Online monitoring method and system for gas in transformer oil, medium and electronic equipment

Abstract

The invention provides an online monitoring method and system for gas in transformer oil, a medium and electronic equipment. The online monitoring method for the gas in the transformer oil comprises the following steps: inputting the gas in the transformer oil into a first oil-gas separation module and / or a second oil-gas separation module, inputting the gas into a photoacoustic spectrum detection module and / or a gas chromatography detection module through a gas circuit, and obtaining detected gas data based on a degassing detection working mode; wherein the degassing detection working mode comprises a first working mode, a second working mode, a third working mode and a fourth working mode, and the gas circuit comprises a first gas circuit, a second gas circuit, a third gas circuit, a fourth gas circuit, a fifth gas circuit and a sixth gas circuit; and performing mutation analysis and data fusion on the detected gas data by using a bispectrum fusion algorithm to obtain a gas monitoring result. According to the online monitoring method for the gas in the transformer oil, the monitoring accuracy of the gas in the transformer oil can be improved.

Description

Technical Field

[0001] The present application belongs to the field of online monitoring technology and relates to a method, system, medium and electronic equipment for online monitoring of gas in transformer oil. Background Art

[0002] Oil-immersed power transformers may suffer from latent faults such as internal overheating and partial discharge during long-term operation. Overheating and discharge will cause the insulating oil to decompose, thereby producing trace amounts of dissolved gases such as H2, CO, CO2, CH4, C2H4, C2H2, and C2H6. Different fault phenomena and deterioration degrees produce different gas components and concentrations. By monitoring the concentrations of various dissolved gases in the oil through an oil chromatography online monitoring device, the operating status of the transformer can be understood in a timely manner, and latent faults can be warned, thereby avoiding catastrophic accidents. This is of great significance and has practical effects for early detection of latent faults inside oil-filled power equipment. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, medium and electronic equipment for online monitoring of gas in transformer oil, so as to improve the accuracy of gas monitoring in transformer oil.

[0004] In a first aspect, the present application provides a method for online monitoring of gas in transformer oil, the method comprising: obtaining gas in transformer oil; inputting the gas in transformer oil into a first oil-gas separation module and / or a second oil-gas separation module, and inputting the gas into a photoacoustic spectroscopy detection module and / or a gas chromatography detection module through a gas path, and obtaining gas data after detection based on a degassing detection working mode; wherein the degassing detection working mode comprises a first working mode, a second working mode, a third working mode, and a fourth working mode, and the gas path comprises a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path; performing mutation analysis and data fusion on the gas data after detection using a dual-spectrum fusion algorithm to obtain a gas monitoring result, wherein the gas monitoring result is used to indicate the credibility of the gas data after detection.

[0005] In an implementation of the first aspect, the online monitoring method for gas in transformer oil further includes: using a communication control module to perform communication control on the first oil-gas separation module, the second oil-gas separation module, the photoacoustic spectroscopy detection module and / or the gas chromatography detection module.

[0006] In an implementation of the first aspect, the first working mode is that the first gas path and the second gas path are executed in parallel; the fourth working mode is that the fifth gas path and the sixth gas path are executed in parallel.

[0007] In an implementation of the first aspect, the process of obtaining detected gas data based on the degassing detection working mode includes: based on the first working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module through the first gas path to obtain first gas path detection gas data; the gas in the transformer oil is input into the second oil-gas separation module, and is input into the photoacoustic spectroscopy detection module through the second gas path to obtain second gas path detection gas data; the detected gas data includes first gas path detection gas data and second gas path detection gas data.

[0008] In an implementation of the first aspect, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the fourth working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the photoacoustic spectroscopy detection module through the fifth gas path to obtain fifth gas path detection gas data; the gas in the transformer oil is input into the second oil-gas separation module, and is input into the gas chromatography detection module through the sixth gas path to obtain sixth gas path detection gas data; the gas data after detection includes the fifth gas path detection gas data and the sixth gas path detection gas data.

[0009] In an implementation of the first aspect, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the second working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the third gas path to obtain third gas path detection gas data, and the gas data after detection is the third gas path detection gas data.

