Transformer thermal insulation life loss calculation method, device, equipment and medium
By weighted scoring and theoretical life loss calculation of the status index of the oil-immersed transformer at the target sampling time, combined with the similarity and mean of the life loss data, the problem of difficult to accurately predict the thermal insulation life loss of the transformer in the prior art is solved, and higher evaluation accuracy and memory ability are achieved.
Patent Information
- Application Number
- CN202411519456.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-02-25
AI Technical Summary
The prior art is difficult to accurately predict the thermal insulation life loss of power transformers, resulting in the insulating performance not meeting the requirements of safe and reliable operation.
By obtaining the various state indicators of the oil-immersed transformer at the target sampling time, performing weighted scores, calculating the theoretical life loss, and determining the life loss data at the target sampling time based on the similarity and mean of the life loss data in the target database.
It improves the accuracy of the thermal insulation life loss assessment of the transformer, comprehensively considers various indicators, and refers to the historical situation of the life loss data, and has a certain memory ability.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of transformers, and in particular to a method, device, equipment and medium for calculating the thermal insulation life loss of a transformer. Background Art
[0002] During operation, power transformers are subjected to the combined effects of multiple stresses such as thermal, electrical and mechanical stresses. Their insulation systems gradually age, which will eventually lead to the insulation performance being unable to meet the requirements for safe and reliable operation.
[0003] Therefore, it is usually necessary to predict the insulation life of power transformers in this field to determine the insulation aging of transformers. In the prior art, the tensile strength and average degree of polymerization of transformer coil insulation paper can be used as important indicators to measure the insulation aging in transformers, but on-site sampling and measurement are difficult; the insulation degradation products have a good linear relationship with the aging degree of transformer insulation sampling, but it is difficult to meet the needs of actual transformer operation. Therefore, there is an urgent need for a method to calculate the loss of thermal insulation life of transformers. Summary of the invention
[0004] In view of this, the present application provides a method, device, equipment and medium for calculating the life loss of thermal insulation of a transformer, which improves the accuracy of life loss assessment. The technical solution is as follows.
[0005] In a first aspect, a method for calculating the loss of thermal insulation life of a transformer is provided, the method comprising:
[0006] Obtain various status indicators of the oil-immersed transformer at the target sampling time;
[0007] According to the indicator weights of the various status indicators, the various status indicators are weighted to obtain the final status score of the oil-immersed transformer;
[0008] According to the final state score, obtaining the theoretical life loss of the oil-immersed transformer;
[0009] If the amount of life loss data in the target database is greater than the first amount, then calculating the similarity between each state indicator at the target sampling time and the state indicators corresponding to the second amount of adjacent life loss data;
[0010] If the similarity is greater than a similarity threshold, calculating the mean of the second number of life loss data;
[0011] The life loss data corresponding to the target sampling moment is determined according to the average of the second number of life loss data and the theoretical life loss.
[0012] In a possible implementation, the method further includes:
[0013] Get the last estimated remaining life in the target database;
[0014] The remaining life of this assessment is determined based on the remaining life of the last assessment and the life loss data corresponding to the target sampling time, and the remaining life of this assessment and the life loss data corresponding to the target sampling time are stored in the target database.
[0015] In a possible implementation, the method further includes:
[0016] If the amount of life loss data in the target database is less than or equal to a first amount, the theoretical life loss is determined as the life loss data corresponding to the target sampling moment.
[0017] In a possible implementation, the method further includes:
[0018] If the similarity is less than a similarity threshold, the theoretical life loss is determined as the life loss data corresponding to the target sampling time.
[0019] In a possible implementation, the calculating the similarity between each state indicator at the target sampling moment and the state indicators corresponding to the second number of adjacent life loss data includes:
[0020] For each adjacent life loss data, calculating the cosine value of the angle between each state index at the target sampling time and the state index of the adjacent life loss data;
[0021] The average of the cosine values of the second number of included angles is determined as the similarity.
[0022] In a possible implementation, the step of obtaining various status indicators of the oil-immersed transformer at a target sampling time includes:
[0023] Obtain variable data of the oil-immersed transformer at the target sampling time; the variable data includes the following variables: top oil temperature of the oil-immersed transformer, oil level, oil chromatography analysis results, partial discharge evaluation results, core clamp monitoring results, infrared monitoring results, three-phase current, three-phase voltage, defect records, winding hot spot temperature and sampling time;
[0024] According to the variable data, various state indicators of the oil-immersed transformer at the target sampling time are obtained.
