Method, device and equipment for evaluating health state of transformer and storage medium

Abstract

The invention belongs to the electromechanical field, and discloses a transformer health state assessment method, device and equipment and a storage medium, and the method comprises the steps: obtaining the operation age limit, oil chromatography data and meteorological condition data at an assessment moment of a to-be-assessed transformer, and determining a comprehensive weight corresponding to the obtained data; a score table is established, the score table divides the operating age limit, the oil chromatography gas concentration and the meteorological condition into different gears, and each gear corresponds to a score; respectively calculating membership weights corresponding to the operating age limit, the oil chromatography data and the meteorological condition data at the evaluation moment of the to-be-evaluated transformer according to the score table, and taking a point product value of the comprehensive weight and the membership weight as an evaluation vector of the transformer; and calculating the health index of the to-be-estimated transformer according to the evaluation vector and the scores of different gears in the evaluation table. According to the method, multi-dimensional data are integrated, the evaluation accuracy is improved, the health state of the transformer is reflected to a user more clearly and directly, and the use experience is improved.

Description

Technical Field

[0001] The present invention belongs to the field of transformers, relates to an evaluation method, and particularly relates to an evaluation method, device, equipment and storage medium for the health state of a transformer. Background Art

[0002] A transformer is an electrical device that uses the principle of electromagnetic induction to change AC voltage, current and impedance, and is widely used in fields such as power transmission, electronic equipment and industrial control. As a core device in the power system, its health state is directly related to the stability of the power grid and the reliability of power supply.

[0003] In related technologies, the evaluation method for the health state of a transformer mainly relies on oil chromatogram data for evaluation, fault diagnosis and early warning. However, a single data source cannot comprehensively reflect the true operating state of the transformer. Especially in a complex environment, the operating state of the transformer is affected by multiple factors, and the health state evaluated only based on oil chromatogram data is not accurate enough. Summary of the Invention

[0004] In view of this, the present invention discloses an evaluation method, device, equipment and storage medium for the health state of a transformer, which can solve the deficiencies existing in related technologies.

[0005] To achieve the above object, the present invention discloses the following technical solutions:

[0006] According to the first aspect of the present invention, an evaluation method for the health state of a transformer is proposed, and the method includes:

[0007] Obtain the operating years, oil chromatogram data of the transformer to be evaluated and the meteorological condition data at the evaluation moment, and determine the comprehensive weight corresponding to the obtained data;

[0008] Establish a scoring table, which divides the operating years, oil chromatogram gas concentration and meteorological conditions into different grades, and each grade corresponds to a score;

[0009] Calculate the membership weights corresponding to the operating years, oil chromatogram data of the transformer to be evaluated and the meteorological condition data at the evaluation moment according to the scoring table respectively, and take the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer;

[0010] Calculate the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

[0011] According to the second aspect of the present invention, an evaluation device for the health state of a transformer is proposed, and the device includes:

[0012] Acquisition unit: Acquire the operation years, oil chromatogram data of the transformer to be evaluated, and meteorological condition data at the evaluation moment, and determine the comprehensive weight corresponding to the acquired data;

[0013] Construction unit: Establish a scoring table, which divides the operation years, oil chromatogram gas concentration, and meteorological conditions into different grades, and each grade corresponds to a score;

[0014] First calculation unit: Calculate the membership weights corresponding to the operation years, oil chromatogram data of the transformer to be evaluated, and meteorological condition data at the evaluation moment according to the scoring table respectively, and take the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer;

[0015] Second calculation unit: Calculate the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

[0016] According to the third aspect of the present invention, an electronic device is provided, including:

[0017] A processor;

[0018] A memory for storing instructions executable by the processor;

[0019] Wherein, the processor realizes the steps of the method as described in the first aspect by running the executable instructions.

[0020] According to the fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method as described in the first aspect are realized.

