CN115468946A - Transformer oil aging diagnosis method and device and storage medium - Google Patents
Transformer oil aging diagnosis method and device and storage medium Download PDFInfo
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Abstract
The invention discloses a transformer oil aging diagnosis method, a device and a storage medium, wherein the method comprises the following steps: obtaining an aging sample of transformer oil; detecting the initial Raman spectrum of the aged sample and the polymerization degree of the aged sample, and denoising the initial Raman spectrum to obtain a preprocessed Raman spectrum; inputting the preprocessed Raman spectrum of the aging sample into a long-time and short-time memory neural network model as input quantity, and training the neural network model by taking the polymerization degree of the aging sample as the output quantity of the long-time and short-time memory neural network model to obtain a transformer oil aging diagnosis model; the method comprises the steps of obtaining transformer oil to be detected, detecting a Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into a transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree. The method can effectively improve the accuracy of diagnosis of the aging state of the transformer oil.
Description
Technical Field
The invention relates to the technical field of power equipment monitoring, in particular to a method and a device for diagnosing oil insulation aging of a transformer and a storage medium.
Background
The power transformer is the most critical and expensive equipment in power transmission and transformation equipment, and the operation reliability of the power transformer is directly related to the economic operation and the safety and stability of a power grid. Transformer accidents are mostly caused by the deterioration of the insulation properties thereof, and the transformer oil, which is a major component of the transformer insulation material, has a crucial influence on the life of the transformer. The transformer oil is mineral insulating oil refined from petroleum products, and mainly comprises alkanes, cyclanes and aromatics, mainly hydrocarbons. Transformer oil is a main liquid insulating medium used for insulation, cooling, and arc extinguishing in electric power equipment, and is required to have excellent insulating properties because it is used as a liquid insulating medium. However, with the increase of the operation time, the transformer oil is oxidized and aged to generate degradation products such as free radicals, alcohol, aldehyde, ketone and the like, and the insulation performance of the transformer oil is seriously affected. For a transformer with an average service life of about three or forty years, the oxidation and aging phenomena of transformer oil can bring serious potential safety hazards to the safe and stable operation of the transformer.
The existing transformer oil aging diagnosis method is generally an insulating oleic acid value measurement method and an oil surface tension test method, wherein the insulating oil acid value measurement and the oil surface tension test require that a sample of insulating oil taken from a transformer is sent to a laboratory for physical and chemical property analysis during the test, and because an oil sample is further oxidized and aged when exposed to the air and is easily polluted in the transportation process, the accurate diagnosis of the polymerization degree of the transformer is difficult.
Disclosure of Invention
The invention provides a transformer oil insulation aging diagnosis method, a transformer oil insulation aging diagnosis device and a storage medium, and aims to solve the technical problems that in the existing transformer oil aging diagnosis method, insulation oil is required to be taken from a transformer and a sample is sent to a laboratory for physical and chemical property analysis during testing, and the polymerization degree of the transformer is difficult to accurately diagnose due to the fact that an oil sample is further oxidized and aged when exposed to air and is easily polluted in the transportation process.
One embodiment of the present invention provides a transformer oil aging diagnosis method, including:
obtaining an aging sample of the transformer oil;
detecting an initial Raman spectrum of the aged sample and the polymerization degree of the aged sample, and denoising the initial Raman spectrum to obtain a preprocessed Raman spectrum;
inputting the preprocessed Raman spectrum of the aged sample into a long-time and short-time memory neural network model as an input quantity, and training the neural network model by taking the polymerization degree of the aged sample as an output quantity of the long-time and short-time memory neural network model to obtain a transformer oil aging diagnosis model;
acquiring transformer oil to be detected, detecting a Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into the transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
Further, the obtaining an aged sample of the transformer oil comprises:
the transformer oil is subjected to accelerated thermal aging tests under the conditions of different oil-paper ratios, and periodic sampling is performed in the aging tests to obtain aging samples of the transformer oil at different sampling times.
