectrum technology and mainly solves the technical problems that the aging state of the oil paper insulation of the transformer lacks a field electrification monitoring means and the detection process is complicated. The invention simplifies the oil paper insulation Raman spectrogram into a characteristic vector, and defines the oil paper insulation aging state by taking the degree of polymerization as an indirect basis; and the Raman spectrum diagnosis of the oil paper insulation aging is realized by establishing a transformer oil paper insulation aging Raman spectrum diagnosis model by taking the spectrum vector as input and the oil paper insulation aging state as output. Therefore, the performance state of the transformer oil paper insulation is effectively monitored, and the operation safety of the power system is guaranteed.
Description
Technical Field
The invention belongs to the field of insulation online monitoring and fault diagnosis of electrical equipment, and particularly relates to a Raman spectrum diagnosis method for insulation aging of transformer oil paper.
Background
The transformer occupies a high proportion in the power equipment, and the aging problem is a key factor related to the safe operation of a power grid. The rapid development of the power grid puts high requirements on the evaluation of the aging state of the transformer. The service life of power transformers is generally associated with the deterioration of the insulation material. After the transformer oil paper insulation system is used for years, the transformer oil paper insulation system is aged under the action of thermal stress and electric stress, and the insulation performance of the transformer is influenced. The insulating oil and the insulating paper are decomposed to generate substances such as carbon monoxide, carbon dioxide, furfural, methanol, acetone and the like which reflect the fault property and the aging degree, and the substances are dissolved in the oil. The insulating oil contains abundant aging information, so that the method has important significance for detecting the insulating oil. In order to evaluate the aging state of oil-immersed power equipment, test results such as the furfural content in oil, dissolved gas in oil, the degree of polymerization of insulating paper, and the like are often used. However, these methods are difficult to sample due to the complex steps, and are often difficult to use for in-situ rapid aging status assessment.
Raman spectroscopy is a scattering spectrum based on the raman scattering effect found in c.v. raman. The scattering spectra of different frequencies of incident light are analyzed to obtain information of molecular vibration and rotation, and the method is applied to the research of molecular structures. Due to the fact that different substances are different in structure, property and content, different Raman signals can be generated under the irradiation of laser, and therefore the analysis of the material characteristics is achieved. The Raman spectrum technology is widely applied to the fields of petrifaction, biological environmental protection, medicine, food safety and the like, and also draws wide attention in the field of oil paper insulation aging diagnosis. Different substances can generate different Raman signals when irradiated by laser due to different structures, properties and contents, so that the analysis of the characteristics of the substances is realized. In the aging process of the oil paper insulation, the substances in the oil are various and continuously generate a series of complex chemical changes, and the Raman spectrum technology can just reflect the complex process.
Disclosure of Invention
In order to solve the problems in the prior art, the invention discloses a Raman spectrum diagnosis method for aging of oil paper insulation of a transformer, which is characterized in that the aging diagnosis of the oil paper insulation is realized based on a Raman spectrum technology, the accuracy is high, and the method is a novel method at present.
In order to achieve the above object, the present invention specifically adopts the following technical solutions.
A Raman spectrum diagnosis method for insulation aging of transformer oil paper is characterized by comprising the following steps: and predicting the aging degree of the oil paper insulation sample by utilizing the improved T-S fuzzy neural network according to the Raman spectrum data of the insulation oil.
The Raman spectrum vector of the insulating oil is connected with the input layer of the neural network, the aging state of the oil paper insulation is used as the output of the neural network, the experimental data is used for training a diagnosis model, membership functions are continuously modified, the internal mathematical relationship between the oil paper insulation Raman spectrum characteristic and the oil paper insulation aging state is mined, and the purpose of predicting the oil paper insulation aging degree by using the insulating oil Raman spectrum data is achieved.
