A method and system for inverting and locating internal faults of a transformer

Abstract

The present invention discloses a method and system for inverting and locating internal faults of a transformer, including establishing a heat transfer simulation model of an oil-immersed transformer based on the internal heat source and the temperature data on the surface of the oil tank; adding an inter-turn short circuit fault module to the heat transfer simulation model of the oil-immersed transformer to implement temperature field simulation calculation; inversely calculating the internal fault heat source parameters of the oil-immersed transformer based on the measured temperature data of the outer shell of the oil tank to achieve inter-turn short circuit fault location; and verifying the heat transfer simulation model and the inversion calculation method through experiments. The proposed method for inverting and locating slight inter-turn short circuit faults can perform online inverse calculation of fault parameters during the fault latency period, which is of great significance for the safe operation of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of inversion and location of inter-turn short-circuit faults in transformers, and particularly to a method and system for inverting and locating internal faults in transformers. Background Art

[0002] As a series device in the power grid, the operation safety and stability of a transformer are the key to ensuring the power supply reliability of the power grid. However, due to reasons such as frequent short circuits at the outlet, lightning strikes, and manufacturing process defects, the transformer is prone to inter-turn short-circuit faults during operation. Differential protection and gas protection are typical main protection forms of transformers. Since differential protection is affected by inrush current and may cause misoperation, and the threshold setting value is relatively high, a slight inter-turn short-circuit fault cannot trigger differential protection. It can only wait for the slight fault to transform into a more serious inter-layer fault or phase-to-phase fault before differential protection can operate reliably. Moreover, the sensitivity of gas protection is seriously insufficient, and among the currently in-service transformers, only some large transformers are equipped with differential protection and light gas protection. Therefore, researching an online location method for slight inter-turn short-circuit faults to avoid their development into serious faults and causing losses is one of the important ways to ensure the safe and stable operation of the power grid.

[0003] The frequency response method is an effective detection method for inter-turn short-circuit faults. In the prior art, the pulse frequency response analysis method is adopted. By injecting a high-voltage nanosecond pulse into the winding, the influence of the inter-turn short-circuit fault on the equivalent circuit parameters of the transformer is studied, and the effective detection of the inter-turn short-circuit fault in the transformer is realized. In the prior art, the equal-distance characteristics of the frequency response trajectory are also used to map and locate the inter-turn faults in the transformer winding. Based on visualization technology, the frequency response is converted into a two-dimensional image, and the graph convolutional neural network method is used for analysis, realizing the rapid and high-precision location of the inter-turn short-circuit fault. In addition, Zhang Lijing et al. comprehensively considered electro-thermal characteristic parameters such as current, hot spot temperature, and oil temperature, and proposed an identification method for inter-turn short-circuit faults based on the fusion analysis of electro-thermal characteristics. Deng Xiangli et al. proposed a new strategy for early fault protection of transformers based on the multi-operation state model of transformers, and realized the detection of transformer winding deformation and slight inter-turn faults by collecting the voltage and current at the transformer ports. However, since the above methods rely on the acquisition of contact measurement signals such as current and voltage, in actual operation and maintenance detection, it is necessary to ensure the continuous power supply of the transformer, and it is difficult to meet the requirement of installing measurement devices during shutdown, which limits the application of the above methods. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for inverting and locating internal faults of a transformer, which can solve the problem of online location of inter-turn short-circuit faults inside an oil-immersed transformer.

[0007] To solve the above technical problems, the present invention provides the following technical solutions. A method for inverting and locating internal faults of a transformer includes:

[0008] Based on the internal heat source and the temperature data on the surface of the oil tank, establish a heat transfer simulation model of the oil-immersed transformer;

[0009] Add an inter-turn short-circuit fault module to the heat transfer simulation model of the oil-immersed transformer to realize the simulation calculation of the temperature field;

[0010] Based on the measured temperature data of the outer shell of the oil tank, perform an inversion calculation on the internal fault heat source parameters of the oil-immersed transformer to realize the location of the inter-turn short-circuit fault;

[0011] Verify the heat transfer simulation model and the inversion calculation method through experiments.

[0012] As a preferred solution of the method for inverting and locating internal faults of the transformer according to the present invention, wherein: the inversion calculation is an indirect inversion solution method, which transforms the problem of inverting the internal temperature field of the transformer into an optimization problem f(x) of the internal heat source parameters of the transformer for solution,

[0013] f(x) = ||Ax - b obs || L

[0014] wherein, A is the forward operator of the model, x is the input signal; b obs is the observed data, which can be the calculation result of the observed value, and L represents the norm.

