Transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics

Abstract

The invention discloses a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics, and the system comprises a monitoring unit which is used for monitoring the deposition state of sulfide in a transformer in real time, and obtaining sulfide deposition characteristic parameters during the operation period of the transformer; the first diagnosis unit is used for judging whether the current sulfide deposition characteristic parameter affects the insulation system or not and obtaining a first diagnosis result; the second diagnosis unit is used for acquiring a second diagnosis result according to the propagation and attenuation characteristics of the electric signals, the thermal signals and the vibration signals under different turn-to-turn discharge positions, types and intensities; the fault diagnosis unit is used for judging whether the insulation defect and the turn-to-turn discharge phenomenon in the transformer reach the early warning level or not according to the first diagnosis result and the second diagnosis result; and the early warning unit is used for generating corresponding fault early warning information according to the current early warning level, and reminding a user or operation and maintenance personnel to take preventive maintenance or emergency treatment measures in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of transformers, and in particular to a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics. Background Art

[0002] Transformers are vital equipment in power systems, and their long-term safe and reliable operation is of great significance to the stability of the entire power grid. However, the insulation paper and insulating oil inside the transformer will deteriorate during long-term operation, and the deposition of cuprous sulfide (Cu2S) is considered to be one of the important reasons for transformer failure. The formation of cuprous sulfide is usually related to the sulfide reaction in the insulation paper, which accelerates the aging of the insulation paper and causes breakdown, thereby causing partial discharge or even full discharge, which greatly threatens the operational safety of the transformer.

[0003] Most of the existing detection technologies are unable to achieve online, real-time monitoring. The internal degradation process of the transformer is slow and difficult to detect, and the occurrence and development of cuprous sulfide deposition is even more hidden. Traditional technologies cannot effectively capture these microscopic changes in the early stages. Current technologies focus more on post-fault diagnosis and lack continuous monitoring and trend analysis of the degradation process, especially on the risk of insulation failure caused by sulfide deposition, and fail to provide forward-looking early warning information. Most technologies rely on only a single type of physical signal (such as electrical or thermal signals) to determine the insulation status, and fail to comprehensively analyze the changes in multiple physical signals (such as ultrasound, electromagnetic signals, optical radiation, etc.), resulting in a lack of in-depth understanding of the degradation process.

[0004] Therefore, there is an urgent need for a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics. Summary of the invention

[0005] The present invention provides a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics to solve the above problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: A transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics, comprising: A monitoring unit, used to monitor the sulfide deposition state inside the transformer in real time and obtain sulfide deposition characteristic parameters during transformer operation; A first diagnostic unit, used to determine whether the current sulfide deposition characteristic parameters have an impact on the insulation system and obtain a first diagnostic result; The second diagnosis unit is used to identify the specific location and fault type of the discharge fault through the propagation and attenuation characteristics of the electrical signal, thermal signal and vibration signal under different inter-turn discharge locations, types and intensities, and obtain a second diagnosis result; A fault diagnosis unit, used to determine whether the insulation defects and inter-turn discharge phenomena inside the transformer have reached a warning level according to the first diagnosis result and the second diagnosis result; The early warning unit is used to generate corresponding fault early warning information according to the current early warning level, and remind users or operation and maintenance personnel to take preventive maintenance or emergency disposal measures in real time.

[0007] Among them, it also includes: The display terminal is used to display the sulfide deposition characteristic parameters at each monitoring time point, detailed information of each potential fault point, the propagation path and attenuation characteristics of the inter-turn discharge signal, the fault type and warning level, as well as the prevention or maintenance suggestions given.

[0008] The monitoring unit includes: The transformer monitoring module is used to set multiple monitoring time points according to preset time intervals during the operation of the transformer, monitor the deposition state of the insulation paper and sulfide inside the transformer in real time, and obtain the sulfide deposition signal and operation status signal at each monitoring time point; A signal processing module is used to filter out noise in the sulfide deposition signal and the operation status signal to obtain sulfide deposition characteristic data and process data, where the process data is the load current of the transformer; A division module is used to align the sulfide deposition characteristic data with the process data, divide the working conditions according to the process data, classify the sulfide deposition characteristic data under each working condition, and obtain multiple groups of sulfide deposition data; The characteristic parameter extraction module is used to extract the deposition thickness and distribution characteristics of sulfides on the insulating paper corresponding to each monitoring time point based on the deposition characteristic samples in each group of sulfide deposition data, and obtain the sulfide deposition characteristic parameters during the operation of the transformer.

[0009] Wherein, the first diagnosis unit comprises: A diagnostic model building module, used to align the acquired copper sulfide deposition characteristic parameters with the aging and degradation state data of the transformer insulation paper, obtain an insulation system characteristic data set, and build a first fault diagnosis model based on the insulation system characteristic data set; The anomaly detection module is used to perform anomaly detection on the sulfide deposition characteristic data under various detection conditions based on the fault diagnosis model, identify potential insulation defect points, evaluate the severity, and obtain the first diagnosis result.

[0010] Wherein, obtaining the second diagnosis result includes: Acquire multi-physical signal data at different inter-turn discharge positions, types and intensities, and identify and obtain initial diagnostic data from the multi-physical signal data, wherein the initial diagnostic data includes electrical signal parameters, thermal signal parameters and vibration signal parameters; According to the electrical signal parameters, thermal signal parameters and vibration signal parameters, the initial diagnostic data is processed in layers to obtain the data to be diagnosed, including: Obtaining propagation path parameters from electrical signal parameters, and obtaining attenuation characteristic parameters from thermal signal parameters and vibration signal parameters; Acquire the attenuation characteristic parameter associated with each propagation path parameter as a data group to be diagnosed; After sorting each data group to be diagnosed according to the propagation path parameter, the data corresponding to each propagation path parameter and the attenuation characteristic parameter are stored in the corresponding data group to be diagnosed, thereby obtaining the data to be diagnosed; Acquire a transformer fault diagnosis knowledge base, and acquire fault mode data from the transformer fault diagnosis knowledge base; After noise elimination processing is performed on the fault mode data, a data feature vector is constructed, and fault-related data is obtained from the transformer fault diagnosis knowledge base; After clustering the data feature vectors according to the fault-related data, a data group to be trained is obtained, and after training the initial model with the data group to be trained, a second fault diagnosis model is obtained; Input the data to be diagnosed into the preset second fault diagnosis model one by one to obtain the specific location and fault type of the discharge fault; A second diagnosis result is generated according to the specific location and fault type of the discharge fault and the initial diagnosis data.

[0011] Among them, judging whether the insulation defects and inter-turn discharge phenomena inside the transformer have reached the warning level includes: According to the first and second diagnostic results, the severity of insulation defects and inter-turn discharge phenomena is graded and warned. The warning levels are divided into the following four categories: Warning level 0: no obvious insulation defects or inter-turn discharge, the transformer is in normal operation; Warning level 1: Minor insulation defects or inter-turn discharge are detected. It is recommended to monitor regularly and record the change trend. Warning level 2: moderate insulation defects or inter-turn discharges exist. It is recommended to take preventive measures and conduct in-depth inspections of relevant areas. Warning level 3: The insulation system or inter-turn discharge has reached a serious level, posing a high risk. It is recommended to shut down the machine for maintenance immediately.

