Transformer fault identification method and system based on fitting weight and equivalent inductance

Abstract

The invention discloses a transformer fault identification method and system based on fitting weight and equivalent inductance, and the method comprises the steps: selecting voltages at two ends of a primary winding and currents of primary and secondary double-end windings in a transformer as sampling points, and generating a matrix of the voltages at the two ends of the primary winding and the currents at the two ends of the primary winding in each unit data window; carrying out equivalent inductance normalization calculation to obtain unweighted equivalent inductance; accumulatively calculating equivalent inductance in the weighted data window to obtain weighted equivalent inductance; and designing a fault protection criterion according to the weighted equivalent inductance, performing transformer fault identification by using the fault protection criterion, designing a protection criterion, and if the value of the equivalent inductance always meets the protection criterion within the time window length, determining that the transformer has an internal fault. Compared with the prior art, the method overcomes the defect that traditional time domain calculation is large in volatility, and the stability and reliability of protection are improved.

Description

Technical Field

[0001] The invention belongs to the field of relay protection of electric power systems, and in particular to a transformer fault determination method and system based on transformer time-domain equivalent inductance. Background Art

[0002] In the new energy transmission system, the transformer plays a key role in voltage conversion and energy transmission, among which the rapid identification and protection after the fault will play a vital role in equipment safety and system stability. At present, the current differential protection based on Kirchhoff's law is generally used as the main protection of the transformer in the project, and the excitation inrush current is determined by calculating the second harmonic content of the three-phase differential current, so as to achieve the effect of timely protection locking and preventing false operation. With the access of large-scale power electronic equipment, the fault characteristics of the transformer show high harmonic characteristics, which aggravates the difficulty of identifying the transformer excitation inrush current. Traditional transformer protection has the risk of refusal to operate and false operation.

[0003] When the harmonic content in the transformer fault current is large, the second harmonic criterion will easily lock the protection until the second harmonic content drops below the threshold value. In order to better distinguish between normal operation, magnetizing inrush current and fault conditions of the transformer, the equivalent inductance of the transformer is calculated in the time domain using the idea of ​​parameter identification, which can intuitively reflect the operating status of the transformer, quickly identify faults and realize reliable protection action. Summary of the invention

[0004] In view of the difficulty in identifying transformer faults and excitation inrush currents under the access of new energy, the present invention proposes a transformer fault judgment method and system based on fitting weights and equivalent inductance, obtains weighted equivalent inductance in the time domain, and forms a judgment criterion based on the weighted equivalent inductance to quickly identify internal faults occurring in the operating state of the transformer and realize reliable protection action.

[0005] In order to achieve the above-mentioned object of the invention, the present invention proposes the following technical solutions:

[0006] A transformer fault identification method based on weighted and equivalent inductance, comprising:

[0007] In the transformer, the voltage across the primary winding and the current across the primary and secondary windings are selected as sampling points, and the voltage across the primary winding at any sampling point k is read. 1 (k), the original secondary double-end winding current sampling value i 1 (k), i 2 (k), calculate the differential current i converted to the voltage collection side for each unit data window d (k), generate the voltage matrix U and current matrix I in each unit data window;

[0008] The voltage equation across the primary winding at sampling point k is obtained as follows:

[0009]

[0010] In the formula, k is the sampling point sequence index, T s is the sampling period, i d (k) is the differential current at sampling point k, i d (k+1) is the differential current at the previous sampling point k+1, i d (k-1) is the differential current at the previous sampling point k-1;

[0011] Select the sampling period T s The multiple sampling points in the primary winding are combined to obtain the primary winding voltage matrix U and current matrix I, which are expressed as follows:

[0012] U=Iβ

[0013] Set the least squares window length n, the primary winding voltage matrix U within the least squares window length n (n-2)×1 and the current matrix I (n-2)×2 It is expressed as follows:

[0014] U (n-2)×1 =[u(2),u(3),...,u(k′),...,u(n-1)] T

[0015]