[0021] As can be seen from the above technical solutions, for the method for evaluating the health state of a transformer disclosed in the present invention, on the one hand, it is no longer limited to a single data source, but combines three data sources, namely operation years, oil chromatogram data, and meteorological condition data, to evaluate the health state of the transformer, increasing the accuracy of the evaluation; on the other hand, by using multiple methods such as the analytical method and the entropy weight method to determine the evaluation weights of different data sources, and combining the established scoring table to quantify the health state of the transformer into a health index, not only integrating multi-dimensional data and increasing the accuracy of the evaluation, but also more clearly and straightforwardly reflecting the health state of the transformer to the user, improving the user experience. Description of the Drawings

[0022] Figure 1 is a flowchart of a method for evaluating the health state of a transformer provided by an exemplary embodiment.

[0023] Figure 2 is a schematic structural diagram of a device provided by an exemplary embodiment.

[0024] Figure 3It is a block diagram of a transformer health status assessment device provided by an exemplary embodiment. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0026] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in the present invention. In some other embodiments, the steps included in the method may be more or less than those described in the present invention. In addition, a single step described in the present invention may be decomposed into multiple steps for description in other embodiments; and multiple steps described in the present invention may be combined into a single step for description in other embodiments.

[0027] To further illustrate the present invention, the following examples are provided:

[0028] A transformer is an electrical device that uses the principle of electromagnetic induction to change AC voltage, current and impedance. It is widely used in power transmission, electronic equipment, industrial control and other fields. As the core equipment in the power system, its health status is directly related to the stability of the power grid and the reliability of power supply.

[0029] In related technologies, the evaluation method of transformer health status mainly relies on oil chromatography data for evaluation, fault diagnosis and early warning. However, a single data source cannot fully reflect the true operating status of the transformer, especially in complex environments, where the operating status of the transformer is affected by many factors, and the health status evaluated solely by oil chromatography data is not accurate enough.

[0030] In order to solve the deficiencies existing in the related art, the present invention proposes a method for evaluating the health status of a transformer.

[0031] Figure 1 FIG. 1 is a flow chart of a method for evaluating the health status of a transformer provided by an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0032] Step 102, obtaining the operating life of the transformer to be evaluated, oil chromatogram data and meteorological condition data at the evaluation time, and determining the comprehensive weight corresponding to the acquired data.

[0033] The transformer to be evaluated is a transformer whose health status needs to be evaluated.

[0034] The operation years of the transformer to be appraised are the length of time since the transformer to be appraised was put into use. The oil chromatographic data of the transformer are the concentration data of various gases in the transformer oil, and the various gases can include: H2, CH4, C2H2, C2H4, and C2H6. The acquisition of the gas concentration data can be regularly collected by an oil-gas analysis instrument and the average value is taken. The meteorological condition data of the transformer to be appraised at the evaluation moment can be the specific weather, for example, it can include: overcast, cloudy, sunny, snow, rain, strong wind, storm, heavy rain, thunderstorm, heavy snow, thunderstorm with heavy rain, etc.

[0035] The comprehensive weight is the weight corresponding to different data sources, namely, the operation years, the oil chromatographic data, and the meteorological condition data. The reason it is called the comprehensive weight is that this weight is obtained through comprehensive calculation based on multiple methods.

[0036] In one embodiment, determining the comprehensive weight corresponding to the acquired data includes: respectively determining the weights corresponding to the operation years, the oil chromatographic data, and the meteorological condition data based on the analytical method and the entropy weight method, and performing weighted summation on the two weight results to obtain the comprehensive weight.

[0037] The analytical method is an artificial analysis method. In response to the judgment matrix given for the importance degrees of the operation years, the oil chromatographic data, and the meteorological condition data, perform consistency verification on the judgment matrix; in the case where the judgment matrix passes the verification, perform normalization processing on the judgment matrix; add the elements in each row of the processed matrix and perform normalization processing again to obtain the analysis weights corresponding to the operation years, the oil chromatographic data, and the meteorological condition data.

[0038] Specifically, feedback the names of the three data sources to the operator. The operator can evaluate the influence degree of each data source on the health status of the transformer, and give the importance score of each data source according to experience and actual application. The 1-9 scale method can be used to compare the importance degrees of each data source with each other to obtain the judgment matrix, perform consistency verification on the judgment matrix, and if it passes, it indicates that the judgment matrix is reasonable. Perform normalization processing on the judgment matrix, then add the elements in each row of the matrix, and after normalization processing, obtain the analysis weight of each data source.

