A transformer operation fault detection method and system

Abstract

The present invention discloses a transformer operation fault detection method and system, which relates to the technical field of transformer operation fault detection, and includes the following steps: when the relevant maintenance and management personnel locate and diagnose the abnormality of the transformer, the operating parameters of the transformer are monitored in real time, and the data is transmitted to the monitoring system, and the real-time data is analyzed by the monitoring system to identify the abnormal situation; based on the data analysis results, an electric shock risk perception model is further established to generate an electric shock risk assessment coefficient. Through real-time monitoring and data analysis, the present invention can timely perceive the electric shock risk of the transformer, and give early warnings and prompts according to the risk level, so that the relevant maintenance and management personnel can take countermeasures in time to avoid accidents. The statistical analysis of the electric shock risk assessment coefficient can accurately assess the degree of electric shock risk, which is helpful to formulate targeted safety management strategies and preventive measures.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer operation fault detection, and in particular to a transformer operation fault detection method and system. Background Art

[0002] Transformer operation fault detection refers to the detection and diagnosis of possible problems or faults of the transformer by monitoring the operating status and parameters of the transformer and analyzing its electrical characteristics. This process usually involves real-time monitoring and recording of key parameters such as current, voltage, temperature, humidity, etc. of the transformer, as well as analysis and comparison of these data. By analyzing these data, it is possible to promptly detect whether the transformer has problems such as overload, short circuit, insulation damage, etc., so as to take corresponding maintenance or repair measures to ensure the safe operation of the transformer and extend its service life.

[0003] When the transformer operation fault detection finds that the transformer has an abnormal operating status, the relevant maintenance managers usually conduct further positioning and diagnosis, which includes a detailed inspection of the inside of the transformer, which may involve checking the insulation system, oil quality, cooling system, grounding system and other aspects. At the same time, the abnormal data monitored, such as temperature rise and current fluctuation, may be analyzed and compared to determine the specific location and type of the abnormality. This further positioning and diagnosis work by the maintenance managers helps to accurately find the root cause of the fault, so as to take corresponding maintenance measures to ensure the safe and stable operation of the transformer.

[0004] The risk of electric shock is an extremely serious safety threat that can cause injury or even death to workers. In high-voltage power systems, the risk of electric shock is particularly serious because high-voltage currents can cause serious harm to the human body, including electric shock, burns, and even cardiac arrest. If the risk of electric shock in a transformer is not discovered in time, the relevant maintenance managers may accidentally touch the high-voltage components during the abnormal location and diagnosis process, resulting in serious casualties. Therefore, it is particularly important to have intelligent perception of the risk of electric shock when locating transformer abnormalities.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a transformer operation fault detection method and system, which can timely perceive the electric shock risk of the transformer through real-time monitoring and data analysis, and issue early warnings and prompts according to the risk level, so that relevant maintenance management personnel can take countermeasures in time to avoid accidents. Statistical analysis of the electric shock risk assessment coefficient can accurately assess the degree of electric shock risk, which is helpful to formulate targeted safety management strategies and preventive measures to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a transformer operation fault detection method, comprising the following steps:

[0008] When the relevant maintenance managers locate and diagnose the abnormality of the transformer, the operating parameters of the transformer are monitored in real time and the data is transmitted to the monitoring system. The monitoring system performs abnormal processing and analysis on the real-time data to identify abnormal conditions;

[0009] Based on the data analysis results, an electric shock risk perception model is further established to generate an electric shock risk assessment coefficient, and the electric shock risk assessment coefficient is used to intelligently identify situations where there is an electric shock risk;

[0010] When a risk of electric shock is identified, the risk level of electric shock is further divided into high risk of electric shock, general risk of electric shock and low risk of electric shock;

[0011] Different response measures are taken for high electric shock risks, general electric shock risks and low electric shock risks to ensure the life safety of relevant maintenance management personnel.