[0012] Among them, the corresponding fault warning information is generated, including: The fault warning model outputs a current warning level according to the input first diagnosis result and the second diagnosis result, and when the fault warning model outputs the current warning level, acquires fault warning information corresponding to the warning level; According to the current warning level, obtain the user reminder strategy corresponding to the warning level and generate corresponding fault warning information; If the warning level is 0, a prompt message is generated to inform that the device is operating normally; If the warning level is 1, a suggestion message is generated to remind the user to regularly monitor and record the change trend; If the warning level is 2, a preventive maintenance suggestion is generated to remind the user or operation and maintenance personnel to conduct in-depth inspections of the relevant areas to prevent the fault from expanding; If the warning level is 3, an emergency shutdown and maintenance suggestion is generated to remind the user to shut down immediately and perform emergency maintenance; Display fault warning information in real time on the display terminal; Obtaining a preset fault decision-making support model, inputting the current warning level and the generated fault warning information into the fault decision-making support model, and obtaining at least one maintenance or disposal suggestion information output by the decision-making support model; Based on the maintenance or disposal suggestion information, provide users or operation and maintenance personnel with specific maintenance plans and strategic basis to help users or operation and maintenance personnel prevent faults or carry out emergency disposal.

[0013] The display terminal includes: The first display module is used to display the sulfide deposition trend at each monitoring time point on the time axis in a graphical manner according to the acquired sulfide deposition characteristic parameters, and to display the sulfide deposition changes in the monitoring area; The second display module is used to mark in detail the geographical location, fault degree and fault type of each potential fault point in a preset distributed fault point map; The third display module is used to display the propagation path and attenuation characteristics of the inter-turn discharge signal, including the initial position, propagation direction and intensity change of the signal, and is visualized by color gradient or line thickness; The fourth display module is used to identify the warning level of each fault type using different colors or symbols according to the acquired warning level information, and to display each fault type by category in the form of a list; The fifth display module is used to display corresponding prevention or maintenance suggestions according to the acquired fault information and warning level, and associate the suggestion information with the corresponding fault point or monitoring time point for display.

[0014] Wherein, building a first fault diagnosis model based on the insulation system characteristic data set includes: Based on the obtained transformer copper sulfide deposition characteristic parameters, combined with the aging and degradation status data of the transformer insulation paper under each working condition, an insulation system characteristic data set is constructed; Based on the insulation system characteristic data set, the correlation between copper sulfide deposition and insulation paper breakdown is analyzed to determine whether copper sulfide deposition has an impact on the transformer insulation system, further determine the severity of insulation defects, and mark potential fault points; For each set of operating condition data in the insulation system characteristic data set, a first fault diagnosis model is constructed, and fault identification is performed on the insulation system characteristic data under each operating condition based on the first fault diagnosis model to obtain a first diagnosis result, including: For each set of insulation system characteristic data, copper sulfide deposition characteristic parameters and insulation paper aging degradation state data are selected to form a sub-sample set, and a diagnostic tree is initialized based on the sub-sample set; Randomly selecting multiple features from the sub-sample set, determining multiple splitting nodes based on each feature, and splitting the sub-sample set into multiple subsets based on each splitting node; Calculate the variance of the insulation system characteristic data corresponding to each split node, and determine the variance reduction coefficient of each split node; For each feature data, calculate the distance between the feature data and other feature data, and select the nearest neighbor feature data for clustering to obtain the clustering coefficient; The variance reduction coefficient and clustering coefficient are normalized, and the weight ratio is set to adjust the contribution ratio of the two to form the final split decision indicator; Determine the target split node based on the maximum split decision index, and recursively split the target node until the leaf node of the diagnosis tree is reached; For each feature data, the path parameter in the diagnosis tree is calculated, and the feature data whose path parameter exceeds the preset threshold is marked as a potential fault point; Analyze potential fault points, and determine the fault type and location of the insulation system by combining copper sulfide deposition characteristic parameters and insulation paper aging and degradation status data through spectrum analysis or related diagnostic methods.

[0015] Among them, the specific location and fault type of the discharge fault are obtained, including: Inputting the first data to be diagnosed into the second fault diagnosis model, performing similarity matching on the first data to be diagnosed in the second fault diagnosis model, and outputting the current diagnosis result according to the matching result; Starting from the second data to be diagnosed, the current diagnosis result is input into the second fault diagnosis model to obtain the corresponding diagnosis result, and the first diagnosis result and all the diagnosis results are integrated to obtain the specific location and fault type of the discharge fault.

[0016] Compared with the prior art, the present invention has the following advantages: A transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics includes: a monitoring unit for real-time monitoring of the sulfide deposition state inside the transformer and obtaining the sulfide deposition characteristic parameters during the operation of the transformer; a first diagnosis unit for judging whether the current sulfide deposition characteristic parameters affect the insulation system and obtaining the first diagnosis result; a second diagnosis unit for identifying the specific location and fault type of the discharge fault through the propagation and attenuation characteristics of the electrical signal, thermal signal and vibration signal under different inter-turn discharge positions, types and intensities, and obtaining the second diagnosis result; a fault diagnosis unit for judging whether the insulation defects and inter-turn discharge phenomena inside the transformer reach the early warning level according to the first diagnosis result and the second diagnosis result; an early warning unit for generating corresponding fault early warning information according to the current early warning level, and reminding the user or operation and maintenance personnel to take preventive maintenance or emergency disposal measures in real time. The accuracy of fault diagnosis and early warning is greatly improved, and the misjudgment and omission are reduced.

[0017] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a structural diagram of a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics in an embodiment of the present invention; Figure 2 This is a structural diagram of a monitoring unit in an embodiment of the present invention; Figure 3 4 is a structural diagram of the first diagnostic unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0021] The embodiment of the present invention provides a transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics, including: A monitoring unit, used to monitor the sulfide deposition state inside the transformer in real time and obtain sulfide deposition characteristic parameters during transformer operation; A first diagnostic unit, used to determine whether the current sulfide deposition characteristic parameters have an impact on the insulation system and obtain a first diagnostic result; The second diagnosis unit is used to identify the specific location and fault type of the discharge fault through the propagation and attenuation characteristics of the electrical signal, thermal signal and vibration signal under different inter-turn discharge locations, types and intensities, and obtain a second diagnosis result; A fault diagnosis unit, used to determine whether the insulation defects and inter-turn discharge phenomena inside the transformer have reached a warning level according to the first diagnosis result and the second diagnosis result; The early warning unit is used to generate corresponding fault early warning information according to the current early warning level, and remind users or operation and maintenance personnel to take preventive maintenance or emergency disposal measures in real time.

[0022] The working principle of the above technical solution is: the monitoring unit monitors the sulfide deposition state inside the transformer in real time. Sulfide deposition is usually caused by the chemical reaction of the insulating oil inside the transformer under high temperature environment to generate sulfide, which is deposited on the surface of the conductor or the insulating material, affecting the insulation performance of the transformer. Through the monitoring unit, the system can continuously collect the sulfide deposition characteristic parameters during the operation of the transformer. These parameters include deposition rate, deposition thickness, sulfide content, etc.

[0023] The first diagnostic unit is responsible for analyzing the sulfide deposition characteristic parameters provided by the monitoring unit and determining whether these depositions have an impact on the insulation system of the transformer. It determines whether the deposition will cause insulation performance degradation through set thresholds or trend analysis.

[0024] The second diagnostic unit focuses on identifying inter-turn discharge faults inside the transformer. It determines the specific location, type and intensity of the fault by analyzing the electrical, thermal and vibration signals generated during inter-turn discharge. The unit collects data through sensors at different locations and uses propagation and attenuation characteristics to distinguish the source of the signal.

[0025] The fault diagnosis unit summarizes the results of the first and second diagnostic units and comprehensively determines whether the insulation defects and interturn discharge phenomena inside the transformer have reached the warning level. If sulfide deposition and interturn discharge abnormalities are detected at the same time, the fault diagnosis unit will generate a higher fault severity judgment.

[0026] The early warning unit generates corresponding early warning information based on the analysis results of the fault diagnosis unit. If the diagnosis results show that the insulation defect or inter-turn discharge has reached a dangerous level, the early warning unit will automatically send an alarm to the user or operation and maintenance personnel, and recommend preventive maintenance or emergency disposal measures. The early warning level is usually divided into three levels: low, medium, and high, and different levels correspond to different response measures.