[0016] Where p(2), p(3), ... p(k′) ..., p(n-1) is the current sampling sequence within the unit data window length n, p d (2),p d (3),...p d (k′)...,p d (n-1) is the differential derivative sequence of the current sampling points within the unit data window length n, U (n-2)×1 ,I (n-2)×2 is the result of the least squares length n of the primary winding voltage matrix U and current matrix I constructed previously. The subscript represents the dimension of the matrix, n is the least squares calculation window length, and k′ is the data index in the least squares calculation window;

[0017] The sampling sequence equations are obtained, which can be expressed as:

[0018] U (n-2)×1 =I (n-2)×2 β

[0019] Perform normalized calculation of equivalent inductance to obtain unweighted equivalent inductance; the specific description is as follows:

[0020] The sampling sequence equations are estimated and solved using the least squares method, that is, the fitting coefficient matrix β makes the calculated value I of the voltage matrix in the data window (n-2)×2 β and the primary winding voltage matrix U within the least squares window length n (n-2)×1 The actual value has the smallest distance in the Euclidean space. The Euclidean distance E between the two is expressed as:

[0021]

[0022] Take the partial derivative of the Euclidean distance E formula and set it equal to 0 to obtain the fitting coefficient matrix β in each unit data window k″ ,, the expression is as follows:

[0023] β (k″) =(I (k″)Τ I (k″) ) -1 I (k″)Τ U (k″)

[0024] The solution to the equation is β 2×1 =[r,L] T , r and L are the primary winding voltage equations respectively The unfitted equivalent resistance and equivalent inductance in ;

[0025] Step 3: cumulatively calculate the weighted data window t q The equivalent inductance inside is calculated to obtain the weighted equivalent inductance, which is described as follows:

[0026] Define the fitting error E of a single fitting calculation r The expression is as follows:

[0027]

[0028] Among them, U(k′) and I(k′) represent the primary winding voltage matrix and current matrix corresponding to any sampling point k′ in the data window respectively;

[0029] Set the weighted data window length m, and calculate the weight coefficient 1 / E of any sampling point k' in the corresponding unit data window in the weighted data window. r (k′) is weighted and normalized as the calculation result of equivalent inductance. At this time, the calculation formula of equivalent inductance is:

[0030] W r(k″) =1 / E r(k″)

[0031] W r =[W r(1) ,W r(2) ,W r(3) ,...,W r(m) ]T

[0032] Cumulative calculation of weighted data window t q The fitted equivalent inductance of the corresponding weighted data window length m is used to obtain the weighted equivalent inductance, which is expressed as follows:

[0033]

[0034] Where W r(k″) is the fitting weight of each unit inductance calculation result in the weighted data window, W r is the weight matrix composed of all weights, k″ is the weighted data window index, m is the number of data in the weighted data window, k″∈m, L is the equivalent inductance value in the weighted data window, is the weighted equivalent inductance, W r(1) ,W r(2) ,W r(3) ,...,W r(m) is the fitting weight corresponding to 1, 2, 3, ...m in the weighted data window;

[0035] A fault protection criterion is designed according to the weighted equivalent inductance, and transformer fault identification is performed using the fault protection criterion:

[0036] The design protection criterion is expressed as:

[0037]

[0038] In the formula, is the equivalent inductance calculated value of the weighted data window index k″, L set is the inductance threshold value, α is the fluctuation coefficient, T 1 For a long time window of protection criterion, if the value of equivalent inductance is T 1 If the criterion is met within the time window, it is determined that the transformer has an in-zone fault.