[0039] The Entropy Weight Method (EWM) is an objective weight assignment method based on the information entropy theory, which is often used in multi-index decision-making or comprehensive evaluation problems. Its core idea is to determine the weight by calculating the data dispersion degree (entropy value) of each index - the higher the dispersion degree of the index (the smaller the entropy), the greater the amount of information provided, and the greater the weight.

[0040] Specifically, the weights corresponding to the operation years, oil chromatogram data, and meteorological condition data are determined by the analysis method and the entropy weight method respectively, including: determining the entropy values of the operation years, oil chromatogram data, and meteorological condition data, and calculating the entropy value weights based on these.

[0041] The entropy values are determined for the data after standardizing the three data sources (operation years, oil chromatogram data, and meteorological condition data), which is described by the formula:

[0042]

[0043] where H j is the entropy value of the j-th data source, and p ij is the probability distribution of the j-th data source after standardization in the i-th sample. k is a constant, usually taking k = 1 / log(n) to ensure the normalization of the entropy value.

[0044]

[0045] where w j is the weight of the j-th data source, and m is the total number of data sources.

[0046] In an embodiment, the comprehensive weights obtained after weighted averaging are shown in Table 1. The comprehensive weights corresponding to the operation years, oil chromatogram data, and meteorological condition data are 0.2634, 0.6343, and 0.1023 respectively. Among them, the comprehensive weights corresponding to H2, CH4, C2H2, C2H4, and C2H6 in the oil chromatogram data are 0.1327, 0.1040, 0.1495, 0.1384, and 0.1097 respectively.

[0047] Table 1

[0048] Data Comprehensive weight Data Comprehensive weight Operation years 0.2634 <![CDATA[CH4]]> 0.1040 Weather condition data 0.1023 <![CDATA[C2H2]]> 0.1495 Oil chromatogram data 0.6343 <![CDATA[C2H4]]> 0.1384 <![CDATA[H2]]> 0.1327 <![CDATA[C2H6]]> 0.1097

[0049] Step 104, establish a scoring table. The scoring table divides the operation years, oil chromatogram gas concentrations, and meteorological conditions into different grades, and each grade corresponds to a score.

[0050] Create a scoring table according to the transformer oil chromatogram data, operation years, and meteorological conditions. For the oil chromatogram data, use the IEC 60599 standard to establish the standard thresholds for gas concentrations. Establish the standard thresholds for the operation years and meteorological conditions through the analysis of the number of faults, as shown in Table 2. Each type of data source includes four grades: normal, attention, abnormal, and serious. Each grade corresponds to the values of the specific data source. For example, the value of the operation years in the normal grade is 5, and the values of the meteorological conditions in the abnormal grade include storm and heavy rain.

[0051] Table 2

[0052]

[0053] Step 106: Calculate the membership weights corresponding to the operation years, oil chromatogram data, and meteorological condition data at the evaluation moment of the transformer to be evaluated according to the scoring table respectively, and take the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer.

[0054] In practical applications, there is a situation where the obtained operation years are between two grades.

[0055] In one embodiment, when any one of the operation years, oil chromatogram data, and meteorological condition data is between the first grade and the second grade, the membership weight a = (a 正 , a 注 , 0, 0), where a is the membership weight of any one of the data, and the two 0s are the weights corresponding to the other grades except the first grade and the second grade.

[0056]

[0057] where n1 and n2 are the values corresponding to the first grade and the second grade respectively, and x is any one of the data;

[0058] When any one of the operation years, oil chromatogram data, and meteorological condition data is at the third grade, the membership weight a = (0, 0, 1, 0), and 1 corresponds to the weight of the third grade.

[0059] As for the calculation formula of the evaluation vector, taking the operation years as an example, it can be specifically described as:

[0060] B1 = ω1 · a1;

[0061] where B1 is the evaluation vector of the operation years, ω1 is the comprehensive weight of the operation years, and a1 is the membership weight of the operation years.

[0062] Step 108: Calculate the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

[0063] In one embodiment, calculating the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table includes: determining the dot product value of the score matrix generated based on the scores corresponding to each grade and the transposed vector of the score vector as the health index.