[0010] In an implementation of the first aspect, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the third working mode, the gas in the transformer oil is input into the second oil-gas separation module, and is input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the fourth gas path to obtain fourth gas path detection gas data, and the gas data after detection is the fourth gas path detection gas data.

[0011] In a second aspect, the present application provides an online monitoring system for gas in transformer oil, the online monitoring system for gas in transformer oil comprising: an oil-gas acquisition module for acquiring gas in transformer oil; a detection gas acquisition module for inputting the gas in transformer oil into a first oil-gas separation module and / or a second oil-gas separation module, and inputting the gas into a photoacoustic spectroscopy detection module and / or a gas chromatography detection module through a gas path, and acquiring gas data after detection based on a degassing detection working mode; wherein the degassing detection working mode comprises a first working mode, a second working mode, a third working mode, and a fourth working mode, and the gas path comprises a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path; a detection gas analysis fusion module for performing mutation analysis and data fusion on the detected gas data using a dual-spectrum fusion algorithm to obtain a gas monitoring result, and the gas monitoring result is used to indicate the credibility of the detected gas data.

[0012] In a third aspect, the present application provides an electronic device, comprising: a memory on which a computer program is stored; and a processor, communicatively connected to the memory, for executing the computer program to implement the above-mentioned method for online monitoring of gas in transformer oil.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned method for online monitoring of gas in transformer oil.

[0014] As described above, the method, system, medium, and electronic device for online monitoring of gas in transformer oil described in this application have the following beneficial effects:

[0015] The online transformer oil gas monitoring method provided in this application utilizes multiple flexible degassing detection operating modes, effectively utilizing gas chromatography and photoacoustic spectroscopy detection modules to accurately detect and analyze gas components in transformer oil. Through four different operating modes and six independent, non-interfering gas path designs, this application achieves efficient and accurate detection of gas in transformer oil, significantly enhancing the flexibility of the monitoring process and the reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a schematic diagram of an application scenario of the online monitoring method for gas in transformer oil described in an embodiment of the present application.

[0017] Figure 2 Shown is a process diagram of the online monitoring method for gas in transformer oil according to an embodiment of the present application.

[0018] Figure 3Shown is a schematic diagram of the process of performing mutation analysis using a dual-spectrum fusion algorithm as described in an embodiment of the present application.

[0019] Figure 4 Shown is a schematic diagram of the process of bi-spectrum data mutation analysis and data fusion described in an embodiment of the present application.

[0020] Figure 5 Shown is a structural schematic diagram of the online monitoring system for gas in transformer oil according to an embodiment of the present application.

[0021] Figure 6 Shown is a structural schematic diagram of an electronic device described in an embodiment of the present application.

[0022] Component number description

[0023] 1 Oil-light dual-spectrum transformer oil gas online monitoring device

[0024] 11 Communication control module

[0025] 12 Environmental Control Module

[0026] 13. First oil-gas separation module

[0027] 14. Second oil-gas separation module

[0028] 15. Photoacoustic spectroscopy detection module

[0029] 16 Gas chromatography detection module

[0030] 2 Transformer Oil Gas Online Monitoring System

[0031] 21 Oil-gas acquisition module

[0032] 22 Detection gas acquisition module

[0033] 23 Detection gas analysis fusion module

[0034] 3 Electronic devices

[0035] 31 Memory

[0036] 32 processors

[0037] 33 Display

[0038] Steps S21 to S23

[0039] Steps S31 to S33 DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0041] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0042] Oil-immersed power transformers may suffer from latent faults such as internal overheating and partial discharge during long-term operation. Overheating and discharge will cause the insulating oil to decompose, thereby producing trace amounts of dissolved gases such as H2, CO, CO2, CH4, C2H4, C2H2, and C2H6. Different fault phenomena and deterioration degrees produce different gas components and concentrations. By monitoring the concentrations of various dissolved gases in the oil through an oil chromatography online monitoring device, the operating status of the transformer can be understood in a timely manner, and latent faults can be warned, thereby avoiding catastrophic accidents. This is of great significance and has practical effects for early detection of latent faults inside oil-filled power equipment.