Further, the denoising processing on the initial raman spectrum to obtain a preprocessed raman spectrum includes:
identifying peaks of the initial Raman spectrum based on a derivative spectrum, and removing peaks of the initial Raman spectrum based on a cubic curve peak removal method;
and denoising the initial Raman spectrum after the peak is removed by adopting a smooth denoising method of three-point circular fast Fourier transform median to obtain a preprocessed Raman spectrum.
Further, the long-time and short-time memory neural network model comprises an input gate, a forgetting gate, an output gate and a memory unit;
an input gate: i all right angle t ＝σ(W t ·[h t-1 ,x t ]+b i )；
Forget the door: f. of t ＝σ(W f ·[h t-1 ,x t ]+b f )；
An output gate: o is t ＝σ(W o ·[h t-1 ,x t ]+b o )，
A memory unit:
wherein f is t ,i t ,O t Forgetting information, input information after memory, output information at time t, W f ,W i ,W C ,W o Are weights respectively; b is a mixture of f ,b i ,b C ,b o Are all offset vectors, C t is Memory state at time t, h t For the output at time t, σ is the excitation function,
is a Hadamard product.
Further, the different ratios of the oilpaper include 10.
Further, the periodic sampling in the aging test comprises:
the aging test duration was set to 30 days, and the transformer oil was sampled once every day.
An embodiment of the present invention provides a transformer oil aging diagnosis apparatus including:
the aging sample acquisition module is used for acquiring an aging sample of the transformer oil;
the aging sample detection module is used for detecting the initial Raman spectrum of the aging sample and the polymerization degree of the aging sample, and carrying out denoising treatment on the initial Raman spectrum to obtain a preprocessed Raman spectrum;
the diagnosis model training module is used for inputting the preprocessed Raman spectrum of the aged sample into the long-time and short-time memory neural network model as an input quantity, and training the neural network model by taking the polymerization degree of the aged sample as an output quantity of the long-time and short-time memory neural network model to obtain a transformer oil aging diagnosis model;
the aging diagnosis module is used for acquiring transformer oil to be detected, detecting the Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into the transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
The embodiment of the invention provides a computer-readable storage medium, which comprises a stored computer program, wherein when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the transformer oil aging diagnosis method.
According to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time and short-time memory neural network, and the polymerization degree of the transformer oil can be diagnosed by establishing the mapping relation between the polymerization degree and the characteristic quantity of the Raman spectrum, so that the accuracy of diagnosis of the transformer oil aging state can be effectively improved; according to the embodiment of the invention, the aging state diagnosis of the transformer oil can be realized without contacting an aging sample, the problem that the transformer oil is not accurately diagnosed due to oxidation of the transformer oil when the transformer oil is exposed in the air is avoided, and the diagnosis efficiency can be effectively improved; according to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time memory neural network, the input parameters of modeling and the training time of the model can be greatly reduced, the efficiency of transformer oil diagnosis can be effectively improved, the diagnosis capability of aging samples with different oil-paper ratios can be effectively improved, and thus the aging state of the transformer oil in the aging stage with different oil-paper ratios can be comprehensively diagnosed.
Furthermore, the peak removing processing and the smoothing denoising processing are carried out on the initial Raman spectrum, so that the accuracy and the reliability of the Raman spectrum can be improved, and the accuracy of transformer oil aging diagnosis can be further improved.
Drawings
FIG. 1 is a schematic flow chart of a transformer oil aging diagnosis method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of the degree of polymerization of transformer oil at different oil-to-paper ratios according to an embodiment of the present invention;
FIG. 3 is another schematic flow chart of a transformer oil aging diagnosis method provided by the embodiment of the invention;
FIG. 4 is a schematic diagram of a long-term and short-term memory neural network model according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an aging stage of transformer oil according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a transformer oil aging diagnosis apparatus provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, the meaning of "a plurality" is two or more unless otherwise specified.