[0064] Taking Table 2 as an example, scores can be assigned to the four grades of normal, attention, abnormal, and serious respectively to generate a score matrix (100, 75, 50, 25). The calculation formula of the evaluation vector can be described as:

[0065] HI = v · B T ;

[0066] B = B1 + B2 + B3;

[0067] Wherein, HI is the health index of the transformer to be evaluated, v is the scoring matrix, B is the evaluation vector of the whole transformer to be evaluated, and B1, B2, and B3 are the evaluation vectors of the operation years, oil chromatogram data, and meteorological condition data respectively.

[0068] In this embodiment, on the one hand, it is no longer limited to a single data source, but combines three data sources of operation years, oil chromatogram data, and meteorological condition data to evaluate the health status of the transformer, increasing the accuracy of the evaluation; on the other hand, the evaluation weights of different data sources are determined through various methods such as the analytic hierarchy process and the entropy weight method, and combined with the established scoring table to quantify the health status of the transformer into a health index. This not only integrates multi-dimensional data, increases the accuracy of the evaluation, but also more clearly and straightforwardly reflects the health status of the transformer to the user, improving the user experience.

[0069] In one embodiment, through the management system of the transformer, it is extracted that this transformer has been in use for 7 years. The concentration data of gases H2, CH4, C2H2, C2H4, and C2H6 in the transformer oil are collected regularly by an oil-gas analyzer, which are 7.5, 7.3, 0.3, 6.7, and 3.4 ppm respectively. At the same time, the weather condition at the current evaluation moment is sunny. The comprehensive weights are shown in Table 1, and the established scoring table is shown in Table 2.

[0070] Calculate the membership weights of the three data sources respectively.

[0071] Taking the operation years as an example, assuming the operation years is 7 years, calculate the membership weight.

[0072]

[0073] Therefore, the membership weight of the operation years is a1 = (0.5, 0.5, 0, 0).

[0074] The evaluation of the operation years B1 = ω1·a1 = 0.2634·(0.5, 0.5, 0, 0) = (0.1317, 0.1317, 0, 0).

[0075] Similarly, the evaluation vectors B2 and B3 of the oil chromatogram data and the meteorological condition data are respectively:

[0076]

[0077] B1 = ω1·a1 = 0.1023·(0, 1, 0, 0) = (0, 0.1023, 0, 0);

[0078] Then, the evaluation vector B of the transformer to be evaluated is B = B1 + B2 + B3 = (0.2042, 0.7257, 0.0711, 0);

[0079] The health index HI of the transformer to be evaluated is:

[0080] HI = v·B T = (100, 75, 50, 25)·(0.2042, 0.7257, 0.0711, 0) T = 78.4025.

[0081] Furthermore, the user can judge the health status of the transformer to be evaluated according to this health index. For example, a health threshold of 80 can be set. When the health index reaches this health threshold, it is determined that the health status of the transformer to be evaluated is healthy; when the health index does not reach this health threshold, it is determined that the health status of the transformer to be evaluated is unhealthy; or, different grades such as normal, abnormal, and attention can be set for the health status, and corresponding thresholds can be set for different grades. When the health index reaches the threshold, the belonging grade is determined, so as to determine the health status. Of course, the health status can also be determined by other means, and the present invention does not limit this.

[0082] In this embodiment, on the one hand, it is no longer limited to a single data source, but combines three data sources of operation years, oil chromatogram data, and meteorological condition data to evaluate the health status of the transformer, increasing the accuracy of the evaluation; on the other hand, the evaluation weights of different data sources are determined by various methods such as the analysis method and the entropy weight method, and the health status of the transformer is quantified as a health index by combining the established scoring table. It not only integrates multi-dimensional data, increases the accuracy of the evaluation, but also more clearly and straightforwardly reflects the health status of the transformer to the user, improving the user experience.