[0025] In a possible implementation, obtaining the theoretical life loss of the oil-immersed transformer according to the final state score includes:
[0026] Determining the relative aging rate of the oil-immersed transformer according to the hot spot temperature of the winding;
[0027] The theoretical life loss of the oil-immersed transformer is obtained according to the relative aging rate of the oil-immersed transformer at the target sampling moment and the last sampling moment of the target sampling moment, and the final state score.
[0028] In a second aspect, a device for calculating the loss of thermal insulation life of a transformer is provided, the device comprising:
[0029] An index acquisition module is used to obtain various state indicators of the oil-immersed transformer at the target sampling time;
[0030] A scoring module, used for weighting each state indicator according to the indicator weight of each state indicator to obtain a final state score of the oil-immersed transformer;
[0031] A theoretical calculation module, used for obtaining the theoretical life loss of the oil-immersed transformer according to the final state score;
[0032] A similarity calculation module, for calculating the similarity between each state indicator at the target sampling time and the state indicators corresponding to the second number of adjacent life loss data if the amount of life loss data in the target database is greater than the first amount;
[0033] a mean value acquisition module, configured to calculate a mean value of the second number of life loss data if the similarity is greater than a similarity threshold;
[0034] The loss acquisition module is used to determine the life loss data corresponding to the target sampling time according to the mean of the second number of life loss data and the theoretical life loss.
[0035] In a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned method for calculating the thermal insulation life loss of the transformer by executing the computer instructions.
[0036] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above-mentioned method for calculating the loss of thermal insulation life of a transformer.
[0037] In a fifth aspect, a computer program product or a computer program is provided, wherein the computer instructions are used to enable a computer to execute the above-mentioned method for calculating the loss of thermal insulation life of a transformer.
[0038] The technical solution provided by this application may have the following beneficial effects:
[0039] The computer device first obtains each state indicator of the oil-immersed transformer at the target sampling time; then weights each state indicator according to the indicator weight to obtain the final state score of the oil-immersed transformer; obtains the theoretical life loss of the oil-immersed transformer according to the final state score; if the amount of life loss data in the target database is greater than the first amount, then calculate the similarity between each state indicator at the target sampling time and the state indicator corresponding to the second amount of adjacent life loss data; if the similarity is greater than the similarity threshold, calculate the mean of the second amount of life loss data; determine the life loss data corresponding to the target sampling time according to the mean of the second amount of life loss data and the theoretical life loss. The life loss data calculated by the above scheme not only comprehensively considers each indicator, but also refers to the historical situation of life loss data, and has a certain memory capacity, so the obtained life loss data has a high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0041] Figure 1 The present invention is a flowchart of a method for calculating the thermal insulation life loss of a transformer according to an exemplary embodiment.
[0042] Figure 2 The present invention is a flowchart of a method for calculating the thermal insulation life loss of a transformer according to an exemplary embodiment.
[0043] Figure 3 A schematic diagram of a life span assessment process involved in an embodiment of the present application is shown.
[0044] Figure 4 It is a structural schematic diagram of a transformer thermal insulation life loss calculation device provided in an embodiment of the present application.
[0045] Figure 5 It is a structural schematic diagram of a computer device provided by an optional embodiment of the present invention. DETAILED DESCRIPTION
[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
[0047] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between two items, or an association relationship between the two items, or a relationship between indication and being indicated, configuration and being configured, and the like.
[0048] An oil-immersed transformer is a transformer that uses oil as both an insulating and cooling medium. The oil not only provides electrical insulation, but also carries away the heat generated inside the transformer through a circulating flow, keeping the equipment within a safe temperature range. Calculating the thermal insulation life loss of an oil-immersed transformer usually involves the following aspects: 1. Temperature effects: The insulating materials of the transformer (such as paper and oil) have different lifespans at different temperatures. 2. Operating time: The total thermal insulation life loss is calculated based on the actual operating time of the transformer, combined with the life estimates at different temperatures. Humidity effects: The humidity of the insulating material will also affect its lifespan. An increase in relative humidity will cause the insulation performance to deteriorate, so the effects of humidity need to be considered in the calculation. Frequent load changes: Frequent changes in load will cause temperature fluctuations, which in turn affect the aging rate of the insulating material. Figure 1 is a method flow chart showing a method for calculating the loss of thermal insulation life of a transformer according to an exemplary embodiment. The method can be executed by a computer device, such as Figure 1 As shown, the method for calculating the loss of thermal insulation life of a transformer may include the following steps:
[0049] Step 101, obtaining various state indicators of the oil-immersed transformer at a target sampling time.