[0015] As a preferred solution of the method for inverting and locating internal faults of the transformer according to the present invention, wherein: the inversion calculation includes,

[0016] Take the infrared temperature measurement data on the surface of the operating transformer oil tank as the observed temperature on the surface of the oil tank, input it into the inversion calculation model, and extract the characteristic parameters of the surface temperature of the oil tank, denoted as the target temperature characteristic vector;

[0017] Set a set of initial values of the fault heat source parameters;

[0018] Input the fault heat source parameters into the heat transfer simulation model for forward calculation to obtain the calculation result of the surface temperature of the oil tank, which is used as the calculated value of the surface temperature of the oil tank;

[0019] Extract the characteristic parameters of the fuel tank surface temperature from the calculated value of the fuel tank surface temperature to obtain the calculated temperature characteristic vector;

[0020] Calculate the objective function value according to the target temperature characteristic vector and the calculated temperature characteristic vector;

[0021] Judge whether the objective function value meets the calculation error.

[0022] As a preferred scheme of the method for inverting and locating internal faults of a transformer according to the present invention, wherein: the judging whether the objective function value meets the calculation error includes,

[0023] When the objective function value meets the calculation error, output the current fault heat source parameters and end the calculation;

[0024] When the objective function value does not meet the calculation error, an intelligent optimization algorithm needs to be used to optimize and iterate the fault heat source parameters to obtain the next set of fault heat source parameters, repeat the inversion calculation until the error calculation requirement is met, output the result, and end the calculation.

[0025] As a preferred scheme of the method for inverting and locating internal faults of a transformer according to the present invention, wherein: the temperature characteristic vector includes,

[0026] b = [T Amax , T Bmax , T Cmax , T Dmax , T Aavr , T Bavr , T Cavr , T Davr , T A(i,j)avr , T C(i,j)avr , T B(m,n)avr , T D(m,n)avr

[0027] wherein, b is the temperature characteristic vector, square sliding windows are respectively arranged on the surfaces of the four fuel tanks ABCD, the sliding range is the surface area of the fuel tank, and the maximum average temperature within the sliding window TAmax , T Bmax , T Cmax and T Dmax , denoted as the maximum surface temperature; the average temperatures T Aavr , T Bavr , T Cav r and T Davr ; both areas AC are 15 small areas with 5 rows and 3 columns, and the average temperature T A(i,j)avr and T C(i,j)avr of each area, where (i, j) represents the small area in the i-th row and the j-th column; both areas BD are 25 small areas with 5 rows and 5 columns, and the average temperature TB of each area​(m,n)avr and TD (m,n)avr where (m,n) represents the small area at the m-th row and the n-th column.

[0028] As a preferred solution of the transformer internal fault inversion and location method described in the present invention, wherein: the objective function includes,

[0029]

[0030] where: k is the k-th particle in the optimization calculation process; b cal(k) (i) is the i-th temperature characteristic parameter in the temperature characteristic vector corresponding to the k-th particle; b obs (i) is the i-th target temperature characteristic parameter in the target temperature characteristic vector.

[0031] As a preferred solution of the transformer internal fault inversion and location method described in the present invention, wherein: the inversion calculation further includes that the parameter to be solved by the inversion calculation is the fault heat source parameter, and the two-dimensional particle swarm optimization algorithm is used to optimize and solve the internal parameters of the inversion model.

[0032] As a preferred solution of the transformer internal fault inversion and location method described in the present invention, wherein: the optimization algorithm includes,

[0033] Updating and iterating its flight speed and particle position:

[0034]

[0035]

[0036] where: is the speed of the i-th particle in the k-th generation; is the position of the i-th particle in the k-th generation; is the individual extreme value of the i-th particle when iterating to the k-th generation; is the global extreme value point of all particles when iterating to the k-th generation; w is the inertia weight coefficient; c1 and c2 are learning coefficients; r1, r2 are random variables uniformly distributed in the range of [0,1].

[0037] The present invention also proposes a transformer internal fault inversion and location system, which is characterized in that it includes,

[0038] A model construction module, which is used to establish a heat transfer simulation model of an oil-immersed transformer according to the internal heat source and the oil tank surface temperature data;

[0039] A temperature field calculation module, which realizes temperature field simulation calculation by adding an inter-turn short circuit fault module to the model construction module;

[0040] A fault location module, which is used to inversely calculate the internal fault heat source parameters of the oil-immersed transformer based on the measured temperature data of the tank shell, so as to realize the inter-turn short circuit fault location.

[0041] A verification module, which verifies the heat transfer simulation model and the inverse calculation method through experiments.

[0042] As a preferred solution of the transformer internal fault inversion and location system of the present invention, wherein: the measured temperature data of the tank shell is obtained by an infrared thermometer, and 4 thermometers are respectively placed at a position 1.5 m away from the tank surface in four directions outside the transformer.