[0012] Preferably, the operating parameters of the transformer include electrical characteristic information and insulation performance information, wherein the electrical characteristic information includes the gas concentration in the insulating oil, and the insulation performance information includes the insulation resistance value. After the monitoring system performs abnormal processing and analysis on the gas concentration in the insulating oil and the insulation resistance value, a gas concentration abnormal increment index and an insulation resistance value decrease index are generated, and abnormal situations are identified through the gas concentration abnormal increment index and the insulation resistance value decrease index.

[0013] Preferably, the steps of performing abnormal processing and analysis on the gas concentration in the insulating oil by the monitoring system to generate the gas concentration abnormal increment index are as follows:

[0014] In a fixed time window, real-time data of gas concentration in insulating oil is collected from the transformer monitoring system. Suppose the sampling time is t i , the corresponding gas concentration of a certain gas in the insulating oil is C i ;

[0015] The gas concentration data C in the insulating oil during the first n sampling times i-n, C i-n+1 , …, C i-1 Perform average calculation to obtain the benchmark value

[0016] For the current time t i The gas concentration in the insulating oil is C i , calculate its difference with the reference value The difference between the two, namely the abnormal increment ΔC i , the calculation expression of abnormal increment is:

[0017] The abnormal increment ΔC i For normalization, use the following formula: Where, ΔC i ' represents the normalized abnormal increment, μ represents the mean of the abnormal increment, and σ represents the standard deviation of the abnormal increment;

[0018] Use the normalized abnormal increment ΔC i Calculate the gas concentration abnormal increment index I i , the calculation formula is: i =α*ΔC i '+β*I i-1 , where α and β are coefficients used for weighting, α represents the weight of the current abnormal increment, and β represents the weight of the abnormal increment index at the previous moment.

[0019] Preferably, the steps of performing abnormal processing and analysis on the insulation resistance value through the monitoring system to generate the insulation resistance value drop index are as follows:

[0020] Set the initial time t 0 , and the insulation resistance value at the initial moment decreases exponentially Set to 0;

[0021] For each time t k , according to the exponentially weighted moving average algorithm, calculate the insulation resistance drop index The expression to be calculated is: in, is time t k The insulation resistance value, is time t k-1 The insulation resistance decrease index is 0 at the initial moment.

[0022] Preferably, within a fixed time window, the gas concentration abnormal increment index I generated when the relevant maintenance management personnel locate and diagnose the abnormality of the transformer is obtained. i And insulation resistance drop index Then, the gas concentration abnormal increment index I generated at the same timev And insulation resistance drop index Establish an electric shock risk perception model and generate an electric shock risk assessment coefficient ψ 风险 , based on the formula: In the formula, x 1 、x 2 They are respectively the gas concentration abnormal increment index I v And insulation resistance drop index The preset scale factor, and x 1 、x 2 Both are greater than 0.

[0023] Preferably, the electric shock risk assessment coefficient generated within a fixed time window when the relevant maintenance personnel locate and diagnose the abnormality of the transformer is compared and analyzed with the pre-set electric shock risk assessment coefficient reference threshold; if the electric shock risk assessment coefficient is greater than or equal to the electric shock risk assessment coefficient reference threshold, the corresponding moment is marked as an electric shock risk mark; if the electric shock risk assessment coefficient is less than the electric shock risk assessment coefficient reference threshold, the corresponding moment is not marked.

[0024] Preferably, when a mark is generated when relevant maintenance personnel locate and diagnose an abnormality in the transformer, an analysis set is established between the electric shock risk assessment coefficient at that moment and several electric shock risk assessment coefficients generated subsequently, and the average value and standard deviation of the electric shock risk assessment coefficient are calculated by analyzing the electric shock risk assessment coefficients of the set.

