Transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system

Abstract

The invention discloses a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system, and belongs to the technical field of transformer detection. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system comprises a comprehensive monitoring unit, a data acquisition unit, an analysis and diagnosis unit and a remote monitoring unit. According to the invention, the problems of single transformer monitoring and low monitoring efficiency in the prior art are solved, various data of the transformer are obtained through the plurality of intelligent monitoring devices, the transformer fault can be diagnosed more comprehensively through the obtained multi-dimensional data of the transformer, the diagnosis result is prevented from deviating, and the fault diagnosis accuracy is improved. Various actually-measured data are compared and analyzed with a large number of existing fault cases, preliminary fault diagnosis can be carried out on the transformer, then various actually-measured data are analyzed and diagnosed through the integrated diagnosis model, fault diagnosis can be further carried out on the transformer, and the accuracy of fault diagnosis can be improved through the combination of the two diagnosis modes.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer monitoring, and in particular to a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system. Background Art

[0002] Gas generator sets use natural gas and other high calorific value gases as fuel to generate electricity. The output voltage of gas generator sets is usually low and needs to be stepped up by transformers for long-distance transmission. The transformer is one of the most important equipment in the power grid. Its reliability is directly related to whether the power grid can operate safely, efficiently and economically. Reducing transformer failures means improving the economic benefits of the power grid. However, the existing fault monitoring of gas generator transformers usually relies on manual or single sensor monitoring methods. Manual monitoring not only increases the workload but also has low monitoring efficiency. The use of a single monitored variable for judgment is prone to deviations in the results and it is difficult to ensure the accuracy of fault diagnosis. Therefore, it does not meet the existing needs. In this regard, we propose a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system. Summary of the invention

[0003] The purpose of the present invention is to provide a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system, which obtains various data of the transformer through multiple intelligent monitoring devices, compares and analyzes the measured data with a large number of existing fault cases, and can perform preliminary fault diagnosis on the transformer. By integrating the diagnostic model and comprehensively analyzing and diagnosing the various measured data from the transformer and historical data, the transformer fault can be further diagnosed. The transformer fault can be diagnosed more comprehensively through the analysis of various data, and the combination of the two diagnostic methods can improve the accuracy of fault diagnosis, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system, comprising:

[0005] Integrated monitoring unit for:

[0006] Deploy multiple intelligent monitoring devices to monitor the transformers of gas turbine generator sets to obtain various data of the transformers, including partial discharge data, temperature data, vibration data, current and voltage data, and oil product data;

[0007] Data acquisition unit for:

[0008] Collect various data obtained by the comprehensive monitoring unit to realize the aggregation of various data, and process the aggregated data;

[0009] Analytical and diagnostic unit for:

[0010] Compare and analyze the measured data with a large number of existing fault cases to achieve preliminary fault diagnosis of the transformer. Then, through the integrated diagnosis model, analyze and diagnose the measured data of the transformer from the data acquisition unit and the historical data, and realize the fault diagnosis of the transformer through analysis.

[0011] Remote monitoring unit for:

[0012] The integrated monitoring unit stores various data obtained, provides a large number of fault cases and historical data for the analysis and diagnosis unit, displays the fault diagnosis results of the analysis and diagnosis unit, and sets permissions for users to enable users to manage the remote monitoring unit.

[0013] Furthermore, the comprehensive monitoring unit includes a plurality of intelligent monitoring devices, specifically:

[0014] Microwave sensors for:

[0015] Monitor the partial discharge signal of the transformer to obtain partial discharge data about the transformer;

[0016] Thermal imaging cameras for:

[0017] Monitor the temperature distribution on the transformer surface to obtain temperature data about the transformer;

[0018] Vibration sensors for:

[0019] Monitor the vibration of the transformer to obtain vibration data about the transformer;

[0020] Current and voltage sensors for:

[0021] Monitor the current and voltage information of the transformer to obtain current and voltage data about the transformer;

[0022] Micro-water sensor for:

[0023] Monitor the trace moisture content in transformer insulating oil to obtain oil quality data about the transformer.

[0024] Furthermore, the data acquisition unit comprises:

[0025] Data acquisition module for:

[0026] Collect various data obtained by multiple deployed intelligent monitoring devices in real time and pre-process the collected data;

[0027] Time interval acquisition module, used for:

[0028] Get the data acquisition time interval of each intelligent monitoring device and set the time threshold;

[0029] Data transmission module for:

[0030] A transmission time interval is set according to the time threshold, and data corresponding to the time threshold is transmitted to the analysis and diagnosis unit according to the transmission time interval.