[0039] A transformer fault identification system based on fitting weight and equivalent inductance, comprising:

[0040] The transformer information acquisition module is used to select the voltage across the primary winding and the current across the primary and secondary windings as sampling points in the transformer, and read the voltage u across the primary winding at any sampling point k. 1 (k), the original secondary double-end winding current sampling value i 1 (k), i 2 (k), calculate the differential current i converted to the voltage collection side for each unit data window d (k), generate the voltage matrix U and current matrix I in each unit data window, and convert the primary winding voltage matrix U in the least squares window length n into (n-2)×1 and the current matrix I(n-2)×2 , we get the sampling sequence equation system U (n-2)×1 =I (n-2)×2 β

[0041] The equivalent inductance normalization calculation module is used to use the least square method to calculate the sampling sequence equation group U (n-2)×1 =I (n-2)×2 β is estimated and solved, and the equivalent inductance is normalized and calculated to obtain the unfitted equivalent resistance and unfitted equivalent inductance in the primary winding voltage equation;

[0042] Fitting weighted equivalent inductance calculation module, used to accumulate and calculate weighted data window t q The equivalent inductance inside is used to obtain the weighted equivalent inductance;

[0043] The fault identification module is used to design a fault protection criterion according to the weighted equivalent inductance, use the fault protection criterion to identify transformer faults, and design a protection criterion, the expression is:

[0044]

[0045] In the formula, is the equivalent inductance calculated value within any unit data window k″, L set is the inductance threshold value, α is the fluctuation coefficient, α ranges from -0.1 to -0.05, T 1 The time window of the protection criterion is long. If the equivalent inductance satisfies the protection criterion, it is determined that the transformer has an in-zone fault.

[0046] Compared with the prior art, the present invention is not affected by power supply characteristics, can effectively eliminate the influence of harmonics and inrush current, and quickly and accurately identify internal and external faults and excitation inrush current of the transformer area. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is an overall flow chart of a transformer fault identification method based on fitting weight and equivalent inductance of the present invention;

[0048] Figure 2 This is an example diagram of the transformer structure;

[0049] Figure 3 This is a module diagram of a transformer fault identification system based on fitting weights and equivalent inductance according to the present invention. DETAILED DESCRIPTION

[0050] The specific implementation modes of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Embodiment 1:

[0052] like Figure 1As shown, a transformer fault identification method based on fitting weight and equivalent inductance of the present invention comprises the following specific steps:

[0053] Step 1: Select the voltage across the primary winding and the current across the primary and secondary windings as sampling points in the transformer, and read the voltage across the primary winding at any sampling point k. 1 (k), the original secondary double-end winding current sampling value i 1 (k), i 2 (k), calculate the differential current i converted to the voltage collection side for each unit data window d (k) Generate the voltage matrix U and current matrix I in each unit data window. This step is described in detail as follows:

[0054] like Figure 2 As shown in the figure, when the transformer has an internal fault, the short-circuited part is used as the third winding, which is equivalent to a three-winding transformer with a short circuit in the third winding, that is, the original excitation inductance L m A small fault inductor L is connected in parallel Sδ . Combining the principle of equivalent inductance, looking from the primary side of the transformer: instantaneous inductance based on primary voltage and differential current. Under normal operating conditions, the equivalent inductance is the sum of the primary leakage inductance and the excitation inductance; while under fault conditions, the equivalent inductance consists of two parts, one is the parallel connection of the excitation inductance and the fault winding, and the other is the leakage inductance of the primary winding. The two parts are added together to obtain the equivalent instantaneous inductance.

[0055] Ignoring the voltage drop of the secondary current on the primary leakage resistor, the voltage equation across the primary winding is constructed, and the expression is as follows:

[0056]

[0057] In the formula, u 1 is the voltage across the primary winding, r is the equivalent resistance, L is the equivalent excitation inductance, i d is the differential current;

[0058] Using difference instead of differentiation, the primary winding voltage equation at sampling point k is obtained according to equation (1), which is as follows:

[0059]

[0060] i d (k) = i 1 (k)+i 2 (k)

[0061] i d (k-1)=i 1 (k-1)+i 2 (k-1)

[0062] In the formula, k is the sampling point sequence index, T s is the sampling period, i d (k) is the differential current at sampling point k, i d (k+1) is the differential current at the next sampling point k+1, i d (k-1) is the differential current of the previous sampling point k-1, i 1 (k) is the sampling value of the primary double-ended winding current at sampling point k, i 2 (k) is the sampling value of the secondary double-end winding current at sampling point k, i 1 (k-1) is the sampling value of the primary double-end winding current at the previous sampling point k-1, i 2 (k-1) is the sampling value of the secondary double-end winding current at the previous sampling point k-1;