[0025] Preferably, the average value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient are compared and analyzed with the pre-set electric shock risk assessment coefficient reference threshold and the standard deviation reference threshold, respectively, and the electric shock risk is divided into high electric shock risk, general electric shock risk and low electric shock risk. The specific division process is as follows:

[0026] If the average value of the electric shock risk assessment coefficient is greater than or equal to the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a high electric shock risk;

[0027] If the standard deviation of the electric shock risk assessment coefficient is greater than or equal to the standard deviation reference threshold, the electric shock risk is classified as general electric shock risk;

[0028] If the average value of the electric shock risk assessment coefficient is less than the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a low electric shock risk;

[0029] The risk of electric shock from a high electric shock risk is greater than the risk of electric shock from a medium electric shock risk, and the risk of electric shock from a medium electric shock risk is greater than the risk of electric shock from a low electric shock risk.

[0030] A transformer operation fault detection system includes an abnormality processing analysis module, an electric shock risk identification module, a risk level classification module and a risk response module;

[0031] The abnormality processing and analysis module monitors the operating parameters of the transformer in real time when the relevant maintenance managers locate and diagnose the abnormalities of the transformer, and transmits the data to the monitoring system. The monitoring system performs abnormality processing and analysis on the real-time data to identify abnormal situations;

[0032] The electric shock risk identification module further establishes an electric shock risk perception model based on the data analysis results, generates an electric shock risk assessment coefficient, and uses the electric shock risk assessment coefficient to intelligently identify situations where there is an electric shock risk;

[0033] The risk level classification module further classifies the electric shock risk level into high electric shock risk, general electric shock risk and low electric shock risk when the electric shock risk is identified;

[0034] The risk response module adopts different response measures for high electric shock risk, general electric shock risk and low electric shock risk to ensure the life safety of relevant maintenance management personnel.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] Through real-time monitoring and data analysis, the present invention can timely perceive the electric shock risk of the transformer, and issue early warnings and prompts according to the risk level, so that relevant maintenance management personnel can take countermeasures in time to avoid accidents. Statistical analysis of the electric shock risk assessment coefficient can accurately assess the degree of electric shock risk, which is helpful to formulate targeted safety management strategies and preventive measures.

[0037] The present invention proposes specific response measures for high, general and low risks of electric shock. These measures take into account both safety and operational effectiveness and cost-effectiveness. By restricting access, strengthening personal protection, safety monitoring and other means, the probability of accidents under high risks of electric shock is effectively reduced. At the same time, it also increases awareness of general and low risks of electric shock and reduces potential safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 The present invention is a flowchart of a transformer operation fault detection method.

[0040] Figure 2 The present invention is a module schematic diagram of a transformer operation fault detection system. DETAILED DESCRIPTION

[0041] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0042] The present invention provides Figure 1 A transformer operation fault detection method shown includes the following steps:

[0043] When the relevant maintenance managers locate and diagnose the abnormality of the transformer, the operating parameters of the transformer are monitored in real time and the data is transmitted to the monitoring system. The monitoring system performs abnormal processing and analysis on the real-time data to identify abnormal conditions;

[0044] The operating parameters of the transformer include electrical characteristic information and insulation performance information, wherein the electrical characteristic information includes the gas concentration in the insulating oil, and the insulation performance information includes the insulation resistance value. After the monitoring system performs abnormal processing and analysis on the gas concentration in the insulating oil and the insulation resistance value, a gas concentration abnormal increment index and an insulation resistance value decrease index are generated, and abnormal conditions are identified through the gas concentration abnormal increment index and the insulation resistance value decrease index.

[0045] When performing transformer operation fault detection, the gas concentration in the insulating oil refers to the gas concentration in the insulating oil. By monitoring the gas concentration in the insulating oil, possible abnormal conditions inside the transformer can be identified, such as damaged insulation materials, partial discharge, aging of oil-paper insulation, etc.