[0027] The beneficial effects of the above technical solution are: through real-time monitoring and multi-dimensional signal analysis (electrical, thermal, and vibration signals) of the monitoring unit, the potential fault types and locations inside the transformer can be accurately identified, which greatly improves the accuracy of fault diagnosis and early warning, and reduces misjudgment and missed reports; early identification of hidden dangers such as sulfide deposition and inter-turn discharge allows maintenance personnel to take preventive maintenance measures before the problem becomes serious, thereby avoiding excessive wear and unplanned downtime of the transformer and extending the service life of the equipment; the automated diagnosis and early warning system reduces the need for manual inspections and reduces the burden on operation and maintenance personnel. At the same time, through the timely reminders of the intelligent early warning system, operation and maintenance personnel can respond to potential problems more quickly and shorten the downtime of equipment failures.

[0028] In another embodiment, it also includes: The display terminal is used to display the sulfide deposition characteristic parameters at each monitoring time point, detailed information of each potential fault point, the propagation path and attenuation characteristics of the inter-turn discharge signal, the fault type and warning level, as well as the prevention or maintenance suggestions given.

[0029] The working principle of the above technical solution is as follows: the core function of the display terminal is to collect and display real-time data from various monitoring units, diagnostic units, fault diagnosis units and early warning units. The monitoring system of the transformer will monitor sulfide deposition, inter-turn discharge and other data in real time through sensors, and transmit these data to the display terminal through the communication network. The display terminal will display the sulfide deposition characteristic parameters at each monitoring time point in the form of charts or numbers. These parameters include the thickness of the deposit, the deposition rate and the trend of change over time. Users can view the data intuitively through the graphical interface of the terminal, which is convenient for real-time grasp of the equipment operation status. When the diagnostic unit detects a potential fault point, the display terminal will display the information of these fault points in detail, including location, signal characteristics, fault type and development trend. This information can help operation and maintenance personnel quickly understand the severity of the problem and its possible impact. By analyzing the propagation path and attenuation characteristics of the discharge signal, the display terminal can display the signal propagation process in the form of a dynamic schematic diagram. For example, after the signal is sent from the discharge point, how it propagates through different windings or insulation layers, how the signal strength gradually decays with distance or time, and other information. This helps to locate the fault point and assess the scope of the fault impact. Based on the analysis results of the fault diagnosis unit, the display terminal will display the identified fault type, such as inter-turn discharge, insulation aging, etc., and its corresponding warning level (low, medium, high). The warning level will be classified according to the severity of the fault and the size of the risk to help users make quick decisions. The display terminal not only displays fault information, but also provides specific prevention or maintenance suggestions based on the system analysis results. The suggestions may include measures such as equipment inspection, component replacement, or adjustment of operating conditions to help users deal with potential problems in a timely manner. For example: When the display terminal detects moderate sulfide deposition, the system will prompt "It is recommended to replace or clean the insulating oil" to prevent the deposits from further deteriorating the insulation performance of the equipment.

[0030] The beneficial effects of the above technical solution are as follows: the display terminal helps operation and maintenance personnel to understand the operating status of the transformer in real time by converting complex data into intuitive charts, dynamic animations and early warning information. Whether it is sulfide deposition, interturn discharge, or fault location, the terminal can present it in a clear form, lowering the threshold for understanding complex technologies; the display terminal can display detailed information and early warning levels of potential faults in real time, helping operation and maintenance personnel to quickly identify problems and take measures. Through the efficient reminders of the early warning system, operation and maintenance personnel can respond before the problem develops to a serious stage, reducing the downtime of transformer failures.

[0031] In another embodiment, the monitoring unit comprises: The transformer monitoring module is used to set multiple monitoring time points according to preset time intervals during the operation of the transformer, monitor the deposition state of the insulation paper and sulfide inside the transformer in real time, and obtain the sulfide deposition signal and operation status signal at each monitoring time point; A signal processing module is used to filter out noise in the sulfide deposition signal and the operation status signal to obtain sulfide deposition characteristic data and process data, where the process data is the load current of the transformer; A division module is used to align the sulfide deposition characteristic data with the process data, divide the working conditions according to the process data, classify the sulfide deposition characteristic data under each working condition, and obtain multiple groups of sulfide deposition data; The characteristic parameter extraction module is used to extract the deposition thickness and distribution characteristics of sulfides on the insulating paper corresponding to each monitoring time point based on the deposition characteristic samples in each group of sulfide deposition data, and obtain the sulfide deposition characteristic parameters during the operation of the transformer.

[0032] The working principle of the above technical solution is as follows: after the transformer starts to operate, the monitoring module sets multiple monitoring time points according to the preset time interval (for example, every 1 hour). The monitoring module can monitor the deposition state of the insulation paper and sulfide inside the transformer in real time. The information obtained at these monitoring time points mainly includes two parts: one is the deposition signal of sulfide, and the other is the operation status signal of the transformer.

[0033] Sulfide deposition signal: This is the raw data collected from the internal sensors of the transformer about the degree of sulfide deposition on the insulation paper. Operation status signal: This is data related to the operation of the transformer, such as load current, etc.

[0034] When the monitoring data is collected, these data contain some noise. The task of the signal processing module is to process these data and filter out the noise. In this way, the sulfide deposition characteristic data and process data can be obtained more accurately. If external electromagnetic interference or sensor errors generate noise, the signal processing module will remove the noise through a specific algorithm. Sulfide deposition characteristic data: After processing, the data reflecting the amount of sulfide deposition on the insulating paper is extracted. Process data: changes in transformer load current, etc. These data help to understand the relationship between the transformer's working load and sulfide deposition.

[0035] Among the acquired data, the sulfide deposition characteristic data and process data are aligned. The classification module classifies these data according to the operating conditions of the transformer. Working condition classification: According to the load current or other process parameters of the transformer, several different working conditions are classified. For example, low load operation, high load operation, overload operation, etc.

[0036] Classified data: Under different working conditions, the deposition characteristics of sulfides will be different. This module classifies and saves the deposition data under each working condition. In this way, multiple sets of sulfide deposition data can be obtained, which is convenient for subsequent analysis.

[0037] Finally, the system extracts characteristic parameters based on each set of sulfide deposition data. By analyzing each set of deposition characteristic samples, the deposition thickness and distribution characteristics of sulfides on the insulating paper can be obtained. Deposition thickness and distribution characteristics: For example, at a certain moment, the sulfide deposition thickness in a certain area on the insulating paper is 0.2 mm, and the distribution is relatively uniform, or the deposition thickness in a certain area is large, which may indicate local aging problems. Deposition characteristic parameters: Based on this information, the system can generate a set of deposition characteristic parameters that reflect the deposition law of sulfides under different operating conditions of the transformer.

[0038] The beneficial effects of the above technical solution are: the system can monitor the sulfide deposition state inside the transformer in real time, avoiding potential problems such as aging of insulating paper due to excessive deposition, and reducing the risk of equipment failure; the signal processing module improves the accuracy of sulfide deposition characteristic data by filtering out noise. In this way, subsequent analysis is more reliable, which helps to detect the deterioration of the insulating paper inside the transformer in advance; by classifying the sulfide deposition data under different operating conditions of the transformer through the division module, it is possible to more clearly understand the impact of different operating conditions on the aging of insulating paper, which helps to optimize the operation strategy of the transformer; by extracting key parameters such as sulfide deposition thickness and distribution characteristics, it is possible to intuitively understand the operating status inside the transformer and provide guidance for operation and maintenance personnel. For example, when the deposition thickness is too large, the system can issue an early warning to indicate that maintenance is required.

