A transformer fault detection method based on gases in oil and related equipment

Abstract

A transformer fault detection method and related equipment based on gases in oil, which relates to the technical field of power equipment monitoring. By adopting the above technical solutions, it effectively solves the problem in traditional methods that due to the oil temperature fluctuation of the transformer, the content of characteristic gases varies greatly under different transformer oil temperatures for the same fault type. The detection system eliminates the influence of oil temperature change on gas content through oil temperature correction and standardization processing, making the fault judgment of the transformer more accurate and reliable. At the same time, the detection system uses a characteristic gas ratio matrix for fault type determination, avoiding misjudgment that may be caused by simply relying on the absolute value of the content, and improving the accuracy and reliability of transformer fault diagnosis.

Description

Technical Field

[0001] This application relates to the technical field of power equipment monitoring, and in particular, to a transformer fault detection method and related equipment based on gases in oil. Background Art

[0002] A transformer is an important device in the power system, and its safe and stable operation is directly related to the reliability of the entire power system. During the operation of the transformer, various faults may occur due to reasons such as insulation aging and overload operation, and these faults are often accompanied by the generation of characteristic gases. Therefore, by analyzing the content and variation law of the characteristic gases in the transformer oil, potential faults of the transformer can be detected in a timely manner, which is of great significance for ensuring the safe operation of the power system.

[0003] Currently, the gas fault detection in transformer oil mainly adopts the method of regular sampling detection, that is, sampling and analyzing the transformer oil at fixed time intervals to obtain the content data of the characteristic gases, and then judging whether there are potential faults in the transformer according to the absolute value of the content data.

[0004] This detection method has certain limitations in practical applications. Since the temperature of the transformer oil fluctuates with the change of the transformer load, and the change of the transformer oil temperature will affect the solubility of the characteristic gases in the oil, resulting in large differences in the content data of the characteristic gases under different oil temperature conditions for the same fault type, it is difficult to accurately judge the fault type, thus easily causing misjudgment or missed judgment. Summary of the Invention

[0005] This application provides a transformer fault detection method and related equipment based on gases in oil, which is used to improve the accuracy of gas fault detection in transformer oil.

[0006] In a first aspect, the present application provides a transformer fault detection method based on gases in oil, which is applied to a detection system. The method includes: collecting transformer oil temperature data, transformer load data, and the content data of each characteristic gas in the transformer oil; determining whether both the transformer oil temperature data and the transformer load data conform to the preset transformer oil temperature - load operation characteristics, where the preset transformer oil temperature - load operation characteristics include the transformer oil temperature change rate standard and the transformer load change rate standard; if they conform, according to the preset oil temperature - gas type - solubility correspondence, determining the theoretical solubility of each characteristic gas at the current transformer oil temperature, where the current transformer oil temperature is determined by the transformer oil temperature data at the current moment; dividing the content data of each characteristic gas by the theoretical solubility to obtain the standardized content of each characteristic gas at the current transformer oil temperature; based on the standardized content of each characteristic gas, calculating the concentration ratio between every two of all the characteristic gases to construct a characteristic gas ratio matrix; and according to the preset characteristic gas ratio fault criterion rules and the characteristic gas ratio matrix, determining the fault type of the transformer, where the preset characteristic gas ratio fault criterion rules include multiple characteristic gas ratio fault intervals and corresponding fault types.

[0007] By adopting the above technical solution, the problem that in the traditional method, due to the fluctuation of the transformer oil temperature, the content of the characteristic gas varies greatly under the same fault type at different transformer oil temperatures is effectively solved. The detection system eliminates the influence of the oil temperature change on the gas content through oil temperature correction and standardization processing, making the fault judgment of the transformer more accurate and reliable. At the same time, the detection system uses the characteristic gas ratio matrix to determine the fault type, avoiding the misjudgment that may be caused by simply relying on the absolute value of the content, and improving the accuracy and reliability of the transformer fault diagnosis.

[0008] Combined with some embodiments of the first aspect, in some embodiments, determining whether both the transformer oil temperature data and the transformer load data conform to the preset transformer oil temperature - load operation characteristics specifically includes: calculating the oil temperature change rate of the transformer oil temperature data and the load change rate of the transformer load data within a preset time window; when the oil temperature change rate is less than the preset first change rate threshold and the load change rate is less than the preset second change rate threshold, determining that the transformer oil temperature data and the transformer load data conform to the preset transformer oil temperature - load operation characteristics; when the oil temperature change rate is greater than or equal to the preset first change rate threshold or the load change rate is greater than or equal to the preset second change rate threshold, determining that the transformer oil temperature data and the transformer load data do not conform to the preset transformer oil temperature - load operation characteristics.

[0009] By adopting the above technical solution, the detection system calculates the oil temperature change rate and the load change rate within a preset time window, and compares them with the transformer oil temperature change rate standard and the transformer load change rate standard to judge the operating state of the transformer. This judgment mechanism can effectively identify whether the transformer is in a state of drastic change or relatively stable operation. When the oil temperature change rate is greater than or equal to the preset first change rate threshold or the load change rate is greater than or equal to the preset second change rate threshold, it indicates that the transformer is in a dynamic change process. At this time, the gas data obtained may have large fluctuations and is not suitable for fault diagnosis, avoiding data interference during the dynamic process and further improving the accuracy of fault diagnosis.

[0010] Combined with some embodiments of the first aspect, in some embodiments, according to the preset characteristic gas ratio fault judgment criterion rules and the characteristic gas ratio matrix, the fault type of the transformer is determined, specifically including: according to each concentration ratio in the characteristic gas ratio matrix, the target characteristic gas ratio fault interval with the highest matching degree is determined in the preset characteristic gas ratio fault judgment criterion rules, and the preset characteristic gas ratio fault judgment criterion rules include multiple characteristic gas ratio fault intervals and corresponding fault types; based on the target characteristic gas ratio fault interval and the preset characteristic gas ratio fault judgment criterion rules, the target fault type of the transformer is determined.