[0043] Online monitoring systems for gas in transformer oil typically employ a single technology approach. Common approaches include gas chromatography, photoacoustic spectroscopy, and absorption spectroscopy. Each of these approaches has its own advantages and disadvantages. For example, gas chromatography offers a simple structure, mature technology, and high detection sensitivity, but suffers from issues such as a short chromatographic column lifespan (typically only 3,000 to 5,000 uses due to sensor aging and degradation). Photoacoustic spectroscopy is unaffected by the number of device uses, and its theoretical lifespan can meet the design lifespan requirements, requiring minimal maintenance. However, it is subject to interference from water vapor, and optical and acoustic components are susceptible to environmental influences, leading to false alarms. Absorption spectroscopy can theoretically achieve higher detection sensitivity by increasing the absorption optical pathlength, but it is also subject to issues such as water vapor interference and high gas consumption.

[0044] At least to address the above problems, the following embodiments of the present application provide an online monitoring method for gas in transformer oil. The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present application.

[0045] Figure 1A schematic diagram of an application scenario of a transformer oil gas online monitoring method is shown in the embodiment of the present application. As shown in Figure 1 The oil light dual spectrum transformer oil gas online monitoring device 1 includes a communication control module 11, an environment control module 12, a first oil gas separation module 13, a second oil gas separation module 14, a photoacoustic spectroscopy detection module 15, and a gas chromatography detection module 16. The communication control module 11 controls the communication of the environment control module 12, the first oil gas separation module 13, the second oil gas separation module 14, the photoacoustic spectroscopy detection module 15, and the gas chromatography detection module 16. The environment control module 12 is used to monitor the temperature, humidity, and other environmental conditions of the system operation, and adjust the operating parameters of the system according to the environmental conditions. The first oil gas separation module 13 and / or the second oil gas separation module 14 are used for degassing, separating dissolved gas from the transformer oil, and transporting the separated gas to the photoacoustic spectroscopy detection module 15 and the gas chromatography detection module 16 through a gas path. The photoacoustic spectroscopy detection module 15 is used to detect the gas composition in the oil using photoacoustic spectroscopy technology. The gas chromatography detection module 16 is used to analyze the gas composition using gas chromatography technology to determine the type and concentration of the gas.

[0046] Figure 2 A process diagram of a transformer oil gas online monitoring method is shown in an embodiment of the present application. As shown in Figure 2 The transformer oil gas online monitoring method includes the following steps S21 to S23.

[0047] Step S21, obtaining the gas in the transformer oil.

[0048] Step S22, inputting the gas in the transformer oil into the first oil gas separation module and / or the second oil gas separation module, and inputting it into the photoacoustic spectroscopy detection module and / or the gas chromatography detection module through a gas path, and obtaining the detected gas data based on a degassing detection working mode. The degassing detection working mode includes a first working mode, a second working mode, a third working mode, and a fourth working mode. The gas path includes a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path. The first gas path, the second gas path, the third gas path, the fourth gas path, the fifth gas path, and the sixth gas path do not interfere with each other.

[0049] Step S23, using a dual spectrum fusion algorithm to analyze the detected gas data and perform data fusion to obtain a gas monitoring result. The gas monitoring result is used to represent the reliability of the detected gas data.

[0050] As can be seen from the above description, the online transformer oil gas monitoring method provided by this application adopts multiple flexible degassing detection operating modes, effectively utilizing gas chromatography detection modules and photoacoustic spectroscopy detection modules to accurately detect and analyze the gas components in transformer oil. Through four different operating modes and six independent, non-interfering gas path designs, this application achieves efficient and accurate detection of gas in transformer oil, greatly improving the flexibility of the monitoring process and the reliability of the detection results.

[0051] In one embodiment of the present application, the online monitoring method for gas in transformer oil further includes: using a communication control module to perform communication control on the first oil-gas separation module, the second oil-gas separation module, the photoacoustic spectroscopy detection module and / or the gas chromatography detection module.

[0052] In one embodiment of the present application, the first working mode is that the first gas path and the second gas path are executed in parallel; the fourth working mode is that the fifth gas path and the sixth gas path are executed in parallel.

[0053] In one embodiment of the present application, the process of obtaining gas data after detection based on the degassing detection working mode includes: based on the first working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module through the first gas path to obtain first gas path detection gas data; the gas in the transformer oil is input into the second oil-gas separation module, and is input into the photoacoustic spectroscopy detection module through the second gas path to obtain second gas path detection gas data; the gas data after detection includes first gas path detection gas data and second gas path detection gas data.