[0046] Insulation resistance refers to the resistance value of the transformer insulation system, which is used to evaluate the insulation performance of the insulation system. Insulation resistance is the impedance of the insulation system to the current, reflecting the isolation ability of the insulation material to the current. Under normal circumstances, the insulation resistance of the transformer insulation system should be within a certain range, and the higher the resistance value, the better the insulation performance.

[0047] Based on the data analysis results, an electric shock risk perception model is further established to generate an electric shock risk assessment coefficient, and the electric shock risk assessment coefficient is used to intelligently identify situations where there is an electric shock risk;

[0048] Increased gas concentration in the insulating oil increases the likelihood of electric shock risk in transformers for three main reasons:

[0049] Risk of electric shock from gas discharge:

[0050] When the gas concentration in the insulating oil increases, it is easy to cause gas foam or bubbles to form in the insulating oil. These gas foam or bubbles will cause gas discharge under the high voltage electric field, forming corona discharge or breakdown discharge. This discharge phenomenon will cause the local temperature of the insulating oil to rise, generate sparks, and increase the possibility of electric shock risk in the transformer.

[0051] Degradation of insulation performance:

[0052] The presence of gas in insulating oil will reduce the insulating properties of the insulating oil and reduce its insulation strength. The reduction in insulation strength will increase the possibility of insulation oil breakdown under high voltage electric fields, thereby increasing the risk of electric shock in transformers.

[0053] Abnormal performance of insulating oil causes failure:

[0054] Increased gas concentrations may mean that there are other abnormalities in the insulating oil, such as the presence of electrolytes or moisture, which can cause the insulating oil to deteriorate in performance and increase the risk of insulating oil breakdown under high voltage electric fields, thereby increasing the possibility of electric shock risk in the transformer.

[0055] The steps for analyzing the abnormal gas concentration in the insulating oil through the monitoring system to generate the abnormal gas concentration increment index are as follows:

[0056] In a fixed time window (i.e., the detection time, the time setting in the fixed time window is not specifically limited here, and can be selected according to the actual situation. Of course, it can also be selected to be infinite, that is, the entire abnormality positioning and diagnosis process is detected), the real-time data of the gas concentration in the insulating oil is collected from the transformer monitoring system. Suppose the sampling time is t i , the corresponding gas concentration of a certain gas in the insulating oil is C i ;

[0057] Gases in insulating oil are usually released due to partial discharge, thermal decomposition or other abnormal conditions inside the transformer. These gases include methane, ethane, acetylene, etc.

[0058] The gas concentration data C in the insulating oil during the first n sampling times i-n , C i-n+1 , …, C i-1 Perform average calculation to obtain the benchmark value

[0059] This baseline value represents the gas concentration level in the insulating oil under normal conditions.

[0060] For the current time t i The gas concentration in the insulating oil is C i , calculate its difference with the reference value The difference between the two, namely the abnormal increment ΔC i , the calculation expression of abnormal increment is:

[0061] The abnormal increment ΔC i For normalization, use the following formula: Where, ΔC i ' represents the normalized abnormal increment, μ represents the mean of the abnormal increment, and σ represents the standard deviation of the abnormal increment;

[0062] Normalization processing can make the abnormal increment index have a unified scale, which is convenient for subsequent analysis.

[0063] Use the normalized abnormal increment ΔC i Calculate the gas concentration abnormal increment index I i (Current time t i ), the calculation formula is: i =α*ΔC i '+β*I i-1 , where α and β are weighted coefficients that can be adjusted according to actual conditions. α represents the weight of the current abnormal increment, and β represents the weight of the previous moment (t i-1 )’s abnormal increment index.

[0064] Within a fixed time window, the larger the performance value of the gas concentration abnormality increment index generated when the relevant maintenance management personnel locate and diagnose the abnormality of the transformer, the higher the risk of electric shock, and vice versa.