[0039] In another embodiment, the first diagnostic unit comprises: A diagnostic model building module, used to align the acquired copper sulfide deposition characteristic parameters with the aging and degradation state data of the transformer insulation paper, obtain an insulation system characteristic data set, and build a first fault diagnosis model based on the insulation system characteristic data set; The anomaly detection module is used to perform anomaly detection on the sulfide deposition characteristic data under various detection conditions based on the fault diagnosis model, identify potential insulation defect points, evaluate the severity, and obtain the first diagnosis result.

[0040] The working principle of the above technical solution is as follows: First, the system will obtain the extracted copper sulfide deposition characteristic parameters, such as the deposition thickness and distribution characteristics of sulfides. At the same time, the system also needs to collect the aging and degradation status data of the transformer's insulation paper, which are historical data or obtained through other monitoring equipment. Before building the model, the system needs to align the copper sulfide deposition characteristic parameters with the insulation paper aging status data. In other words, the two types of data must be synchronized so that they correspond to each other in the same time dimension or operating conditions. For example, at the same monitoring time point, there must be both insulation paper aging data and sulfide deposition data. After alignment, the system merges these data into an insulation system characteristic data set, which contains the complete characteristics of the copper sulfide deposition data and insulation paper aging status of the transformer under different operating conditions.

[0041] Construction of the first fault diagnosis model: Based on this data set, the system will build a first fault diagnosis model through a machine learning model. The model can identify the impact of copper sulfide deposition on the insulation system under different conditions and predict possible faults in the insulation system. If the system finds that the thickness of the sulfide deposition exceeds a certain threshold and the degree of deterioration of the insulation paper also increases, the model may judge this situation as a potential insulation system failure and predict that the failure may occur at some point in the future.

[0042] When the diagnostic model is built, the system will use the model to perform real-time anomaly detection on the subsequent monitored sulfide deposition characteristic data. Based on the diagnostic model, the system detects the sulfide deposition data under the current working conditions and identifies any situation that does not conform to the normal range. For example, in a certain monitoring, the system found that the deposition thickness of copper sulfide suddenly increased, and the model predicted that this increase in deposition may cause the aging of the insulation paper to increase. The system will mark the situation as abnormal. By analyzing the abnormal data, the system can identify possible defects in the insulation system. For example, excessive concentration of sulfide deposition in a certain area may indicate that the insulation paper in that area is more seriously aged, or even partially damaged. In addition to identifying defects, the system will also evaluate the severity of defects based on the diagnostic model. For example, excessive deposition may accelerate the time of insulation failure. The system can inform the operation and maintenance personnel of the urgency of the problem through prediction and provide corresponding maintenance suggestions. Finally, the system will generate the first diagnostic result, which details the current transformer operation status, the abnormalities found, and the recommended maintenance measures.

[0043] The beneficial effects of the above technical solution are as follows: through the construction of the diagnostic model, the system can accurately predict the impact of copper sulfide deposition on the aging of insulating paper, and identify possible fault points in advance, helping the transformer operation and maintenance team to take measures in advance to avoid sudden failures; the system can monitor the copper sulfide deposition in the operation of the transformer in real time through automated anomaly detection, and promptly identify potential insulation defects, reducing the burden of manual inspections and greatly improving the efficiency of detection; in addition to simply identifying faults, the system can also assess the severity of the fault. This means that operation and maintenance personnel can reasonably arrange maintenance plans based on the system's recommendations, thereby avoiding unnecessary downtime or repairs and optimizing the operation and management of the transformer; through the diagnostic model, the system can make maintenance decisions based on big data analysis based on the insulation system characteristic data. This approach reduces the traditional experience-based maintenance model, is more scientific and accurate, and thus reduces operation and maintenance costs.

[0044] In another embodiment, obtaining the second diagnosis result includes: Acquire multi-physical signal data at different inter-turn discharge positions, types and intensities, and identify and obtain initial diagnostic data from the multi-physical signal data, wherein the initial diagnostic data includes electrical signal parameters, thermal signal parameters and vibration signal parameters; According to the electrical signal parameters, thermal signal parameters and vibration signal parameters, the initial diagnostic data is processed in layers to obtain the data to be diagnosed, including: Obtaining propagation path parameters from electrical signal parameters, and obtaining attenuation characteristic parameters from thermal signal parameters and vibration signal parameters; Acquire the attenuation characteristic parameter associated with each propagation path parameter as a data group to be diagnosed; After sorting each data group to be diagnosed according to the propagation path parameter, the data corresponding to each propagation path parameter and the attenuation characteristic parameter are stored in the corresponding data group to be diagnosed, thereby obtaining the data to be diagnosed; Acquire a transformer fault diagnosis knowledge base, and acquire fault mode data from the transformer fault diagnosis knowledge base; After noise elimination processing is performed on the fault mode data, a data feature vector is constructed, and fault-related data is obtained from the transformer fault diagnosis knowledge base; After clustering the data feature vectors according to the fault-related data, a data group to be trained is obtained, and after training the initial model with the data group to be trained, a second fault diagnosis model is obtained; Input the data to be diagnosed into the preset second fault diagnosis model one by one to obtain the specific location and fault type of the discharge fault; A second diagnosis result is generated according to the specific location and fault type of the discharge fault and the initial diagnosis data.

[0045] The working principle of the above technical solution is: first, the test is carried out at different inter-turn discharge positions (for example, inter-turn discharge between the primary winding and the secondary winding). In this process, a variety of physical signal data will be collected, such as: Electrical signal: Due to the change of electric field, the discharge will produce instantaneous high-frequency voltage fluctuations and current pulses; Thermal signal: Energy is released during the discharge process, causing the local temperature to rise; Vibration signal: The shock wave generated by the discharge will cause tiny vibrations in the physical structure of the transformer.

[0046] These signals are monitored in real time through different sensors and the data is stored. These data constitute the initial diagnostic data, mainly including: Electrical signal parameters: such as voltage waveform and pulse frequency; Thermal signal parameters: such as temperature change rate; Vibration signal parameters: such as vibration frequency and intensity.

[0047] Next, the initial diagnostic data is processed in layers, that is, feature data at different levels are extracted according to different signal types: Obtaining propagation path parameters from electrical signals: This refers to the conduction path of the discharge current in the transformer winding or other components, such as the time of current propagation, path length, etc.; Obtain attenuation characteristic parameters from thermal signals and vibration signals: These parameters describe the energy attenuation of thermal signals and vibration signals when they are transmitted in space, such as the ratio of temperature attenuation with distance, the attenuation rate of vibration amplitude, etc.

[0048] Through this hierarchical processing, key information in multi-physical signal data can be extracted to form "data to be diagnosed".

[0049] For each propagation path parameter, the system will find the attenuation characteristic parameters associated with it and combine them into a set of data to be diagnosed. For example, if the propagation path of a discharge is short and the attenuation is small, the system will mark its data separately, and then sort the data to finally generate a complete data set to be diagnosed.

[0050] The historical fault data is extracted from the transformer fault diagnosis knowledge base, which contains a wealth of fault mode and fault association data, such as the location, type and corresponding signal characteristics of known discharge faults. The data feature vector is generated by performing noise elimination on these fault modes.

[0051] The system uses a clustering algorithm to classify these feature vectors to form a "training data group". Next, these training data groups are input into the initial fault diagnosis model for training, and finally a second fault diagnosis model is obtained, which can more accurately identify the specific location and type of discharge faults.

[0052] During the diagnosis process, the system will input the data to be diagnosed into the trained second fault diagnosis model one by one, and the model will identify the specific location and type of the discharge fault based on the input data. For example: The system recognizes that the discharge is occurring in the transformer primary winding; The discharge type is local discharge and the intensity is high-frequency short-time discharge.

[0053] Combined with the initial diagnostic data, the system generates a second diagnostic result and feeds it back to the user.