A Raman spectrum diagnosis method for insulation aging of transformer oil paper is characterized by comprising the following steps:
step 1: simulating the aging state of a real transformer to obtain an aging insulating oil sample;
and (3) placing the real transformer in a sealed system, and simulating the aging state of the real transformer at 120 ℃ to obtain an aged insulating oil sample.
Accelerated aging oil samples with aging time of 0, 1,5,10,20,35,50 and 80 days are respectively obtained, and 40 training samples and 15 testing samples are obtained in total. Monitoring the insulation aging of the oil paper, and in order to guarantee the safe and stable operation of equipment, research focuses on more concerned aging degree. The distribution of a total of 55 samples at 0, 1,5,10,20,35,50 and 80 days is therefore: 5, 6, 11, 6. The distribution of training samples at 0, 1,5,10,20,35,50 and 80 days is: 5, 5. Test samples 15, aged 10 days 1, aged 20 days 1, aged 35 days 6, aged 50 days 6, aged 80 days 1.
And (3) for the division of the aging stage of the training sample, taking the average polymerization degree of the insulating paper in each aging time stage as a judgment basis, and calculating the aging degree of the oil paper insulating sample according to the definition so as to initially establish a training sample library.
The aging state of the oiled paper insulation sample is defined as follows:
the polymerization Degree (DP) of the insulating paper is a recognized index of the insulation aging of the oil paper, but the polymerization degree cannot be detected in an actually-operated transformer. Therefore, a plurality of oil paper insulation aging diagnosis researches are focused on finding out a substitute detection method of polymerization degree, and the invention also aims to establish indirect correlation between the polymerization degree and the oil paper insulation aging Raman spectrum data so as to achieve the diagnosis effect. And (3) detecting the polymerization degree of the insulating paper in the oil paper insulating and aging sample according to national standard detection standards by dividing the oil paper insulating sample in the aging stage, and defining the aging state of the oil paper insulating sample by taking the polymerization degree of the insulating paper in each aging time stage as a judgment basis. Typically, the degree of polymerization of the new sample is between 1200 and 1600. Thus, under this definition, the samples in good insulation state have a degradation value between 0 and 1. It should be noted that when the DP value is less than 400, the sample has been severely aged and should be noted. At this time, the aging degree value is between 3 and 4. In summary, the aging degree is defined as a range between 0 and 4, and the larger the value, the deeper the aging degree is.
Step 2: dividing the aged insulating oil sample obtained in the step 1 into a training sample and a test sample;
training a sample: test sample 8: 3, in general case 9: 1,8: 2,7: 3 are useful.
And step 3: constructing an oil paper insulation aging Raman spectrum diagnosis model;
the oil paper insulation aging Raman spectrum diagnosis model is defined by adopting an if-then rule form and is divided into 4 layers, namely an input layer, a fuzzy rule calculation layer and an output layer;
wherein the fuzzy layer adopts a membership function
Fuzzifying the input value to obtain a fuzzy membership value mu; mu is a fuzzy membership value; x is the number ofjIs the jth input variable;
a fuzzy set of a fuzzy system corresponding to the ith input variable;
the center and the width of the membership function of the jth input variable of the ith sample are respectively;
the fuzzy rule calculation layer calculates the fuzzy operator omega by adopting the following fuzzy multiplication formula:
ωiis a fuzzy operator, mu is a fuzzy membership value;
for the ith input variable pairA fuzzy set of a corresponding fuzzy system, wherein k is the number of input parameters; n is the number of fuzzy subsets.
The output layer is calculated using the following formula:
wherein,
to blur the system parameters, yiFor the output obtained according to fuzzy rules, ωiFor the fuzzy operator, n is the fuzzy subset number k is the maximum number of subscript j in fuzzy system parameters, and k is the input parameter number.