[0011] By adopting the above technical solution, the detection system finds the target fault interval with the highest matching degree in the preset characteristic gas ratio fault judgment criterion rules to determine the fault type. This comprehensive matching method based on multiple characteristic gas ratios overcomes the problem of fuzzy judgment that may be caused by traditional single-ratio criteria, so as to comprehensively reflect the relative relationship between various characteristic gases, more accurately identify the fault type, improve the credibility of the fault diagnosis result, and provide a reliable basis for subsequent maintenance decisions.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after the step of calculating the concentration ratio between every two of all characteristic gases based on the standardized content of each characteristic gas to construct the characteristic gas ratio matrix, the method further includes: calculating the standardized content change rate of each characteristic gas within a preset time window; judging whether the standardized content change rate exceeds the preset third change rate threshold; if the standardized content change rate exceeds the preset third change rate threshold, determining the mutant characteristic gas; identifying the equipment components whose correlation with the mutant characteristic gas exceeds the preset correlation threshold.

[0013] By adopting the above technical solution, after the detection system constructs the characteristic gas ratio matrix, it calculates the standardized content change rate of each characteristic gas within a preset time window, and compares it with a preset third change rate threshold to identify the mutant characteristic gas, and then identifies the equipment components with a high degree of relevance to the mutant characteristic gas. This method can not only timely detect the abnormal change of gas content, but also quickly locate the specific components that may fail.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the step of determining the mutant characteristic gas if the standardized content change rate exceeds the preset third change rate threshold, the method further includes: determining the concentration ratio related to the mutant characteristic gas in the characteristic gas ratio matrix as the mutant concentration ratio, and the mutant concentration ratio is used to represent the concentration ratio of the mutant characteristic gas to the non-mutant characteristic gas; based on the mutant concentration ratio, determining the correlation of the content change between the mutant characteristic gas and the non-mutant characteristic gas; based on the correlation, screening the associated characteristic gases affected by the mutant characteristic gas.

[0015] By adopting the above technical solution, after determining the mutant characteristic gas, the detection system analyzes the concentration ratio relationship between the mutant gas and the non-mutant gas, establishes a correlation network of gas content changes, and screens out the associated characteristic gases affected by the mutant characteristic gas, so as to identify the chain reaction caused by the same fault cause, accurately grasp the scope and development trend of the fault expansion, and provide a scientific basis for evaluating the severity of the fault and formulating maintenance strategies.

[0016] Combined with some embodiments of the first aspect, in some embodiments, before the step of determining the fault type of the transformer according to the preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix, the method further includes: obtaining historical transformer oil temperature data, historical transformer load data, historical characteristic gas ratio matrix and the corresponding historical fault type; determining the historical characteristic gas ratio fault interval according to the historical characteristic gas ratio matrix; determining the preset characteristic gas ratio fault criterion rule based on the historical characteristic gas ratio fault interval and the historical fault type.

[0017] By adopting the above technical solution, the detection system obtains historical transformer oil temperature data, historical transformer load data, historical characteristic gas ratio matrix and the corresponding historical fault type to establish the corresponding relationship between the characteristic gas ratio fault interval and the fault type, and form a preset characteristic gas ratio fault criterion rule. This learning method based on historical data makes the preset characteristic gas ratio fault criterion rule have strong practical guiding significance, so that different characteristic gas ratio fault intervals corresponding to different fault types can be accurately divided, and the accuracy and applicability of the fault criterion are improved.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the fault type of the transformer according to the preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix, the method further includes: inputting the transformer oil temperature data, the transformer load data, and the characteristic gas ratio matrix into a preset fault evolution prediction model to predict the evolution trajectory of the characteristic gas ratio; determining the suspected fault type according to the evolution trajectory of the characteristic gas ratio and the characteristic gas ratio matrix; and generating a corresponding fault reminder item based on the suspected fault type, where the fault reminder item includes the evolution trajectory of the characteristic gas ratio, the suspected fault type, and the fault solution corresponding to the suspected fault type.

[0019] By adopting the above technical solution, the detection system predicts the change trend of the characteristic gas ratio through a preset fault evolution prediction model, and based on the prediction result, it identifies the suspected fault type in advance and generates a warning message including the evolution trajectory of the characteristic gas ratio, the suspected fault type, and the fault solution corresponding to the suspected fault type. This predictive diagnosis method changes the fault diagnosis from passive response to active prevention, so that potential fault hazards can be detected early, the development direction of the fault can be predicted, and the risk of serious faults in the transformer is effectively reduced.

[0020] In a second aspect, an embodiment of the present application provides a detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the detection system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, when the above computer program product runs on the detection system, it enables the above detection system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the detection system, it enables the above detection system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the detection system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By adopting the above technical solution, the problem that in the traditional method, due to the temperature fluctuation of the transformer oil, the content of characteristic gases varies greatly under different transformer oil temperatures for the same fault type is effectively solved. Through oil temperature correction and standardization processing, the detection system eliminates the influence of oil temperature change on the gas content, making the fault judgment of the transformer more accurate and reliable. At the same time, the detection system uses the characteristic gas ratio matrix to determine the fault type, avoiding misjudgment that may be caused by simply relying on the absolute value of the content, and improving the accuracy and reliability of transformer fault diagnosis.

[0026] By adopting the above technical solution, the detection system searches for the target fault interval with the highest matching degree in the preset characteristic gas ratio fault criterion rules to determine the fault type. This comprehensive matching method based on multiple characteristic gas ratios overcomes the problem of fuzzy judgment that may be caused by traditional single ratio criteria, can thus comprehensively reflect the relative relationship between various characteristic gases, more accurately identify the fault type, improve the credibility of the fault diagnosis result, and provide a reliable basis for subsequent maintenance decisions.

[0027] 3. By adopting the above technical solution, after determining the mutation characteristic gas, the detection system analyzes the concentration ratio relationship between the mutation gas and the non-mutation gas, establishes a correlation network of gas content changes, so as to screen out the associated characteristic gases affected by the mutation characteristic gas, thereby identifying the chain reaction caused by the same fault reason, accurately grasping the expansion range and development trend of the fault, and providing a scientific basis for evaluating the severity of the fault and formulating maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a schematic flow chart of a transformer fault detection method based on gases in oil in an embodiment of the present application;

[0029] Figure 2 is another schematic flow chart of a transformer fault detection method based on gases in oil in an embodiment of the present application;

[0030] Figure 3 is a schematic structural diagram of an entity device of the detection system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term " / and" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0033] The following describes the process of the method provided in this embodiment. Please refer to Figure 1 , which is a schematic flowchart of a method for transformer fault detection based on gases in oil in an embodiment of the present application.