In the description of the present application, it should be noted that, unless otherwise explicitly stated or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, a fixed connection, a detachable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a transformer oil aging diagnosis method, including:
s1, obtaining an aging sample of transformer oil;
in the embodiment of the invention, the aging sample of the transformer oil can be obtained through an aging test, and the embodiment of the invention performs a 130-degree accelerated thermal aging test under the conditions of different oil-paper ratios and obtains the aging sample of the transformer oil by sampling periodically.
The oil paper material in the embodiment of the invention is 25# transformer oil-cellulose insulating paper, and different oil paper proportions can be set according to requirements, for example, the oil paper proportions are 10.
S2, detecting the initial Raman spectrum of the aged sample and the polymerization degree of the aged sample, and denoising the initial Raman spectrum to obtain a preprocessed Raman spectrum;
in the embodiment of the invention, 16 samples are sampled at each oil paper ratio to measure the initial Raman spectrum of the oil paper, the oil paper is preprocessed, and the detected Raman spectrum and the polymerization degree of an aging sample construct a Raman spectrum database for diagnosing the aging state of the transformer oil according to a transformer oil aging diagnosis model in the follow-up process. One embodiment of constructing the raman spectrum database is as follows: and arranging all the Raman spectrum data by adopting Matlab software, wherein each Raman spectrum data corresponds to a polymerization degree, and organizing all the Raman spectrum data and the corresponding polymerization degrees to construct a Raman spectrum database.
Referring to fig. 2, in the embodiment of the present invention, the aging state of the Degree of Polymerization (DP) includes good edge, early stage of aging, middle stage of aging, and final stage of aging, the degree of polymerization of the oiled paper insulation aging sample is detected according to the national standard detection standard, and the oiled paper insulation aging sample is divided into four aging states, including good insulation I: DP is more than or equal to 500; and (2) early stage of aging II: (DP more than or equal to 250 is less than 500); and (3) aging middle stage III: (DP is more than or equal to 150 and less than 250); end of aging IV (DP < 150).
According to the embodiment of the invention, the initial Raman spectrum is subjected to pretreatment such as denoising, so that the accurate pretreated Raman spectrum can be obtained, and the accuracy and reliability of subsequent transformer oil aging diagnosis can be effectively improved.
S3, inputting the preprocessed Raman spectrum of the aged sample into a long-time memory neural network model as an input quantity, and training the neural network model by taking the polymerization degree of the aged sample as an output quantity of the long-time memory (LSTM) neural network model to obtain a transformer oil aging diagnosis model;
in the embodiment of the invention, the mapping relation between the polymerization degree and the characteristic quantity of the Raman spectrum can be established based on the long-term and short-term memory neural network, so that the diagnosis result of the transformer oil aging degree can be accurately improved, and the accuracy can reach more than 90%.
S4, obtaining the transformer oil to be detected, detecting the Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into the transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
According to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time and short-time memory neural network, and the polymerization degree of the transformer oil can be diagnosed by establishing the mapping relation between the polymerization degree and the characteristic quantity of the Raman spectrum, so that the accuracy of diagnosis of the transformer oil aging state can be effectively improved; according to the embodiment of the invention, the aging state diagnosis of the transformer oil can be realized without contacting an aging sample, the problem that the transformer oil is not accurately diagnosed due to oxidation of the transformer oil when the transformer oil is exposed in the air is avoided, and the diagnosis efficiency can be effectively improved; according to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time memory neural network, the input parameters of modeling and the training time of the model can be greatly reduced, the efficiency of transformer oil diagnosis can be effectively improved, the diagnosis capability of aging samples with different oil-paper ratios can be effectively improved, and thus the aging state of the transformer oil in the aging stage with different oil-paper ratios can be comprehensively diagnosed.