[0050] In the embodiment of the present application, in order to calculate the loss of thermal insulation life of the oil-immersed transformer, the relevant data of the oil-immersed transformer can be sampled according to the period, so as to calculate the various state indicators of the oil-immersed transformer at the target sampling time according to the relevant data of the oil-immersed transformer at the target sampling time. Optionally, the state indicator can be used to indicate the temperature of the oil-immersed transformer, the insulation strength of the oil, the load current, the humidity, the aging degree of the insulating material, etc., and these indicators are the basis for evaluating the health status of the transformer. After obtaining the various state indicators of the oil-immersed transformer at the target sampling time, the loss of thermal insulation life can be calculated according to the state indicators.
[0051] Step 102: weighting each state indicator according to the indicator weight of each state indicator to obtain a final state score of the oil-immersed transformer.
[0052] In the embodiment of the present application, a comprehensive final status score can be calculated by assigning weights to each status indicator. The final score is obtained by multiplying the value of each status indicator by its corresponding weight and then summing them.
[0053] Optionally, a weight can be set based on the degree of impact of each indicator on the transformer life. For example, temperature may have a greater impact on the life and therefore have a higher weight.
[0054] In a possible implementation of an embodiment of the present application, the indicator weights of each state indicator can be obtained by the entropy weight method. The entropy weight method is an objective weighting method used to determine the weights of multiple indicators in a comprehensive evaluation. Its basic principle is based on the concept of information entropy, which is used in information theory to measure the degree of disorder of a system or the degree of concentration of information. The entropy weight method calculates the information entropy of each indicator and then determines the weight of each indicator, thereby realizing multi-indicator decision-making.
[0055] Step 103: Obtain the theoretical life loss of the oil-immersed transformer according to the final state score.
[0056] When the final state score is obtained, the theoretical life loss of the oil-immersed transformer at the target sampling time can be determined according to the final state score. The theoretical life loss is used to indicate the theoretical value of the life loss of the oil-immersed transformer from the last sampling to the current sampling.
[0057] Step 104 : If the amount of life loss data in the target database is greater than the first amount, then calculate the similarity between each state indicator at the target sampling moment and the state indicators corresponding to the second amount of adjacent life loss data.
[0058] After obtaining the theoretical life loss, in the embodiment of the present application, the accuracy of the life loss estimation can also be improved according to the historical data in the target database. Specifically, if the amount of life loss data in the target database is greater than the first amount, it can be considered that there are more historical data in the target database, and the historical data has a certain reference significance. Therefore, the second amount of adjacent life loss data can be selected from the life loss data in the target data. The adjacent life loss data is the life loss data obtained at adjacent sampling moments, and the time difference between the adjacent sampling moments and the target sampling moment is less than the time threshold. For example, if the sampling interval is 1 day, that is, the interval of life loss estimation is 1 day, if the first amount is 10 and the second amount is 5, when the target database stores more than ten days of life loss estimation data, the computer device can take out the life loss data of the previous five days as the adjacent life loss data, and the computer device then determines the similarity between the state indicator corresponding to the target sampling moment and the state indicator of the previous five days.
[0059] Step 105: If the similarity is greater than a similarity threshold, then calculate the mean of the second number of life loss data.
[0060] If the similarity is greater than the similarity threshold, it means that the state indicator collected at the target sampling time is similar to the previous state indicator, that is, the life loss of the oil-immersed transformer should also be similar to the previous situation. Therefore, the mean of the second number (for example, five days) of life loss data can be calculated as the approximate situation of the life loss data calculated for the oil-immersed transformer during this sampling.
[0061] Step 106: Determine the life loss data corresponding to the target sampling time according to the mean of the second number of life loss data and the theoretical life loss.
[0062] After obtaining the mean of the second amount of life loss data and the theoretical life loss, the two are weighted and summed to determine the life loss data corresponding to the target sampling time, that is, the life loss data from the last sampling to the current sampling. The life loss data calculated through the above steps not only comprehensively considers various indicators, but also refers to the historical situation of life loss data, and has a certain memory ability, so the obtained life loss data has a high accuracy.
