Transformer fault positioning method and device based on multi-point oil chromatography data

Abstract

The invention relates to a transformer fault positioning method and device based on multi-point oil chromatographic data. The method specifically comprises the following steps: setting more than or equal to 3 sampling points on a transformer to obtain concentration sequence data of dissolved gas in transformer oil; establishing a relation function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time; and based on the acquired concentration sequence data, solving the relation function through a global optimization algorithm to obtain the coordinates of the fault point of the transformer. According to the method, the coordinate of the fault point with the maximum correlation coefficient can be quickly and effectively searched in the range of the transformer oil tank, and compared with a traditional fault positioning method, the method is free of electromagnetic interference, wider in coverage range, higher in safety and capable of reducing the possibility of misjudgment.

Description

Technical Field

[0001] The present invention relates to the field of power detection technologies, and particularly to a transformer fault location method and device based on multi-point oil chromatographic data. Background Art

[0002] Large transformers are core equipment of power systems. Their failures may lead to serious accidents and economic losses. At the same time, it is necessary to distinguish the severity and urgency of the failures as much as possible to reduce the economic losses caused by the unplanned outage of transformers. The main purpose of fault location is to infer whether the fault location is in the key area of the transformer, which is an important means to distinguish the severity of transformer faults. Currently, the main means is partial discharge detection, but there are problems such as being easily interfered, limited sampling point coverage, and relying on expert experience. For example, ultrasonic detection requires personnel to approach the transformer shell and continuously adjust the sampling point position, which is relatively dangerous.

[0003] Dissolved gas analysis (DGA) in oil is a relatively common means of transformer fault detection. By detecting gases such as H2, CH4, C2H2, and CO dissolved in transformer oil, the fault type is judged based on the gas type and concentration. However, currently, it is mainly used to judge the fault type and cannot perform fault location. Summary of the Invention

[0004] The present invention provides a transformer fault location method and device based on multi-point oil chromatographic data. By collecting a concentration data sequence at multiple points in the transformer oil tank, the peak time and peak concentration of each sampling point are obtained. An equation set is established based on the relationship between the distance from the fault point to the sampling point, the peak time, and the peak concentration, and the equation set is solved through a global optimization algorithm to determine the fault point.

[0005] The solution of the present invention to the above technical problems is as follows: A transformer fault location method based on multi-point oil chromatographic data includes the following steps:

[0006] Set ≥3 sampling points on the transformer to obtain the concentration sequence data of the gases dissolved in the transformer oil;

[0007] Establish a relationship function based on the distance from each sampling point to the fault point, the peak concentration, and the peak concentration time;

[0008] Based on the obtained concentration sequence data, solve the relationship function through a global optimization algorithm to obtain the coordinates of the transformer fault point.

[0009] Preferably, any two sampling points are in a non - collinear and non - coplanar relationship, and at the same time, the distance between any two sampling points is the farthest. It is recommended to arrange a sampling point at the top of the highest riser of the transformer first. The transformer oil tank is generally a cuboid, and other sampling points are preferably arranged near both ends of the space diagonal of the oil tank. The installation position of the sampling points is not limited to the wall of the transformer oil tank and can be installed inside the transformer. An on - line oil chromatograph monitoring device, or a gas in - situ detection sampling point, or a regular manual sampling is installed at each sampling point, and the sampling interval is generally not more than 1 hour.

[0010] Preferably, the establishment of the relationship function based on the distance, peak concentration, and peak concentration time from each sampling point to the fault point includes:

[0011] Based on Fick's second law, the concentration data sequence during the period when the fault gas detected at each sampling point reaches the peak concentration is expressed in the form of a relationship function as:

[0012]

[0013] where Q is the total amount of gas generated by the fault, D i is the diffusion coefficient of the fault gas from the fault point to the sampling point i, i is the sampling point serial number, t is the sampling detection time, r i is the coordinate position of the sampling point i, and r0 is the coordinate position of the fault point;

[0014] where,

[0015] where K is the constant to be solved, t i，max is the time when the sampling point i reaches the peak starting from the fault time t0, c i，max is the peak concentration measured at the sampling point i;

[0016] The specific derivation process is as follows:

[0017] The diffusion process of dissolved gas can be described by Fick's second law:

[0018]

[0019] where:

[0020] -c is the dissolved gas concentration,

[0021] -D is the diffusion coefficient,

[0022] - is the Laplace operator.