[0043] The beneficial effects of the present invention: The present invention proposes a method and system for inverting and locating internal faults of a transformer. The present invention establishes a transformer heat transfer simulation model to simulate and calculate the internal and external temperature field distributions of the transformer. A simulation module for inter-turn short circuit faults is added to effectively simulate the heat generation of internal inter-turn short circuit faults. An inverse model for inter-turn short circuit fault location is established, and the internal fault inter-turn short circuit parameters are inversely solved by converting the inverse calculation problem into an optimization problem. Randomly generate a target fault parameter group and perform inverse solution, which can realize the effective calculation of on-line inverse location of inter-turn short circuit faults in oil-immersed transformers. The inverse location method for slight inter-turn short circuit faults proposed by the present invention can realize on-line inverse calculation of fault parameters during the fault latency period, which is of great significance for the safe operation of transformers. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor. Among them:

[0045] Figure 1 It is a flow chart of a method for inverting and locating internal faults of a transformer provided by an embodiment of the present invention;

[0046] Figure 2 It is a frame diagram of the inverse location of inter-turn short circuit faults of an oil-immersed transformer for a method and system for inverting and locating internal faults of a transformer provided by an embodiment of the present invention;

[0047] Figure 3 It is a simulation result diagram of the temperature on the surface of the tank under normal operating conditions for a method and system for inverting and locating internal faults of a transformer provided by an embodiment of the present invention;

[0048] Figure 4Thermal fault operating condition fuel tank surface temperature simulation result diagram of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0049] Figure 5 Fault location error and fault heat calculation error diagram of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0050] Figure 6 Inter-turn short circuit fault simulation schematic diagram of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0051] Figure 7 C-phase winding temperature comparison diagram under normal operating condition and fault operating condition of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0052] Figure 8 Fuel tank surface temperature diagram under inter-turn short circuit fault of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0053] Figure 9 Temperature feature vector comparison diagram under normal operating condition and fault operating condition of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention;

[0054] Figure 10 Temperature change diagram of fault operation compared with normal operation of a transformer internal fault inversion and positioning method and system provided by an embodiment of the present invention; Detailed implementation manners

[0055] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Second, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0058] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width and depth should be included.

[0059] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, connected" should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0061] Embodiment 1

[0062] Refer to Figure 1-5 , which is the first embodiment of the present invention. This embodiment provides a method for inverting and locating internal faults of a transformer, including:

[0063] S1: Based on the internal heat source and the oil tank surface temperature data, establish a heat transfer simulation model of the oil-immersed transformer;

[0064] It should be noted that the forward problem refers to the process of calculating the output signal / result forward given the model and the input signal / cause. The inverse problem describes the reverse physical solution process, where the model is known and the cause / input signal is obtained from the result / output signal.

[0065] Furthermore, the mathematical model of the forward problem can be expressed as: A x = b cal , A is the forward operator of the model, x is the input signal, b calThis is the forward calculation result. The mathematical model of the inverse problem corresponding to the forward problem can be expressed as: x = A -1 b obs , A -1 is the inverse operator of the forward operator A, and b obs is the observed data (or the calculation result that can be used as an observed value), that is, the input signal x is obtained according to the observed data bobs and the inverse operator A -1 .

[0066] It should be noted that for the physical process of transformer heat transfer, the above description method can be used. Its internal heat source is the input signal / cause x, the temperature on the tank surface is the output signal / result b, and the heat transfer process from the inside to the outside is the heat transfer model, which can be described by the forward operator A of the heat transfer model (i.e., the control equations of the heat transfer model). The process of inversely calculating the internal heat source through the temperature data observed on the tank surface is the inverse calculation problem.

[0067] Furthermore, the present invention adopts an indirect inversion solution method to transform the inverse problem of the internal temperature field of the transformer into an optimization problem f(x) of the internal heat source parameters of the transformer for solution.

[0068] f(x) = ||Ax - b obs || L

[0069] where A is the forward operator of the model, x is the input signal; b obs is the observed data, which can be used as the calculation result of the observed value, and L represents the norm.

[0070] Furthermore, the inverse model of the fault heat source of the oil-immersed transformer mainly includes the establishment of the transformer heat transfer model (including the setting of the fault module), the extraction of the surface temperature eigenvector, the construction of the objective function, the intelligent optimization algorithm, the input data and the output result, etc. The solution steps of the inverse solution framework for the fault heat source are described as follows:

[0071] Take the infrared temperature measurement data on the surface of the operating transformer tank as the observed temperature on the tank surface, input it into the inverse calculation model, and extract the characteristic parameters of the tank surface temperature, denoted as the target temperature eigenvector b obs ;

[0072] Furthermore, set a set of initial values of the fault heat source parameters;

[0073] Furthermore, input the fault heat source parameters into the heat transfer model for forward calculation to obtain the calculation result of the tank surface temperature as the calculated value of the tank surface temperature;

[0074] Furthermore, extract the characteristic parameters of the tank surface temperature from the calculated value of the tank surface temperature to obtain the calculated temperature eigenvector bcal ;

[0075] Further, according to the target temperature feature vector b obs and the calculated temperature feature vector b cal calculate the objective function value;

[0076] Further, determine whether the objective function value meets the calculation error. If so, output the current fault heat source parameters and end the calculation; if not, an intelligent optimization algorithm needs to be used to optimize and iterate the fault heat source parameters to obtain the next set of fault heat source parameters, repeat the solution until the error calculation requirement is met, output the result, and end the calculation.