[0063] In summary, the computer device first obtains the various state indicators of the oil-immersed transformer at the target sampling time; then weights the various state indicators according to the indicator weight to obtain the final state score of the oil-immersed transformer; according to the final state score, obtains the theoretical life loss of the oil-immersed transformer; if the amount of life loss data in the target database is greater than the first amount, then calculate the similarity between the various state indicators at the target sampling time and the state indicators corresponding to the second amount of adjacent life loss data; if the similarity is greater than the similarity threshold, calculate the mean of the second amount of life loss data; according to the mean of the second amount of life loss data and the theoretical life loss, determine the life loss data corresponding to the target sampling time. The life loss data calculated by the above scheme not only comprehensively considers various indicators, but also refers to the historical situation of life loss data, and has a certain memory capacity, so the obtained life loss data has a high accuracy.
[0064] Figure 2 is a method flow chart showing a method for calculating the loss of thermal insulation life of a transformer according to an exemplary embodiment. The method is executed by a computer device. Figure 3 As shown, the method for calculating the loss of thermal insulation life of a transformer may include the following steps:
[0065] Step 201, obtaining variable data of the oil-immersed transformer at the target sampling time; the variable data includes the following variables: top oil temperature of the oil-immersed transformer, oil level, oil chromatography analysis results, partial discharge evaluation results, core clamp monitoring results, infrared monitoring results, three-phase current, three-phase voltage, defect records, winding hot spot temperature and sampling time.
[0066] First, it is necessary to sample the key variable data of the oil-immersed transformer at the target sampling time, wherein the key variable data at least includes:
[0067] Top oil temperature: Monitor the temperature of the transformer oil. Excessive temperature will accelerate the aging of the insulation material.
[0068] Oil level: Make sure the oil level is within the normal range to ensure insulation and cooling effects.
[0069] Oil chromatography results: Analyze the composition of dissolved gases in the oil to detect potential failures or aging.
[0070] Partial discharge evaluation results: Evaluate the partial discharge situation inside the transformer. Partial discharge can damage the insulation material.
[0071] Core clamp monitoring results: Check the temperature and condition of the core to ensure normal magnetic performance.
[0072] Infrared Monitoring Results: Detect hot spots through infrared imaging to help identify potential problems.
[0073] Three-phase current and voltage: Monitor load and power quality and determine transformer operating status.
[0074] Defect Log: Record known defects to help assess equipment health.
[0075] Winding hot spot temperature and sampling time: Monitor the temperature of the winding to assess the degree of aging.
[0076] Step 202: acquiring various state indicators of the oil-immersed transformer at a target sampling time according to the variable data.
[0077] After the variable data is obtained, the various state indicators of the oil-immersed transformer at the target sampling time can be calculated accordingly. Optionally, in the embodiment of the present application, the various state indicators include oil temperature score s1, oil level score s2, oil chromatogram state score s3, partial discharge monitoring result score s4, core clamp grounding monitoring score s5, infrared monitoring score s6, load rate score s7, transformer ratio score s8, active loss change rate score s9, and defect score s10.
[0078] Step 203: weighting each state indicator according to the indicator weight of each state indicator to obtain a final state score of the oil-immersed transformer.
[0079] In the embodiment of the present application, the indicator weight of each state indicator can be calculated by the entropy weight method. After the indicator weight of each state indicator is obtained, the weighted sum of each state indicator can be performed to obtain the final state score of the oil-immersed transformer.
[0080] Step 204: determining the relative aging rate of the oil-immersed transformer according to the hot spot temperature of the winding.
[0081] In the embodiment of the present application, the relative aging rate V(i) of the oil-immersed transformer can be calculated by the following formula:
[0082] V(i)=exp(39.164-15000 / (Tem(i)+273));
[0083] Where Tem(i) is the hot spot temperature of the winding at the i-th moment (that is, the target sampling moment).
[0084] Step 205 , obtaining the theoretical life loss of the oil-immersed transformer according to the relative aging rate of the oil-immersed transformer at the target sampling time and the last sampling time before the target sampling time, and the final state score.