[0063] Select the sampling period T s The multiple sampling points in the primary winding are combined to obtain the primary winding voltage matrix U and current matrix I, which are expressed as follows:

[0064] U=Iβ (3)

[0065] Set the least squares window length n. According to equation (3), the primary winding voltage matrix U within the least squares window length n is (n-2)×1 and the current matrix I (n-2)×2 It is expressed as follows:

[0066] U (n-2)×1 =[u(2),u(3),...,u(k′),...,u(n-1)] T (4)

[0067]

[0068] Where p(2), p(3), ... p(k′) ..., p(n-1) is the current sampling sequence within the least squares window length n, p d (2),p d (3),...p d (k′)...,p d (n-1) is the differential derivative sequence of the current sampling points within the least squares window length n, U (n-2)×1 ,I (n-2)×2 are the primary winding voltage matrix U and current matrix I within the least squares window length n, the subscripts represent the dimensions of the matrix, n is the least squares calculation window length, and k′ is the data index within the least squares calculation window;

[0069] in:

[0070] p(k′)=i d (k) (6)

[0071]

[0072] Among them, T s is the sampling period, i d (k) is the differential current at sampling point k, i d (k+1) is the differential current at the next sampling point k+1, i d (k-1) is the differential current at the previous sampling point k-1;

[0073] According to equations (3), (4), and (5), the following sampling sequence equations are obtained:

[0074] U (n-2)×1 =I (n-2)×2 β (8)

[0075] Step 2, perform normalized calculation of equivalent inductance to obtain unweighted equivalent inductance, which is described in detail as follows:

[0076] 2.1, based on certain redundant data, the least square method is used to estimate and solve the equation group (8), that is, the fitting coefficient matrix β makes the calculated value of the voltage matrix in the data window I (n-2)×2 β and the primary winding voltage matrix U within the least squares window length n (n-2)×1 The actual value has the smallest distance in the Euclidean space. The Euclidean distance E between the two is expressed as:

[0077]

[0078] The above-mentioned certain redundant data means that the least squares window length n is set to at least 2. When the computation load allows, the value of n is as large as possible to satisfy the primary winding voltage matrix U within the least squares window length n. (n-2)×1 and the current matrix I (n-2)×2 There are multiple redundant data involved in the least squares method.

[0079] 2.2, find the partial derivative of the above formula (9) and set it equal to 0, and get the fitting coefficient matrix β of the data k′ in the least squares calculation window: k′ ,, the expression is as follows:

[0080] β (k′) =(I (k′)Τ I (k′) ) -1 I (k′)Τ U (k′) (10)

[0081] The solution of equation (10) is β 2×1 =[r,L] T , r and L are the equivalent resistance and equivalent inductance that are not fitted in formula (2);

[0082] The actual magnetization curve of the transformer is a curve with a constantly changing slope, that is, the solution equation of the equivalent inductance is nonlinear. Even if the least squares linear method is used to solve it in a shorter window length, there will be large errors and fluctuations in the calculation results, which is not conducive to constant value setting and fault identification. It is necessary to improve the accuracy and reliability of the calculation.

[0083] Step 3: cumulatively calculate the weighted data window t q The equivalent inductance inside is calculated to obtain the weighted equivalent inductance, which is described as follows:

[0084] The size of the Euclidean distance in formula (9) directly reflects the accuracy of the calculation. The smaller the Euclidean distance, the better the fitting calculation effect is, that is, the calculated value of the fault distance is closer to the steady-state calculated value. Otherwise, the fitting effect is poor. Based on this, the fitting error E of a single fitting calculation is defined as r The expression is as follows:

[0085]

[0086] Among them, U(k′) and I(k′) represent the primary winding voltage matrix and current matrix corresponding to any sampling point k′ in the data window respectively;