[0034] S101. Collect the transformer oil temperature data, transformer load data, and the content data of each characteristic gas in the transformer oil;

[0035] Among them, the transformer oil temperature data is used to represent the real-time temperature value of the transformer oil medium; the transformer load data is used to represent the real-time power load value borne by the transformer during operation, usually expressed as a percentage of the rated capacity; the characteristic gas refers to the characteristic gas that may be generated when the transformer fails, such as hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), etc.; the content data refers to the concentration value of the characteristic gas in the transformer oil, usually in μL / L (ppm).

[0036] Specifically, the detection system collects the transformer oil temperature data in real time through the oil temperature sensor installed on the transformer. The detection system obtains the transformer load data through the power monitoring system. The detection system obtains the content data of the dissolved gases (characteristic gases) in the transformer oil through an on-line chromatograph analyzer or regular sampling analysis. The acquisition frequency of these data can be set according to actual needs. Usually, the acquisition interval of the transformer oil temperature data and the transformer load data is at the minute level, and the acquisition interval of the gas content data is at the hour level or the day level.

[0037] S102. Determine whether both the transformer oil temperature data and the transformer load data conform to the preset transformer oil temperature load operation characteristics, and the preset transformer oil temperature load operation characteristics include the transformer oil temperature change rate standard and the transformer load change rate standard;

[0038] Among them, the transformer oil temperature load operation characteristics represent the change law of the oil temperature and the load of the transformer in the normal operation state; the oil temperature change rate standard refers to the allowable range of the oil temperature change amplitude per unit time; the load change rate standard represents the allowable range of the load change amplitude per unit time; the change rate refers to the ratio of the numerical change amount between two adjacent sampling times to the time interval.

[0039] Specifically, first, the detection system calculates the oil temperature change rate and the load change rate within a preset time window (such as 15 minutes or 30 minutes) based on the transformer oil temperature data and the transformer load data. Then, the detection system compares the calculated oil temperature change rate and load change rate with the preset transformer oil temperature change rate standard and the transformer load change rate standard. For example, the oil temperature change rate threshold in the transformer oil temperature change rate standard can be set to 2 °C / min, and the load change rate threshold in the transformer load change rate standard can be set to 5% / min. Only when both change rates are less than their respective thresholds is it considered that the transformer is in a relatively stable operating state and suitable for subsequent fault diagnosis and analysis.

[0040] The following gives an example of a detection system calculating the oil temperature change rate and the load change rate within a preset time window (such as 15 minutes or 30 minutes) based on the transformer oil temperature data and the transformer load data:

[0041] Suppose the detection system collects the following data of the transformer within 15 minutes:

[0042] Time point 1 (0 minutes):

[0043] Transformer oil temperature data: 45.0 °C; Transformer load data: 70.0%;

[0044] Time point 2 (15 minutes):

[0045] Transformer oil temperature data: 46.5 °C; Transformer load data: 72.5%;

[0046] Calculate the change rates:

[0047] Oil temperature change rate = (46.5 °C - 45.0 °C) ÷ 15 minutes = 0.1 °C / min;

[0048] Load change rate = (72.5% - 70.0%) ÷ 15 minutes = 0.167% / min;

[0049] Judgment result:

[0050] Oil temperature change rate (0.1 °C / min) < oil temperature change rate threshold (2 °C / min);

[0051] Load change rate (0.167% / min) < load change rate threshold (5% / min);

[0052] Since both change rates are less than their respective thresholds, it can be determined that the transformer is in a stable operating state during this period and is suitable for fault diagnosis and analysis.

[0053] Optionally, generally, determining whether the transformer oil temperature data and the transformer load data both conform to the preset transformer oil temperature - load operation characteristics can be achieved in the following ways, which are not limited herein: calculating the oil temperature change rate of the transformer oil temperature data and the load change rate of the transformer load data within a preset time window; when the oil temperature change rate is less than the preset first change rate threshold and the load change rate is less than the preset second change rate threshold, determining that the transformer oil temperature data and the transformer load data conform to the preset transformer oil temperature - load operation characteristics; when the oil temperature change rate is greater than or equal to the preset first change rate threshold or the load change rate is greater than or equal to the preset second change rate threshold, determining that the transformer oil temperature data and the transformer load data do not conform to the preset transformer oil temperature - load operation characteristics.

[0054] S103. If they conform, according to the preset oil temperature - gas type - solubility correspondence, determine the theoretical solubility of each characteristic gas at the current transformer oil temperature, where the current transformer oil temperature is determined by the transformer oil temperature data at the current moment;

[0055] Among them, the preset oil temperature - gas type - solubility correspondence is used to represent the theoretical solubility of various characteristic gases in transformer oil at different transformer oil temperatures; the theoretical solubility refers to the maximum dissolved amount of the characteristic gas in transformer oil at a specific transformer oil temperature; the current transformer oil temperature refers to the real - time temperature value of the transformer oil medium at the moment of fault diagnosis and analysis.

[0056] Specifically, first, the detection system obtains the current transformer oil temperature at the current moment. Then, the detection system looks up or interpolates and calculates the theoretical solubility of each characteristic gas at this transformer temperature according to the pre - established preset oil temperature - gas type - solubility correspondence. By determining the theoretical solubility, it can prepare for the subsequent standardization processing of the content data of the characteristic gases.

[0057] The following gives an example of the preset oil temperature - gas type - solubility correspondence:

[0058] ① Solubility of hydrogen (H2):

[0059] 20°C: 7%;

[0060] 40°C: 5%;

[0061] 60°C: 3.5%;

[0062] 80°C: 2.5%

[0063] ② Solubility of methane (CH4):

[0064] 20°C: 30%;

[0065] 40°C: 25%;

[0066] 60°C: 21%;

[0067] 80°C: 18%;

[0068] ③ Solubility of ethane (C2H6):

[0069] 20°C: 280%;

[0070] 40°C: 230%;

[0071] 60°C: 190%;

[0072] 80°C: 160%;

[0073] ④ Solubility of ethylene (C2H4):

[0074] 20°C: 280%;

[0075] 40°C: 230%;

[0076] 60°C: 190%;

[0077] 80°C: 160%;

[0078] ⑤ Solubility of acetylene (C2H2):

[0079] 20°C: 400%;

[0080] 40°C: 330%;

[0081] 60°C: 270%;

[0082] 80°C: 220%...