[0031] Furthermore, the time threshold is divided into a first time threshold and a second time threshold, specifically:

[0032] If the data acquisition time interval of the intelligent monitoring device is less than the first time threshold, a first transmission time interval is set for the data acquired by the intelligent monitoring device;

[0033] If the data acquisition time interval of the intelligent monitoring device is equal to or greater than the first time threshold but less than the second time threshold, a second transmission time interval is set for the data acquired by the intelligent monitoring device;

[0034] If the data acquisition time interval of the intelligent monitoring device is greater than the second time threshold, a third transmission time interval is set for the data acquired by the intelligent monitoring device.

[0035] Furthermore, the preprocessing includes:

[0036] Data cleaning: Process and correct the collected data to remove or repair errors, incomplete or redundant data;

[0037] Data conversion: Convert cleaned data into a unified format to ensure data consistency.

[0038] Furthermore, the analysis and diagnosis unit comprises:

[0039] Comparative analysis module for:

[0040] Compare the measured partial discharge data, temperature data, vibration data, current and voltage data, and oil data with a large number of existing fault cases one by one, and compare and analyze whether there are any abnormalities in the data to achieve preliminary diagnosis of transformer faults;

[0041] Model analysis module for:

[0042] An integrated diagnostic model is constructed and trained using the historical data of the transformer. Based on the trained integrated diagnostic model, the current measured data are analyzed and diagnosed to realize intelligent diagnosis of transformer faults.

[0043] Furthermore, the model analysis module includes the following process:

[0044] Convolutional neural network is used as the basis for building an integrated diagnostic model;

[0045] The integrated diagnostic model is trained using the historical data of the transformer, and the trained integrated diagnostic model is tested to evaluate the performance of the integrated diagnostic model;

[0046] After training and testing are completed, the integrated diagnostic model is deployed to practical applications to achieve intelligent diagnosis of transformer faults.

[0047] Furthermore, the remote monitoring unit comprises:

[0048] Data management module for:

[0049] Store various data obtained by the comprehensive monitoring unit and provide users with query, output and printing functions;

[0050] Diagnostic information module for:

[0051] Store fault cases and historical data about the transformer so as to provide a large amount of fault cases and historical data to the analysis and diagnosis unit;

[0052] The result display module is used to:

[0053] Visualize the fault diagnosis results of the analysis and diagnosis unit, including histograms, bar charts and pie charts;

[0054] User permissions module, used to:

[0055] Different access rights are set for different users, and users obtain corresponding management rights through the access rights they have, so as to realize the management of the remote monitoring unit by the users.

[0056] Furthermore, the diagnostic information module classifies and stores the fault cases and historical data of the transformer, and deletes or updates the fault cases and historical data regularly or irregularly.

[0057] Furthermore, the user authority module includes the following process:

[0058] The user creates an account and applies for the required permissions;

[0059] Perform further authentication on the user to ensure that the user is a legitimate user;

[0060] Grant appropriate access rights based on user identity;

[0061] After the user successfully logs in, he is allowed to access authorized resources and use specific functions, including management, reading, deletion, input and output functions.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The comprehensive monitoring unit of the present invention can obtain various data of the transformer through multiple intelligent monitoring devices. The obtained multi-dimensional data of the transformer can be used to diagnose the transformer fault more comprehensively, avoid deviations in the diagnosis results, and thus ensure the normal operation of the transformer. The analysis and diagnosis unit first compares and analyzes the measured data with a large number of existing fault cases. After comparing the data one by one, it can be determined whether there is a fault in the current transformer, thereby achieving preliminary fault diagnosis of the transformer. Then, the analysis and diagnosis unit uses an integrated diagnostic model and combines the measured data from the transformer with historical data for analysis and diagnosis, so as to further diagnose the transformer fault. Through the combination of the two diagnostic methods, the accuracy of the transformer fault diagnosis can be improved, so as to provide a basis for the staff to supervise the transformer, thereby ensuring the normal operation of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a structural schematic diagram of the transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In order to solve the existing fault monitoring of gas generator transformers, it is usually dependent on manual or single sensor monitoring methods. Manual monitoring not only increases the workload but also has low monitoring efficiency. Using a single monitoring variable for judgment is prone to result deviation and it is difficult to ensure the accuracy of fault diagnosis. Please refer to Figure 1 , this embodiment provides the following technical solutions:

[0067] A transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system, comprising:

[0068] Integrated monitoring unit for:

[0069] Deploy multiple intelligent monitoring devices to monitor the transformers of gas turbine generator sets to obtain various data of the transformers, including partial discharge data, temperature data, vibration data, current and voltage data, and oil product data;

[0070] Data acquisition unit for:

[0071] Collect various data obtained by the comprehensive monitoring unit to realize the aggregation of various data, and process the aggregated data;

[0072] Analytical and diagnostic unit for:

[0073] Compare and analyze the measured data with a large number of existing fault cases to achieve preliminary fault diagnosis of the transformer. Then, through the integrated diagnosis model, analyze and diagnose the measured data of the transformer from the data acquisition unit and the historical data, and realize the fault diagnosis of the transformer through analysis.

[0074] Remote monitoring unit for:

[0075] The integrated monitoring unit stores various data obtained, provides a large number of fault cases and historical data for the analysis and diagnosis unit, displays the fault diagnosis results of the analysis and diagnosis unit, and sets permissions for users to enable users to manage the remote monitoring unit.

[0076] The technical effects of the above content are as follows: the comprehensive monitoring unit can obtain various data of the transformer through multiple intelligent monitoring devices, so as to diagnose the transformer fault more comprehensively; the data acquisition unit can collect and summarize the various data obtained by the comprehensive monitoring unit, and can process the summarized data to improve the quality of the data; the analysis and diagnosis unit can compare and analyze the measured data and analyze and diagnose through an integrated diagnostic model, so as to realize intelligent diagnosis of transformer faults; finally, the fault diagnosis results can be displayed through the remote monitoring unit, and the remote monitoring unit can also store the acquired data so that the staff can view the required data according to the actual situation.

[0077] The integrated monitoring unit includes multiple intelligent monitoring devices, specifically:

[0078] Microwave sensors for:

[0079] Monitor the partial discharge signal of the transformer to obtain partial discharge data about the transformer;

[0080] Thermal imaging cameras for:

[0081] Monitor the temperature distribution on the transformer surface to obtain temperature data about the transformer;

[0082] Vibration sensors for:

[0083] Monitor the vibration of the transformer to obtain vibration data about the transformer;

[0084] Current and voltage sensors for:

[0085] Monitor the current and voltage information of the transformer to obtain current and voltage data about the transformer;

[0086] Micro-water sensor for:

[0087] Monitor the trace moisture content in transformer insulating oil to obtain oil quality data about the transformer.

[0088] The technical effects of the above contents are as follows: the comprehensive monitoring unit can monitor the partial discharge signal of the transformer through a microwave sensor, the comprehensive monitoring unit can monitor the temperature fault of the transformer through an infrared thermal imager, the comprehensive monitoring unit can monitor the vibration condition of the transformer through a vibration sensor, the comprehensive monitoring unit can monitor the current and voltage information of the transformer through a current and voltage sensor, and the comprehensive monitoring unit can monitor the micro-water content in the transformer oil through a micro-water sensor. Through the above monitoring, multi-dimensional data about the transformer, such as partial discharge data, temperature data, vibration data, current and voltage data, and oil product data, can be obtained. Compared with single monitoring data, multi-dimensional data can provide more comprehensive and accurate transformer fault diagnosis results and avoid deviations in the diagnosis results of the transformer.

[0089] Data acquisition unit, including:

[0090] Data acquisition module for:

[0091] Collect various data obtained by multiple deployed intelligent monitoring devices in real time and pre-process the collected data;

[0092] Time interval acquisition module, used for:

[0093] Get the data acquisition time interval of each intelligent monitoring device and set the time threshold;

[0094] Data transmission module for:

[0095] A transmission time interval is set according to the time threshold, and data corresponding to the time threshold is transmitted to the analysis and diagnosis unit according to the transmission time interval.