[0085] Optionally, within the evaluation period T formed by the target sampling moment and the previous sampling moment of the target sampling moment, the theoretical life loss Lifeloss_A can be calculated by the following formula:
[0086] Lifeloss_A=∑(2-Score) / 100*(V(i)+V(i-1))*interval(i) / 2;
[0087] Where Score is the final status score, V(i) is the relative aging rate of the oil-immersed transformer at the target moment, V(i-1) is the relative aging rate of the oil-immersed transformer at the previous moment; interval(i) is the sampling interval of the winding hot spot temperature.
[0088] Step 206 , if the amount of life loss data in the target database is greater than the first amount, then calculate the similarity between each state indicator at the target sampling moment and the state indicators corresponding to the second amount of adjacent life loss data.
[0089] Optionally, for each adjacent life loss data, a cosine value of an angle between each state index at the target sampling moment and a state index of the adjacent life loss data is calculated;
[0090] The average of the cosine values of the second number of included angles is determined as the similarity.
[0091] For example, in the embodiment of the present application, the cosine of the angle between the current 10 state indicators and the 10 state indicators of the previous 5 adjacent times (reflecting the memory capacity of the algorithm) cos(i), i=1-5, can be calculated respectively:
[0092] cos(i)=(X01*X1i+X02*X2i+…+X010*X10i) / [sqrt(X01^2+X02^2+…+X010^2)*sqrt(X1i^2+X2i^2+…+X10i^2)];
[0093] Among them, X01 to X010 are the 10 status indicators of this time; X1i to X10i are the 10 status indicators of the adjacent i-th time.
[0094] Optionally, if the amount of life loss data in the target database is less than or equal to a first quantity, the theoretical life loss is determined as the life loss data corresponding to the target sampling moment.
[0095] For example, when the amount of life loss data in the target database (that is, the number of samplings) is less than or equal to a first number, such as 10, it means that the amount of data in the target database is too small and does not have much reference value. At this time, there is no need to consider the impact of historical data on the life loss data. The theoretical life loss can be directly determined as the life loss data corresponding to the target sampling moment.
[0096] Step 207: If the similarity is greater than the similarity threshold, calculate the mean of the second number of life loss data.
[0097] If the similarity is greater than the similarity threshold, it means that the state of the oil-immersed transformer at the previous sampling moments is similar to the state of the oil-immersed transformer at the target sampling moment. Therefore, the life loss data at the previous sampling moments and the life loss data of the oil-immersed transformer at the target sampling moment should have a certain correlation. At this time, the mean of the second number of life loss data can be calculated to characterize the approximate situation of the life loss data of the oil-immersed transformer at the target sampling moment.
[0098] Optionally, if the similarity is less than a similarity threshold, the theoretical life loss is determined as the life loss data corresponding to the target sampling moment.
[0099] If the similarity is less than the similarity threshold, it means that the life loss data at the previous sampling moments are not highly correlated with the life loss data of the oil-immersed transformer at the target sampling moment. In this case, the theoretical life loss can be directly determined as the life loss data corresponding to the target sampling moment.
[0100] Step 208: Determine the life loss data corresponding to the target sampling time according to the mean of the second number of life loss data and the theoretical life loss.
[0101] After obtaining the mean of the second amount of life loss data and the theoretical life loss, the two are weighted and summed to determine the life loss data corresponding to the target sampling time, that is, the life loss data from the last sampling to the current sampling. The life loss data calculated through the above steps not only comprehensively considers various indicators, but also refers to the historical situation of life loss data, and has a certain memory ability, so the obtained life loss data has a high accuracy.
[0102] Step 209, obtaining the last evaluated remaining life in the target database.
[0103] Step 210, determining the remaining life of this assessment based on the remaining life of the last assessment and the life loss data corresponding to the target sampling time, and storing the remaining life of this assessment and the life loss data corresponding to the target sampling time in the target database.
[0104] After obtaining the life loss data corresponding to the target sampling time, the remaining life of the last assessment can be subtracted from the life loss data calculated this time to obtain the remaining life of this assessment.
[0105] Please refer to Figure 3 , which shows a schematic diagram of a life span assessment process involved in an embodiment of the present application.