[0083] S104. Divide the content data of each characteristic gas by the theoretical solubility to obtain the standardized content of each characteristic gas at the current transformer oil temperature;

[0084] Among them, the standardized content refers to the relative value of the gas content after being corrected by the transformer oil temperature, which is the ratio of the actual content to the theoretical solubility; the actual content refers to the original gas concentration data obtained by chromatographic analysis; the theoretical solubility refers to the maximum dissolution amount of the characteristic gas under the condition of the current transformer oil temperature.

[0085] Specifically, the detection system divides the actual content of each characteristic gas by its corresponding theoretical solubility to obtain a dimensionless relative content value. For example, if the actual content of acetylene at a certain moment is 5 ppm and the theoretical solubility is 100 ppm, then its standardized content is 0.05. This kind of standardization processing eliminates the influence of the transformer oil temperature on the gas solubility.

[0086] S105. Based on the standardized content of each characteristic gas, calculate the concentration ratio between every two of all characteristic gases to construct a characteristic gas ratio matrix;

[0087] Among them, the concentration ratio refers to the ratio of the standardized contents of two characteristic gases; the characteristic gas ratio matrix is used to represent the square matrix formed by the concentration ratios between all pairs of characteristic gases; the matrix elements represent the concentration ratios of the characteristic gas corresponding to the row and the characteristic gas corresponding to the column; the diagonal elements are always 1; the upper and lower triangular matrix elements are reciprocals of each other.

[0088] Specifically, the detection system calculates the concentration ratios between any two characteristic gases and arranges these concentration ratios in matrix form in a fixed order. For example, for five characteristic gases, namely H2, CH4, C2H6, C2H4, and C2H2, a 5×5 ratio matrix will be formed. Each element aij in the matrix represents the concentration ratio of the i-th characteristic gas to the j-th characteristic gas, satisfying the characteristics of aii = 1 and aij = 1 / aji. This characteristic gas ratio matrix comprehensively reflects the relative content relationship between various characteristic gases.

[0089] Next, a specific example is used to illustrate the construction process of the characteristic gas ratio matrix:

[0090] Suppose the five characteristic gases are H2, CH4, C2H6, C2H4, and C2H2, and their standardized contents are as follows:

[0091] H2: 100 μL / L;

[0092] CH4: 200 μL / L;

[0093] C2H6: 50 μL / L;

[0094] C2H4: 150 μL / L;

[0095] C2H2: 25 μL / L;

[0096] The constructed 5×5 ratio matrix is as follows (arranged in the order of H2, CH4, C2H6, C2H4, C2H2):

[0097] Explanation:

[0098] 1. The diagonal elements are all 1 (the concentration ratio of a characteristic gas to itself);

[0099] 2. For example, a12 = 100 / 200 = 0.5, representing the concentration ratio of H2 / CH4;

[0100] 3. The corresponding a21 = 200 / 100 = 2, which is the reciprocal of a12;

[0101] 4. The upper and lower triangular elements of the matrix are reciprocals of each other;

[0102] 5. Each element aij represents the gas content in the i-th row divided by the gas content in the j-th column.

[0103] S106. Determine the fault type of the transformer according to the preset characteristic gas ratio fault criterion rules and the characteristic gas ratio matrix. The preset characteristic gas ratio fault criterion rules include multiple characteristic gas ratio fault intervals and corresponding fault types.

[0104] Among them, the characteristic gas ratio fault criterion rules refer to the fault diagnosis criteria established based on historical data and expert experience; the characteristic gas ratio fault intervals refer to the characteristic gas ratio ranges corresponding to different fault types; the fault types refer to different types of faults that may occur in the transformer, such as overheating, discharge, etc.

[0105] Specifically, first, the detection system compares each element in the characteristic gas ratio matrix with the preset characteristic gas ratio fault criterion rules. These preset characteristic gas ratio fault criterion rules usually include multiple groups of concentration ratio ranges. For example, C2H2 / C2H4 > 1 indicates a discharge fault, and CH4 / H2 > 1 indicates an overheating fault, etc. The detection system calculates the criterion matching degree of each fault type, and methods such as fuzzy membership degree or weighted score can be used. Finally, the detection system selects the fault type with the highest matching degree as the diagnosis result. If the matching degrees of multiple fault types are close, there may be a composite fault, and further analysis or combination with other diagnostic methods is required for confirmation.

[0106] Optionally, generally, according to the preset characteristic gas ratio fault criterion rules and the characteristic gas ratio matrix, determining the fault type of the transformer can be achieved in the following ways, which are not limited here: According to each concentration ratio in the characteristic gas ratio matrix, determine the target characteristic gas ratio fault interval with the highest matching degree in the preset characteristic gas ratio fault criterion rules. The preset characteristic gas ratio fault criterion rules include multiple characteristic gas ratio fault intervals and corresponding fault types; based on the target characteristic gas ratio fault interval and the preset characteristic gas ratio fault criterion rules, determine the target fault type of the transformer.

[0107] Suppose the preset characteristic gas ratio fault criterion rules are as follows:

[0108] ①Criterion for overheating fault (low temperature < 300°C):

[0109] C2H2 / C2H4 < 0.1;

[0110] CH4 / H2 > 1;

[0111] C2H4 / C2H6 < 1;

[0112] ②Criterion for overheating fault (high temperature 300 - 700°C):

[0113] C2H2 / C2H4 < 0.2

[0114] CH4 / H2 > 1;

[0115] C2H4 / C2H6 > 1;

[0116] ③ Arc discharge fault criterion:

[0117] C2H2 / C2H4 > 0.6;

[0118] CH4 / H2 < 1;

[0119] C2H4 / C2H6 > 2;

[0120] Now assume that the key concentration ratios in the detected characteristic gas ratio matrix are:

[0121] C2H2 / C2H4 = 0.167;

[0122] CH4 / H2 = 2.0;

[0123] C2H4 / C2H6 = 3.0;

[0124] Analysis process:

[0125] Check C2H2 / C2H4 = 0.167:

[0126] Meets the high-temperature overheat fault range (<0.2);

[0127] Does not meet the low-temperature overheat fault range (<0.1);

[0128] Does not meet the arc discharge fault range (>0.6);

[0129] Check CH4 / H2 = 2.0:

[0130] Meets the high-temperature overheat fault range (>1);

[0131] Meets the low-temperature overheat fault range (>1);

[0132] Does not meet the arc discharge fault range (<1);

[0133] Check C2H4 / C2H6 = 3.0:

[0134] Meets the high-temperature overheat fault range (>1);

[0135] Does not meet the low-temperature overheat fault range (<1);

[0136] Meets the arc discharge fault range (>2);

[0137] Comprehensive judgment:

[0138] This set of concentration ratios has the highest matching degree with the criterion rules for "overheating fault (high temperature 300 - 700 °C)" because all three key concentration ratios are within the criterion range for this type of fault. Therefore, it can be determined that the current fault type of the transformer is high-temperature overheating fault.