[0087] Fitting error E r It reflects the effect of the fault distance fitting calculation in the unit data window. The smaller the value of the fitting error, the higher the matching degree between the coefficient matrix obtained by the solution and the voltage and current matrix obtained by sampling. That is, it is believed that the voltage matrix and current matrix in the fitting calculation data window are more stable, and the confidence of the calculation result based on this is higher. Therefore, the reciprocal of the fitting error 1 / E r As the weight coefficient of the fitting calculation result, the larger the weight coefficient, the higher the confidence of the fitting calculation result. To this end, the present invention proposes to calculate the fault distance fitting calculation result L in each unit data window. k″ ,

[0088] Set the weighted data window length m, and calculate the weight coefficient 1 / E of any sampling point k' in the corresponding unit data window in the weighted data window. r (k′) is weighted and normalized as the calculation result of equivalent inductance. At this time, the calculation formula of equivalent inductance is:

[0089] W r(k′) =1 / E r(k′) (12)

[0090] W r =[W r(1) ,W r(2) ,W r(3) ,...,W r(k ″),...,Wr(m) ] T (13)

[0091] Cumulative calculation of weighted data window t q The fitted equivalent inductance of the corresponding weighted data window length m is obtained to obtain the weighted equivalent inductance, which is expressed as follows:

[0092]

[0093] Where W r(k″) is the fitting weight of each unit inductance calculation result in the weighted data window, W r is the weight matrix composed of all weights, k″ is the weighted data window index, m is the weighted data window length, k″∈m, L is the equivalent inductance, is the equivalent inductance of the weighted data window index k″, W r(1) ,W r(2) ,W r(3) ,...,W r(k ″),...,W r(m) is the fitting weight corresponding to the data window index 1, 2, 3, ..., k', ... m in the weighted data window;

[0094] In summary, by performing fast calculation based on the sliding short-time data window and fitting the error weight coefficient, the weight of the equivalent inductance calculation value with low confidence can be greatly reduced, thereby reducing its impact on the overall result; based on the historical calculation data, the weighted equivalent inductance calculation results are accumulated, thereby accelerating the calculation convergence and improving the stability of the overall calculation result.

[0095] Step 3, designing a fault protection criterion according to the equivalent inductance, and using the fault protection criterion to identify transformer faults, the specific steps are as follows:

[0096] The design protection criterion is expressed as:

[0097]

[0098] In the formula, is the equivalent inductance of the weighted data window index k″, L set is the inductance threshold value (the inductance setting value), α is the fluctuation coefficient, α ranges from -0.1 to -0.05, T 1 For a long time window of protection criterion, if the value of equivalent inductance is T 1 If the formula (10) is satisfied within the time window of , it is determined that the transformer has an internal fault, T 1Set to 8~12ms; that is, under normal circumstances, when the transformer is closed without load, the time that the core is in the saturation zone in one cycle will not exceed 8~12ms. Therefore, when the equivalent inductance value is within a smaller value range within a time span of 8~12ms in one cycle, it is determined that the transformer has an internal fault; otherwise, it is determined that the transformer has an out-of-zone fault;

[0099] Furthermore, the threshold value L in formula (10) set The setting principle is to be able to distinguish whether the core is in a saturated state and avoid the interference of the excitation inrush current on fault identification. The calculation method is:

[0100]

[0101] Where, L m0 is the equivalent inductance value when no load is applied, K b-min It is the ratio of the unsaturated magnetic permeability to the saturated magnetic permeability of the core material. It can be set by referring to the silicon steel sheet model. It is generally between 50 and 200, K k is the reliability coefficient, which is set between 0.5 and 1.0. In summary, the time domain transformer protection action process based on fitting weights is as follows: Figure 2 shown.

[0102] Embodiment 2

[0103] like Figure 3 As shown, a transformer fault identification system based on fitting weight and equivalent inductance of the present invention includes a transformer information acquisition module, an equivalent inductance normalization calculation module, a fitting weight equivalent inductance calculation module and a fault identification module.