[0065] A drop in insulation resistance may increase the risk of electric shock in transformers. Here are three reasons why:

[0066] Reduced insulation capacity:

[0067] The insulation resistance of the transformer reflects the health of the insulation system. Its decrease may indicate aging, moisture or damage of the insulation system. The decrease of insulation resistance will reduce the insulation capacity of the insulation system, making the insulating oil or other insulating materials unable to effectively block the flow of current, thereby increasing the risk of electric shock such as breakdown and leakage of the insulation system.

[0068] Increase the possibility of discharge:

[0069] The decrease in insulation resistance may lead to insulation breakdown or partial discharge in the insulation system. These discharge phenomena will generate sparks or high temperatures, causing the burning of insulating oil or other insulating materials, thereby increasing the risk of fire in the insulation system. At the same time, the discharge may also produce a strong electric field effect, exposing the surrounding personnel or equipment to the risk of electric shock.

[0070] Influence on insulation system stability:

[0071] A drop in insulation resistance could be a sign of an internal transformer fault or equipment aging, which could lead to unstable operation of electrical equipment. For example, instability in the insulation system could lead to voltage fluctuations or short circuits, increasing the risk of electric shock.

[0072] The steps for analyzing the insulation resistance abnormality through the monitoring system to generate the insulation resistance drop index are as follows:

[0073] Set the initial time t 0 , and the insulation resistance value at the initial moment decreases exponentially Set to 0;

[0074] Determine the weighting coefficient τ in the exponentially weighted moving average algorithm, where the value range of τ is 0<τ<1;

[0075] The weighting coefficient τ in the exponentially weighted moving average algorithm can usually be determined in the following way. First, determine the selection range of the weighting coefficient according to the application requirements and data characteristics, and then consider the impact of historical data on the current results. If you pay more attention to the impact of recent data, you should choose a smaller τ value, otherwise choose a larger τ value. In addition, you can use methods such as empirical rules, data characteristic analysis, cross-validation and parameter optimization algorithms to find the optimal τ value from actual data.

[0076] For each time t k , according to the exponentially weighted moving average algorithm, calculate the insulation resistance drop index The expression to be calculated is: in, is time t k The insulation resistance value, is time t k-1 The insulation resistance decrease index is 0 at the initial moment.

[0077] Within a fixed time window, the greater the value of the insulation resistance drop index generated by the relevant maintenance personnel when locating and diagnosing the abnormality of the transformer, the higher the risk of electric shock, and vice versa.

[0078] In a fixed time window, the gas concentration abnormal increment index I generated by the relevant maintenance managers when locating and diagnosing the abnormality of the transformer is obtained. i And insulation resistance drop index Then, the gas concentration abnormal increment index I generated at the same time v And insulation resistance drop index (I v and For I i and The electric shock risk perception model is established and the electric shock risk assessment coefficient ψ is generated. 风险 , based on the formula: In the formula, x 1 、x 2 They are gas concentration abnormal increment index I v And insulation resistance drop index The preset scale factor, and x 1 、x 2 Both are greater than 0.

[0079] It can be seen from the calculation formula that within a fixed time window, when the relevant maintenance managers locate and diagnose the abnormalities of the transformer, the greater the value of the gas concentration abnormal increment index and the greater the value of the insulation resistance decrease index, the higher the electric shock risk assessment coefficient ψ generated. 风险 The larger the performance value, the higher the potential risk of electric shock, and vice versa.

[0080] The electric shock risk assessment coefficient generated within a fixed time window when the relevant maintenance personnel locate and diagnose the abnormalities in the transformer is compared and analyzed with the pre-set electric shock risk assessment coefficient reference threshold. If the electric shock risk assessment coefficient is greater than or equal to the electric shock risk assessment coefficient reference threshold, the corresponding moment will be marked as an electric shock risk mark; if the electric shock risk assessment coefficient is less than the electric shock risk assessment coefficient reference threshold, the corresponding moment will not be marked.