[0054] The beneficial effects of the above technical solution are: by integrating the parameters of various physical signals such as electricity, heat, and vibration, the characteristics of the discharge fault can be captured more comprehensively and accurately, avoiding the misjudgment of a single signal; the mechanism of layered processing of electrical signals, thermal signals, and vibration signals enables the diagnostic system to extract key information more efficiently. In particular, the extraction and combination of propagation path parameters and attenuation characteristic parameters can enable the system to locate faults more quickly; the system can achieve self-learning and optimization of the diagnostic model through the rich fault modes and historical data in the knowledge base. With the input of more historical fault data, the diagnostic accuracy of the model will continue to improve; by performing noise elimination on the fault mode data, the system can significantly improve the quality of the diagnostic signal, and classify the faults through feature clustering, greatly reducing the probability of misjudgment; the data to be diagnosed can be quickly input into the model for real-time processing, and finally generate a second diagnostic result. This real-time performance provides strong support for timely discovery and resolution of potential faults in transformers, avoiding the risk of fault expansion.

[0055] In another embodiment, determining whether insulation defects and inter-turn discharge phenomena inside the transformer have reached a warning level includes: According to the first and second diagnostic results, the severity of insulation defects and inter-turn discharge phenomena is graded and warned. The warning levels are divided into the following four categories: Warning level 0: no obvious insulation defects or inter-turn discharge, the transformer is in normal operation; Warning level 1: Minor insulation defects or inter-turn discharge are detected. It is recommended to monitor regularly and record the change trend. Warning level 2: moderate insulation defects or inter-turn discharges exist. It is recommended to take preventive measures and conduct in-depth inspections of relevant areas. Warning level 3: The insulation system or inter-turn discharge has reached a serious level, posing a high risk. It is recommended to shut down the machine for maintenance immediately.

[0056] The working principle of the above technical solution is as follows: First, the operating parameters of the transformer are collected through an online or offline monitoring system, such as current, voltage, temperature, gas content in oil (through gas chromatography), and partial discharge (PD) data. These parameters can be used to evaluate the insulation status of the transformer.

[0057] Gas chromatography: By analyzing the gas composition in the transformer oil, arc discharge and overheating inside the transformer can be detected. Gases such as hydrogen, acetylene, methane, etc. are indicative gases of faults.

[0058] Partial discharge monitoring: Sensors detect the intensity and location of partial discharge phenomena, which are important indicators of insulation defects, especially in the case of interturn discharges.

[0059] Preliminary diagnosis (first diagnosis result): Perform a preliminary diagnosis based on the above data to determine whether there is insulation defect or inter-turn discharge. If the monitoring data is obviously beyond the normal range, the preliminary diagnosis can give an abnormal phenomenon and prompt further analysis.

[0060] In-depth analysis (second diagnosis result): Based on the results of the initial diagnosis, more sophisticated analysis tools and algorithms (such as pattern recognition, trend analysis, etc.) are used to further confirm the severity of the problem. For example, the amplitude and frequency of the partial discharge signal can be used to assess the severity of the insulation defect. At the same time, trend analysis can be used to determine whether the problem is getting worse.

[0061] Warning level 0: If the analysis results show that the data is completely within the normal range and there is no obvious abnormality, the transformer is judged to be in normal operation.

[0062] Warning level 1: If the analysis finds minor insulation defects or occasional inter-turn discharge phenomena, the risk is small and the system recommends regular monitoring and recording of changes for continuous tracking.

[0063] Warning level 2: When there is a moderate insulation defect or obvious inter-turn discharge phenomenon, it is recommended to take preventive measures, such as reducing the load, improving ventilation and cooling efficiency, and conducting detailed inspections of abnormal areas to avoid further deterioration of the fault.

[0064] Warning Level 3: When the insulation system or inter-turn discharge phenomenon reaches a serious level, it means there is a high risk of short circuit or equipment failure, and the machine must be shut down for maintenance immediately to avoid greater losses or equipment damage.

[0065] Execution feedback: Based on the warning classification results, the operation and maintenance personnel can take corresponding actions to ensure the safe and reliable operation of the transformer. The early warning system automatically records all operations and results to provide a basis for subsequent analysis.

[0066] The beneficial effects of the above technical solution are: through timely detection and early warning, measures can be taken before insulation defects or interturn discharge problems worsen to prevent transformer failure or damage, thereby improving the safety and reliability of transformer operation; through regular monitoring and preventive maintenance, the damage caused by insulation aging and interturn discharge can be reduced, thereby extending the service life of the transformer.

[0067] In another embodiment, generating corresponding fault warning information includes: The fault warning model outputs a current warning level according to the input first diagnosis result and the second diagnosis result, and when the fault warning model outputs the current warning level, acquires fault warning information corresponding to the warning level; According to the current warning level, obtain the user reminder strategy corresponding to the warning level and generate corresponding fault warning information; If the warning level is 0, a prompt message is generated to inform that the device is operating normally; If the warning level is 1, a suggestion message is generated to remind the user to regularly monitor and record the change trend; If the warning level is 2, a preventive maintenance suggestion is generated to remind the user or operation and maintenance personnel to conduct in-depth inspections of the relevant areas to prevent the fault from expanding; If the warning level is 3, an emergency shutdown and maintenance suggestion is generated to remind the user to shut down immediately and perform emergency maintenance; Display fault warning information in real time on the display terminal; Obtaining a preset fault decision-making support model, inputting the current warning level and the generated fault warning information into the fault decision-making support model, and obtaining at least one maintenance or disposal suggestion information output by the decision-making support model; Based on the maintenance or disposal suggestion information, provide users or operation and maintenance personnel with specific maintenance plans and strategic basis to help users or operation and maintenance personnel prevent faults or carry out emergency disposal.

[0068] The working principle of the above technical solution is as follows: the fault warning model first receives the operating data of the transformer, including the first diagnostic result and the second analysis result. The first diagnostic result is usually derived from conventional monitoring data, such as temperature, current, voltage, insulation status, etc. The second analysis result is obtained through in-depth analysis technology (such as trend analysis, pattern recognition, partial discharge monitoring, etc.), and contains more detailed fault information, such as insulation degradation trend, frequency and intensity of partial discharge, etc.

[0069] Based on the first diagnosis result and the second analysis result, the fault warning model determines the current warning level through a built-in algorithm. The warning levels are divided into four: Warning level 0: No fault, the equipment operates normally.

[0070] Warning level 1: Minor insulation defect or mild inter-turn discharge, the equipment is in good condition.

[0071] Warning level 2: Moderate insulation defect or interturn discharge, the equipment is in poor condition and preventive maintenance is required.

[0072] Warning level 3: Severe insulation defects or severe inter-turn discharges, the equipment is at risk of major failure and must be shut down immediately.

[0073] Fault warning information generation: The model generates corresponding fault warning information according to different warning levels. The specific generation steps are as follows: Warning level 0: Generates a warning message to inform that the device is operating normally and no action is required.

[0074] Warning Level 1: Generates advisory information to remind users or operation and maintenance personnel to regularly monitor and record changing trends in equipment operation.

[0075] Warning level 2: Generates preventive maintenance recommendations to remind operation and maintenance personnel to conduct in-depth inspection and maintenance of the areas where problems are found to prevent the problems from further expanding.

[0076] Warning level 3: Generate emergency shutdown and maintenance recommendations, remind users to shut down immediately, and suggest arranging emergency maintenance to prevent equipment damage or shutdown accidents.

[0077] Real-time display of warning information: Fault warning information is displayed in real time on the display terminal, including the current warning level and corresponding operation suggestions, to help users and operation and maintenance personnel quickly understand the equipment status and take action.

[0078] Calling of auxiliary decision-making model: After generating fault warning information, the system inputs the warning level and the generated fault warning information into the preset fault auxiliary decision-making model. The auxiliary decision-making model is a model based on data and empirical rules. It will provide more specific maintenance or disposal suggestions based on the input data. For example, for the situation of warning level 2, the auxiliary decision-making model will recommend detailed testing of transformer windings in a specific area or replacement of insulation materials.