[0077] S2: Add the inter-turn short-circuit fault module to the heat transfer simulation model of the immersed transformer to implement temperature field simulation calculation;

[0078] Further, in order to implement the simulation calculation of the heat generation caused by the inter-turn short-circuit fault, a fault module is set in the transformer simulation model to simulate the fault heat source. This fault module is located in the high-voltage winding of phase C, and the fault heat source parameters are set as h and Q. h represents the height of the fault coil position from the bottom end of the winding, and Q represents the heat generation amount at the fault position. The fault heat source parameters (h, Q) are the optimization solution parameters for the fault inversion calculation.

[0079] It should be noted that the transformer internally contains solids such as iron cores and windings, as well as transformer oil fluids. The heat transfer methods mainly include solid-solid heat conduction and solid-liquid heat convection, involving the coupling theory of temperature fields and flow fields. The heat transfer process and fluid motion both follow the three major conservation equations.

[0080] Further, the mass conservation equation (continuity equation):

[0081]

[0082] where: ρ is the density; t is the time; u is the velocity vector.

[0083] Further, the momentum conservation equation (Navier-Stokes equation):

[0084]

[0085] where: ρ is the density; t is the time; u is the velocity vector; p is the pressure; τ is the viscous force; F is the body force.

[0086] Further, the energy conservation equation:

[0087]

[0088] where: ρ is the density; t is the time; e is the internal energy per unit mass; u 2is the square of velocity; u is the velocity vector; q is the heat flux vector; σ is the total stress tensor; F is the volume force.

[0089] Furthermore, the natural circulation flow heat transfer phenomenon of transformer oil caused by temperature difference is a non-isothermal flow process, which is described by the viscous heat transfer equation.

[0090]

[0091] Where: Q vd is the work done by the viscous force; τ is the viscous force; u is the velocity vector.

[0092] Furthermore, the above equations are combined to obtain a set of fluid-solid coupling differential equations describing the transformer heat transfer model, and the finite element numerical calculation method is used to solve the above partial differential equations.

[0093] It should be noted that the construction of the objective function is an important step that affects the accuracy of the inversion calculation, so the objective function must meet the requirements of fully reflecting the characteristics of the target temperature for optimization. The present invention adopts a method of constructing the objective function based on the characteristic parameters of the oil tank surface temperature. The surface temperature characteristic parameters need to be extracted based on the simulation results of the oil tank surface temperature of the transformer heat transfer model.

[0094] It should be noted that the inversion solution parameters of the present invention are the fault heat source parameters h and Q, so a two-dimensional particle swarm optimization algorithm is used to optimize the internal parameters of the inversion model. When a particle swarm is used to solve the optimization problem, a group of initial solutions is randomly generated, that is, a group of particles, each of which has its own position x(h,Q) and flight speed v(v h ,v Q ), the objective function is used to evaluate the fitness value of the particle and calculate the individual extreme value points and the global extreme value points.

[0095] Furthermore, the updated and iterated flight speed and particle position formulas are expressed as follows:

[0096]

[0097]

[0098] in: is the velocity of the i-th particle of the k-th generation; is the position of the i-th particle of the k-th generation; is the individual extreme value of the i-th particle when iterating to the k-th generation; is the global extreme point of all particles when iterating to the kth generation; w is the inertia weight coefficient; c1 and c2 are learning coefficients; r1 and r2 are random variables uniformly distributed in the range [0,1].

[0099] It should be noted that the iterative process of particles is a process in which each particle continuously approaches the optimal value. Therefore, the global optimal value can be obtained by the collective movement of all particles. The two-dimensional particle swarm algorithm has the characteristics of high computational efficiency and is suitable for solving the optimization problem of the present invention.

[0100] S3: Based on the measured temperature data of the fuel tank shell, perform inverse calculation on the internal fault heat source parameters of the oil-immersed transformer to achieve inter-turn short circuit fault location;

[0101] Furthermore, the simulation model calculation results can obtain two-dimensional continuous temperature data on the fuel tank surface, and the infrared imager can measure two-dimensional dot matrix data on the fuel tank surface. In order to extract sensitive parameters that can reflect the temperature distribution characteristics of the fuel tank surface, select the normal operating condition and the fault operating condition (the fault is an inter-turn short circuit fault at the 1 / 4 position from the bottom of the C-phase high-voltage winding) under rated load.

[0102] Furthermore, by comparing the temperature distribution characteristics on the fuel tank surface under normal operating conditions and fault operating conditions, extract the following temperature characteristic parameters:

[0103] Set square sliding windows with a side length of 4 cm on the four fuel tank surfaces of ABCD respectively. The sliding range is the fuel tank surface area, and find the maximum average temperature T within the sliding window Amax , T Bmax , T Cmax and T Dmax , denoted as the maximum surface temperature;

[0104] The average temperatures T of the four surfaces of ABCD Aavr , T Bavr , T Cavr and T Davr ;

[0105] Both the AC areas are 15 small areas with 5 rows and 3 columns. The average temperature T of each area A(i,j)avr and T C(i,j)avr , where (i, j) represents the i-th row and the j-th column of the small area;

[0106] Both the BD areas are 25 small areas with 5 rows and 5 columns. The average temperature T of each area B(m,n)avr and T D(m,n)avr , where (m, n) represents the m-th row and the n-th column of the small area.