[0083] In one embodiment, the method further includes: predicting the operation years, oil chromatogram data, and meteorological condition data of the transformer to be evaluated at at least one future moment, and calculating the health index of the transformer to be evaluated at at least one future moment based on the predicted data; when the health index at any moment among the at least one future moment and the evaluation moment is the index corresponding to the unhealthy state of the transformer to be evaluated, determining the abnormal reason of the transformer to be evaluated; if the abnormal reason of the transformer to be evaluated is abnormal meteorological condition data, feedback operation and maintenance suggestions to the user; if the abnormal reason of the transformer to be evaluated is abnormal oil chromatogram data, diagnose the fault type according to the three-ratio analysis method, and trigger a warning, and the warning content is the diagnosis result and the health status of the transformer to be evaluated.

[0084] Hypothesis: The calculated health indexes for the next three days are 73.29, 81.02, and 71.67 respectively, and their status evaluations are abnormal, attention, and abnormal. State transitions occur on the first and third days. Regarding the specific situation on the first day, it is found that there are no obvious changes in the operating years and oil chromatogram data, only the meteorological conditions have changed. It can be inferred that the state transition is caused by the change in meteorological conditions. A warning is issued, and the warning content is that the transformer may malfunction due to the influence of thunderstorm weather and the predicted health status is given. Please strengthen the inspection around the transformer. Since the state does not change on the second day, no warning is issued, and the predicted health status is given. On the third day, it can be seen that the gas change is relatively obvious. It is concluded that the important influencing factor for the transformer is the gas change, and the transformer gas is analyzed. The three-ratio coding rules are shown in Table 3.

[0085] Table 3

[0086]

[0087] The judgment of the fault type is shown in Table 4. Calculate the gas ratio, C2H2 / C2H4 = 0.028 < 0.1, numbered 0, CH4 / H2 = 1.377, 1 < 1.377 < 3, numbered 2, C2H4 / C2H6 = 0.935, 0.1 < 0.935 < 1, labeled 0. Based on the coding rules and the fault type judgment, it is obtained that the transformer may have a low-temperature overheat (150 - 300) °C fault, and a warning is issued. The warning content is: low-temperature overheat and the predicted health status is given. It is recommended to check the contact points of the tap changer of the transformer and the welding quality of the joints.

[0088] Table 4

[0089]

[0090] In this embodiment, the first-level warning is carried out through the calculation of the health index, and the oil chromatogram data and meteorological conditions are predicted at the same time. When the state changes, the main factors causing the state change are calculated. If the abnormality is caused by meteorological conditions, a warning is issued, and operation and maintenance suggestions are given through daily experience analysis and the health status is given; if the oil chromatogram data is abnormal, a warning is issued, the oil chromatogram data is analyzed by the three-ratio method, the fault type is obtained, and operation and maintenance suggestions are given through the fault type and the health status is given. The two-level warning makes the fault prediction of the transformer more accurate and reliable, thus effectively extending the equipment life and improving the safety

[0091] Figure 2 It is a schematic structural diagram of a device provided by an exemplary embodiment. Please refer to Figure 2, at the hardware level, the device includes a processor 202, an internal bus 204, a network interface 206, a memory 208, and a non-volatile memory 210. Of course, it may also include other hardware required for other functions. One or more embodiments of the present invention can be implemented in a software manner. For example, the processor 202 reads the corresponding computer program from the non-volatile memory 210 into the memory 208 and then runs it. Of course, in addition to the software implementation manner, one or more embodiments of the present invention do not exclude other implementation manners, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0092] Please refer to Figure 3 , an evaluation device for the health status of a transformer can be applied to a device as shown in Figure 3 to implement the technical solution of the present invention. The device may include:

[0093] An acquisition unit 302, configured to acquire the operation years, oil chromatogram data, and meteorological condition data at the evaluation moment of the transformer to be evaluated, and determine the comprehensive weight corresponding to the acquired data;

[0094] A construction unit 304, configured to establish a scoring table, where the scoring table divides the operation years, oil chromatogram gas concentration, and meteorological conditions into different grades, and each grade corresponds to a score;

[0095] A first calculation unit 306, configured to calculate the membership weights corresponding to the operation years, oil chromatogram data, and meteorological condition data at the evaluation moment of the transformer to be evaluated according to the scoring table respectively, and use the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer;

[0096] A second calculation unit 308, configured to calculate the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

[0097] Optionally, the acquisition unit 302 is specifically configured to:

[0098] Based on the analytic hierarchy process and the entropy weight method, determine the weights corresponding to the operation years, oil chromatogram data, and meteorological condition data respectively, and perform weighted summation on the two weight results to obtain the comprehensive weight.