[0104] The transformer information acquisition module is used to execute step 1, the equivalent inductance normalization calculation module is used to execute step 2, the fitting weight equivalent inductance calculation module is used to execute step 3, and the fault identification module is used to execute step 4.

[0105] In response to the problem of delayed action of transformer protection caused by the high harmonic characteristics of new energy systems, it has been found that the transformer time-domain equivalent inductance protection based on fitting weights proposed in the present invention is not affected by the power supply characteristics, can effectively eliminate the influence of harmonics and inrush currents, and can quickly and accurately identify internal and external faults and excitation inrush currents of the transformer area.

[0106] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. A transformer fault identification method based on fitting weight and equivalent inductance, characterized in that: include: In the transformer, the voltage across the primary winding and the current across the primary and secondary windings are selected as sampling points. The voltage across the primary winding u1(k) and the current sampling values ​​i1(k) and i2(k) of the primary and secondary windings at any sampling point k are read. The differential current i converted from each unit data window to the voltage side is calculated. d (k), generate the voltage matrix U and current matrix I in each unit data window; The voltage equation across the primary winding at sampling point k is obtained as follows: In the formula, k is the sampling point sequence index, T s is the sampling period, i d (k) is the differential current at sampling point k, i d (k+1) is the differential current at the previous sampling point k+1, i d (k-1) is the differential current at the previous sampling point k-1; Select the sampling period T s The combination of multiple sampling points in the primary winding voltage matrix U and current matrix I is obtained, and the expressions are as follows: U=Iβ Set the least squares window length n, the primary winding voltage matrix U within the least squares window length n (n-2)×1 and the current matrix I (n-2)×2 It is expressed as follows: U (n-2)×1 =[u(2),u(3),...,u(k′),...,u(n-1)] T Where p(2), p(3), ... p(k′) ..., p(n-1) is the current sampling sequence within the unit data window length n, p d (2),p d (3),...p d (k′)...,p d (n-1) is the differential derivative sequence of the current sampling points within the unit data window length n, n is the least squares calculation window length, and k′ is the data index within the least squares calculation window; The sampling sequence equations are obtained, which can be expressed as: AT (n-2)×1 =I (n-2)×2 β Perform normalized calculation of equivalent inductance to obtain unweighted equivalent inductance; the specific description is as follows: The sampling sequence equations are estimated and solved using the least squares method, that is, the fitting coefficient matrix β makes the calculated value I of the voltage matrix in the data window (n-2)×2 β and the primary winding voltage matrix U within the least squares window length n (n-2)×1 The actual value has the smallest distance in the Euclidean space. The Euclidean distance E between the two is expressed as: Take the partial derivative of the Euclidean distance E formula and set it equal to 0 to obtain the fitting coefficient matrix β in each unit data window k″ , the expression is as follows: β (k″) =(I (k″)Τ I (k″) ) -1 I (k″)Τ U (k″) The solution to the equation is β 2×1 =[r,L] T , r and L are the primary winding voltage equations respectively The unfitted equivalent resistance and equivalent inductance in ; Step 3: cumulatively calculate the weighted data window t q The equivalent inductance inside is calculated to obtain the weighted equivalent inductance, which is described as follows: Define the fitting error E of a single fitting calculation r The expression is as follows: Among them, U(k′) and I(k′) represent the primary winding voltage matrix and current matrix corresponding to any sampling point k′ in the data window respectively; Set the weighted data window length m, and calculate the weight coefficient 1 / E of any sampling point k' in the corresponding unit data window in the weighted data window. r (k′) is weighted and normalized as the calculation result of equivalent inductance. At this time, the calculation formula of equivalent inductance is: W r(k″) =1 / E r(k″) IN r =[W r(1) ,IN r(2) ,IN r(3) ,...,IN r(m) ] T Cumulative calculation of weighted data window t q The fitted equivalent inductance of the corresponding weighted data window length m is obtained to obtain the weighted equivalent inductance, which is expressed as follows: Where W r(k″) is the fitting weight of each unit inductance calculation result in the weighted data window, W r is the weight matrix composed of all weights, k″ is the weighted data window index, m is the number of data in the weighted data window, k″∈m, L is the equivalent inductance value in the weighted data window, is the weighted equivalent inductance, W r(1) ,W r(2) ,W r(3) ,...,W r(m) is the fitting weight corresponding to 1, 2, 3, ...m in the weighted data window; A fault protection criterion is designed according to the weighted equivalent inductance, and transformer fault identification is performed using the fault protection criterion: The design protection criterion is expressed as: In the formula, is the equivalent inductance of the weighted data window index k″, L set is the inductance threshold value, α is the fluctuation coefficient, and T1 is the time window length of the protection criterion. If the value of the equivalent inductance meets the criterion within the time window length of T1, it is determined that the transformer has an in-zone fault.