[0079] Generate maintenance recommendations: The decision-making support model outputs at least one maintenance or disposal recommendation. These recommendations include: specific maintenance operation steps (such as parts or materials that need to be replaced), priority of inspection areas (such as windings, insulation media, etc.), or emergency response measures (such as emergency shutdown plans).

[0080] Finally, the system integrates the recommended information of the auxiliary decision-making model into an actionable maintenance or prevention plan and displays it on the terminal for users or maintenance personnel to refer to. These plans provide users with a specific basis for maintenance strategies and help them take appropriate measures to prevent the fault from further deteriorating.

[0081] The beneficial effects of the above technical solution are as follows: by combining the first diagnostic result and the second analysis result, the system can more accurately assess the health of the transformer and issue an early warning of the fault. This precise early warning reduces the possibility of false alarms or missed alarms, allowing users to make more accurate maintenance decisions; through graded early warnings and corresponding user reminder strategies, operation and maintenance personnel can take preventive measures in advance to effectively avoid the expansion of potential faults and reduce losses caused by equipment damage or downtime; when a fault occurs, the system can quickly generate emergency processing suggestions and provide specific maintenance plans through auxiliary decision-making models. This speeds up the processing of problems and shortens the downtime after equipment failure.

[0082] In another embodiment, the display terminal includes: The first display module is used to display the sulfide deposition trend at each monitoring time point on the time axis in a graphical manner according to the acquired sulfide deposition characteristic parameters, and to display the sulfide deposition changes in the monitoring area; The second display module is used to mark in detail the geographical location, fault degree and fault type of each potential fault point in a preset distributed fault point map; The third display module is used to display the propagation path and attenuation characteristics of the inter-turn discharge signal, including the initial position, propagation direction and intensity change of the signal, and is visualized by color gradient or line thickness; The fourth display module is used to identify the warning level of each fault type using different colors or symbols according to the acquired warning level information, and to display each fault type by category in the form of a list; The fifth display module is used to display corresponding prevention or maintenance suggestions according to the acquired fault information and warning level, and associate the suggestion information with the corresponding fault point or monitoring time point for display.

[0083] The working principle of the above technical solution is as follows: the first display module (sulfide deposition trend display) obtains the sulfide deposition characteristic parameters at multiple monitoring time points, such as sulfide concentration, deposition rate, etc., and corresponds these data to time, and displays the deposition trend of sulfide on the time axis in a graphical manner (such as a line graph, a bar graph, etc.). After the numerical value of each monitoring time point is processed by an algorithm, a trend curve is generated to show the change of sulfide deposition. The second display module (distributed fault point location and display) first obtains potential fault point information such as geographical location, fault severity and fault type through a sensor network or other monitoring equipment. This information will be combined with the preset distributed fault point map, and the specific location of the fault point will be marked through a graphical interface, and the degree of fault will be indicated by color, symbol or other marks (such as red for severe, yellow for medium, and green for mild). For example: in the power grid monitoring system, the sensor finds that there are multiple faults in the cable. The second display module marks them in the geographical distribution map according to the specific coordinates of each fault point, and uses different color marks to indicate the severity of the fault.

[0084] The third display module (visualization of the propagation path of the inter-turn discharge signal) captures the propagation information of the inter-turn discharge signal, including the initial position of the signal, the propagation direction and the intensity change. Through the signal propagation path, attenuation rate and other data, this module uses color gradient (from light to dark) or line thickness (from thick to thin) to intuitively display the change in signal strength. For example: in the winding of a transformer, inter-turn discharge is detected. The third display module generates a line diagram with a color gradient based on the discharge signal collected by the sensor, showing the process of the discharge signal propagating along the winding from the discharge point and gradually attenuating. The line color changes from red to blue, and the line changes from thick to thin, indicating the gradual weakening of the signal strength.

[0085] The fourth display module (fault warning level display) uses different colors or symbols to identify different warning levels (such as minor, warning, and serious) through the warning level information obtained from the fault monitoring system. At the same time, the system will display the types of all current faults in a list and classify them according to the warning level. For example, the high-level warning is ranked first in the list to remind the operator to handle it first. For example: in the power system, when the temperature of a certain transmission line is too high, the system will issue a warning signal. The fourth display module indicates "serious warning" with a red triangle sign and displays this fault at the top of the warning list to remind relevant personnel to take immediate action. The fifth display module (prevention and maintenance suggestion display) generates corresponding prevention or maintenance suggestions by analyzing the acquired fault information and warning level. These suggestions will be displayed in association with different fault points or monitoring time points. For example, if a certain fault point detects that the temperature is too high, the system will provide corresponding cooling measures; if the sulfide deposition exceeds the warning value at a certain time point, the system will recommend relevant governance measures. The suggestion content can be displayed in the form of graphics, text, etc., and associated with specific fault points or time points. When the temperature of a generator bearing is too high, the system generates a maintenance suggestion, prompting that the component needs to be lubricated immediately. The fifth display module displays this suggestion in a pop-up window on the monitoring interface and marks the bearing failure point to remind maintenance personnel to pay attention to this problem.

[0086] The beneficial effects of the above technical solution are: it helps to monitor the changes in sulfide deposition in real time, helps relevant departments understand the emission trends of pollution sources, adjusts environmental protection measures in a timely manner, and reduces environmental pollution risks; it can quickly locate potential fault points and assess the severity of the fault, greatly improving the efficiency of troubleshooting, helping maintenance personnel to respond quickly and reduce system downtime; through the intuitive display of the signal propagation path, it enables technicians to more clearly understand the impact range of the discharge phenomenon and its intensity changes, which helps to accurately determine the cause of equipment failure and take targeted measures.

[0087] In another embodiment, constructing a first fault diagnosis model based on an insulation system characteristic data set includes: Based on the obtained transformer copper sulfide deposition characteristic parameters, combined with the aging and degradation status data of the transformer insulation paper under each working condition, an insulation system characteristic data set is constructed; Based on the insulation system characteristic data set, the correlation between copper sulfide deposition and insulation paper breakdown is analyzed to determine whether copper sulfide deposition has an impact on the transformer insulation system, further determine the severity of insulation defects, and mark potential fault points; For each set of operating condition data in the insulation system characteristic data set, a first fault diagnosis model is constructed, and fault identification is performed on the insulation system characteristic data under each operating condition based on the first fault diagnosis model to obtain a first diagnosis result, including: For each set of insulation system characteristic data, copper sulfide deposition characteristic parameters and insulation paper aging degradation state data are selected to form a sub-sample set, and a diagnostic tree is initialized based on the sub-sample set; Randomly selecting multiple features from the sub-sample set, determining multiple splitting nodes based on each feature, and splitting the sub-sample set into multiple subsets based on each splitting node; Calculate the variance of the insulation system characteristic data corresponding to each split node, and determine the variance reduction coefficient of each split node; For each feature data, calculate the distance between the feature data and other feature data, and select the nearest neighbor feature data for clustering to obtain the clustering coefficient; The variance reduction coefficient and clustering coefficient are normalized, and the weight ratio is set to adjust the contribution ratio of the two to form the final split decision indicator; Determine the target split node based on the maximum split decision index, and recursively split the target node until the leaf node of the diagnosis tree is reached; For each feature data, the path parameter in the diagnosis tree is calculated, and the feature data whose path parameter exceeds the preset threshold is marked as a potential fault point; Analyze potential fault points, and determine the fault type and location of the insulation system by combining copper sulfide deposition characteristic parameters and insulation paper aging and degradation status data through spectrum analysis or related diagnostic methods.