[0099] Optionally, the acquisition unit 302 is specifically configured to:

[0100] In response to a judgment matrix given for the importance degrees of the operation years, oil chromatogram data, and meteorological condition data, perform consistency verification on the judgment matrix;

[0101] When the judgment matrix passes the verification, normalize the judgment matrix;

[0102] Add up each row of the processed matrix and normalize it again to obtain the analysis weights corresponding to the operation years, oil chromatogram data, and meteorological condition data.

[0103] Optionally, the obtaining unit 302 is specifically configured to:

[0104] Determine the entropy values of the operation years, oil chromatogram data, and meteorological condition data, and calculate the entropy value weights based on these.

[0105] Optionally,

[0106] When any one of the operation years, oil chromatogram data, and meteorological condition data is between the first gear and the second gear, the membership weight a = (a 正 , a 注 , 0, 0), where a is the membership weight of the any one data, and the two 0s are the weights corresponding to the other gears except the first gear and the second gear.

[0107]

[0108] where n1 and n2 are the values corresponding to the first gear and the second gear respectively, and x is the any one data;

[0109] When any one of the operation years, oil chromatogram data, and meteorological condition data is on the third gear, the membership weight a = (0, 0, 1, 0), and 1 corresponds to the weight of the third gear.

[0110] Optionally, the second calculation unit 308 is specifically configured to:

[0111] Determine the dot product value of the scoring matrix generated based on the scores corresponding to each gear and the transposed vector of the scoring vector as the health index.

[0112] Optionally, the device further includes:

[0113] A prediction unit 310, configured to predict the operation years, oil chromatogram data, and meteorological condition data of the transformer to be estimated at at least one future moment, and calculate the health index of the transformer to be estimated at at least one future moment based on the predicted data;

[0114] A determination unit 312, configured to determine the abnormal cause of the transformer to be estimated when the health index at any one of the at least one future moment and the evaluation moment is the index corresponding to the transformer to be estimated being in an unhealthy state;

[0115] A feedback unit 314, configured to feed back operation and maintenance suggestions to a user if the abnormal cause of the transformer to be estimated is abnormal meteorological condition data;

[0116] An early warning unit 316, configured to diagnose a fault type according to the three-ratio analysis method and trigger an early warning if the abnormal cause of the transformer to be estimated is abnormal oil chromatogram data, where the early warning content is the diagnosis result and the health status of the transformer to be estimated.

[0117] The system, device, module or unit illustrated in the above embodiments may be specifically implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, and the specific form of the computer may be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver device, a game console, a tablet computer, a wearable device, or a combination of any several of these devices.

[0118] In a typical configuration, a computer includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0119] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0120] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage, quantum memory, graphene-based storage media, or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0121] For a computer-readable medium as described above or in any other form (or, a computer-readable storage medium), computer instructions can be stored thereon, and when the instructions are executed by a processor, one or more of the above-described embodiments are implemented, thereby implementing the technical solution of the present invention.

[0122] The present invention also provides a computer program, and when the program is executed by a processor, one or more of the above-described embodiments are implemented, thereby implementing the technical solution of the present invention. Among them, the computer program can be specifically recorded on a computer-readable medium as described above or in any other form, and the present invention does not limit this.

[0123] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the said element.

[0124] The above specifically describes certain embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit one or more embodiments of the present invention. The singular forms "a", "the" and "said" used in one or more embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0126] It should be understood that although the terms first, second, third, etc. may be used in one or more embodiments of the present invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0127] The above are only the preferred embodiments of one or more embodiments of the present invention, and are not intended to limit one or more embodiments of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention shall be included within the scope of protection of one or more embodiments of the present invention.