The embodiment of the invention is suitable for the aging state evaluation and fault diagnosis of the oil-immersed transformer, can effectively guide the operation and maintenance work of power equipment, is beneficial to ensuring the stable operation of a power grid, and can be widely applied to the fields of power energy, power systems and the like.
Referring to fig. 2, another flow chart of a transformer oil aging diagnosis method according to an embodiment of the present invention is shown.
In one embodiment, obtaining an aged sample of transformer oil comprises:
the transformer oil is subjected to accelerated thermal aging tests under the conditions of different oil-paper ratios, and periodic sampling is performed in the aging tests to obtain aging samples of the transformer oil at different sampling times.
In the present example, the different oilpaper ratios include 10. Periodic sampling was performed in the aging test, including: the aging test duration is set to be 30 days, the transformer oil is sampled once every day, and all 720 prepared transformer oil samples (the number of samples in each oil paper ratio is 240) in a laboratory are subjected to Raman data acquisition. It should be noted that the aging duration and the sampling interval can be adjusted according to actual needs.
In one embodiment, denoising the initial raman spectrum to obtain a preprocessed raman spectrum includes:
identifying a peak of the initial Raman spectrum based on the derivative spectrum, and removing the peak of the initial Raman spectrum based on a peak removal method of the cubic curve;
and denoising the initial Raman spectrum after the peak is removed by adopting a smooth denoising method of a three-point circular fast Fourier transform median to obtain a preprocessed Raman spectrum.
In the embodiment of the invention, the peak of the initial Raman spectrum is identified and removed, so that the influence of the peak on the aging state diagnosis can be effectively reduced, the accuracy of the transformer oil aging state diagnosis can be effectively improved, and the noise removal processing is carried out on the initial Raman spectrum by a smooth noise removal method, so that the accuracy of the transformer oil aging state diagnosis can be further improved.
Referring to fig. 4, the long-term and short-term memory neural network model includes an input gate, a forgetting gate, an output gate, and a memory unit;
an input gate: i.e. i t ＝σ(W t ·[h t-1 ,x t ]+b i )；
Forget the door: f. of t ＝σ(W f ·[h t-1 ,x t ]+b f )；
An output gate: o is t ＝σ(W o ·[h t-1 ,x t ]+b o )，
A memory unit:
wherein f is t ,i t ,O t Respectively, forgetting information, input information after memory, output information at time t, W f ,W i ,W C ,W o Are weights respectively; b f ,b i ,b C ,b o Are all offset vectors, C t is Memory state at time t, h t For the output at time t, σ is the excitation function,
is a Hadamard product.
In the embodiment of the invention, 240 samples of each oil-paper ratio type in the Raman spectrum database are randomly divided into 165 training samples in a training set and 75 testing samples in a testing set, wherein the training set is used for establishing a relationship model of characteristic quantity and polymerization degree of a Raman spectrum, and the testing set is used for verifying the relationship model. The framework of the long-time memory neural network adopts an input-2 LSTM layers-1 full-connection layer-1 Softmax layer-output structure, the preprocessed Raman spectrum of the aging sample is input into the long-time memory neural network model as input quantity, the degree of polymerization of the aging sample is used as output quantity of the long-time memory neural network model to train the neural network model, and the transformer oil aging diagnosis model is obtained.
Referring to tables 1-3, the degree of polymerization is obtained by detecting the transformer aging diagnosis model, and the aging state of the transformer oil can be accurately judged.
Table 1 test sample aging state diagnosis result with oilpaper ratio of 10
Table 2 results of diagnosing the aging state of test samples with an oil-to-paper ratio of 15
Table 3 results of diagnosing the aging state of test samples with a 20 oilpaper ratio
Referring to fig. 5, taking a transformer oil sample with a paper-oil ratio of 10. The aging degree of the sample is obtained through the aging diagnosis output of the transformer oil, the polymerization degree of the transformer oil is 798, the transformer oil is in a good insulation state, and the transformer oil and the polymerization degree 835 obtained through actual testing are in the same aging state.