[0107] It should be noted that the above-mentioned 88 sensitive characteristic parameters can basically cover the temperature information of the four surfaces of the transformer fuel tank shell ABCD, and constitute the temperature characteristic vector b, as shown in the following formula.

[0108] b = [T Amax , T Bmax , T Cmax , TDmax , T Aavr , T Bavr , T Cavr , T Davr , T A(i,j)avr , T C(i,j)avr , T B(m,n)avr , T D(m,n)avr

[0109] Furthermore, in order to make full use of the characteristic information contained in each characteristic parameter, a dimensionless objective function is established as shown in the formula.

[0110]

[0111] Where: k is the k-th particle in the optimization calculation process; b cal(k) (i) is the i-th temperature characteristic parameter in the temperature characteristic vector corresponding to the k-th particle; b obs (i) is the i-th target temperature characteristic parameter in the target temperature characteristic vector.

[0112] Furthermore, in order to verify the effectiveness of the inversion model, the present invention randomly generates 10 groups of target fault parameters (h obs , Q obs ), and performs temperature field simulation calculations to obtain the surface oil tank temperature distribution data, extracts the target characteristic vector b obs , as the input information of the inversion model, performs inversion solution, and obtains the inversion calculation fault parameters (h obs , Q obs ). The solution results are compared and analyzed with the target fault parameters, and the fault location error and the fault heat calculation error are calculated.

[0113] The formula for calculating the fault location error e h is as follows:

[0114]

[0115] The formula for calculating the fault heat calculation error e Q is as follows:

[0116]

[0117] Where, H is the height of the transformer winding, h cal and Q cal represent the height of the fault coil position from the bottom end of the winding and the heat generation amount at the fault position in the forward calculation result, h obs and Q obs represent the height of the fault coil position from the bottom end of the winding and the heat generation amount at the fault position in the inversion calculation fault parameters.

[0118] ​S4: Verify the heat transfer simulation model and the inversion calculation method through experiments.

[0119] Furthermore, the present invention uses the short - circuit method to conduct the temperature rise experiment of the transformer. By adjusting the amplitude of the input current, the operating conditions of the transformer under different load ratios can be simulated respectively.

[0120] It should be noted that in order to accurately measure the temperature in the harsh environment of high voltage, strong electromagnetic interference and full oil immersion inside the transformer, a BA - OFS type point - type fiber optic temperature sensor wrapped with Teflon material is selected for temperature measurement. During the production process of the transformer, the BA - OFS type point - type fiber optic temperature sensor is arranged in the winding, iron core and transformer oil.

[0121] Furthermore, according to the calculation results of the simulation model, the hot - spot temperature of the transformer winding is generally located in the upper half of the winding. In order to effectively capture the hot - spot temperature, the temperature - measuring points in the upper half are densely distributed.

[0122] It should be noted that the present invention selects 4 infrared thermometers of model HY - 2100 to measure the temperature of the transformer oil tank shell. The 4 thermometers are respectively placed at a position 1.5 m away from the surface of the oil tank in four directions outside the transformer. The temperature - measuring range of the HY - 2100 infrared thermometer is 0 - 200 °C, and the resolution is 384×288 Pixel.

[0123] Furthermore, under the normal operating condition of rated load (load ratio 1.0), when the measured value at room temperature is 4.16 °C and there is no wind indoors, the fiber optic temperature - measuring system and the infrared temperature - measuring system are used to measure the temperature of the internal winding and the surface of the oil tank of the transformer.

[0124] Embodiment 2

[0125] Refer to Figure 6-10 , which is an embodiment of the present invention, provides a method and system for in - transformer fault inversion and location. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0126] The schematic diagram of the inter - turn short - circuit fault simulation is as Figure 6 shown. During the production process of the transformer, similar to the method of setting the transformer tap, at a position 1 / 4 from the bottom end of the C - phase high - voltage winding (i.e., h = 75 mm), two winding lead - out wires are reserved. The number of winding turns between the two lead - out wires is 39 turns, accounting for 2.4% of the total number of turns of the C - phase high - voltage coil.

[0127] The heat generation of the inter - turn short - circuit fault is an important parameter for the simulation calculation of the internal temperature field of the transformer. Since it is impossible to directly measure the current in the inter - turn short - circuited coil, the present invention selects the method of experimental measurement and indirect calculation to obtain the heat generation of the inter - turn short - circuit. The experimental data are shown in Table 1 below.

[0128] Table 1 Calculation of heat generation in turn - to - turn short - circuit fault

[0129]

[0130] After calculation, the heat generation in turn - to - turn short - circuit under this fault is 720.2W (i.e., Q = 720.2W).

[0131] Comparison of the temperature of phase - C winding under normal operation condition and fault operation condition (turn - to - turn short - circuit fault at the position 1 / 4 from the bottom of phase - C high - voltage winding) Figure 7 is shown as follows.