And 4, step 4: the training sample obtained in the step 2 is used for testing and training the oil paper insulation aging Raman spectrum diagnosis model constructed in the step 3, and the oil paper insulation aging Raman spectrum diagnosis model is obtained after the test sample is tested and trained;
taking the Raman spectrogram vector of a training sample as input, taking the aging state of the training sample as output, and training the oil paper insulation aging Raman spectrum diagnosis model, wherein the training method specifically comprises the following steps:
the error between the desired output and the actual output is calculated as follows:
in the formula, ydExpected output of the oil paper insulation aging Raman spectrum diagnosis model; y iscThe actual output of the oil paper insulation aging Raman spectrum diagnosis model; e is the error of the desired output and the actual output;
correcting the fuzzy system parameters according to the calculated errors:
in the formula,
is a fuzzy system parameter; alpha is the network learning rate; x is the number ofjIs the jth input variable; omegaiIs the product of the membership degree of the input parameters;
modifying the center and width of the membership function:
in the formula,
respectively the center and width of the membership function.
And 5: collecting transformer insulating oil to be diagnosed in operation, acquiring a Raman spectrogram spectral vector of the insulating oil, inputting the Raman spectrogram spectral vector as input sample data into the oil paper insulation aging Raman spectral diagnosis model obtained in the step 4 after training and testing;
step 6: and 5, judging the aging degree of the transformer insulating oil according to the output result of the oil paper insulating aging Raman spectrum diagnosis model in the step 5.
The aging state of the oiled paper insulation sample is defined as follows:
the polymerization Degree (DP) of the insulating paper is a recognized index of the insulation aging of the oil paper, but the polymerization degree cannot be detected in an actually-operated transformer. Therefore, a plurality of oil paper insulation aging diagnosis researches are focused on finding out a substitute detection method of polymerization degree, and the patent also aims to establish indirect correlation between the polymerization degree and the oil paper insulation aging Raman spectrum data so as to achieve the diagnosis effect. And (3) detecting the polymerization degree of the insulating paper in the oil paper insulating and aging sample according to national standard detection standards by dividing the oil paper insulating sample in the aging stage, and defining the aging state of the oil paper insulating sample by taking the polymerization degree of the insulating paper in each aging time stage as a judgment basis. Typically, the degree of polymerization of the new sample is between 1200 and 1600. Thus, under this definition, the samples in good insulation state have a degradation value between 0 and 1. It should be noted that when the DP value is less than 400, the sample has been severely aged and should be noted. At this time, the aging degree value is between 3 and 4. In summary, the aging degree is defined as a range between 0 and 4, and the larger the value, the deeper the aging degree is.
Compared with the prior art, the invention has the following beneficial technical effects:
the oiled paper insulating material can generate various characteristic quantities reflecting the aging state, such as furfural, methanol, acetone, CO2 and the like, in the electric or thermal aging process, and is dissolved in oil. At present, the aging diagnosis of the running transformer is mainly based on detection and analysis of the aging characteristic quantities, laboratory judgment is carried out on the corresponding guide rule threshold values, and the problems that a single characteristic quantity needs different equipment for analysis, cannot be used for effective field diagnosis and the like exist. The laser raman technology has the advantage of non-contact nondestructive analysis in the fields of material composition analysis and state diagnosis, however, researchers have few researches on raman data analysis of transformer oil at present. The invention relates to a Raman spectrum diagnosis method for aging of oil paper insulation of a transformer based on a T-S fuzzy neural network, which can realize non-contact lossless aging diagnosis of the oil paper insulation based on a Raman spectrum technology and mainly solves the technical problems that the aging state of the oil paper insulation of the transformer lacks a field charged monitoring means and the detection process is complicated. A new idea is opened up for diagnosing the insulation aging state of the transformer oil paper.
Drawings
FIG. 1 is a Raman spectrum of insulating oil;
FIG. 2 is a schematic flow chart of a Raman spectrum diagnosis method for the aging of the oil paper insulation of the transformer according to the present invention;
FIG. 3 is a graph showing the results of aging diagnosis of 10 samples with deeper aging according to the embodiment of the present invention;
FIG. 4 is a graph showing the results of aging diagnosis of 5 samples with different aging degrees according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only some embodiments of the invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without any inventive step based on the spirit of the present invention are within the scope of the present invention.