[0054] Exemplarily, based on the first working mode, the gas in the transformer oil is input into the first oil-gas separation module, input into the gas chromatography detection module through the first gas path, and the gas in the transformer oil is input into the second oil-gas separation module, and input into the photoacoustic spectroscopy detection module through the second gas path. These two gas paths are executed separately and do not interfere with each other.

[0055] In one embodiment of the present application, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the fourth working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the photoacoustic spectroscopy detection module through the fifth gas path to obtain fifth gas path detection gas data; the gas in the transformer oil is input into the second oil-gas separation module, and is input into the gas chromatography detection module through the sixth gas path to obtain sixth gas path detection gas data; the gas data after detection includes the fifth gas path detection gas data and the sixth gas path detection gas data.

[0056] Exemplarily, based on the fourth working mode, the gas in the transformer oil is input into the first oil-gas separation module, input into the photoacoustic spectroscopy detection module through the fifth gas path, and the gas in the transformer oil is input into the second oil-gas separation module, and input into the gas chromatography detection module through the sixth gas path. These are executed simultaneously in parallel, and these two gas paths are executed separately without interfering with each other.

[0057] In one embodiment of the present application, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the second working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the third gas path to obtain third gas path detection gas data, and the gas data after detection is the third gas path detection gas data.

[0058] In some embodiments, when the second oil-gas separation module is in an inoperative state, the operator selects the second operating mode, and the gas separated by the first oil-gas separation module can be simultaneously input into the gas chromatography detection module and the photoacoustic spectroscopy detection module, that is, the gas chromatography detection module and the photoacoustic spectroscopy detection module can simultaneously detect the gas separated by the first oil-gas separation module through the third gas path.

[0059] In one embodiment of the present application, the process of obtaining gas data after detection based on the degassing detection working mode also includes: based on the third working mode, the gas in the transformer oil is input into the second oil-gas separation module, and input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the fourth gas path to obtain the fourth gas path detection gas data, and the gas data after detection is the fourth gas path detection gas data.

[0060] In some embodiments, when the first oil-gas separation module is in an inoperative state, the operator selects the third operating mode, and the gas separated by the second oil-gas separation module can be simultaneously input into the gas chromatography detection module and the photoacoustic spectroscopy detection module, that is, the gas chromatography detection module and the photoacoustic spectroscopy detection module can simultaneously detect the gas separated by the second oil-gas separation module through the fourth gas path.

[0061] In some implementations, the second working mode and the third working mode can be selected based on the working conditions, detection cycle, equipment life and / or working efficiency under actual operation.

[0062] Specifically, when either the first oil-gas separation module or the second oil-gas separation module fails, for example, when the first oil-gas separation module fails, the third operating mode may be selected, and when the second oil-gas separation module fails, the second operating mode may be selected.

[0063] Furthermore, the operating mode is selected based on the detection cycle. For example, if a certain amount of detection data needs to be output per day, and a detection data is output every time a period of time, the detection task is allocated using the first and second operating modes. When the detection work is carried out in the first operating mode, the second operating mode is suspended. By alternating the operating modes, the detection task is shared, thereby ensuring the detection cycle while taking into account the service life of the system.

[0064] Furthermore, the first working mode, the second working mode, the third working mode, and the fourth working mode can also select different working modes for monthly, weekly, or daily testing tasks according to actual testing needs, and this application is not limited to this.

[0065] Figure 3 Shown is a schematic diagram of the process of performing mutation analysis using a dual-spectrum fusion algorithm in one embodiment of the present application. Figure 3 As shown, the process of performing mutation analysis on the detected gas data using the dual-spectrum fusion algorithm includes the following steps S31 to S33.

[0066] Step S31 : performing smoothing processing on the detected gas data to obtain a predicted trend of the gas data.