[0081] When a risk of electric shock is identified, the risk level of electric shock is further divided into high risk of electric shock, general risk of electric shock and low risk of electric shock;

[0082] When relevant maintenance management personnel locate and diagnose abnormalities in the transformer and a mark is generated, an analysis set is established between the electric shock risk assessment coefficient at that moment and several subsequently generated electric shock risk assessment coefficients. The average value and standard deviation of the electric shock risk assessment coefficient are calculated by analyzing the electric shock risk assessment coefficients of the set.

[0083] The average value and standard deviation of the electric shock risk assessment coefficient are compared and analyzed with the pre-set reference threshold of the electric shock risk assessment coefficient and the standard deviation reference threshold, respectively, and the electric shock risk is divided into high electric shock risk, general electric shock risk and low electric shock risk. The specific division process is as follows:

[0084] If the average value of the electric shock risk assessment coefficient is greater than or equal to the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a high electric shock risk;

[0085] If the standard deviation of the electric shock risk assessment coefficient is greater than or equal to the standard deviation reference threshold, the electric shock risk is classified as general electric shock risk;

[0086] If the average value of the electric shock risk assessment coefficient is less than the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a low electric shock risk.

[0087] It should be noted that the risk of electric shock due to high electric shock risk is greater than the risk of electric shock due to general electric shock risk, and the risk of electric shock due to general electric shock risk is greater than the risk of electric shock due to low electric shock risk.

[0088] Different response measures are taken for high electric shock risk, general electric shock risk and low electric shock risk to ensure the life safety of relevant maintenance managers;

[0089] Take the following actions for high, moderate, and low risk of electric shock:

[0090] High risk of electric shock:

[0091] Isolate the danger zone immediately: Once a high risk of electric shock is confirmed, the danger zone should be isolated immediately to ensure that workers are away from the danger zone.

[0092] Stop maintenance operations: All maintenance operations should be stopped immediately to prevent any potential electric shock injury.

[0093] Emergency Notification and Evacuation: Issue an emergency notification to all relevant personnel, requiring them to immediately evacuate the danger area and take appropriate safety measures.

[0094] Professional intervention: In situations with high risk of electric shock, a rescue team composed of professionals should intervene to carry out necessary emergency treatment and disposal.

[0095] Safety inspection and restoration: While ensuring safety, conduct a comprehensive safety inspection and take measures to restore the normal operating status of the transformer.

[0096] General risk of electric shock:

[0097] Restricted access: Only professionally trained personnel can enter the abnormal area, ensuring that only qualified personnel are on site.

[0098] Strengthen personal protection: All staff are required to strengthen personal protection measures, including wearing insulating gloves, insulating shoes, etc.

[0099] Safety monitoring and alarm: Strengthen safety monitoring of the transformer area to ensure that any abnormal situation is detected at any time and the alarm is issued in time.

[0100] Real-time tracking and notification: Track the operating status of the transformer in real time and notify relevant personnel of changes in risk levels.

[0101] Low risk of electric shock:

[0102] Warnings and Alerts: Provide workers with warnings and reminders about low-risk electric shocks, ensuring they remain alert while working.

[0103] Regular inspection and maintenance: Regularly inspect and maintain the transformer to detect and repair any potential safety hazards in a timely manner.

[0104] Training and education: Regularly provide workers with electric shock risk awareness training and safety operating procedures education to improve their understanding of and ability to respond to safety risks.

[0105] Through the above measures, corresponding countermeasures can be taken for different degrees of electric shock risks to maximize the safety of workers and ensure the smooth progress of transformer abnormality positioning and diagnosis.

[0106] Through real-time monitoring and data analysis, the present invention can timely perceive the electric shock risk of the transformer, and issue early warnings and prompts according to the risk level, so that relevant maintenance management personnel can take countermeasures in time to avoid accidents. Statistical analysis of the electric shock risk assessment coefficient can accurately assess the degree of electric shock risk, which is helpful to formulate targeted safety management strategies and preventive measures.