[0139] By adopting the above technical solution, the problem that in the traditional method, due to the fluctuation of the transformer oil temperature, the content of characteristic gases varies greatly under different transformer oil temperatures for the same fault type is effectively solved. Through oil temperature correction and standardization processing, the detection system eliminates the influence of oil temperature change on the gas content, making the fault judgment of the transformer more accurate and reliable. At the same time, the detection system uses the characteristic gas ratio matrix to determine the fault type, avoiding misjudgment that may be caused by simply relying on the absolute value of the content, and improving the accuracy and reliability of transformer fault diagnosis.

[0140] After combining the above scenarios, the following further and more specific process description of the method provided in this embodiment will be given. Please refer to Figure 2 , which is another process schematic diagram of the transformer fault detection method based on gases in oil in the embodiments of the present application.

[0141] After step S105, the following steps may also be executed, or may not be executed, and no limitation is made here:

[0142] S201. Calculate the standardized content change rate of each characteristic gas within a preset time window;

[0143] Among them, the preset time window refers to the time range for calculating the standardized content change rate, which can usually be set to several hours; the standardized content change rate refers to the change amplitude of the standardized content of the characteristic gas within the preset time window.

[0144] Specifically, the steps for calculating the standardized content change rate of each characteristic gas within a preset time window can refer to step S102. First, the detection system obtains the standardized variables of the characteristic gas at multiple time points within the preset time window. Then, the detection system calculates the standardized content change of the characteristic gas within the preset time window. Next, the detection system divides the standardized content change by the preset time window to obtain the standardized content change rate of the characteristic gas. Specific examples are not listed here.

[0145] S202. Determine whether the standardized content change rate exceeds a preset third change rate threshold;

[0146] Among them, the preset third change rate threshold refers to the standard value for determining whether the content of the characteristic gas has an abnormal change; the preset third change rate threshold refers to the upper limit of the allowable change range determined according to historical operation experience and expert knowledge.

[0147] Specifically, the detection system compares the standardized content change rate of each characteristic gas with a preset third change rate threshold. The preset third change rate threshold can be determined in the following ways: (1) Statistically analyze the change rate distribution during normal operation in historical data and take the upper limit of the 95% or 99% confidence interval; (2) Set different thresholds according to the physical and chemical properties of different characteristic gases; (3) Dynamically adjust the threshold considering factors such as seasonal changes. The detection system not only focuses on single-time over-threshold situations but also examines the duration and extent of the over-threshold state to improve the reliability of judgment.

[0148] S203. If the standardized content change rate exceeds the preset third change rate threshold, determine the mutant characteristic gas;

[0149] Among them, the mutant characteristic gas refers to the characteristic gas with abnormal content change; the mutant characteristic refers to the sharp or abnormal change characteristic of the content; the mutant degree is used to represent the degree to which the content change exceeds the normal range; the mutant moment is the time point when the content starts to change abnormally; the mutant duration is the duration of the abnormal change; the mutant mode is the specific form of the content change, such as step type, gradual change type, etc.

[0150] Specifically, first, the detection system marks all characteristic gases whose standardized content change rates exceed the preset third change rate threshold; then, the detection system conducts in-depth analysis on each over-threshold characteristic gas, including: (1) Calculate the multiple of over-threshold to evaluate the mutant degree; (2) Determine the starting moment and duration of the mutation; (3) Analyze the time characteristics of the mutation process to judge the mutant mode; (4) Combine historical data to evaluate the abnormality degree of the mutation. Finally, the detection system determines the characteristic gas that meets the mutant characteristic criterion as the mutant characteristic gas.

[0151] S204. Identify the equipment components whose relevance to the mutant characteristic gas exceeds the preset relevance threshold;

[0152] Among them, the relevance refers to the degree of association between the characteristic gas and the equipment component; the preset relevance threshold is the standard value for judging the significance of the association; the equipment component refers to the specific component in the transformer that may have a fault; the fault mode is used to represent the possible fault type of the equipment component;

[0153] Specifically, the detection system performs analysis based on a pre-established gas-component association knowledge base, which includes: (1) the correspondence between various characteristic gases and different equipment components; (2) characteristic gas combinations generated by different failure modes; (3) typical gas characteristic spectra of component failures. The detection system calculates the correlation between the mutation characteristic gas and each equipment component, and can use expert rules, fuzzy reasoning or machine learning methods. When the correlation exceeds the preset correlation threshold, the corresponding equipment component is marked as a potential fault site. At the same time, the detection system will also consider the combined characteristics of multiple mutation gases to improve the accuracy of fault location.

[0154] S205, determining the concentration ratio related to the mutation characteristic gas in the characteristic gas ratio matrix as the mutation concentration ratio, where the mutation concentration ratio is used to represent the concentration ratio of the mutation characteristic gas to the non-mutation characteristic gas;

[0155] Among them, the mutation concentration ratio refers to the concentration ratio between the mutation characteristic gas and other characteristic gases; the non-mutation characteristic gas refers to the characteristic gas whose content has not undergone abnormal changes.

[0156] Specifically, the detection system extracts all concentration ratios related to the mutation characteristic gas from the characteristic gas ratio matrix, including: (1) the ratio of the mutation characteristic gas to other characteristic gases; (2) the ratio between the mutation characteristic gases (if there are multiple mutation characteristic gases). These concentration ratios include both the ratio with the mutation characteristic gas as the numerator and the ratio with the mutation characteristic gas as the denominator. The detection system will record the change process of these concentration ratios before and after the mutation, establish the ratio evolution sequence during the mutation process, and provide a data basis for the subsequent analysis of the correlation between gases.