[0106] like Figure 3 As shown, the process includes the following steps:
[0107] The following variables are collected: top oil temperature of oil-immersed transformer, oil level, oil chromatography analysis results, partial discharge evaluation results, core clamp monitoring results, infrared monitoring results, three-phase current, three-phase voltage, defect records, winding hot spot temperature Tem(i) and its sampling time time(i);
[0108] Calculate the relative aging rate V: V(i)=exp(39.164-15000 / (Tem(i)+273));
[0109] Calculate the scores of 10 sub-status indicators of oil-immersed transformers: oil temperature score s1, oil level score s2, oil chromatography status score s3, partial discharge monitoring result score s4, core clamp grounding monitoring score s5, infrared monitoring score s6, load factor score s7, transformer ratio score s8, active loss change rate score s9, defect score s10 (Reference: Du Lin, Yuan Lei, Extensional hierarchical evaluation of power transformer operating status [J]. High Voltage Technology, 2011, 37(04): 897-903.);
[0110] Use the entropy weight method to calculate the sub-state indicator weight wi;
[0111] Calculate the final state score score = ∑wi*si, s = 1 to 10;
[0112] Calculate the winding hot spot temperature sampling interval interval(i)=time(i)-time(i-1);
[0113] In the evaluation period T, calculate the theoretical life loss Lifeloss_A:
[0114] Lifeloss_A=∑(2-Score) / 100*(V(i)+V(i-1))*interval(i) / 2;
[0115] Determine whether the life loss assessment data in the database is greater than 10? If yes, proceed to step (7); if not, proceed to step (11);
[0116] Calculate the cosine of the angle between the current 10 state indicators and the 10 state indicators of the previous 5 times (reflecting the memory ability of the algorithm) cos(i), i = 1 to 5:
[0117] cos(i)=(X01*X1i+X02*X2i+…+X010*X10i) / [sqrt(X01^2+X02^2+…+X010^2)*sqrt(X1i^2+X2i^2+…+X10i^2)];
[0118] Determine whether cos(i)≥0.8? i=1~5; if yes, go to step (9); if no, go to step (11)
[0119] Calculate the final average life loss Lifeloss_B for cos(i)≥0.8, where N is the number of Lifeloss_AB that meet the condition:
[0120] Lifeloss_B=∑Lifeloss_ABj / N;
[0121] Calculate Lifeloss_AB=2 / 3*Lifeloss_A+1 / 3*Lifeloss_B; proceed to step (12);
[0122] Calculate the final life loss Lifeloss_AB = Lifeloss_A;
[0123] Read the last estimated remaining life LastLifetime from the database;
[0124] Calculate the remaining life of this assessment: LastLifetime = LastLifetime - Lifeloss_AB;
[0125] Process calculation parameters and remaining life data are stored in the database;
[0126] End the remaining life calculation and wait for the next calculation.
[0127] In summary, the computer device first obtains the various state indicators of the oil-immersed transformer at the target sampling time; then weights the various state indicators according to the indicator weight to obtain the final state score of the oil-immersed transformer; according to the final state score, obtains the theoretical life loss of the oil-immersed transformer; if the amount of life loss data in the target database is greater than the first amount, then calculate the similarity between the various state indicators at the target sampling time and the state indicators corresponding to the second amount of adjacent life loss data; if the similarity is greater than the similarity threshold, calculate the mean of the second amount of life loss data; according to the mean of the second amount of life loss data and the theoretical life loss, determine the life loss data corresponding to the target sampling time. The life loss data calculated by the above scheme not only comprehensively considers various indicators, but also refers to the historical situation of life loss data, and has a certain memory capacity, so the obtained life loss data has a high accuracy.
[0128] In the embodiments of the present application, a transformer thermal insulation life loss calculation device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.
[0129] The present application provides a device for calculating the loss of thermal insulation life of a transformer. Figure 4 : is a schematic diagram of a transformer thermal insulation life loss calculation device provided in an embodiment of the present application, the device comprising:
[0130] The indicator acquisition module 401 is used to obtain various state indicators of the oil-immersed transformer at the target sampling time;
[0131] A scoring module 402 is used to weight each state indicator according to the indicator weight of each state indicator to obtain a final state score of the oil-immersed transformer;
[0132] Theoretical calculation module 403, used to obtain the theoretical life loss of the oil-immersed transformer according to the final state score;
[0133] A similarity calculation module 404 is used to calculate the similarity between each state indicator at the target sampling time and the state indicators corresponding to the second number of adjacent life loss data if the amount of life loss data in the target database is greater than the first amount;
[0134] A mean value acquisition module 405 is configured to calculate a mean value of the second number of life loss data if the similarity is greater than a similarity threshold;
[0135] The loss acquisition module 406 is used to determine the life loss data corresponding to the target sampling time according to the mean of the second number of life loss data and the theoretical life loss.