[0096] The time threshold is divided into a first time threshold and a second time threshold, specifically:

[0097] If the data acquisition time interval of the intelligent monitoring device is less than the first time threshold, a first transmission time interval is set for the data acquired by the intelligent monitoring device;

[0098] If the data acquisition time interval of the intelligent monitoring device is equal to or greater than the first time threshold but less than the second time threshold, a second transmission time interval is set for the data acquired by the intelligent monitoring device;

[0099] If the data acquisition time interval of the intelligent monitoring device is greater than the second time threshold, a third transmission time interval is set for the data acquired by the intelligent monitoring device.

[0100] The technical effects of the above content are as follows: first, the data acquisition module can collect various data acquired by multiple intelligent monitoring devices. Then, different time thresholds are set according to the data acquisition time interval of the intelligent monitoring device, so that the data acquired by the intelligent monitoring device can be divided, which can better reflect the real-time nature of the data. The data acquired by the intelligent monitoring device with a shorter data acquisition time interval will be transmitted and processed faster. Finally, the data transmission module can set the transmission time interval for the time threshold, and transmit the data corresponding to the time threshold to the analysis and diagnosis unit according to the transmission time interval. In this way, the data acquired by the intelligent monitoring devices with different data acquisition time intervals can have personalized time arrangements during transmission, better adapt to the data acquisition characteristics of different intelligent monitoring devices, thereby improving the flexibility of data transmission. The data acquired by the intelligent monitoring devices with a shorter data acquisition time interval can be transmitted more frequently to ensure real-time nature, while the data sets acquired by the intelligent monitoring devices with a longer data acquisition time interval can be appropriately reduced in the number of transmissions as needed to reduce resource consumption.

[0101] Pre-processing, including:

[0102] Data cleaning: Process and correct the collected data to remove or repair errors, incomplete or redundant data;

[0103] This includes missing value processing, outlier processing and duplicate data processing, specifically:

[0104] Missing value processing: To fill missing values, you can use interpolation, mean, median and other methods;

[0105] Outlier processing: detect and process outliers, which can be deleted or replaced;

[0106] Duplicate data processing: remove duplicate data;

[0107] Data conversion: Convert cleaned data into a unified format to ensure data consistency.

[0108] The technical effects of the above content are: the purpose of data cleaning is to improve the quality of data and make it more suitable for data analysis or data mining. Through data cleaning, the accuracy and reliability of the data can be ensured, thereby improving the accuracy of subsequent analysis and modeling. The purpose of data conversion is to ensure the quality and consistency of data, improve the reliability and availability of data, promote data interoperability and integrability, and is very important for data analysis and decision support.

[0109] Analytical diagnostic unit, including:

[0110] Comparative analysis module for:

[0111] Compare the measured partial discharge data, temperature data, vibration data, current and voltage data, and oil data with a large number of existing fault cases one by one, and compare and analyze whether there are any abnormalities in the data to achieve preliminary diagnosis of transformer faults;

[0112] Model analysis module for:

[0113] Construct an integrated diagnostic model and use the historical data of the transformer to train the integrated diagnostic model. Based on the trained integrated diagnostic model, analyze and diagnose the current measured data to achieve intelligent diagnosis of transformer faults.

[0114] Among them, the model analysis module includes the following processes:

[0115] Convolutional neural network is used as the basis for building an integrated diagnostic model;

[0116] The integrated diagnostic model is trained using the historical data of the transformer, and the trained integrated diagnostic model is tested to evaluate the performance of the integrated diagnostic model;

[0117] After training and testing are completed, the integrated diagnostic model is deployed to practical applications to achieve intelligent diagnosis of transformer faults.

[0118] The technical effect of the above content is: the analysis and diagnosis unit adopts comparative analysis and model analysis. First, the comparative analysis module compares the local discharge data, temperature data, vibration data, current and voltage data and oil product data with a large number of existing fault cases one by one. By comparing and analyzing whether there are abnormalities in each data one by one, it can be determined whether the transformer is currently in a fault condition. The comparative analysis module can realize the preliminary diagnosis of the transformer fault. Then, the model analysis module uses a convolutional neural network to construct an integrated diagnosis model as the basis for analysis and diagnosis. The integrated diagnosis model is trained using historical data provided by the remote monitoring unit, and the trained integrated diagnosis model is tested to evaluate the performance of the integrated diagnosis model and ensure the accuracy of the integrated diagnosis model analysis. After the training and testing are completed, the integrated diagnosis model is deployed for actual application. By analyzing and diagnosing the measured data through the integrated diagnosis model, the intelligent diagnosis of transformer faults can be further realized. The analysis and diagnosis unit adopts two fault diagnosis methods, which can improve the accuracy of transformer fault diagnosis, thereby providing a better basis for the supervision of staff.