[0067] For example, the exponentially weighted moving average (EWMA) algorithm is used to perform a weighted average of the current gas data and the historical smoothed value to obtain the smoothed value of the current gas data. By using the exponentially weighted moving average to smooth the time series data, the weights of the historical data and the current data are dynamically adjusted to reduce random fluctuations in the data and generate a more stable long-term trend value. The weighted average process is expressed as:

[0068] C smoothed (t) = α·C(t) + (1-α)·C smoothed (t-1)

[0069] Among them, C smoothed (t) represents the smoothed value of the gas data detected at time point t, that is, the trend value after smoothing, which represents the expected state of the gas data. C(t) is the current detection value of the gas data detected at time point t, and C smoothed (t-1) is the smoothed value of the gas data detected at time point t-1, and α is the smoothing factor (0<α<1), which determines the degree of influence of the weight of historical data on the current smoothed value. When α is large, it means that the recent data C(t) has a high weight and is sensitive to changes, which is suitable for rapid response to mutations. When α is small, it means that the historical data C(t) has a high weight and is sensitive to changes and is suitable for rapid response to mutations. smoothed (t-1) has a higher weight and a stronger smoothing effect, which is suitable for long-term stable trends.

[0070] The concentration trend value is calculated through historical data to serve as the basis for judging data mutations, which is specifically expressed as:

[0071] C smoothed (t-1) = α·C(t-1) + (1-α)·C smoothed (t-2)

[0072] Step S32 : performing data mutation judgment on the detected gas data based on the predicted trend of the gas data to obtain a mutation judgment result.

[0073] For example, a consistency check algorithm is used to perform a threshold judgment on the current detection value. If the threshold is exceeded, it is judged as a mutation. The judgment process is expressed as:

[0074] a. Get the current detection value C(t) and the corresponding smoothing value C smoothrd The error between (t-1).

[0075] b. When the error is greater than 3 times the standard deviation of the error, it is considered a data mutation. current (t)-C smoothed (t-1)|>3σ, where σ is the standard deviation of the corresponding equipment detection error.

[0076] c, when the corresponding smoothing value is 0, but the current detection value is greater than 0, it is considered a data mutation. smoothed (t-1)=0,

[0077] C current (t)>0.

[0078] Step S33: performing an abnormality decision on the processed gas data based on the mutation judgment result to obtain final detection data.

[0079] In one embodiment of the present application, the process of performing an abnormality decision on the processed gas data based on the mutation judgment result to obtain final detection data includes the following steps S41 to S42.

[0080] Step S41 , obtaining the number of spectrum detections and the number of spectrum mutations during detection in a chromatographic detection process.

[0081] Step S42 , performing an abnormality decision based on the mutation judgment result, the number of spectrum detections during the one chromatographic detection process, and the number of mutations of the spectrum during detection, to obtain the final detection data.

[0082] For example, the total number of spectra detected during one chromatographic detection process is N PAS , the number of mutations in the spectrum during detection is n PAS,mutation .

[0083] It should be noted that no matter whether the chromatographic data output by the gas chromatography detection module undergoes a sudden change, the final detection data shall be based on the chromatographic data.

[0084] When 0 <n PAS,mutation <N PAS , it indicates that the spectrum detection of the photoacoustic spectrum detection module is unstable and the reliability is low.

[0085] When n PAS,mutation =0 or n PAS,mutation =N PAS , it indicates that the spectrum detection of the photoacoustic spectroscopy detection module is stable and has high reliability.

[0086] When the chromatographic data of the gas chromatography detection module suddenly changes, and n PAS,mutation =0, the final detection data is based on the spectrum data.

[0087] When the chromatographic data does not undergo a sudden change, and n PAS,mutation =N PAS When the spectrum data is used as the final test data,

[0088] In one embodiment of the present application, the process of performing data fusion on the detected gas data using a dual-spectrum fusion algorithm includes the following steps S51 to S52.

[0089] Step S51, obtaining a gas chromatography detection data weight coefficient and a photoacoustic spectroscopy detection data weight coefficient;

[0090] It should be noted that the dual-spectrum data fusion of the chromatographic data of the gas chromatography detection module and the spectral data of the photoacoustic spectroscopy detection module needs to take into account the aging factors of the chromatographic equipment and the photoacoustic spectroscopy equipment.

[0091] Exemplarily, the weight coefficient is obtained using the aging variance growth model, where the aging variance growth model is expressed as:

[0092]

[0093] in, It is represented as the initial error variance of the new device sensor, n is the number of detections, and λ is the aging rate. λ can be fitted by historical data. For example, the variance increases by 20% every 100 detections.