[0023] In the case of spherical symmetry, the solution of Fick's second law is:

[0024]

[0025] Wherein:

[0026] -Q is the total amount of gas generated by the fault,

[0027] -r is the distance from the fault point,

[0028] -t is the time.

[0029] At a fixed r, take the derivative of c(r, t) with respect to t and set the derivative to zero to solve for the time t when the maximum concentration is reached at the distance r from the fault point max , and we get:

[0030]

[0031] It can be verified through the second derivative or physical meaning that is the maximum point of c(r, t), indicating that the farther the distance from the fault point, the longer the time to reach the peak. Substitute Equation (3) into Equation (2) to obtain the maximum concentration c max :

[0032]

[0033] The diffusion coefficient D and the total amount of gas Q generated by the fault can be regarded as constants. From Equations (3) and (4), it can be seen that r 2 is proportional to the time t to reach the maximum concentration max , and r 3 is inversely proportional to the maximum concentration value c max , so we have

[0034]

[0035] where k is a number related to Q and D. Here, Q is the total amount of gas generated by this fault and can be regarded as an unknown constant. D is the equivalent diffusion coefficient from the fault point to the sampling point, which is affected by the specific structure of the transformer, the oil temperature distribution, the oil flow velocity distribution, etc. This equivalent diffusion coefficient D can be measured in advance for transformers with the same structure to obtain more accurate calculation results. If there is no historical data on the equivalent diffusion coefficient D, an alternative method is provided below. During the same period in the same transformer, the oil temperature distribution and the oil flow velocity distribution do not differ much, and the D values from the fault point to different sampling ports can be considered approximately equal. Therefore, k can be regarded as an unknown constant. The time t to reach the maximum concentration max , the maximum concentration c max can be obtained through actual measurement. For multiple sampling points, t max , c max Set up a system of equations according to Equation (5) to calculate the location of the fault point.

[0036] Take the moment corresponding to the concentration value as Each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0;

[0037] Since it takes a certain amount of time for the gas produced at the fault point to diffuse to each sampling point, and the sampling point precision limit cannot detect the slight change in the previous concentration, the fault time t0 is earlier than the time when the data of each sampling point starts to increase, and the time t0 cannot be directly read from the concentration series data of each sampling point. Regarding the determination of the time t0, use formula (3) to subtract half of the peak time Substituting into formula (2), the corresponding concentration data can be calculated as calculate That is, when the peak time is half, the corresponding concentration is about 0.6 times the peak concentration. Based on the above derivation and experimental measurement, it is recommended to regard the moment when the peak concentration is about 0.5 times as half of the peak time, that is, The moment corresponding to the concentration value is regarded as Each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0.

[0038] make By squaring both sides of formula (5), we can get The data of each sampling point are combined to obtain the relationship function:

[0039]

[0040] Preferably, the method of solving the relationship function based on the acquired concentration sequence data by a global optimization algorithm to obtain the coordinates of the transformer fault point includes:

[0041] Suppose a point (x0, y0, z0) is inside the transformer tank, so that the distance from (x0, y0, z0) to n sampling points is equal to the given value {d1, d2, ..., d n} has the largest correlation coefficient Corr, n≥3;

[0042] The global optimization algorithm is used to maximize the correlation coefficient Corr as the objective function to obtain the transformer fault coordinates.

[0043] Preferably, the objective function expression with the maximum correlation coefficient Corr is:

[0044]

[0045] in:

[0046] is the calculated distance from the fault point to the i-th sampling point, μ D Calculate the mean distance from n sampling points to the fault point, μY is the mean value of the actual distances from n sampling points to the fault point;

[0047] Maximizing Corr is transformed into minimizing the negative correlation coefficient:

[0048] Minimize f(x0, y0, z0) = -Corr

[0049] The constraint condition is the range of the transformer (including the riser) oil tank:

[0050] 0 ≤ x0 ≤ L, 0 ≤ y0 ≤ W, 0 ≤ z0 ≤ H

[0051] L, W, and H are the dimensions of the oil tank in the x, y, and z directions respectively.

[0052] The present invention also provides a transformer fault location device based on multi-point oil chromatogram data, including:

[0053] A data extraction module for setting ≥3 sampling points on the transformer to obtain the concentration sequence data of the dissolved gases in the transformer oil;

[0054] A relationship function establishment module for establishing a relationship function based on the distances from each sampling point to the fault point, the peak concentration, and the peak concentration time;

[0055] A transformer fault coordinate location module for solving the relationship function through a global optimization algorithm based on the obtained concentration sequence data to obtain the coordinates of the transformer fault point.