[0107] The present invention proposes specific response measures for high, general and low risks of electric shock. These measures take into account both safety and operational effectiveness and cost-effectiveness. By restricting access, strengthening personal protection, safety monitoring and other means, the probability of accidents under high risks of electric shock is effectively reduced. At the same time, it also increases awareness of general and low risks of electric shock and reduces potential safety hazards.

[0108] The present invention provides Figure 2 A transformer operation fault detection system shown includes an abnormality processing analysis module, an electric shock risk identification module, a risk level classification module and a risk response module;

[0109] The abnormality processing and analysis module monitors the operating parameters of the transformer in real time when the relevant maintenance managers locate and diagnose the abnormalities of the transformer, and transmits the data to the monitoring system. The monitoring system performs abnormality processing and analysis on the real-time data to identify abnormal situations;

[0110] The electric shock risk identification module further establishes an electric shock risk perception model based on the data analysis results, generates an electric shock risk assessment coefficient, and uses the electric shock risk assessment coefficient to intelligently identify situations where there is an electric shock risk;

[0111] The risk level classification module further classifies the electric shock risk level into high electric shock risk, general electric shock risk and low electric shock risk when the electric shock risk is identified;

[0112] The risk response module takes different response measures for high electric shock risk, general electric shock risk and low electric shock risk to ensure the life safety of relevant maintenance managers;

[0113] A transformer operation fault detection method provided in an embodiment of the present invention is implemented by the above-mentioned transformer operation fault detection system. The specific method and process of a transformer operation fault detection system are detailed in the embodiment of the above-mentioned transformer operation fault detection method, which will not be repeated here.

[0114] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0116] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A transformer operation fault detection method, characterized in that: The following steps are involved: When the relevant maintenance managers locate and diagnose the abnormality of the transformer, the operating parameters of the transformer are monitored in real time and the data is transmitted to the monitoring system. The monitoring system performs abnormal processing and analysis on the real-time data to identify abnormal conditions; The operating parameters of the transformer include electrical characteristic information and insulation performance information, wherein the electrical characteristic information includes the gas concentration in the insulating oil, and the insulation performance information includes the insulation resistance value. After the monitoring system performs abnormal processing and analysis on the gas concentration in the insulating oil and the insulation resistance value, a gas concentration abnormal increment index and an insulation resistance value decrease index are generated, and the abnormal situation is identified through the gas concentration abnormal increment index and the insulation resistance value decrease index; The steps for analyzing the abnormal gas concentration in the insulating oil through the monitoring system to generate the abnormal gas concentration increment index are as follows: In a fixed time window, real-time data of gas concentration in insulating oil is collected from the transformer monitoring system. The sampling time is set to , the corresponding gas concentration of a certain gas in the insulating oil is ; Before n Gas concentration data in insulating oil at sampling time , , …, Perform average calculation to obtain the benchmark value ; For the current moment Gas concentration in insulating oil , calculate its difference with the reference value The difference between , the calculation expression of abnormal increment is: ; Increase the abnormal For normalization, use the following formula: , where represents the normalized abnormal increment, μ represents the mean of the abnormal increment, represents the standard deviation of abnormal increments; Using normalized abnormal increments Calculate the gas concentration abnormal increment index , the calculation formula is: , where α and β are coefficients used for weighting, α represents the weight of the current abnormal increment, and β represents the weight of the abnormal increment index at the previous moment; The steps for analyzing the insulation resistance abnormality through the monitoring system to generate the insulation resistance drop index are as follows: Set the initial time , and the insulation resistance value at the initial moment decreases exponentially Set to 0; For every moment , according to the exponentially weighted moving average algorithm, calculate the insulation resistance drop index , the calculation expression is: ,in, For the moment The insulation resistance value, For the moment The insulation resistance decrease index is 0 at the initial moment; Based on the data analysis results, an electric shock risk perception model is further established to generate an electric shock risk assessment coefficient, and the electric shock risk assessment coefficient is used to intelligently identify situations where there is an electric shock risk; In a fixed time window, obtain the gas concentration abnormal increment index generated by the relevant maintenance managers when locating and diagnosing the abnormalities in the transformer And insulation resistance drop index Then, the gas concentration abnormal increment index generated at the same time And insulation resistance drop index Establish an electric shock risk perception model and generate an electric shock risk assessment coefficient , based on the formula: , where , Gas concentration abnormal increment index And insulation resistance drop index The preset scaling factor of , All are greater than 0; When a risk of electric shock is identified, the risk level of electric shock is further divided into high risk of electric shock, general risk of electric shock and low risk of electric shock; Different response measures are taken for high electric shock risks, general electric shock risks and low electric shock risks to ensure the life safety of relevant maintenance management personnel.