The transformer oil aging diagnosis model is obtained based on the long-time memory neural network model training, long-term rules can be learned based on the long-time memory neural network model, and the time sequence is classified, processed and predicted through experience learning, so that an accurate transformer oil aging diagnosis result is obtained.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time memory neural network, and the polymerization degree of the transformer oil can be diagnosed by establishing the mapping relation between the polymerization degree and the characteristic quantity of the Raman spectrum, so that the accuracy of diagnosis of the aging state of the transformer oil can be effectively improved; according to the embodiment of the invention, the aging state diagnosis of the transformer oil can be realized without contacting an aging sample, the problem that the transformer oil is not accurately diagnosed due to oxidation of the transformer oil when the transformer oil is exposed in the air is avoided, and the diagnosis efficiency can be effectively improved; according to the embodiment of the invention, the transformer oil aging diagnosis model is constructed based on the long-time memory neural network, the input parameters of modeling and the training time of the model can be greatly reduced, the efficiency of transformer oil diagnosis can be effectively improved, the diagnosis capability of aging samples with different oil-paper ratios can be effectively improved, and thus the aging state of the transformer oil in the aging stage with different oil-paper ratios can be comprehensively diagnosed.
Furthermore, the embodiment of the invention can improve the accuracy and reliability of the Raman spectrum by carrying out peak removing processing and smooth denoising processing on the initial Raman spectrum, thereby further improving the accuracy of transformer oil aging diagnosis.
An embodiment of the present invention provides a transformer oil aging diagnosis apparatus including:
the aging sample obtaining module 10 is used for obtaining an aging sample of the transformer oil;
the aging sample detection module 20 is used for detecting the initial raman spectrum of the aging sample and the polymerization degree of the aging sample, and performing denoising treatment on the initial raman spectrum to obtain a preprocessed raman spectrum;
the diagnosis model training module 30 is used for inputting the preprocessed raman spectrum of the aging sample into the long-term and short-term memory neural network model as input quantity, and training the neural network model by taking the polymerization degree of the aging sample as the output quantity of the long-term and short-term memory neural network model to obtain a transformer oil aging diagnosis model;
and the aging diagnosis module 40 is used for acquiring the transformer oil to be detected, detecting the raman spectrum of the transformer oil to be detected, inputting the raman spectrum of the transformer oil to be detected into the transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
In one embodiment, the aged sample acquisition module 10 is further configured to:
the transformer oil is subjected to accelerated thermal aging tests under the conditions of different oil-paper ratios, and periodic sampling is performed in the aging tests to obtain aging samples of the transformer oil at different sampling times.
In one embodiment, the aged sample detection module 20 is further configured to:
identifying a peak of the initial Raman spectrum based on the derivative spectrum, and removing the peak of the initial Raman spectrum based on a peak removal method of a cubic curve;
and denoising the initial Raman spectrum after the peak is removed by adopting a smooth denoising method of a three-point circular fast Fourier transform median to obtain a preprocessed Raman spectrum.
In one embodiment, the long-time memory neural network model comprises an input gate, a forgetting gate, an output gate and a memory unit;
an input gate: i all right angle t ＝σ(W t ·[h t- 1,x t ]+b i )；
Forgetting the door: f. of t ＝σ(W f ·[h t- 1,x t ]+b f )；
An output gate: o is t ＝σ(W o ·[h t-1 ,x t ]+b o )，
A memory unit:
wherein f is t ,i t ,O t Forgetting information, input information after memory, output information at time t, W f ,W i ,W C ,W o Are weights respectively; b f ,b i ,b C ,b o Are all offset vectors, C t is Memory state at time t, h t For the output at time t, σ is the excitation function,
is a Hadamard product.
In one embodiment, the different oilpaper ratios include 10.