[0132] From Figure 7 it can be found that: after the fault, the temperature of the phase - C low - voltage winding rises as a whole, but the temperature distribution trend from top to bottom does not change; after the fault, the temperature of the phase - C high - voltage winding rises significantly, and the temperature at the turn - to - turn short - circuit position rises significantly to 114.9℃ (temperature rise is 111.41℃), becoming a new hot spot, and the temperature rise of 111.41℃ has far exceeded the hot - spot temperature - rise limit value (75℃) specified in the national standard, and has begun to accelerate the deterioration of the transformer insulation performance. Although the hot - spot temperature of the fault winding reaches 114.9℃, it does not reach the 140℃ limit specified in the national standard. This shows that a slight turn - to - turn short - circuit will not cause thermal runaway and damage the transformer in the short term, and the transformer can still maintain normal power supply operation.

[0133] The temperature of the transformer tank shell under the turn - to - turn short - circuit fault at the position 1 / 4 from the bottom of phase - C high - voltage winding is as follows Figure 8 shown. In order to quantitatively analyze the temperature distribution characteristics of the transformer tank surface under normal operation condition and fault operation condition, calculate the temperature eigenvectors bN and bF of the transformer tank surface under normal operation condition and fault operation condition, and the difference between them, as Figure 9 and Figure 10 shown.

[0134] Observing Figure 9 and Figure 10 , it can be found that: when a 2.4% turn - to - turn short - circuit fault occurs inside the transformer, the temperature of the transformer tank shell rises significantly. After calculation, the average rising temperature is 4.32℃. Figure 9 In, whether it is the surface average temperature or the surface maximum temperature, the C - surface is higher than the ABD - surfaces. Figure 10 In, due to the turn - to - turn short - circuit fault occurring in the phase - C high - voltage winding, the temperature rise value of the horizontal mapping area corresponding to the turn - to - turn short - circuit fault on the C - surface (the fifth row of the C - surface partition) is the largest, exceeding 11℃.

[0135] Taking the infrared temperature - measurement data of the transformer tank shell under the above - mentioned turn - to - turn short - circuit fault as the input data of the inversion model, perform inversion positioning calculation on the internal turn - to - turn short - circuit position under this fault, and the calculation results are shown in Table 2 below.

[0136] Table 2 Inversion calculation results of turn - to - turn short - circuit fault

[0137]

[0138] In this case, the positioning error of the turn - to - turn short - circuit fault by the inversion calculation method is 4 cm, and the inversion calculation error of the fault heat generation is 39.6 W. It verifies the feasibility of the method for inversely locating internal turn - to - turn short - circuit faults based on infrared temperature measurement data proposed in the present invention in the application scenario of operating transformers.

[0139] Taking the infrared temperature measurement data of the transformer tank shell under the above - mentioned turn - to - turn short - circuit fault as the input data of the inversion model, the internal turn - to - turn short - circuit position under this fault is inversely located and calculated, and the calculation results are shown in Table 3 below.

[0140] Table 3 Inversion calculation results of turn - to - turn short - circuit fault

[0141]

[0142]

[0143] In this case, the positioning error of the turn - to - turn short - circuit fault by the inversion calculation method is 4 cm, and the inversion calculation error of the fault heat generation is 39.6 W. It verifies the feasibility of the method for inversely locating internal turn - to - turn short - circuit faults based on infrared temperature measurement data proposed in the present invention in the application scenario of operating transformers.

[0144] The transformer heat transfer simulation model established in the present invention can simulate and calculate the internal and external temperature field distributions of the transformer. The error between the internal winding experimental measurement and the simulation calculation can be controlled within ±7°C; and a turn - to - turn short - circuit fault simulation module is added to effectively simulate the heat generation of internal turn - to - turn short - circuit faults.

[0145] The present invention establishes an inversion model for locating turn - to - turn short - circuit faults, and uses the method of transforming the inversion calculation problem into an optimization problem to inversely solve the parameters of internal turn - to - turn short - circuit faults. Randomly generate the target fault parameter group and perform inversion calculation. The results show that the average value of the fault position positioning error is 5.60%, and the average value of the fault heat generation calculation error is 8.62%, which can effectively calculate the online inversion location of turn - to - turn short - circuit faults in oil - immersed transformers.

[0146] The effectiveness of the inversion method proposed in the present invention is verified through the transformer turn - to - turn short - circuit fault experiment. In the experimental case, the inversion location error of the turn - to - turn short - circuit fault is 4 cm, and the inversion calculation error of the fault heat generation is 39.6 W.

[0147] In some embodiments, it further includes a system for inversely analyzing and locating internal faults of a transformer, including:

[0148] A model construction module, which is used to establish a heat transfer simulation model of an oil-immersed transformer according to the internal heat source and the oil tank surface temperature data;

[0149] A temperature field calculation module, which realizes the simulation calculation of the temperature field by adding an inter-turn short circuit fault module to the model construction module;

[0150] A fault location module, which is used to inversely calculate the internal fault heat source parameters of the oil-immersed transformer based on the measured temperature data of the oil tank shell, and realizes the location of the inter-turn short circuit fault;

[0151] A verification module, which verifies the heat transfer simulation model and the inverse calculation method through experiments.