2. The transformer fault identification method based on fitting weight and equivalent inductance according to claim 1 is characterized in that: in, Threshold value L set The setting calculation formula is: Where, L m0 is the equivalent inductance value when no load is applied, K b-min It is the ratio of the unsaturated magnetic permeability to the saturated magnetic permeability of the core material.

3. The transformer fault identification method based on fitting weight and equivalent inductance according to claim 1 is characterized in that: in, When the transformer is closed under no-load conditions, the time that the core is in the saturation zone within one cycle will not exceed T1, and T1 is set to 8 to 12ms.

4. The transformer fault identification method based on fitting weight and equivalent inductance according to claim 1 is characterized in that: in, The differential current i at the next sampling point k+1 d (k+1), the differential current i at the previous sampling point k-1 d The expression for (k-1) is as follows: i d (k)=i1(k)+i2(k) i d (k-1)=i1(k-1)+i2(k-1)。 5. The transformer fault identification method based on fitting weight and equivalent inductance according to claim 1 is characterized in that: in, The current sampling sequence p(k′) within the window length n and the current sampling point differential derivative sequence p within the window length n d The expression of (k′) is as follows: p(k′)=i d (k) Among them, T s is the sampling period, i d (k) is the differential current at sampling point k, i d (k+1) is the differential current at the next sampling point k+1, i d (k-1) is the differential current at the previous sampling point k-1.

6. A transformer fault identification system based on fitting weights and equivalent inductance that implements a transformer fault identification method based on fitting weights and equivalent inductance according to any one of claims 1 to 5, characterized in that: include: The transformer information acquisition module is used to select the voltage across the primary winding and the current of the primary and secondary windings as sampling points in the transformer, read the voltage across the primary winding u1(k) and the sampling values ​​of the primary and secondary windings i1(k) and i2(k) at any sampling point k, and calculate the differential current i converted from each unit data window to the acquisition voltage side. d (k), generate the voltage matrix U and current matrix I in each unit data window, and convert the primary winding voltage matrix U in the least squares window length n into (n-2)×1 and the current matrix I (n-2)×2 , we get the sampling sequence equation system U (n-2)×1 =I (n-2)×2 β; The equivalent inductance normalization calculation module is used to use the least square method to calculate the sampling sequence equation group U (n-2)×1 =I (n-2)×2 β is estimated and solved, and the equivalent inductance is normalized and calculated to obtain the unfitted equivalent resistance and unfitted equivalent inductance in the primary winding voltage equation; Fitting weighted equivalent inductance calculation module, used to accumulate and calculate weighted data window t q The equivalent inductance inside is used to obtain the weighted equivalent inductance; The fault identification module is used to design a fault protection criterion according to the weighted equivalent inductance, use the fault protection criterion to identify transformer faults, and design a protection criterion, the expression is: In the formula, is the equivalent inductance calculated value within any unit data window k″, L set is the inductance threshold value, α is the fluctuation coefficient, α ranges from -0.1 to -0.05, T1 is the time window length of the protection criterion, if the equivalent inductance satisfies the protection criterion described in the formula, it is determined that the transformer has an in-zone fault.

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