[0157] S206. Determine the correlation between the content change of the mutation characteristic gas and the non-mutation characteristic gas based on the mutation concentration ratio;

[0158] Specifically, the detection system uses a variety of data analysis methods to evaluate the degree of correlation between changes in different characteristic gas contents: (1) Calculate the Pearson correlation coefficient to evaluate the degree of linear correlation; (2) Use time series analysis methods to identify the synchronization and lag of characteristic gas content changes; (3) Use dynamic time warping algorithms to compare the similarity of change patterns; (4) Apply Granger causality test to evaluate the causal relationship between characteristic gas content changes. The detection system comprehensively considers correlation indicators in multiple dimensions and constructs a correlation network of characteristic gas content changes.

[0159] Assume that the content change data of two characteristic gases, H2 and C2H2, are monitored within 3 days (one data point every 6 hours):

[0160] H2 (μL / L):

[0161] Day1: [100, 105, 110, 115];

[0162] Day2: [150, 200, 250, 300];

[0163] Day3: [320, 325, 330, 335];

[0164] C2H2 (μL / L):

[0165] Day1: [20, 22, 24, 25];

[0166] Day2: [30, 45, 60, 75];

[0167] Day3: [80, 82, 84, 85];

[0168] Analysis process:

[0169] (1) Pearson correlation coefficient analysis:

[0170] Calculation result: 0.98;

[0171] Explanation: There is a strong positive correlation between the changes in the contents of the two characteristic gases;

[0172] Interpretation: The growth trends of H2 and C2H2 are highly consistent;

[0173] (2) Time series analysis:

[0174] Growth rate comparison:

[0175] Growth rate of H2 on the first day: 15% / day;

[0176] Growth rate of H2 on the second day: 160% / day;

[0177] Growth rate of H2 on the third day: 12% / day;

[0178] Growth rate of C2H2 on the first day: 25% / day;

[0179] Growth rate of C2H2 on the second day: 150% / day;

[0180] Growth rate of C2H2 on the third day: 13% / day;

[0181] Explanation: The two characteristic gases both showed a sharp increase on the second day, showing obvious time synchronization;

[0182] (3) Dynamic Time Warping (DTW) analysis:

[0183] Similarity score of change patterns: 0.85 (full score 1.0);

[0184] Note: The change patterns of the two characteristic gases are highly similar;

[0185] Characteristic: Both show a three-stage change pattern of "stable - sharp increase - stable";

[0186] (4) Granger causality test:

[0187] Test results:

[0188] H2 → C2H2: p-value = 0.03 (< 0.05, significant);

[0189] C2H2 → H2: p-value = 0.45 (> 0.05, not significant);

[0190] Note: The change in H2 may be the cause of the change in C2H2;

[0191] Comprehensive analysis conclusion:

[0192] 1. The two characteristic gases show strong correlation (correlation coefficient 0.98);

[0193] 2. The changes have good time synchronization, especially in the mutation period;

[0194] 3. The change patterns are highly similar (DTW score 0.85)

[0195] 4. The change in H2 may lead to the change in C2H2

[0196] Fault diagnosis suggestion:

[0197] Based on this correlation characteristic, combined with the result that H2 is a potential inducement of C2H2, it may point to a discharge-type fault. It is recommended to further check the partial discharge situation of the transformer.

[0198] S207. Based on correlation, screen the associated characteristic gases affected by the mutant characteristic gases;

[0199] Among them, the associated characteristic gas refers to the characteristic gas that changes due to the influence of the mutant characteristic gas.

[0200] Specifically, the detection system screens the associated characteristic gases according to the following steps: (1) Set the correlation screening threshold, which can be determined based on the statistical significance level or expert experience; (2) Sort the characteristic gases according to the correlation strength, and identify the characteristic gases whose correlation exceeds the correlation screening threshold; (3) Analyze the time series characteristics of the influence to determine the propagation order of the influence; (4) Evaluate the persistence of the influence to distinguish short-term influence and long-term influence; (5) Combine the physical and chemical properties of the gas to verify the rationality of the association. Finally, the detection system outputs a characteristic spectrum containing the mutant characteristic gas and its associated characteristic gases for guiding subsequent fault diagnosis.

[0201] Assume that the initial discovery is that C2H2 (acetylene) has a mutation, and its associated characteristic gas needs to be screened. The monitoring data is for 7 consecutive days:

[0202] Data of C2H2 (mutant characteristic gas):

[0203] Day1 - 7: [20, 25, 80, 150, 180, 200, 210];

[0204] Data of other characteristic gases:

[0205] H2: [100, 120, 300, 500, 550, 580, 590];

[0206] CH4: [150, 155, 160, 165, 170, 175, 180];

[0207] C2H4: [30, 35, 90, 160, 185, 200, 205];

[0208] C2H6: [40, 42, 45, 48, 50, 52, 55];

[0209] Analysis process:

[0210] ① Setting the correlation screening threshold:

[0211] Pearson correlation coefficient threshold: 0.8; p - value significance level: 0.05;

[0212] ② Sorting the correlation strength:

[0213] Calculate the correlation coefficient of each characteristic gas with C2H2:

[0214] H2: 0.95 (p = 0.001);

[0215] CH4: 0.72 (p = 0.068);

[0216] C2H4: 0.98 (p = 0.000);

[0217] C2H6: 0.75 (p = 0.052);

[0218] ③ Temporal sequence feature analysis:

[0219] Mutation sequence (a daily growth rate exceeding 20% is considered a mutation):

[0220] Day3: C2H2 mutates first (220% growth);

[0221] Day3: H2 follows the mutation (150% growth);

[0222] Day 3: C2H4 follows mutation (157% increase);

[0223] No obvious mutation in other gases;

[0224] ④ Influence persistence assessment

[0225] Short-term influence (within 3 days): H2, C2H4;

[0226] Long-term influence (lasting for 7 days): None;

[0227] No obvious influence: CH4, C2H6;

[0228] ⑤ Physical and chemical property verification:

[0229] Association between C2H2 and H2: Conforms to discharge fault characteristics;

[0230] Association between C2H2 and C2H4: Conforms to high-energy discharge decomposition characteristics;

[0231] Weak association between CH4 and C2H6: Conforms to conventional rules;

[0232] Final screening result:

[0233] Strongly associated characteristic gases (main association spectrum):

[0234] H2:

[0235] Correlation coefficient: 0.95;

[0236] Response characteristic: Fast follow-up;

[0237] Physical basis: Discharge decomposition products;

[0238] C2H4:

[0239] Correlation coefficient: 0.98;

[0240] Response characteristic: Synchronous change;

[0241] Physical basis: Thermal decomposition chain reaction;

[0242] Non-associated characteristic gases:

[0243] CH4: Correlation coefficient is below the threshold; No obvious response characteristic;

[0244] C2H6: Correlation coefficient is below the threshold; The change trend is not relevant;

[0245] Diagnostic suggestions:

[0246] Based on the associated characteristic spectrum of C2H2-H2-C2H4 and combined with its change characteristics, it is preliminarily judged that there may be a high-energy discharge fault, and it is recommended to conduct further electrical tests for confirmation.

[0247] S208. Obtain historical transformer oil temperature data, historical transformer load data, historical characteristic gas ratio matrix, and corresponding historical fault types.

[0248] Among them, the historical transformer oil temperature data refers to the historical oil temperature measurement values recorded during the operation of the transformer; the historical transformer load data refers to the historical load record values recorded during the operation of the transformer; the historical characteristic gas ratio matrix is used to represent the records of the concentration ratios of various characteristic gases during historical operation; the historical fault type refers to the confirmed transformer fault cases and their type labels.

[0249] Specifically, the detection system extracts historical data from the database, including: (1) historical transformer oil temperature data, historical transformer load data, and historical characteristic gas ratio matrix arranged in chronological order; (2) fault case records confirmed by experts; (3) relevant operating environment parameters. The detection system preprocesses the data, including: removing outliers, filling in missing values, unifying data formats, marking data quality, etc.

[0250] S209. Determine the historical characteristic gas ratio fault interval based on the historical characteristic gas ratio matrix.

[0251] Specifically, for each historical fault type, the detection system conducts the following analysis: (1) Calculate the statistical characteristics of the concentration ratios of various characteristic gases when this type of fault occurs, such as mean value, standard deviation, quantile, etc.; (2) Use methods such as kernel density estimation to fit the probability distribution of the concentration ratios; (3) Determine the concentration ratio interval based on a set confidence level (such as 95%); (4) Analyze the overlapping situation of intervals for different fault types and adjust the interval range if necessary; (5) Verify the time stability of the intervals and evaluate whether it is necessary to set intervals by time period.

[0252] S210. Determine the preset characteristic gas ratio fault criterion rules based on the historical characteristic gas ratio fault interval and historical fault types.

[0253] Among them, the preset characteristic gas ratio fault criterion rules refer to the decision-making criteria for fault diagnosis.

[0254] Specifically, the detection system establishes a preset characteristic gas ratio fault criterion rule through the following steps: (1) For each fault type, select the gas ratio combination with the highest discrimination as the main criterion; (2) Design a multi-level criterion structure, including necessary conditions, sufficient conditions, and auxiliary conditions; (3) Set weights for different criteria to reflect their diagnostic value; (4) Develop a strategy for handling rule conflicts, such as priority sorting or fuzzy synthesis; (5) Evaluate the diagnostic accuracy rate of the rule through cross-validation; (6) Define the usage conditions and constraints of the rule. Finally, a complete criterion rule system is formed to guide real-time fault diagnosis.

[0255] S211. Determine the fault type of the transformer according to the preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix. The preset characteristic gas ratio fault criterion rule includes multiple characteristic gas ratio fault intervals and corresponding fault types.

[0256] Specifically, reference can be made to step S106, which will not be elaborated here.

[0257] S212. Input the transformer oil temperature data, transformer load data, and characteristic gas ratio matrix into the preset fault evolution prediction model to predict the characteristic gas ratio evolution trajectory.

[0258] Among them, the preset fault evolution prediction model refers to a mathematical model used to predict the change trend of the gas concentration ratio; the characteristic gas ratio evolution trajectory refers to the predicted path of the gas concentration ratio changing with time.

[0259] Specifically, first, the detection system preprocesses the input data, including data standardization, feature extraction, etc.; then, the detection system inputs the processed input data into the preset fault evolution prediction model. The preset fault evolution prediction model may adopt the following methods: (1) Time series prediction algorithms, such as ARIMA, LSTM, etc.; (2) Multivariate regression models, considering the influence of factors such as transformer oil temperature and transformer load; (3) Hybrid methods of physical models and data-driven models. The detection system predicts the concentration ratio of each characteristic gas and outputs a time series containing predicted values and confidence intervals. The prediction time span can be set according to actual needs, usually ranging from several hours to several days.

[0260] S213. Determine the suspected fault type according to the characteristic gas ratio evolution trajectory and the characteristic gas ratio matrix.

[0261] Specifically, the detection system analyzes through the following steps to determine the suspected fault types, where the suspected fault types refer to the fault types that may occur in the future: (1) Dynamically match the predicted evolution trajectory of the characteristic gas ratio with the preset fault criterion rules for the characteristic gas ratio to identify the ratio combinations that may exceed the limit; (2) Calculate the similarity between the ratio combinations that may exceed the limit and the characteristics of various faults to evaluate the possibility of the fault types; (3) Analyze the fault development trend and speed in combination with the current characteristic gas ratio matrix; (4) Determine the fault warning level according to the matching result and the development trend; (5) Consider the results of multiple prediction time points to evaluate the stability of the fault judgment.

[0262] S214. Generate corresponding fault reminder items based on the suspected fault types. The fault reminder items include the evolution trajectory of the characteristic gas ratio, the suspected fault types, and the fault solutions corresponding to the suspected fault types.

[0263] Among them, the fault reminder items refer to the set of fault warning information generated by the detection system; the fault solutions refer to the treatment suggestions for the suspected fault types.