[0152] The inverse calculation proposed by the fault location module is an indirect inverse solution method, including transforming the inverse problem of the transformer internal temperature field into an optimization problem f(x) of the internal heat source parameters of the transformer for solution,

[0153] f(x) = ||Ax - b obs || L

[0154] where A is the forward operator of the model, x is the input signal; b obs is the observed data, which can be used as the calculation result of the observed value, and L represents the norm.

[0155] The inverse calculation proposed by the fault location module also includes,

[0156] Taking the infrared temperature measurement data of the oil tank surface of the operating transformer as the observed temperature of the oil tank surface, inputting it into the inverse calculation model, and extracting the characteristic parameters of the oil tank surface temperature, which is denoted as the target temperature characteristic vector;

[0157] Setting a set of initial values of the fault heat source parameters;

[0158] Inputting the fault heat source parameters into the heat transfer simulation model for forward calculation to obtain the calculation result of the oil tank surface temperature, which is used as the calculated value of the oil tank surface temperature;

[0159] Extracting the characteristic parameters of the oil tank surface temperature from the calculated value of the oil tank surface temperature to obtain the calculated temperature characteristic vector;

[0160] Calculating the objective function value according to the target temperature characteristic vector and the calculated temperature characteristic vector;

[0161] Judging whether the objective function value meets the calculation error.

[0162] The temperature characteristic vector includes,

[0163] b = [T Amax , T Bmax , T Cmax , T Dmax , T Aavr , T Bavr , T Cavr , T Davr , T A(i,j)avr , T C(i,j)avr , T B(m,n)avr , T D(m,n)avr

[0164] Among them, b is the temperature feature vector. Square sliding windows are respectively set on the surfaces of the four fuel tanks A, B, C, and D, and the sliding range is the surface area of the fuel tank. The maximum average temperature T Amax , T Bmax , T Cmax and T Dmax in the sliding window are denoted as the maximum surface temperature; the average temperatures T Aavr , T Bavr , T Cav r and T Davr on the surfaces of the four fuel tanks A, B, C, and D; both areas A and C are 15 small areas with 5 rows and 3 columns, and the average temperature T A(i,j)avr and T C(i,j)avr of each area, where (i, j) represents the small area in the i-th row and the j-th column; both areas B and D are 25 small areas with 5 rows and 5 columns, and the average temperature TB (m,n)avr and TD (m,n)avr of each area, where (m, n) represents the small area in the m-th row and the n-th column.

[0165] The objective function includes,

[0166]

[0167] where: k is the k-th particle in the optimization calculation process; b cal(k) (i) is the i-th temperature feature parameter in the temperature feature vector corresponding to the k-th particle; b obs (i) is the i-th target temperature feature parameter in the target temperature feature vector.

[0168] Furthermore, the slight inter-turn short-circuit fault has a very strong latency in the initial stage of the fault. Although it can maintain normal power supply operation, it seriously endangers the insulation performance of the transformer. The slight inter-turn short-circuit fault inversion and location method proposed by the present invention can realize the online inversion calculation of fault parameters during the fault latency period, which is of great significance to the safe operation of the transformer.

[0169] ​It should be noted that the error sources of the inter-turn short-circuit fault inversion model mainly include the following two aspects: During the establishment of the heat transfer model, the internal structure and material parameters of the transformer are simplified and equivalent, and the numerical solution method is used to solve the heat transfer model. The approximate solution is obtained through continuous iterative calculation by the finite element method, which will lead to errors in the calculation accuracy of the heat transfer model, and further increase the calculation error of the inversion algorithm.

[0170] Furthermore, continuous distribution data of the tank surface temperature can be obtained through simulation calculation, while two-dimensional lattice data of the tank surface temperature is obtained through experimental measurement. Due to the non-uniformity of the data format, the present invention proposes a method for extracting the characteristic vector of the tank surface temperature to conduct a comparative analysis of the simulation calculation results and the experimental measurement results. The extraction of the characteristic vector of the tank surface temperature will lose some temperature information, which also increases the calculation error of the inversion algorithm.

[0171] It should be noted that from the perspective of calculation requirements, calculation efficiency and calculation accuracy restrict each other. Since the calculation amount of the fault inversion model is huge, the starting point of the present invention is to verify the effectiveness of the fault inversion model. Therefore, a physical model with a large degree of simplification and a relatively coarse meshing is adopted, and a relatively rough zoning method is also adopted when extracting the characteristic vector of the tank surface temperature to improve the calculation speed and realize the verification of the fault inversion model.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0173] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0174] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application 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. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0175] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, 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 generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0176] 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 work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0178] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0179] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to cover these modifications and variations.