In one embodiment, the periodic sampling is performed in a burn-in test comprising:
the aging test duration was set to 30 days, and the transformer oil was sampled once every day.
The embodiment of the invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, an apparatus where the computer-readable storage medium is located is controlled to execute the transformer oil aging diagnosis method.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.
Claims (8)
1. A transformer oil aging diagnosis method is characterized by comprising the following steps:
obtaining an aging sample of transformer oil;
detecting the initial Raman spectrum of the aged sample and the polymerization degree of the aged sample, and carrying out denoising treatment on the initial Raman spectrum to obtain a preprocessed Raman spectrum;
inputting the preprocessed Raman spectrum of the aged sample into a long-time and short-time memory neural network model as an input quantity, and training the neural network model by taking the polymerization degree of the aged sample as an output quantity of the long-time and short-time memory neural network model to obtain a transformer oil aging diagnosis model;
the method comprises the steps of obtaining transformer oil to be detected, detecting a Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into a transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
2. The transformer oil aging diagnostic method of claim 1, wherein the obtaining an aging sample of transformer oil comprises:
the transformer oil is subjected to accelerated thermal aging tests under the conditions of different oil-paper ratios, and periodic sampling is performed in the aging tests to obtain aging samples of the transformer oil at different sampling times.
3. The transformer oil aging diagnostic method of claim 1, wherein the denoising the initial raman spectrum to obtain a preprocessed raman spectrum comprises:
identifying peaks of the initial raman spectrum based on a derivative spectrum, and removing peaks of the initial raman spectrum based on a peak removal method of a cubic curve;
and denoising the initial Raman spectrum after the peak is removed by adopting a smooth denoising method of a three-point circular fast Fourier transform median to obtain a preprocessed Raman spectrum.
4. The transformer oil aging diagnosis method according to claim 1, wherein the long-time and short-time memory neural network model comprises an input gate, a forgetting gate, an output gate and a memory unit;
an input gate: i.e. i t ＝σ(W t ·[h t-1 ,x t ]+b i )；
Forget the door: f. of t ＝σ(W f ·[h t-1 ,x t ]+b f )；
An output gate: o is t ＝σ(W o ·[h t-1 ,x t ]+b o )，
A memory unit:
wherein, f t ,i t ,O t Respectively, forgetting information, input information after memory, output information at time t, W f ,W i ,W C ,W o Are respectively weight; b f ,b i ,b C ,b o Are all offset vectors, C t is Memory state at time t, h t For the output at time t, σ is the excitation function,
is a Hadamard product.
5. The transformer oil aging diagnostic method according to claim 2, wherein the different paper ratios include 10.
6. The transformer oil aging diagnostic method of claim 2, wherein the periodically sampling in the aging test comprises:
the aging test duration is set to be 30 days, and the transformer oil is sampled once every day.
7. A transformer oil aging diagnostic apparatus, characterized by comprising:
the aging sample acquisition module is used for acquiring an aging sample of the transformer oil;
the aging sample detection module is used for detecting the initial Raman spectrum of the aging sample and the polymerization degree of the aging sample, and denoising the initial Raman spectrum to obtain a preprocessed Raman spectrum;
the diagnosis model training module is used for inputting the preprocessed Raman spectrum of the aged sample into the long-time and short-time memory neural network model as an input quantity, and training the neural network model by taking the polymerization degree of the aged sample as an output quantity of the long-time and short-time memory neural network model to obtain a transformer oil aging diagnosis model;
the aging diagnosis module is used for acquiring the transformer oil to be detected, detecting the Raman spectrum of the transformer oil to be detected, inputting the Raman spectrum of the transformer oil to be detected into the transformer oil aging diagnosis model to obtain the polymerization degree of the transformer oil to be detected, and judging the aging state of the transformer oil to be detected according to the polymerization degree.
8. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the transformer oil aging diagnosis method according to any one of claims 1 to 7.
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