The dimension of the training sample for explaining the construction of the fuzzy neural network in fig. 2 determines the number of input/output nodes of the fuzzy neural network, and in the patent, 1023-dimensional spectral data is input and 1-dimensional aging state is output. During the training of the fuzzy neural network, the center and the width of the fuzzy membership function are initialized randomly, and a diagnosis model is trained by iterating for 100 times by using error calculation and a coefficient/parameter correction method along with the training. And finally, inputting the test data into a trained diagnostic model to verify the accuracy of the prediction result.
The invention discloses a Raman spectrum diagnosis method for the insulation aging of transformer oil paper, which comprises the following steps as shown in figure 2:
firstly, simulating the aging state of a real transformer to obtain an aging insulating oil sample;
and (3) placing the real transformer in a sealed system, and simulating the aging state of the real transformer at 120 ℃ to obtain an aged insulating oil sample.
The invention defines the insulation aging state of the oiled paper by taking the polymerization degree as an indirect basis.
The aging state of the oiled paper insulation sample is defined as follows:
the polymerization Degree (DP) of the insulating paper is a recognized index of the insulation aging of the oil paper, but the polymerization degree cannot be detected in an actually-operated transformer. Therefore, a plurality of oil paper insulation aging diagnosis researches are focused on finding out a substitute detection method of polymerization degree, and the patent also aims to establish indirect correlation between the polymerization degree and the oil paper insulation aging Raman spectrum data so as to achieve the diagnosis effect. And (3) detecting the polymerization degree of the insulating paper in the oil paper insulating and aging sample according to national standard detection standards by dividing the oil paper insulating sample in the aging stage, and defining the aging state of the oil paper insulating sample by taking the polymerization degree of the insulating paper in each aging time stage as a judgment basis. Typically, the degree of polymerization of the new sample is between 1200 and 1600. Thus, under this definition, the samples in good insulation state have a degradation value between 0 and 1. It should be noted that when the DP value is less than 400, the sample has been severely aged and should be noted. At this time, the aging degree value is between 3 and 4. In summary, the aging degree is defined as a range between 0 and 4, and the larger the value, the deeper the aging degree is.
And carrying out Raman spectrum detection on the aged insulating oil to obtain a Raman spectrogram. Insulating oil is a very complex mixture containing many substances, which also results in a complex raman spectrum. Each substance theoretically has a corresponding Raman signal, and as the oil-paper insulation system ages, the substance composition and content of the insulation oil change and the Raman spectrum changes. Therefore, the aging degree of the oiled paper insulation can be judged through the difference of the Raman spectrums of the insulating oil. The raman spectrum of the insulating oil consists of a series of spectral points, as shown in fig. 1. The Raman spectrum of the insulating oil can be expressed as a two-dimensional vector { (U)1,v1),(U2,v2),…,(Un,vn) In the abscissa of the unit Raman frequency shift (cm)-1) Ordinate is Raman intensity (a.u.) when Raman detection instrument parameters are kept unchanged, the sampling interval of the spectrum points in the Raman spectrum is unchanged, namely U of each samplingiKeeping the same; therefore, the Raman spectrum of the insulating oil can be simplified into a one-dimensional vector v1,v2,…,vnAs input variables for the neural network.
Step 2: dividing the aged insulating oil sample obtained in the step 1 into a training sample and a test sample;
training a sample: test sample 8: 3, in general case 9: 1,8: 2,7: 3 are useful.
And step 3: constructing an oil paper insulation aging Raman spectrum diagnosis model based on a fuzzy neural network;
the oil paper insulation aging Raman spectrum diagnosis model takes the Raman spectrogram vector of the insulation oil as input, and takes the aging stage of the oil paper insulation sample as output to construct the diagnosis model.