Claims

1. A method for evaluating the health status of a transformer, characterized in that, The method includes: Obtaining the operating years, oil chromatogram data of the transformer to be evaluated, and meteorological condition data at the evaluation moment, and determining the comprehensive weight corresponding to the obtained data; Establishing a scoring table, which divides the operating years, oil chromatogram gas concentration, and meteorological conditions into different grades, and each grade corresponds to a score; Calculating the membership weights corresponding to the operating years, oil chromatogram data, and meteorological condition data of the transformer to be evaluated respectively according to the scoring table, and taking the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer; Calculating the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

2. The method according to claim 1, characterized in that The determining the comprehensive weight corresponding to the obtained data includes: Based on the analytic hierarchy process and the entropy weight method, determining the weights corresponding to the operating years, oil chromatogram data, and meteorological condition data respectively, and performing weighted summation on the two weight results to obtain the comprehensive weight.

3. The method according to claim 2, wherein The based on the analytic hierarchy process and the entropy weight method respectively determining the weights corresponding to the operating years, oil chromatogram data, and meteorological condition data includes: In response to a judgment matrix given for the importance degrees of the operating years, oil chromatogram data, and meteorological condition data, performing consistency verification on the judgment matrix; In the case where the judgment matrix passes the verification, performing normalization processing on the judgment matrix; Adding the elements of each row of the processed matrix, and performing normalization processing again to obtain the analytic weights corresponding to the operating years, oil chromatogram data, and meteorological condition data.

4. The method according to claim 2, characterized in that The based on the analytic hierarchy process and the entropy weight method respectively determining the weights corresponding to the operating years, oil chromatogram data, and meteorological condition data includes: Determining the entropy values of the operating years, oil chromatogram data, and meteorological condition data, and calculating the entropy weight based on this.

5. The method according to claim 1, wherein In the case where any one of the operating years, oil chromatogram data, and meteorological condition data is between the first grade and the second grade, the membership weight a=(a positive, a note, 0, 0), a is the membership weight of the any one data, and the two 0s are the weights corresponding to other grades except the first grade and the second grade, where n1 and n2 are the values corresponding to the first grade and the second grade respectively, and x is the any one data; In the case where any one of the operating years, oil chromatogram data, and meteorological condition data is in the third grade, the membership weight a=(0, 0, 1, 0), and 1 corresponds to the weight of the third grade.

6. The method according to claim 1, characterized in that, The calculating the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table includes: Determining the dot product value of the scoring matrix generated based on the scores corresponding to each grade and the transposed vector of the scoring vector as the health index.

7. The method according to claim 1, characterized in that The method further includes: Predicting the operating years, oil chromatogram data, and meteorological condition data of the transformer to be evaluated at at least one future moment, and calculating the health index of the transformer to be evaluated at at least one future moment based on the predicted data; When the health index at any one of the at least one future moment and the evaluation moment is the index corresponding to the unhealthy state of the transformer to be evaluated, determine the abnormal cause of the transformer to be evaluated; If the abnormal cause of the transformer to be evaluated is abnormal meteorological condition data, feedback operation and maintenance suggestions to the user; If the abnormal cause of the transformer to be evaluated is abnormal oil chromatogram data, diagnose the fault type according to the three-ratio analysis method, and trigger an early warning, and the early warning content is the diagnosis result and the health state of the transformer to be evaluated.

8. An evaluation device for the health state of a transformer, characterized in that, The device includes: An acquisition unit: acquire the operation years, oil chromatogram data of the transformer to be evaluated, and meteorological condition data at the evaluation moment, and determine the comprehensive weight corresponding to the acquired data; A construction unit: establish a scoring table, which divides the operation years, oil chromatogram gas concentration, and meteorological conditions into different grades, and each grade corresponds to a score; A first calculation unit: calculate the membership weights corresponding to the operation years, oil chromatogram data of the transformer to be evaluated, and meteorological condition data at the evaluation moment respectively according to the scoring table, and use the dot product value of the comprehensive weight and the membership weight as the evaluation vector of the transformer; A second calculation unit: calculate the health index of the transformer to be evaluated according to the evaluation vector and the scores of different grades in the evaluation table.

9. An electronic device, characterized in that, It includes: A processor; A memory for storing instructions executable by the processor; Wherein, the processor realizes the steps of the method according to any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the instruction is executed by the processor, the steps of the method according to any one of claims 1-7 are realized.

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