[0119] Remote monitoring unit, including:

[0120] Data management module for:

[0121] Store various data obtained by the comprehensive monitoring unit and provide users with query, output and printing functions;

[0122] Diagnostic information module for:

[0123] Store fault cases and historical data about the transformer so as to provide a large amount of fault cases and historical data to the analysis and diagnosis unit;

[0124] Among them, the diagnostic information module classifies and stores the fault cases and historical data of the transformer. Classified storage can improve the data management efficiency, so that the fault cases and historical data can be provided to the analysis and diagnosis unit on demand faster and more accurately, and the fault cases and historical data can be deleted or updated regularly or irregularly, which can ensure the accuracy and effectiveness of fault diagnosis;

[0125] The result display module is used to:

[0126] Visualize the fault diagnosis results of the analysis and diagnosis unit, including histograms, bar charts and pie charts;

[0127] User permissions module, used to:

[0128] Different access rights are set for different users. Users obtain corresponding management rights according to the access rights they have, so as to realize the management of remote monitoring units by users;

[0129] Among them, the user authority module includes the following processes:

[0130] The user creates an account and applies for the required permissions;

[0131] Perform further authentication on the user to ensure that the user is a legitimate user;

[0132] Grant appropriate access rights based on user identity;

[0133] After the user successfully logs in, he is allowed to access authorized resources and use specific functions, including management, reading, deletion, input and output functions.

[0134] The technical effects of the above content are as follows: first, the data management module can store various data obtained by the comprehensive monitoring unit, so that users can query, output and print. Then, the diagnostic information module can store the fault cases and historical data of the transformer, so that a large number of fault cases and historical data can be provided to the analysis and diagnosis unit, so that the analysis and diagnosis unit can perform data analysis. The fault diagnosis results of the analysis and diagnosis sheet can be visualized by the result display module using histograms, bar charts and pie charts, so that staff and users can view the fault diagnosis results of the transformer more intuitively, so as to take corresponding measures in time according to the fault diagnosis results. Finally, different access rights can be set for different users through the user authority module, so that users can access authorized resources and use specific functions such as management, reading, deletion, input and output, so that users can manage the remote monitoring unit and prevent unauthorized access and data leakage.

[0135] Working principle: The comprehensive monitoring unit can obtain various data of the transformer through multiple intelligent monitoring devices, and can summarize the obtained data through the data acquisition unit, and perform data cleaning and data conversion on the summarized data, so as to improve the quality of the data. After the data are processed, different time thresholds are set according to the data acquisition time interval of the intelligent monitoring device, so that the data acquired by the intelligent monitoring devices with different data acquisition time intervals can have personalized time arrangements when transmitted, better adapt to the data acquisition characteristics of different intelligent monitoring devices, thereby improving the flexibility of data transmission, and first compare and analyze the measured data with a large number of existing fault cases. After comparing the data one by one, it can be judged whether the current transformer is Whether there is a fault, thereby achieving preliminary fault diagnosis of the transformer. Then, the analysis and diagnosis unit can further diagnose the transformer fault through the integrated diagnosis model and comprehensive analysis and diagnosis of various data from the actual measurement of the transformer and historical data. The use of two fault diagnosis methods can improve the accuracy of transformer fault diagnosis. Finally, the remote monitoring unit can visualize the fault diagnosis results so that staff and users can view the fault diagnosis results of the transformer more intuitively. In addition, the remote monitoring unit can set different access rights for different users, allowing users to access authorized resources and use specific functions, so that users can manage the remote monitoring unit and prevent unauthorized access and data leakage.

[0136] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0137] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system, characterized in that: include: Integrated monitoring unit for: Deploy multiple intelligent monitoring devices to monitor the transformers of gas turbine generator sets and obtain various data of the transformers, including partial discharge data, temperature data, vibration data, current and voltage data, and oil product data; Data acquisition unit for: Collect various data obtained by the comprehensive monitoring unit, summarize the data, and process the summarized data; Analytical diagnostic unit for: Compare and analyze the measured data with a large number of existing fault cases to achieve preliminary fault diagnosis of the transformer. Then, through the integrated diagnosis model, analyze and diagnose the measured data of the transformer from the data acquisition unit and the historical data, and realize the fault diagnosis of the transformer through analysis. Remote monitoring unit for: The integrated monitoring unit stores various data obtained, provides a large number of fault cases and historical data for the analysis and diagnosis unit, displays the fault diagnosis results of the analysis and diagnosis unit, and sets permissions for users to enable users to manage the remote monitoring unit.