[0056] Preferably, any two sampling points satisfy the non-collinear and non-coplanar relationship, and at the same time, the distance between any two sampling points is the farthest.

[0057] Preferably, the establishment of the relationship function based on the distances from each sampling point to the fault point, the peak concentration, and the peak concentration time includes:

[0058] Based on Fick's second law, the concentration data sequence within the time period when the fault gas detected at each sampling point reaches the peak concentration is expressed in the form of a relationship function as:

[0059]

[0060] where Q is the total amount of fault gas production, D i is the diffusion coefficient of the fault gas from the fault point to the sampling point i, i is the sampling point serial number, t is the sampling detection time, r i is the coordinate position of the sampling point i, and r0 is the coordinate position of the fault point;

[0061] where,

[0062] where K is the constant to be solved, ti，max is the time when sampling point i reaches the peak value from the fault time t0, c i，max is the peak concentration measured at sampling point i;

[0063] Pick The moment corresponding to the concentration value is regarded as Each sampling point is calculated to obtain t i，0 , each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0;

[0064] make The data of each sampling point are combined to obtain the relationship function:

[0065]

[0066] Preferably, the method of solving the relationship function based on the acquired concentration sequence data by a global optimization algorithm to obtain the coordinates of the transformer fault point includes:

[0067] Suppose a point (x0, y0, z0) is inside the transformer tank, so that the distance from (x0, y0, z0) to n sampling points is equal to the given value {d1, d2, ..., d n} has the largest correlation coefficient Corr, n≥3;

[0068] The global optimization algorithm is used to maximize the correlation coefficient Corr as the objective function to obtain the transformer fault coordinates.

[0069] Preferably, the objective function expression with the maximum correlation coefficient Corr is:

[0070]

[0071] in:

[0072] is the calculated distance from the fault point to the i-th sampling point,

[0073] Convert maximizing Corr into minimizing the negative correlation coefficient:

[0074] Minimize f(x0, y0, z0) = -Corr

[0075] The constraints are the range of the transformer oil tank (including the riser):

[0076] 0≤x0≤L,0≤y0≤W,0≤z0≤H

[0077] L, W, H are the dimensions of the tank in the x, y, and z directions respectively.

[0078] The present invention also provides a computer storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the transformer fault location method based on multi-point oil chromatographic data as described above are implemented.

[0079] The present invention also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the transformer fault location method based on multi-point oil chromatographic data as described above are implemented.

[0080] The beneficial effects of the present invention are as follows: Based on a richer and more comprehensive data foundation, the present invention establishes a relationship function between the distance, peak time, and peak concentration from the fault point to each sampling point, and uses a global optimization algorithm to solve the established relationship function, which can quickly and effectively search for the fault point coordinates that maximize the correlation coefficient within the transformer oil tank range. Compared with the traditional fault location method, this method is not affected by electromagnetic interference, has a wider coverage range, higher safety, and reduces the possibility of misjudgment.

[0081] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following takes the preferred embodiments of the present invention and combines with the drawings to describe in detail as follows. The specific implementation manners of the present invention are given in detail by the following embodiments and their accompanying drawings. Description of the Drawings

[0082] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0083] Figure 1 It is a flowchart of a transformer fault location based on multi-point oil chromatographic data provided for Embodiment 1;

[0084] Figure 2 It is a schematic diagram of the arrangement of sampling points for dissolved gases in transformer oil in Embodiment 1;

[0085] Figure 3 It is a module diagram of a transformer fault device based on multi-point oil chromatographic data provided for Embodiment 2. Detailed Description of the Invention

[0086] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0087] Embodiment 1

[0088] AsFigure 1 As shown in the figure, this embodiment provides a transformer fault location method based on multi-point oil chromatogram data, including the following steps:

[0089] S1. Set 4 sampling points on the transformer to obtain the concentration sequence data of the dissolved gases in the transformer oil;

[0090] The 4 sampling points are in a non-collinear and non-coplanar relationship, and at the same time, the distances between the sampling points are kept as far as possible. It is recommended to arrange a sampling point at the top of the highest riser on the transformer first. The transformer oil tank is generally a cuboid, and the other sampling points are preferably arranged near the two ends of the space diagonal of the oil tank. The installation positions of the sampling points are not limited to the wall of the transformer oil tank and can be installed inside the transformer. An on-line oil chromatogram monitoring device, or a gas in-situ detection sampling point, or a timed manual sampling is installed at each sampling point, and the sampling interval is generally not more than 1 hour.