The model can not only be automatically updated, but also continuously modify membership functions of fuzzy subsets, which is defined by the following form of 'if-then' rule, where the rule is RiIn the case of (2), fuzzy inference is as follows:
wherein,
a fuzzy set which is a fuzzy system;
is a fuzzy system parameter; y isiFor an output derived from a fuzzy rule, the input part (i.e., if part) is fuzzy and the output part (i.e., then part) is deterministic, the fuzzy inference representing the output as a linear combination of the inputs.
Let x be [ x ] for the input quantity x1,x2,…,xk]First, each input variable x is calculated according to a fuzzy rulejDegree of membership of:
where μ is fuzzy slaveryA genus value; x is the number ofjIs an input variable;
respectively the center and the width of the membership function; k is the number of input parameters; n is the number of fuzzy subsets.
And carrying out fuzzy calculation on each membership degree, and adopting a fuzzy operator as a continuous multiplication operator.
Mu is a fuzzy membership value;
a fuzzy set of a fuzzy system is obtained, and k is the number of input parameters; n is the number of fuzzy subsets.
Calculating the output value y of the fuzzy model according to the fuzzy calculation resulti。
The oil paper insulation aging Raman spectrum diagnosis model is divided into 4 layers of an input layer, a fuzzy layer and a fuzzy rule calculation and output layer. Input layer and input vector xiAnd connecting, wherein the number of nodes is the same as the dimension of the input vector. The fuzzy layer uses membership function
And fuzzifying the input value to obtain a fuzzy membership value mu. The fuzzy rule calculation layer calculates by adopting a fuzzy continuous multiplication formula to obtain omega:
the output layer adopts the following formula to calculate the output of the oil paper insulation aging Raman spectrum diagnosis model:
and 4, step 4: the training sample obtained in the step 2 is used for testing and training the oil paper insulation aging Raman spectrum diagnosis model constructed in the step 3, and the oil paper insulation aging Raman spectrum diagnosis model is obtained after the test sample is tested and trained;
taking the Raman spectrogram vector of a training sample as input, taking the aging state of the training sample as output, and training the oil paper insulation aging Raman spectrum diagnosis model, wherein the training method specifically comprises the following steps:
and (3) error calculation:
in the formula, ydExpected output of the oil paper insulation aging Raman spectrum diagnosis model; y iscThe actual output of the oil paper insulation aging Raman spectrum diagnosis model; e is the error between the desired output and the actual output.
And (3) coefficient correction:
in the formula,
is a fuzzy system parameter; alpha is the network learning rate; x is the number ofjInputting parameters for the network; omegatTo be transportedAnd the membership degree of the input parameter is a product of the continuous degree of membership.
Parameter correction:
in the formula,
respectively the center and width of the membership function.
And 5: collecting transformer insulating oil to be diagnosed in operation, acquiring a Raman spectrogram spectral vector of the insulating oil, inputting the Raman spectrogram spectral vector as input sample data into the oil paper insulation aging Raman spectral diagnosis model obtained in the step 4 after training and testing;
step 6: and 5, judging the aging degree of the transformer insulating oil according to the output result of the oil paper insulating aging Raman spectrum diagnosis model in the step 5.
The aging state of the oiled paper insulation sample is defined as follows:
the polymerization Degree (DP) of the insulating paper is a recognized index of the insulation aging of the oil paper, but the polymerization degree cannot be detected in an actually-operated transformer. Therefore, a plurality of oil paper insulation aging diagnosis researches are focused on finding out a substitute detection method of polymerization degree, and the patent also aims to establish indirect correlation between the polymerization degree and the oil paper insulation aging Raman spectrum data so as to achieve the diagnosis effect. And (3) detecting the polymerization degree of the insulating paper in the oil paper insulating and aging sample according to national standard detection standards by dividing the oil paper insulating sample in the aging stage, and defining the aging state of the oil paper insulating sample by taking the polymerization degree of the insulating paper in each aging time stage as a judgment basis. Typically, the degree of polymerization of the new sample is between 1200 and 1600. Thus, under this definition, the samples in good insulation state have a degradation value between 0 and 1. It should be noted that when the DP value is less than 400, the sample has been severely aged and should be noted. At this time, the aging degree value is between 3 and 4. In summary, the aging degree is defined as a range between 0 and 4, and the larger the value, the deeper the aging degree is.