Claims

1. A method for inverting and locating internal faults of a transformer, characterized in that: including, establishing a heat transfer simulation model of an oil-immersed transformer based on internal heat source and oil tank surface temperature data; adding an inter-turn short circuit fault module to the heat transfer simulation model of the immersed transformer to achieve temperature field simulation calculation; inverting and calculating the internal fault heat source parameters of the oil-immersed transformer based on the measured temperature data of the oil tank shell to achieve inter-turn short circuit fault location; verifying the heat transfer simulation model and the inversion calculation method through experiments; the inversion calculation is an indirect inversion solution method, which transforms the inversion problem of the internal temperature field of the transformer into an optimization problem f(x) of the internal heat source parameters of the transformer for solution, f(x) = ||Ax - b obs || L where A is the forward operator of the model, x is the input signal; b obs is the observed data, which can be used as the calculation result of the observed value, and L represents the norm; the inversion calculation includes, taking the infrared temperature measurement data of the oil tank surface of the operating transformer as the observed temperature of the oil tank surface, inputting it into the inversion calculation model, and extracting the characteristic parameters of the oil tank surface temperature, denoted as the target temperature characteristic vector; setting a set of initial values of the fault heat source parameters; inputting the fault heat source parameters into the heat transfer simulation model for forward calculation to obtain the calculation result of the oil tank surface temperature, which is used as the calculated value of the oil tank surface temperature; extracting the characteristic parameters of the oil tank surface temperature from the calculated value of the oil tank surface temperature to obtain the calculated temperature characteristic vector; calculating the objective function value according to the target temperature characteristic vector and the calculated temperature characteristic vector; judging whether the objective function value meets the calculation error.

2. The internal fault inversion and location method of a transformer according to claim 1, characterized in that: The judgment of whether the objective function value meets the calculation error includes, when the objective function value meets the calculation error, output the current fault heat source parameters and end the calculation; when the objective function value does not meet the calculation error, an intelligent optimization algorithm needs to be used to optimize and iterate the fault heat source parameters to obtain the next set of fault heat source parameters, repeat the inversion calculation until the error calculation requirements are met, output the result, and end the calculation.

3. The internal fault inversion and location method of a transformer according to claim 2, characterized in that: The temperature characteristic vector includes, b = [T Amax , T Bmax , T Cmax , T Dmax , T Aavr , T Bavr , T Cavr , T Davr , T A(i,j)avr , T C(i,j)avr , T B(m,n)avr , T D(m,n)avr ​ Among them, b is the temperature feature vector. Square sliding windows are respectively set on the surfaces of the four fuel tanks A, B, C, and D, and the sliding range is the surface area of the fuel tank. The maximum average temperature within the sliding window TAmax , T Bmax , T Cmax and T Dmax , which is denoted as the maximum surface temperature; The average temperatures T Aavr , T Bavr , T Cav r and T Davr on the surfaces of the four fuel tanks A, B, C, and D; Both areas A and C have 15 small areas in total, with 5 rows and 3 columns. The average temperature T A(i,j)avr and T C(i,j)avr of each area, where (i, j) represents the small area in the i-th row and the j-th column; Both areas B and D have 25 small areas in total, with 5 rows and 5 columns. The average temperature TB (m,n)avr and TD (m,n)avr of each area, where (m, n) represents the small area in the m-th row and the n-th column.

4. The method for inverting and locating internal faults of a transformer according to claim 3, characterized in that: The objective function includes, Where: k is the k-th particle in the optimization calculation process; b cal(k) (i) is the i-th temperature characteristic parameter in the temperature characteristic vector corresponding to the k-th particle; b obs (i) is the i-th target temperature characteristic parameter in the target temperature characteristic vector.

5. A method for inverting and locating internal faults of a transformer according to claim 4, characterized in that: The inversion calculation further includes that the inversion calculation solution parameter is the fault heat source parameter, and a two-dimensional particle swarm optimization algorithm is used to optimize and solve the internal parameters of the inversion model.

6. A method for inverting and locating internal faults of a transformer according to claim 5, characterized in that: The optimization algorithm includes, updating and iterating its flight speed and particle position: Wherein: is the velocity of the i-th particle in the k-th generation; is the position of the i-th particle in the k-th generation; is the personal extreme value of the i-th particle when iterating to the k-th generation; is the global extreme value point of all particles when iterating to the k-th generation; w is the inertia weight coefficient; c1 and c2 are learning coefficients; r1, r2 are random variables uniformly distributed in the range [0, 1].

7. A system applying the method for inverting and locating internal faults of a transformer as described in claim 1, characterized in that: including, a model construction module, which is used to establish a heat transfer simulation model of an oil-immersed transformer according to internal heat source and oil tank surface temperature data; a temperature field calculation module, which realizes temperature field simulation calculation by adding an inter-turn short circuit fault module to the model construction module; a fault location module, which is used to invert and calculate the internal fault heat source parameters of the oil-immersed transformer based on the measured temperature data of the oil tank shell to achieve inter-turn short circuit fault location; a verification module, which verifies the heat transfer simulation model and the inversion calculation method through experiments.

8. The internal fault inversion and positioning system of a transformer according to claim 7, characterized in that: The measured temperature data of the oil tank shell is obtained by an infrared thermometer, and 4 thermometers are respectively placed at positions 1.5 m away from the oil tank surface in four directions outside the transformer.

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