[0091] As Figure 2 shown in the figure, in this embodiment, the lower left corner of the transformer is used as the origin coordinate, and the position coordinates of the 4 sampling points are r1=(3.4, 0.1, 0.4), r2=(0.9, 4.7, 9.4), r3=(1.1, 7.1, 3.8), r4=(0, 3.8, 1.4) respectively, with the unit of meter.

[0092] S2. Establish a relationship function based on the distances from each sampling point to the fault point, the peak concentration, and the peak concentration time;

[0093] Based on Fick's second law, the concentration data sequence within the time period when the fault gas detected at each sampling point reaches the peak concentration is expressed in the form of a relationship function as:

[0094]

[0095] where Q is the total amount of fault gas production, D i is the fault gas diffusion coefficient from the fault point to the sampling point i, i is the sampling point serial number, t is the sampling detection time, r i is the coordinate position of the sampling point i, and r0 is the coordinate position of the fault point;

[0096] where

[0097] where K is the constant to be solved, t i，max is the time when the sampling point i reaches the peak starting from the fault time t0, c i，max is the peak concentration measured at the sampling point i;

[0098] Take the moment corresponding to the concentration value as According to this relationship, each sampling point calculates to obtain t i，0, take the earliest moment as t0;

[0099] make The data of each sampling point are combined to obtain the relationship function:

[0100]

[0101] S3. Based on the acquired concentration series data, the relationship function is solved by a global optimization algorithm to obtain the coordinates of the transformer fault point.

[0102] The C2H2 concentration sequence data of each sampling point is:

[0103] c1(t)={0,0,0.17,0.56,1.21,1.87,2.02,2.80,2.63,2.79,2.60,2.61}. c2(t)={0,0,0.23,0.74,1.59,4.35,3.79,3.03,2.81,2.73,2.57,2.53}. c3(t)={0,0,0,0.31,0.92,1.34,1.78,2.02,2.31,2.73,2.47,2.35}. c t (t) = {0, 0, 0.21, 0.80, 1.25, 2.00, 2.66, 2.80, 2.79, 2.64, 2.73, 2.79}, unit is μL / L.

[0104] The peak value c of c1(t) 1,max =2.80, then The closest value is 1.21. If we consider the time corresponding to 1.21 as half of the time from t0 to the peak value, we can get the time corresponding to the second 0 value of c1(t) as t0. 2,max =4.35, calculate the time t0 as the time corresponding to 0.74 in c2(t). 3,max =2.73, calculate the time t0 as the time corresponding to the second 0 value in c3(t). 4，max =2.80, calculate the time t0 as the time corresponding to the second 0 value in c4(t). Select the time t0 calculated by all sampling points that is closest to the first as the result, that is, the time corresponding to the second value in the sequence is t0. Then the time t when each sampling point reaches the peak can be determined 1，max =3, t 2，max =2,t 3，max =4,t i，max =3, unit is hours.

[0105] Substituting the time when each sampling point reaches the peak value, the peak concentration and the coordinates of the sampling point into the relationship function, the following equation is obtained:

[0106]

[0107] Let a point (x0, y0, z0) inside the transformer oil tank be such that the correlation coefficient Corr between the distances from (x0, y0, z0) to 4 sampling points and a given value is the largest, where n = 4;

[0108] Solve through a global optimization algorithm with the maximum correlation coefficient Corr as the objective function to obtain the transformer fault coordinates;

[0109] The objective function expression with the maximum correlation coefficient Corr is as follows:

[0110]

[0111] Where:

[0112] is the calculated distance from the fault point to the i-th sampling point,

[0113] Convert maximizing Corr to minimizing the negative correlation coefficient:

[0114] Minimize f(x0, y0, z0) = -Corr

[0115] The constraint condition is the range of the transformer (including riser) oil tank:

[0116] 0 ≤ x0 ≤ L, 0 ≤ y0 ≤ W, 0 ≤ z0 ≤ H

[0117] L, W, H are the dimensions of the oil tank in the x, y, and z directions respectively.