[0094] The acquisition process of the aging rate λ is expressed as:

[0095] 1) Obtain detection data during equipment use.

[0096] 2) Based on the test data, nonlinear regression is used to fit the exponential curve to obtain different test times The corresponding error variance.

[0097] 3) Utilize The aging rate λ is obtained by linear fitting.

[0098] Step S52: Weighted fusion is performed on the gas chromatography detection data output by the gas chromatography detection module and the photoacoustic spectroscopy detection data output by the photoacoustic spectroscopy detection module based on the gas chromatography detection data weight coefficient and the photoacoustic spectroscopy detection data weight coefficient to obtain the gas monitoring result. The gas monitoring result is the output result of the online transformer oil gas monitoring device.

[0099] For example, the gas chromatography detection data weight coefficient is The weight coefficient of photoacoustic spectroscopy detection data is

[0100] in, and It represents the variance of the aging measurement of the two devices. The smaller the variance, the greater the weight.

[0101] when When ω is small, GC Large, dominated by gas chromatography detection data (GC data); on the contrary When ω is small, PAS The data are relatively large, and are dominated by photoacoustic spectroscopy data (PAS data).

[0102] The process of weighted fusion of gas chromatography detection data and photoacoustic spectroscopy detection data to obtain gas monitoring results is as follows:

[0103] X fused =ω GC X GC +ω PAS X PAS

[0104] Among them, X fused The output result of the online monitoring equipment for gas in transformer oil is the gas monitoring result.

[0105] Furthermore, when the chromatographic data does not undergo a sudden change and n PAS,mitation = 0, the final detection data is based on the dual spectrum weight data fusion. PAS,mutation =N PAS When , the final detection data is based on the fusion of bispectral weighted data.

[0106] In some embodiments, the process of mutation analysis and data fusion on the detected gas data by using the bispectrum fusion algorithm to obtain the gas monitoring result is described in detail by the following steps. It should be noted that the content in this example is only used to explain and illustrate the transformer oil gas online monitoring method provided by the embodiments of the present application, and is not used to limit the protection scope of the present application in any way. In specific applications, corresponding steps can be added or deleted on the basis of this example according to actual needs. Figure 4 The flowchart of the transformer oil gas online monitoring method in this example is shown. As shown in Figure 4 The mutation analysis and data fusion by using the bispectrum fusion algorithm in this example includes the following steps S300 to S422.

[0107] Step S300, mutation judgment is performed on the gas detection data. Exponential weighted moving average algorithm is used to smooth the gas chromatography data and photoacoustic spectrum data to generate trend values. Whether the current detection data has mutation is judged based on the threshold value.

[0108] Step S400, time synchronization is performed on the gas chromatography data and photoacoustic spectrum data, and whether the photoacoustic spectrum data has mutation is judged.

[0109] Step S410, when all the photoacoustic spectrum data has no mutation, whether the gas chromatography data has mutation is judged.

[0110] Step S411, when all the photoacoustic spectrum data has no mutation and the gas chromatography data has mutation, the final detection data is based on the spectrum data.

[0111] Step S412, when all the photoacoustic spectrum data has no mutation and the gas chromatography data has no mutation, the final detection data is based on the bispectrum weight data fusion. The gas chromatography data and photoacoustic spectrum data are fused by weight through the sensor aging variance model and weighted average algorithm.

[0112] Step S420, when all the photoacoustic spectrum data has mutation, whether the gas chromatography data has mutation is judged.

[0113] Step S421, when all the photoacoustic spectrum data has mutation and the gas chromatography data has no mutation, the final detection data is based on the spectrum data.

[0114] Step S422, when all the photoacoustic spectrum data has mutation and the gas chromatography data has mutation, the final detection data is based on the bispectrum weight data fusion.