In conclusion, the process of constructing the oil paper insulation aging Raman spectrum diagnosis model by using the T-S fuzzy neural network is shown in FIG. 2. The number of input/output nodes of the fuzzy neural network is determined by the dimensionality of a constructed training sample of the fuzzy neural network, 1023-dimensional spectral data is input in the patent, and 1-dimensional aging state is output. During the training of the fuzzy neural network, the center and the width of the fuzzy membership function are initialized randomly, and a diagnosis model is trained by iterating for 100 times by using error calculation and a coefficient/parameter correction method along with the training. And finally, inputting the test data into a trained diagnostic model to verify the accuracy of the prediction result.
The technical scheme of the present invention is further explained by the following examples
The invention provides a method for monitoring the aging state of the oil paper insulation by detecting the Raman signal of the transformer oil. The mechanism is as follows: when substance molecules are irradiated by laser with a certain frequency, scattering occurs, only most of light is scattered by changing the direction, and the frequency of the light is the same as that of exciting light, and the light is elastic scattering, namely Rayleigh scattering; meanwhile, a small part of light not only changes the propagation direction of the light, but also changes the frequency of scattered light, and belongs to inelastic scattering, namely Raman scattering. The frequency difference between the scattered light and the incident light becomes a raman shift, which depends on the change of the vibrational energy level of the molecule, and the molecular vibration characterized by different chemical bonds or genes can be used as the basis for judging the molecular structure. With the aging of the oil paper insulation, the aging characteristics of the oil and the paper are dissolved into the oil, and the aging state monitoring of the oil paper insulation of the transformer is realized through the Raman detection of the oil.
The technical scheme for realizing the invention is as follows: the invention simulates the aging state of a real transformer under the condition of 120 ℃ according to the IEEE guide rule in a sealing system by an accelerated thermal aging method to obtain an aged insulating oil sample. Accelerated aging oil samples with aging time of 0, 1,5,10,20,35,50 and 80 days are respectively obtained, and 40 training samples and 15 testing samples are obtained in total. And (3) dividing the aging stage of the training sample, detecting the polymerization Degree (DP) of the insulating paper in the oil paper insulating aging sample according to the national standard detection standard, and calculating the aging degree of the oil paper insulating sample according to the definition by taking the average polymerization degree of the insulating paper in each aging time stage as a judgment basis so as to initially establish a training sample library.
The oil paper insulation aging Raman spectrum diagnosis model based on the T-S fuzzy neural network is trained by using 40 training samples, the Raman spectrogram vector of the sample is used as input, the aging stage of the sample is used as output, and the construction is started by using a structure with 12 hidden nodes and 100 iterations. (sample Nos. 1 to 5 for 35 days of aging and 6 to 10 for 50 days of aging)
Monitoring the insulation aging of the oil paper, and researching the diagnosis accuracy rate when the aging degree is deep in order to ensure the safe and stable operation of equipment. Therefore, 10 samples with aging state values of about 3 were selected for the intensive monitoring, and the diagnosis results are shown in fig. 3. (sample number 1 for 10 days, sample number 2 for 20 days, sample number 3 for 35 days, sample number 4 for 50 days, and sample number 5 for 80 days)
The 5 test samples with different aging states are selected for the overall test, and the diagnosis result is shown in fig. 4.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. A Raman spectrum diagnosis method for insulation aging of transformer oil paper is characterized by comprising the following steps:
step 1: simulating the aging state of a real transformer to obtain an aging insulating oil sample;
step 2: dividing the aged insulating oil sample obtained in the step 1 into a training sample and a test sample;
and step 3: constructing an oil paper insulation aging Raman spectrum diagnosis model;
and 4, step 4: training the oil paper insulation aging Raman spectrum diagnosis model constructed in the step 3 by using the training sample obtained in the step 2, and testing and training the test sample to obtain the oil paper insulation aging Raman spectrum diagnosis model;
and 5: collecting transformer insulating oil to be diagnosed in operation, acquiring a Raman spectrogram spectral vector of the insulating oil, inputting the Raman spectrogram spectral vector as input sample data into the oil paper insulation aging Raman spectral diagnosis model obtained in the step 4 after training and testing;
step 6: and 5, judging the aging degree of the transformer insulating oil according to the output result of the oil paper insulating aging Raman spectrum diagnosis model in the step 5.
2. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper according to claim 1, wherein the Raman spectrum diagnosis method comprises the following steps:
in step 1, a real transformer is placed in a sealed system, and the aging state of the real transformer is simulated at 120 ℃ to obtain an aged insulating oil sample.
3. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper according to claim 1 or 2, wherein the Raman spectrum diagnosis method comprises the following steps:
in step 1, accelerated aging oil samples were taken for aging times of 0, 1,5,10,20,35,50 and 80 days, respectively.
4. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper as claimed in claim 3, wherein:
in step 3, the oil paper insulation aging Raman spectrum diagnosis model is defined by adopting an if-then rule form and comprises an input layer, a fuzzy rule calculation layer and an output layer;
the input layer is the Raman spectrogram vector of the oiled paper insulation, and the output layer is the oiled paper insulation aging degree.
5. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper as claimed in claim 4, wherein the Raman spectrum diagnosis method comprises the following steps:
in the oil paper insulation aging Raman spectrum diagnosis model, the fuzzy layer adopts a membership function
Fuzzifying the input value to obtain a fuzzy membership value mu;
wherein mu is a fuzzy membership value; x is the number ofjIs the jth input variable;
a fuzzy set of a fuzzy system corresponding to the ith input variable;
the center and width of the membership function of the jth input variable of the ith sample, respectively.
6. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper as claimed in claim 5, wherein the Raman spectrum diagnosis method comprises the following steps:
the fuzzy rule calculation layer adopts the following fuzzy multiplication formula to calculate and obtain a fuzzy operator omegai：
ωiIs a fuzzy operator, mu is a fuzzy membership value;
for the ith input variable correspondingK is the number of input parameters; n is the number of fuzzy subsets.
7. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper as claimed in claim 6, wherein:
the output layer is calculated using the following formula:
wherein,
to blur the system parameters, yiFor the output obtained according to fuzzy rules, ωiFor the fuzzy operator, n is the fuzzy subset number, and k is the maximum number of subscript j in the fuzzy system parameter, i.e. the input parameter number.
8. The Raman spectrum diagnosis method for the insulation aging of the transformer oil paper as claimed in claim 7, wherein:
in step 4, training the oil paper insulation aging raman spectrum diagnosis model by taking the raman spectrogram vector of the training sample as input and the aging state of the training sample as output, and specifically comprises the following steps:
the error between the desired output and the actual output is calculated as follows:
in the formula, ydExpected output of the oil paper insulation aging Raman spectrum diagnosis model; y iscThe actual output of the oil paper insulation aging Raman spectrum diagnosis model; e is the error of the desired output and the actual output;
correcting the fuzzy system parameters, the centers and the widths of the membership functions according to the calculated errors, and then iterating further until a set iteration number is reached or the error e is smaller than a set threshold:
in the formula,
is a fuzzy system parameter; alpha is the network learning rate; x is the number ofjIs the jth input variable;
in the formula,
respectively the center and width of the membership function.
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