[0118] Calculate using the differential evolution algorithm to obtain x0 = 1.37, y0 = 0, z0 = 6.99, and the correlation coefficient is close to 1, that is, the fault point coordinates are obtained.

[0119] Embodiment 2

[0120] A transformer fault location device based on multi-point oil chromatogram data includes:

[0121] A data extraction module for setting ≥3 sampling points on the transformer to obtain the concentration sequence data of dissolved gases in the transformer oil;

[0122] A relationship function establishment module for establishing a relationship function based on the distances from each sampling point to the fault point, the peak concentration, and the peak concentration time;

[0123] A transformer fault coordinate location module for solving the relationship function through a global optimization algorithm based on the obtained concentration sequence data to obtain the transformer fault point coordinates.

[0124] Any two sampling points are in a non-collinear and non-coplanar relationship, and the distance between any two sampling points is kept as far as possible.

[0125] The establishment of a relationship function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time includes:

[0126] Based on Fick's second law, the concentration data sequence of each sampling point during the time period when the fault gas reaches the peak concentration is expressed in the form of a relational function:

[0127]

[0128] Where Q is the total amount of gas produced by the fault, D i is the fault gas diffusion coefficient from the fault point to sampling point i, i is the sampling point number, t is the sampling detection time, r i is the coordinate position of sampling point i, r0 is the coordinate position of the fault point;

[0129] in,

[0130] Where K is a constant, t i，max is the time when sampling point i reaches the peak value from the fault time t0, c i，max is the peak concentration measured at sampling point i;

[0131] Pick The moment corresponding to the concentration value is regarded as Each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0;

[0132] make The data of each sampling point are combined to obtain the relationship function:

[0133]

[0134] The method of solving the relationship function based on the acquired concentration sequence data through a global optimization algorithm to obtain the coordinates of the transformer fault point includes:

[0135] Suppose a point (x0, y0, z0) is inside the transformer tank, so that the distance from (x0, y0, z0) to n sampling points is equal to the given value {d1, d2, ..., d n} has the largest correlation coefficient Corr, n≥3;

[0136] The global optimization algorithm is used to maximize the correlation coefficient Corr as the objective function to obtain the transformer fault coordinates.

[0137] The objective function expression with the maximum correlation coefficient Corr is:

[0138]

[0139] Wherein:

[0140] is the calculated distance from the fault point to the i-th sampling point,

[0141] Converting the maximization of Corr into the minimization of the negative correlation coefficient:

[0142] Minimize f(x0, y0, z0) = -Corr

[0143] The constraint condition is the transformer oil tank range:

[0144] 0 ≤ x0 ≤ L, 0 ≤ y0 ≤ W, 0 ≤ z0 ≤ H

[0145] L, W, and H are the dimensions of the oil tank in the x, y, and z directions respectively.

[0146] Example 3

[0147] This embodiment provides a computer storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of the transformer fault location method based on multi-point oil chromatogram data as described above.

[0148] Example 4

[0149] This embodiment provides an electronic device, including a memory and a processor: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, it implements the steps of the transformer fault location method based on multi-point oil chromatogram data as described above.

[0150] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer program product. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0152] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

Claims

1. A transformer fault location method based on multi-point oil chromatographic data, characterized in that: The following steps are involved: Set ≥3 sampling points on the transformer to obtain concentration series data of dissolved gas in transformer oil; Establish a relationship function based on the distance from each sampling point to the fault point, peak concentration and peak concentration time; Based on the acquired concentration series data, the relationship function is solved by the global optimization algorithm to obtain the coordinates of the transformer fault point.

2. According to claim 1, a transformer fault location method based on multi-point oil chromatogram data is characterized in that: Any two sampling points are in a non-collinear and non-coplanar relationship, and the distance between any two sampling points is kept as far as possible.