[0115] In summary, the online monitoring method of gases in transformer oil provided in the application adopts multiple flexible degassing detection working modes, which can effectively utilize the gas chromatography detection module and the photoacoustic spectroscopy detection module to accurately detect and analyze the gas components in the transformer oil. Through four different working modes and six independent and non-interfering gas path designs, the inherent principle defects of the traditional online monitoring system of gases in transformer oil due to reliance on a single technical principle are solved, and the reliability of the monitoring equipment is significantly improved. Through innovative design, we shorten the detection cycle and ensure that the entire monitoring system will not be paralyzed when a single component fails, thereby improving the service life of the device, and this improvement does not lead to an extension of the detection cycle. At the same time, the EWMA algorithm is used to smooth the noise and extract the trend, combined with threshold rules (such as 3σ) to accurately identify mutations and reduce false positives. Based on the sensor aging model, the gas chromatography data and the photoacoustic spectroscopy data are adjusted in weight, and a weighted average fusion is adopted, which not only suppresses the interference of the aging equipment, but also improves the consistency of multi-source data.

[0116] The protection scope of the online monitoring method of gases in transformer oil described in the embodiments of the application is not limited to the order of steps listed in the embodiments, and any scheme realized by adding, replacing or changing the steps of the prior art according to the principles of the application is included in the protection scope of the application.

[0117] The embodiments of the application also provide an online monitoring system of gases in transformer oil, which can implement the online monitoring method of gases in transformer oil described in the application, but the implementation device of the online monitoring method of gases in transformer oil described in the application includes but is not limited to the structure of the online monitoring system of gases in transformer oil listed in the embodiments, and any structural deformation and replacement of the prior art according to the principles of the application is included in the protection scope of the application.

[0118] Figure 5 The structure of the online monitoring system of gases in transformer oil in an embodiment of the application is shown in the structure schematic diagram. Figure 5As shown, the transformer oil gas online monitoring system 2 includes: an oil gas acquisition module 21, a detection gas acquisition module 22, and a detection gas analysis and fusion module 23. The oil gas acquisition module 21 is used to acquire gas in the transformer oil. The detection gas acquisition module 22 is used to input the transformer oil gas into the first oil-gas separation module and / or the second oil-gas separation module, and then input it into the photoacoustic spectroscopy detection module and / or the gas chromatography detection module via a gas path, thereby acquiring detected gas data based on a degassing detection operating mode. The degassing detection operating modes include a first operating mode, a second operating mode, a third operating mode, and a fourth operating mode, and the gas paths include a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path. The detection gas analysis and fusion module 23 is used to perform mutation analysis and data fusion on the detected gas data using a dual-spectrum fusion algorithm to obtain gas monitoring results, which are used to indicate the credibility of the detected gas data.

[0119] It should be noted that Figure 5 The modules in the transformer oil gas online monitoring system 1 are shown as follows Figure 2 The steps in the method for online monitoring of gas in transformer oil correspond to each other and are not described in detail here.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0121] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0122] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for online monitoring of gas in transformer oil provided by the present application. A person skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, wherein the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0124] An embodiment of the present application may also provide an electronic device. Figure 6 The diagram shows the structure of the electronic device 3 in one embodiment of the present application. Figure 6 As shown, in this embodiment, the electronic device 3 includes a memory 31 and a processor 32.

[0125] The memory 31 is used to store computer programs. In some possible implementations, the memory 31 may include various media capable of storing program codes, such as ROM, RAM, a magnetic disk, a USB flash drive, a memory card, or an optical disk.

[0126] In the embodiment of the present application, the memory 31 may include a computer system readable medium in the form of a volatile memory, such as RAM and / or cache memory. The electronic device 3 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 31 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of each embodiment of the present application.

[0127] The processor 32 is connected to the memory 31 and is used to execute the computer program stored in the memory 31 so as to enable the electronic device 3 to perform the method for online monitoring of gas in transformer oil.

[0128] Exemplarily, the processor 32 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, the processor 32 may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0129] In some implementations, the electronic device 3 provided in the embodiments of the present application may further include a display 33. The display 33 is communicatively connected to the memory 31 and the processor 32, and is configured to display a graphical user interface (GUI) related to the method for online monitoring of gas in transformer oil.

[0130] In the embodiment of the present application, the display 33 may include a display screen (display panel). In some implementations, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. In addition, the display 33 may also be a touch panel (touch screen, touch screen), which may include a display screen and a touch-sensitive surface. When the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor 32 to determine the type of touch event, and then the processor 32 provides a corresponding visual output on the display device according to the type of touch event.