[0264] Specifically, the detection system constructs the reminder items in the following manner: (1) Visualize the predicted evolution trajectory in a graphical manner; (2) List all the suspected fault types and sort them according to the possibility size; (3) Configure corresponding fault solutions for each suspected fault type, including: emergency treatment measures, inspection points, required tools and materials, safety precautions, etc.

[0265] The following describes the detection system in the embodiment of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the detection system in the embodiment of the present application.

[0266] It should be noted that Figure 3 The structure of the detection system shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present invention.

[0267] Such as Figure 3As shown, the detection system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 302 or the program loaded from the storage section 308 into the Random Access Memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0268] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a Liquid Crystal Display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0269] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0270] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0271] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0272] Specifically, the detection system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the transformer fault detection method based on gases in oil provided in the above embodiment is implemented.

[0273] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the detection system described in the above embodiment; or it may exist separately without being assembled into the detection system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the detection system, the detection system is enabled to implement the transformer fault detection method based on gases in oil provided in the above embodiment.

[0274] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0275] As used in the foregoing embodiments, depending on the context, the term "when" may be construed to mean "if", "after", "in response to determining", or "in response to detecting". Similarly, depending on the context, the phrase "when determining" or "if (the stated condition or event) is detected" may be construed to mean "if determined", "in response to determining", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0276] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented. The processes can be completed by relevant hardware instructed by a computer program, which can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the foregoing method embodiments. The foregoing storage media include various media that can store program codes, such as ROM, random access memory (RAM), magnetic disks, or optical discs.

Claims

1. A transformer fault detection method based on gas in oil, characterized in that: Applied to a detection system, the method comprises: Collect transformer oil temperature data, transformer load data, and content data of each characteristic gas in transformer oil; Determine whether the transformer oil temperature data and the transformer load data both meet preset transformer oil temperature load operation characteristics, wherein the preset transformer oil temperature load operation characteristics include a transformer oil temperature change rate standard and a transformer load change rate standard; If it is in accordance with the preset oil temperature-gas type-solubility correspondence, the theoretical solubility of each characteristic gas at the current transformer oil temperature is determined, and the current transformer oil temperature is determined by the transformer oil temperature data at the current moment; Dividing the content data of each characteristic gas by the theoretical solubility to obtain the standardized content of each characteristic gas at the current transformer oil temperature; Based on the standardized content of each characteristic gas, the concentration ratios of all characteristic gases are calculated to construct a characteristic gas ratio matrix; The fault type of the transformer is determined according to a preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix, wherein the preset characteristic gas ratio fault criterion rule includes a plurality of characteristic gas ratio fault intervals and corresponding fault types.

2. The method according to claim 1, characterized in that The determining whether the transformer oil temperature data and the transformer load data both meet the preset transformer oil temperature and load operating characteristics specifically includes: Calculate the oil temperature change rate of the transformer oil temperature data and the load change rate of the transformer load data within a preset time window; When the oil temperature change rate is less than a preset first change rate threshold and the load change rate is less than a preset second change rate threshold, determining that the transformer oil temperature data and the transformer load data meet the preset transformer oil temperature and load operating characteristics; When the oil temperature change rate is greater than or equal to the preset first change rate threshold or the load change rate is greater than or equal to the preset second change rate threshold, it is determined that the transformer oil temperature data and the transformer load data do not meet the preset transformer oil temperature and load operating characteristics.

3. The method according to claim 1, characterized in that The determining the fault type of the transformer according to the preset characteristic gas ratio fault judgment rule and the characteristic gas ratio matrix specifically includes: According to each concentration ratio in the characteristic gas ratio matrix, determining a target characteristic gas ratio fault interval with the highest matching degree in the preset characteristic gas ratio fault criterion rule, wherein the preset characteristic gas ratio fault criterion rule includes a plurality of characteristic gas ratio fault intervals and corresponding fault types; Based on the target characteristic gas ratio fault interval and the preset characteristic gas ratio fault criterion rule, a target fault type of the transformer is determined.

4. The method according to claim 2, characterized in that: After the step of calculating the concentration ratios of all characteristic gases in pairs based on the standardized contents of the respective characteristic gases to construct a characteristic gas ratio matrix, the method further includes: Calculating the standardized content change rate of each characteristic gas within the preset time window; Determining whether the change rate of the standardized content exceeds a preset third change rate threshold; If the standardized content change rate exceeds the preset third change rate threshold, determining a mutation characteristic gas; Identify equipment components whose correlation with the mutation characteristic gas exceeds a preset correlation threshold.

5. The method according to claim 4, characterized in that After the step of determining the mutation characteristic gas if the standardized content change rate exceeds the preset third change rate threshold, the method further includes: Determine the concentration ratio related to the mutation characteristic gas in the characteristic gas ratio matrix as the mutation concentration ratio, wherein the mutation concentration ratio is used to represent the concentration ratio of the mutation characteristic gas to the non-mutation characteristic gas; Based on the mutation concentration ratio, determining the correlation between the content change of the mutation characteristic gas and the non-mutation characteristic gas; Based on the correlation, the associated characteristic gases affected by the sudden change characteristic gas are screened.

6. The method according to claim 1, characterized in that Before the step of determining the fault type of the transformer according to the preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix, the method further includes: Obtain historical transformer oil temperature data, historical transformer load data, historical characteristic gas ratio matrix and corresponding historical fault types; Determining a historical characteristic gas ratio fault interval according to the historical characteristic gas ratio matrix; The preset characteristic gas ratio fault criterion rule is determined based on the historical characteristic gas ratio fault interval and the historical fault type.

7. The method according to claim 1, characterized in that After the step of determining the fault type of the transformer according to the preset characteristic gas ratio fault criterion rule and the characteristic gas ratio matrix, the method further includes: Inputting the transformer oil temperature data, the transformer load data and the characteristic gas ratio matrix into a preset fault evolution prediction model to predict the characteristic gas ratio evolution trajectory; Determining a suspected fault type according to the characteristic gas ratio evolution trajectory and the characteristic gas ratio matrix; Based on the suspected fault type, a corresponding fault reminder item is generated, and the fault reminder item includes the characteristic gas ratio evolution trajectory, the suspected fault type and a fault solution corresponding to the suspected fault type.

8. A detection system, characterized in that: The detection system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the detection system executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a detection system, the detection system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a detection system, the detection system is caused to perform the method according to any one of claims 1 to 7.

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