2. A transformer operation fault detection method according to claim 1, characterized in that: The electric shock risk assessment coefficient generated within a fixed time window when the relevant maintenance personnel locate and diagnose the abnormalities in the transformer is compared and analyzed with the pre-set electric shock risk assessment coefficient reference threshold. If the electric shock risk assessment coefficient is greater than or equal to the electric shock risk assessment coefficient reference threshold, the corresponding moment will be marked as an electric shock risk mark; if the electric shock risk assessment coefficient is less than the electric shock risk assessment coefficient reference threshold, the corresponding moment will not be marked.

3. A transformer operation fault detection method according to claim 2, characterized in that: When relevant maintenance management personnel locate and diagnose abnormalities in the transformer and a mark is generated, an analysis set is established between the electric shock risk assessment coefficient at that moment and several subsequently generated electric shock risk assessment coefficients. The average value and standard deviation of the electric shock risk assessment coefficient are calculated by analyzing the electric shock risk assessment coefficients of the set.

4. A transformer operation fault detection method according to claim 3, characterized in that: The average value and standard deviation of the electric shock risk assessment coefficient are compared and analyzed with the pre-set reference threshold of the electric shock risk assessment coefficient and the standard deviation reference threshold, respectively, and the electric shock risk is divided into high electric shock risk, general electric shock risk and low electric shock risk. The specific division process is as follows: If the average value of the electric shock risk assessment coefficient is greater than or equal to the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a high electric shock risk; If the standard deviation of the electric shock risk assessment coefficient is greater than or equal to the standard deviation reference threshold, the electric shock risk is classified as general electric shock risk; If the average value of the electric shock risk assessment coefficient is less than the reference threshold value of the electric shock risk assessment coefficient and the standard deviation of the electric shock risk assessment coefficient is less than the standard deviation reference threshold value, the electric shock risk is classified as a low electric shock risk; The risk of electric shock from a high electric shock risk is greater than the risk of electric shock from a medium electric shock risk, and the risk of electric shock from a medium electric shock risk is greater than the risk of electric shock from a low electric shock risk.

5. A transformer operation fault detection system, used to implement a transformer operation fault detection method as described in any one of claims 1 to 4, characterized in that: It includes an abnormality handling analysis module, an electric shock risk identification module, a risk level classification module, and a risk response module; The abnormality processing and analysis module monitors the operating parameters of the transformer in real time when the relevant maintenance managers locate and diagnose the abnormalities of the transformer, and transmits the data to the monitoring system. The monitoring system performs abnormality processing and analysis on the real-time data to identify abnormal situations; The electric shock risk identification module further establishes an electric shock risk perception model based on the data analysis results, generates an electric shock risk assessment coefficient, and uses the electric shock risk assessment coefficient to intelligently identify situations where there is an electric shock risk; The risk level classification module further classifies the electric shock risk level into high electric shock risk, general electric shock risk and low electric shock risk when the electric shock risk is identified; The risk response module adopts different response measures for high electric shock risk, general electric shock risk and low electric shock risk to ensure the life safety of relevant maintenance management personnel.

Images