[0088] The working principle of the above technical solution is: during the operation of the transformer, copper sulfide deposition will affect the insulation performance of the insulation paper and oil. Therefore, it is first necessary to collect the copper sulfide deposition characteristic parameters (such as copper sulfide deposition thickness, concentration, etc.) and the aging and degradation status data of the transformer insulation paper (such as dielectric loss, moisture content, fiber strength, etc.) under multiple working conditions. These data are summarized to form a complete insulation system characteristic data set for subsequent analysis. Assuming that under a certain working condition, the copper sulfide deposition concentration of the transformer during operation is 10ppm, the dielectric loss of the insulation paper is 0.5%, and the moisture content is 2%. These parameters constitute part of the characteristic data.

[0089] Based on the insulation system characteristic data set, statistical analysis and correlation analysis were conducted to study whether copper sulfide deposition has a significant effect on the breakdown phenomenon of insulation paper, and to further determine whether copper sulfide deposition will lead to a decrease in insulation performance. By analyzing 100 sets of data, it was found that when the copper sulfide concentration exceeded 15ppm, the dielectric loss of the insulation paper increased significantly and the breakdown voltage decreased significantly, indicating that there is a significant positive correlation between copper sulfide deposition and insulation paper degradation.

[0090] Based on the above data, a fault diagnosis model is constructed. First, copper sulfide deposition characteristic parameters and insulation paper aging and degradation status data are selected as sub-sample sets to initialize the diagnosis tree model. By randomly selecting multiple features in the sub-sample set (such as copper sulfide concentration, insulation paper moisture content, etc.), multiple possible split nodes are determined. In a certain diagnosis, copper sulfide concentration was selected as a feature, and the split node was set to 10ppm, that is, when the copper sulfide concentration is higher or lower than 10ppm, the health state of the insulation system is different. Through analysis, this node can effectively distinguish between normal and degraded insulation systems.

[0091] For the data corresponding to each splitting node, calculate its variance. The variance reduction coefficient can reflect the purity of the data set after splitting. At the same time, by calculating the distance between the feature data, perform feature clustering, and obtain the clustering coefficient. Through normalization processing and setting the weight ratio, determine the final splitting decision index, and select the splitting node corresponding to the maximum decision index. In a certain calculation, the variance reduction coefficient of the splitting node of copper sulfide concentration is 0.8, and the clustering coefficient is 0.9. After combining these two coefficients, the final splitting decision index is formed, and it is decided to split at a copper sulfide concentration of 15ppm.

[0092] Through recursive splitting, a diagnostic tree model is finally constructed. The path of each feature data in the diagnostic tree generates specific path parameters. For feature data whose path parameters exceed the preset threshold, they are marked as potential fault points. A diagnostic result showed that the copper sulfide deposition concentration exceeded 15ppm, the dielectric loss exceeded 1%, and the path parameters exceeded the normal value range, which was judged as a potential insulation system fault point.

[0093] Conduct a detailed analysis of the marked potential fault points. The fault type and location can be further determined through diagnostic methods such as spectrum analysis and oil sample analysis, combined with copper sulfide deposition and insulation paper aging data. For a potential fault point, after oil sample analysis, it was found that the copper sulfide concentration continued to rise. Combined with the insulation paper aging data, it was determined that the transformer insulation system was in a degraded state and was located at the A phase winding.

[0094] The beneficial effects of the above technical solution are as follows: by constructing an insulation system feature data set and a fault diagnosis model, the impact of copper sulfide deposition on the transformer insulation system can be analyzed more accurately. The diagnostic tree model can recursively split multiple nodes, making the fault identification process more refined, and ultimately accurately locating potential fault points; the model can monitor the health status of the insulation system in real time during the operation of the transformer, and provide early warnings for copper sulfide deposition and insulation paper aging, avoiding sudden failures caused by insulation degradation, thereby improving the overall operational reliability of the transformer; by discovering potential defects in the insulation system in advance, targeted maintenance can be carried out to avoid unnecessary downtime or replacement operations and reduce maintenance costs. At the same time, through the continuous optimization of the diagnostic model, the diagnostic strategy can be dynamically adjusted under different working conditions to improve the efficiency and accuracy of fault diagnosis.

[0095] In another embodiment, obtaining the specific location and fault type of the discharge fault includes: Inputting the first data to be diagnosed into the second fault diagnosis model, performing similarity matching on the first data to be diagnosed in the second fault diagnosis model, and outputting the current diagnosis result according to the matching result; Starting from the second data to be diagnosed, the current diagnosis result is input into the second fault diagnosis model to obtain the corresponding diagnosis result, and the first diagnosis result and all the diagnosis results are integrated to obtain the specific location and fault type of the discharge fault.

[0096] The working principle of the above technical solution is as follows: suppose we have a set of data to be diagnosed, representing the sensor readings of a certain device, such as temperature, vibration frequency, etc. The first data to be diagnosed may be a temperature reading of 75°C and a vibration frequency of 50Hz.

[0097] The first data to be diagnosed is input into the second fault diagnosis model. This model is a machine learning-based algorithm, such as KNN (K-Nearest Neighbors), which is used to find the most similar record to the current data in the historical fault data. The model finds a similar record in the historical data, indicating that the device has had an overheating fault in the past under similar conditions.

[0098] Based on the similarity matching results, the model outputs the current diagnosis result. For example, the diagnosis result is "overheating fault".

[0099] Starting from the second data to be diagnosed, the current diagnosis result is input into the model together with the new data to be diagnosed. The second data is a vibration frequency of 55Hz, which is input into the model in combination with the previous "overheating fault" result.

[0100] The model outputs updated diagnostic results based on the new input data and previous diagnostic results. For example, the new diagnostic result is "overheating and vibration abnormality".

[0101] The first diagnosis result and all subsequent diagnosis results are integrated to determine the specific location and type of the fault. For example, the final diagnosis result is "the motor part of equipment A has overheating and abnormal vibration."

[0102] The beneficial effects of the above technical solution are: through similarity matching, the model can use historical data to improve the accuracy of diagnosis and reduce misjudgment; with the input of new data, the model can update the diagnosis results in real time to help discover new fault characteristics in a timely manner; integrate multiple diagnostic results to provide a more comprehensive fault analysis and help locate the specific location and type of the fault.

[0103] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics, characterized in that: include: A monitoring unit, used to monitor the sulfide deposition state inside the transformer in real time and obtain sulfide deposition characteristic parameters during transformer operation; A first diagnostic unit, used to determine whether the current sulfide deposition characteristic parameters have an impact on the insulation system and obtain a first diagnostic result; The second diagnosis unit is used to identify the specific location and fault type of the discharge fault through the propagation and attenuation characteristics of the electrical signal, thermal signal and vibration signal under different inter-turn discharge locations, types and intensities, and obtain a second diagnosis result; A fault diagnosis unit, used to determine whether the insulation defects and inter-turn discharge phenomena inside the transformer have reached a warning level according to the first diagnosis result and the second diagnosis result; The early warning unit is used to generate corresponding fault early warning information according to the current early warning level, and remind users or operation and maintenance personnel to take preventive maintenance or emergency disposal measures in real time.

2. A transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1, characterized in that: Also includes: The display terminal is used to display the sulfide deposition characteristic parameters at each monitoring time point, detailed information of each potential fault point, the propagation path and attenuation characteristics of the inter-turn discharge signal, the fault type and warning level, as well as the prevention or maintenance suggestions given.

3. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1 is characterized in that: The monitoring unit includes: The transformer monitoring module is used to set multiple monitoring time points according to preset time intervals during the operation of the transformer, monitor the deposition state of the insulation paper and sulfide inside the transformer in real time, and obtain the sulfide deposition signal and operation status signal at each monitoring time point; A signal processing module is used to filter out noise in the sulfide deposition signal and the operation status signal to obtain sulfide deposition characteristic data and process data, where the process data is the load current of the transformer; A division module is used to align the sulfide deposition characteristic data with the process data, divide the working conditions according to the process data, classify the sulfide deposition characteristic data under each working condition, and obtain multiple groups of sulfide deposition data; The characteristic parameter extraction module is used to extract the deposition thickness and distribution characteristics of sulfides on the insulating paper corresponding to each monitoring time point based on the deposition characteristic samples in each group of sulfide deposition data, and obtain the sulfide deposition characteristic parameters during the operation of the transformer.

4. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1 is characterized in that: The first diagnostic unit includes: A diagnostic model building module, used to align the acquired copper sulfide deposition characteristic parameters with the aging and degradation state data of the transformer insulation paper, obtain an insulation system characteristic data set, and build a first fault diagnosis model based on the insulation system characteristic data set; The anomaly detection module is used to perform anomaly detection on the sulfide deposition characteristic data under various detection conditions based on the fault diagnosis model, identify potential insulation defect points, evaluate the severity, and obtain the first diagnosis result.

5. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1 is characterized in that: Obtain a second diagnosis, including: Acquire multi-physical signal data at different inter-turn discharge positions, types and intensities, and identify and obtain initial diagnostic data from the multi-physical signal data, wherein the initial diagnostic data includes electrical signal parameters, thermal signal parameters and vibration signal parameters; According to the electrical signal parameters, thermal signal parameters and vibration signal parameters, the initial diagnostic data is processed in layers to obtain the data to be diagnosed, including: Obtaining propagation path parameters from electrical signal parameters, and obtaining attenuation characteristic parameters from thermal signal parameters and vibration signal parameters; Acquire the attenuation characteristic parameter associated with each propagation path parameter as a data group to be diagnosed; After sorting each data group to be diagnosed according to the propagation path parameter, the data corresponding to each propagation path parameter and the attenuation characteristic parameter are stored in the corresponding data group to be diagnosed, thereby obtaining the data to be diagnosed; Acquire a transformer fault diagnosis knowledge base, and acquire fault mode data from the transformer fault diagnosis knowledge base; After noise elimination processing is performed on the fault mode data, a data feature vector is constructed, and fault-related data is obtained from the transformer fault diagnosis knowledge base; After clustering the data feature vectors according to the fault-related data, a data group to be trained is obtained, and after training the initial model with the data group to be trained, a second fault diagnosis model is obtained; Input the data to be diagnosed into the preset second fault diagnosis model one by one to obtain the specific location and fault type of the discharge fault; A second diagnosis result is generated according to the specific location and fault type of the discharge fault and the initial diagnosis data.

6. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1 is characterized in that: Determine whether the insulation defects and inter-turn discharge phenomena inside the transformer have reached the warning level, including: According to the first and second diagnostic results, the severity of insulation defects and inter-turn discharge phenomena is graded and warned. The warning levels are divided into the following four categories: Warning level 0: no obvious insulation defects or inter-turn discharge, the transformer is in normal operation; Warning level 1: Minor insulation defects or inter-turn discharge are detected. It is recommended to monitor regularly and record the change trend. Warning level 2: moderate insulation defects or inter-turn discharges exist. It is recommended to take preventive measures and conduct in-depth inspections of relevant areas. Warning level 3: The insulation system or inter-turn discharge has reached a serious level, posing a high risk. It is recommended to shut down the machine for maintenance immediately.

7. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 1 is characterized in that: Generate corresponding fault warning information, including: The fault warning model outputs a current warning level according to the input first diagnosis result and the second diagnosis result, and when the fault warning model outputs the current warning level, acquires fault warning information corresponding to the warning level; According to the current warning level, obtain the user reminder strategy corresponding to the warning level and generate corresponding fault warning information; If the warning level is 0, a prompt message is generated to inform that the device is operating normally; If the warning level is 1, a suggestion message is generated to remind the user to regularly monitor and record the change trend; If the warning level is 2, a preventive maintenance suggestion is generated to remind the user or operation and maintenance personnel to conduct in-depth inspections of the relevant areas to prevent the fault from expanding; If the warning level is 3, an emergency shutdown and maintenance suggestion is generated to remind the user to shut down immediately and perform emergency maintenance; Display fault warning information in real time on the display terminal; Obtaining a preset fault decision-making support model, inputting the current warning level and the generated fault warning information into the fault decision-making support model, and obtaining at least one maintenance or disposal suggestion information output by the decision-making support model; Based on the maintenance or disposal suggestion information, provide users or operation and maintenance personnel with specific maintenance plans and strategic basis to help users or operation and maintenance personnel prevent faults or carry out emergency disposal.

8. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 2 is characterized in that: The display terminal includes: The first display module is used to display the sulfide deposition trend at each monitoring time point on the time axis in a graphical manner according to the acquired sulfide deposition characteristic parameters, and to display the sulfide deposition changes in the monitoring area; The second display module is used to mark in detail the geographical location, fault degree and fault type of each potential fault point in a preset distributed fault point map; The third display module is used to display the propagation path and attenuation characteristics of the inter-turn discharge signal, including the initial position, propagation direction and intensity change of the signal, and is visualized by color gradient or line thickness; The fourth display module is used to identify the warning level of each fault type using different colors or symbols according to the acquired warning level information, and to display each fault type by category in the form of a list; The fifth display module is used to display corresponding prevention or maintenance suggestions according to the acquired fault information and warning level, and associate the suggestion information with the corresponding fault point or monitoring time point for display.

9. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 4 is characterized in that: The first fault diagnosis model is constructed based on the insulation system characteristic data set, including: Based on the obtained transformer copper sulfide deposition characteristic parameters, combined with the aging and degradation status data of the transformer insulation paper under each working condition, an insulation system characteristic data set is constructed; Based on the insulation system characteristic data set, the correlation between copper sulfide deposition and insulation paper breakdown is analyzed to determine whether copper sulfide deposition has an impact on the transformer insulation system, further determine the severity of insulation defects, and mark potential fault points; For each set of operating condition data in the insulation system characteristic data set, a first fault diagnosis model is constructed, and fault identification is performed on the insulation system characteristic data under each operating condition based on the first fault diagnosis model to obtain a first diagnosis result, including: For each set of insulation system characteristic data, copper sulfide deposition characteristic parameters and insulation paper aging degradation state data are selected to form a sub-sample set, and a diagnostic tree is initialized based on the sub-sample set; Randomly selecting multiple features from the sub-sample set, determining multiple splitting nodes based on each feature, and splitting the sub-sample set into multiple subsets based on each splitting node; Calculate the variance of the insulation system characteristic data corresponding to each split node, and determine the variance reduction coefficient of each split node; For each feature data, calculate the distance between the feature data and other feature data, and select the nearest neighbor feature data for clustering to obtain the clustering coefficient; The variance reduction coefficient and clustering coefficient are normalized, and the weight ratio is set to adjust the contribution ratio of the two to form the final split decision indicator; Determine the target split node based on the maximum split decision index, and recursively split the target node until the leaf node of the diagnosis tree is reached; For each feature data, the path parameter in the diagnosis tree is calculated, and the feature data whose path parameter exceeds the preset threshold is marked as a potential fault point; Analyze potential fault points, and determine the fault type and location of the insulation system by combining copper sulfide deposition characteristic parameters and insulation paper aging and degradation status data through spectrum analysis or related diagnostic methods.

10. The transformer dynamic diagnosis and early warning system based on sulfide deposition characteristics according to claim 5, characterized in that: Get the specific location and fault type of the discharge fault, including: Inputting the first data to be diagnosed into the second fault diagnosis model, performing similarity matching on the first data to be diagnosed in the second fault diagnosis model, and outputting the current diagnosis result according to the matching result; Starting from the second data to be diagnosed, the current diagnosis result is input into the second fault diagnosis model to obtain the corresponding diagnosis result, and the first diagnosis result and all the diagnosis results are integrated to obtain the specific location and fault type of the discharge fault.

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