2. According to claim 1, a transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system is characterized by: The comprehensive monitoring unit includes a plurality of intelligent monitoring devices, specifically: Microwave sensors for: Monitor the partial discharge signal of the transformer and obtain partial discharge data about the transformer; Thermal imaging cameras for: Monitor the temperature distribution on the transformer surface and obtain temperature data about the transformer; Vibration sensors for: Monitor the vibration of the transformer and obtain vibration data about the transformer; Current and voltage sensors for: Monitor the current and voltage information of the transformer and obtain the current and voltage data about the transformer; Micro-water sensor for: Monitor the trace moisture content in transformer insulating oil and obtain oil quality data about the transformer.

3. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 1 is characterized by: The data acquisition unit comprises: Data acquisition module for: Collect various data obtained by multiple deployed intelligent monitoring devices in real time and pre-process the collected data; Time interval acquisition module, used for: Get the data acquisition time interval of each intelligent monitoring device and set the time threshold; Data transmission module for: A transmission time interval is set according to the time threshold, and data corresponding to the time threshold is transmitted to the analysis and diagnosis unit according to the transmission time interval.

4. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 3 is characterized by: The time threshold is divided into a first time threshold and a second time threshold, specifically: If the data acquisition time interval of the intelligent monitoring device is less than the first time threshold, a first transmission time interval is set for the data acquired by the intelligent monitoring device; If the data acquisition time interval of the intelligent monitoring device is equal to or greater than the first time threshold but less than the second time threshold, a second transmission time interval is set for the data acquired by the intelligent monitoring device; If the data acquisition time interval of the intelligent monitoring device is greater than the second time threshold, a third transmission time interval is set for the data acquired by the intelligent monitoring device.

5. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 3 is characterized by: The preprocessing comprises: Data cleaning: Process and correct the collected data to remove or repair errors, incomplete or redundant data; Data conversion: Convert cleaned data into a unified format to ensure data consistency.

6. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 1 is characterized by: The analysis and diagnosis unit comprises: Comparative analysis module for: Compare the measured partial discharge data, temperature data, vibration data, current and voltage data, and oil data with a large number of existing fault cases one by one, and make preliminary diagnosis of transformer faults by comparing and analyzing whether there are any abnormalities in the data; Model analysis module for: An integrated diagnostic model is constructed and trained using the historical data of the transformer. Based on the trained integrated diagnostic model, the current measured data are analyzed and diagnosed to realize intelligent diagnosis of transformer faults.

7. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 6 is characterized by: The model analysis module includes the following processes: Convolutional neural network is used as the basis for building an integrated diagnostic model; The integrated diagnostic model is trained using the historical data of the transformer, and the trained integrated diagnostic model is tested to evaluate the performance of the integrated diagnostic model; After training and testing are completed, the integrated diagnostic model will be deployed to practical applications to achieve intelligent diagnosis of transformer faults.

8. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 1 is characterized by: The remote monitoring unit comprises: Data management module for: Store various data obtained by the comprehensive monitoring unit and provide users with query, output and printing functions; Diagnostic information module for: Store fault cases and historical data about transformers, and provide a large amount of fault cases and historical data to the analysis and diagnosis unit; The result display module is used to: Visualize the fault diagnosis results of the analysis and diagnosis unit, including histograms, bar charts and pie charts; User permissions module, used to: Different access rights are set for different users. Users obtain corresponding management rights through the access rights they have, thereby enabling users to manage the remote monitoring unit.

9. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 8 is characterized by: The diagnostic information module classifies and stores the fault cases and historical data of the transformer, and deletes or updates the fault cases and historical data regularly or irregularly.

10. The transformer multi-dimensional comprehensive monitoring fault intelligent diagnosis system according to claim 8, characterized in that: The user authority module includes the following processes: The user creates an account and applies for the required permissions; Perform further identity verification on the user to ensure that the user is a legitimate user; Grant appropriate access rights based on user identity; After the user successfully logs in, he is allowed to access authorized resources and use specific functions, including management, reading, deletion, input and output functions.

Images