3. According to claim 1, a transformer fault location method based on multi-point oil chromatogram data is characterized in that: The establishment of a relationship function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time includes: Based on Fick's second law, the concentration data sequence of each sampling point during the time period when the fault gas reaches the peak concentration is expressed in the form of a relationship function: Where Q is the total amount of gas produced by the fault, S i is the fault gas diffusion coefficient from the fault point to sampling point i, i is the sampling point number, t is the sampling detection time, r i is the coordinate position of sampling point i, r0 is the coordinate position of the fault point; the diffusion coefficient D and the total amount of fault gas Q in, Among them, K is the constant to be solved, t i，max is the time when sampling point i reaches the peak value from the fault time t0, c i，max is the peak concentration measured at sampling point i; Pick The moment corresponding to the concentration value is regarded as Each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0; make The data of each sampling point are combined to obtain the relationship function:

4. According to claim 3, a transformer fault location method based on multi-point oil chromatogram data is characterized in that: The method of solving the relationship function based on the acquired concentration sequence data through a global optimization algorithm to obtain the coordinates of the transformer fault point includes: Suppose a point (x0, y0, z0) is inside the transformer tank, so that the distance from (x0, y0, z0) to n sampling points is equal to the given value {d1, d2, ..., d n } has the largest correlation coefficient Corr, n≥3; The global optimization algorithm is used to maximize the correlation coefficient Corr as the objective function to solve the transformer fault coordinates.

5. According to claim 4, a transformer fault location method based on multi-point oil chromatogram data is characterized in that: The objective function expression with the maximum correlation coefficient Corr is: in: is the calculated distance from the fault point to the i-th sampling point, Transform maximizing Corr into minimizing the negative correlation coefficient: Minimize f(x0,y0,z0) = -Corr The constraints are the transformer tank range: 0≤x0≤L,0≤y0≤W,0≤z0≤H L, W, H are the dimensions of the tank in the x, y, and z directions respectively.

6. A transformer fault location device based on multi-point oil chromatographic data, comprising: A data extraction module is used to set ≥3 sampling points on the transformer to obtain concentration sequence data of dissolved gas in transformer oil; A relationship function establishment module is used to establish a relationship function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time; The transformer fault coordinate positioning module is used to solve the relationship function based on the acquired concentration sequence data through a global optimization algorithm to obtain the coordinates of the transformer fault point.

7. The transformer fault location device based on multi-point oil chromatographic data according to claim 6, characterized in that: Any two sampling points are in a non-collinear and non-coplanar relationship, and the distance between any two sampling points is kept as far as possible.

8. The transformer fault location device based on multi-point oil chromatographic data according to claim 6, characterized in that: The establishment of a relationship function based on the distance from each sampling point to the fault point, the peak concentration and the peak concentration time includes: Based on Fick's second law, the concentration data sequence of each sampling point during the time period when the fault gas reaches the peak concentration is expressed in the form of a relational function: Where Q is the total amount of gas produced by the fault, D i is the fault gas diffusion coefficient from the fault point to sampling point i, i is the sampling point number, t is the sampling detection time, r i is the coordinate position of sampling point i, r0 is the coordinate position of the fault point; in, Among them, K is the constant to be solved, t i，max is the time when sampling point i reaches the peak value from the fault time t0, c i，max is the peak concentration measured at sampling point i; Pick The moment corresponding to the concentration value is regarded as Each sampling point is calculated according to this relationship to obtain t i，0 , take the earliest moment as t0; make The data of each sampling point are combined to obtain the relationship function:

9. A transformer fault location device based on multi-point oil chromatographic data according to claim 8, characterized in that: The method of solving the relationship function based on the acquired concentration sequence data through a global optimization algorithm to obtain the coordinates of the transformer fault point includes: Assume a point (x0, y0, z0) inside the transformer tank, such that the distance from (x0, y0, z0) to n sampling points is equal to the given value {d1, d2, ..., d n } has the largest correlation coefficient Corr, n≥3; The global optimization algorithm is used to maximize the correlation coefficient Corr as the objective function to obtain the transformer fault coordinates.

10. The transformer fault location device based on multi-point oil chromatogram data according to claim 9, characterized in that: The objective function expression with the maximum correlation coefficient Corr is: in: is the calculated distance from the fault point to the i-th sampling point, Transform maximizing Corr into minimizing the negative correlation coefficient: Minimize f(x0, y0, z0) = -Corr The constraints are the transformer tank range: 0≤x0≤L,0≤y0≤W,0≤z0≤H L, W, H are the dimensions of the tank in the x, y, and z directions respectively.

11. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the transformer fault location method based on multi-point oil chromatogram data as described in any one of claims 1 to 5 are implemented.

12. An electronic device, characterized in that: It comprises a memory and a processor: the memory is used to store computer executable instructions, the processor is used to execute the computer executable instructions, and when the computer executable instructions are executed by the processor, the steps of the transformer fault locating method based on multi-point oil chromatographic data as described in any one of claims 1 to 5 are implemented.

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