[0131] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0132] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for online monitoring of gas in transformer oil, characterized in that: The method for online monitoring of gas in transformer oil comprises: Obtain gas from transformer oil; Inputting the gas in the transformer oil into the first oil-gas separation module and / or the second oil-gas separation module, and then inputting it into the photoacoustic spectroscopy detection module and / or the gas chromatography detection module through the gas path, and obtaining gas data after detection based on the degassing detection working mode; wherein the degassing detection working mode includes a first working mode, a second working mode, a third working mode, and a fourth working mode, and the gas path includes a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path; The dual-spectrum fusion algorithm is used to perform mutation analysis and data fusion on the detected gas data to obtain a gas monitoring result, which is used to indicate the credibility of the detected gas data.

2. The method for online monitoring of gas in transformer oil according to claim 1, characterized in that: The online monitoring method for gas in transformer oil further includes: utilizing a communication control module to perform communication control on the first oil-gas separation module, the second oil-gas separation module, the photoacoustic spectroscopy detection module and / or the gas chromatography detection module.

3. The method for online monitoring of gas in transformer oil according to claim 1, characterized in that: The first working mode is that the first gas path and the second gas path are executed in parallel; the fourth working mode is that the fifth gas path and the sixth gas path are executed in parallel.

4. The method for online monitoring of gas in transformer oil according to claim 3, characterized in that: The process of obtaining gas data after detection based on the degassing detection working mode includes: Based on the first working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module through the first gas path to obtain first gas path detection gas data; The gas in the transformer oil is input into the second oil-gas separation module, and then input into the photoacoustic spectrum detection module through the second gas path to obtain the second gas path detection gas data; The detected gas data includes first gas path detection gas data and second gas path detection gas data.

5. The method for online monitoring of gas in transformer oil according to claim 3, characterized in that: The process of obtaining the gas data after detection based on the degassing detection working mode also includes: Based on the fourth working mode, the gas in the transformer oil is input into the first oil-gas separation module and input into the photoacoustic spectroscopy detection module through the fifth gas path to obtain gas detection data of the fifth gas path; The gas in the transformer oil is input into the second oil-gas separation module and input into the gas chromatography detection module through the sixth gas path to obtain gas detection data of the sixth gas path; The detected gas data includes the fifth gas path detected gas data and the sixth gas path detected gas data.

6. The method for online monitoring of gas in transformer oil according to claim 1, characterized in that: The process of obtaining the gas data after detection based on the degassing detection working mode also includes: Based on the second working mode, the gas in the transformer oil is input into the first oil-gas separation module, and is input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the third gas path to obtain third gas path detection gas data, and the gas data after detection is the third gas path detection gas data.

7. The method for online monitoring of gas in transformer oil according to claim 1, characterized in that: The process of obtaining the gas data after detection based on the degassing detection working mode also includes: Based on the third working mode, the gas in the transformer oil is input into the second oil-gas separation module, and is input into the gas chromatography detection module and the photoacoustic spectroscopy detection module through the fourth gas path to obtain fourth gas path detection gas data, and the gas data after detection is the fourth gas path detection gas data.

8. An online monitoring system for gas in transformer oil, characterized in that: The transformer oil gas online monitoring system includes: Oil gas acquisition module, used to obtain gas in transformer oil; a detection gas acquisition module, configured to input the gas in the transformer oil into the first oil-gas separation module and / or the second oil-gas separation module, and then input the gas into the photoacoustic spectroscopy detection module and / or the gas chromatography detection module through the gas path, and obtain gas data after detection based on a degassing detection working mode; wherein the degassing detection working mode includes a first working mode, a second working mode, a third working mode, and a fourth working mode, and the gas path includes a first gas path, a second gas path, a third gas path, a fourth gas path, a fifth gas path, and a sixth gas path; The detection gas analysis fusion module is used to perform mutation analysis and data fusion on the detected gas data using a dual-spectrum fusion algorithm to obtain a gas monitoring result, which is used to indicate the credibility of the detected gas data.

9. An electronic device, characterized in that: The electronic device comprises: a memory having a computer program stored thereon; A processor is communicatively connected to the memory, and is used to execute the computer program to implement the method for online monitoring of gas in transformer oil according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for online monitoring of gas in transformer oil according to any one of claims 1 to 7 is implemented.

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