[0136] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.
[0137] The device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.
[0138] The embodiment of the present invention also provides a computer device having the above Figure 4 The transformer thermal insulation life loss calculation device shown.
[0139] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphic information in a graphical user interface on an external input / output device (such as a display device coupled to an interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.
[0140] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.
[0141] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.
[0142] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0143] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.
[0144] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.
[0145] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.
[0146] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.
[0147] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.
Claims
1. A method for calculating the loss of thermal insulation life of a transformer, characterized in that: The method comprises: Obtain various status indicators of the oil-immersed transformer at the target sampling time; According to the indicator weights of the various status indicators, the various status indicators are weighted to obtain the final status score of the oil-immersed transformer; According to the final state score, obtaining the theoretical life loss of the oil-immersed transformer; If the amount of life loss data in the target database is greater than the first amount, then calculating the similarity between each state indicator at the target sampling time and the state indicators corresponding to the second amount of adjacent life loss data; If the similarity is greater than a similarity threshold, calculating the mean of the second number of life loss data; The life loss data corresponding to the target sampling moment is determined according to the average of the second number of life loss data and the theoretical life loss.
2. The method according to claim 1, characterized in that The method further comprises: Get the last estimated remaining life in the target database; The remaining life of this assessment is determined based on the remaining life of the last assessment and the life loss data corresponding to the target sampling time, and the remaining life of this assessment and the life loss data corresponding to the target sampling time are stored in the target database.
3. The method according to claim 2, characterized in that The method further comprises: If the amount of life loss data in the target database is less than or equal to a first amount, the theoretical life loss is determined as the life loss data corresponding to the target sampling moment.
4. The method according to claim 2, characterized in that: The method further comprises: If the similarity is less than a similarity threshold, the theoretical life loss is determined as the life loss data corresponding to the target sampling time.
5. The method according to claim 3 or 4, characterized in that: The calculating the similarity between each state index at the target sampling moment and the state index corresponding to the second number of adjacent life loss data includes: For each adjacent life loss data, calculating the cosine value of the angle between each state index at the target sampling time and the state index of the adjacent life loss data; The average of the cosine values of the second number of included angles is determined as the similarity.
6. The method according to any one of claims 1 to 4, characterized in that: The step of obtaining various status indicators of the oil-immersed transformer at the target sampling time includes: Obtain variable data of the oil-immersed transformer at the target sampling time; the variable data includes the following variables: top oil temperature of the oil-immersed transformer, oil level, oil chromatography analysis results, partial discharge evaluation results, core clamp monitoring results, infrared monitoring results, three-phase current, three-phase voltage, defect records, winding hot spot temperature and sampling time; According to the variable data, various state indicators of the oil-immersed transformer at the target sampling time are obtained.
7. The method according to claim 6, characterized in that The obtaining of the theoretical life loss of the oil-immersed transformer according to the final state score includes: Determining the relative aging rate of the oil-immersed transformer according to the hot spot temperature of the winding; The theoretical life loss of the oil-immersed transformer is obtained according to the relative aging rate of the oil-immersed transformer at the target sampling moment and the last sampling moment of the target sampling moment, and the final state score.
8. A transformer thermal insulation life loss calculation device, characterized in that: The device comprises: An indicator acquisition module is used to obtain various state indicators of the oil-immersed transformer at the target sampling time; A scoring module, used for weighting each state indicator according to the indicator weight of each state indicator to obtain a final state score of the oil-immersed transformer; A theoretical calculation module, used for obtaining the theoretical life loss of the oil-immersed transformer according to the final state score; A similarity calculation module, for calculating the similarity between each state indicator at the target sampling time and the state indicators corresponding to the second number of adjacent life loss data if the amount of life loss data in the target database is greater than the first amount; a mean value acquisition module, configured to calculate a mean value of the second number of life loss data if the similarity is greater than a similarity threshold; The loss acquisition module is used to determine the life loss data corresponding to the target sampling time according to the mean of the second number of life loss data and the theoretical life loss.
9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the transformer thermal insulation life loss calculation method according to any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the transformer thermal insulation life loss calculation method according to any one of claims 1 to 7.