Transformer fault detection method and device based on temperature-magnetic conductivity coupling effect

Abstract

The invention relates to a transformer fault detection method and device based on a temperature-magnetic conductivity coupling effect. The method comprises the following steps: acquiring current leakage magnetic field measured data, current temperature data and current winding terminal voltage data of a transformer; calculating the time-varying phase permeability, the time-varying winding inductance and the time-varying winding resistance according to the current temperature data; inputting the time-varying winding inductance, the time-varying winding resistance and the current winding end voltage data into a winding current time-varying response model to calculate a time-varying winding current; determining current leakage magnetic field theoretical data in the transformer; the current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type; calculating a magnetic balance tensor and a deformation positioning tensor; and under the condition that the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion, executing fault alarm. By adopting the method, rapid and accurate detection of transformer winding deformation and turn-to-turn short circuit faults can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformers, in particular to a transformer fault detection method and device based on temperature-permeability coupling effect. BACKGROUND

[0002] In the power system, the faults of transformer windings, such as winding deformation and inter-turn short circuit, seriously threaten the safety of the power grid. The existing technology mainly uses electromagnetic sensors to measure the magnetic field or electrical quantities, which has the following disadvantages: Firstly, the electromagnetic sensor is susceptible to strong electromagnetic interference, resulting in low measurement accuracy. Secondly, the traditional magnetic balance algorithm does not consider the temperature drift of the core permeability (the change rate of the permeability is up to 30% when the temperature is 20-100℃), resulting in an increase in the misjudgment rate under high temperature conditions. In addition, the detection method based on electrical quantities has the problem of lag in fault feature extraction. These problems make it difficult for the existing technology to meet the accurate detection requirements of transformer faults under complex conditions.

[0003] Therefore, it is necessary to propose a new transformer fault detection method to solve the key technical bottlenecks of low defect measurement accuracy, poor temperature stability, and slow response speed of the existing transformer. SUMMARY

[0004] Therefore, it is necessary to propose a new transformer fault detection method to solve the key technical bottlenecks of low defect measurement accuracy, poor temperature stability, and slow response speed of the existing transformer.

[0005] In a first aspect, the present application provides a transformer fault detection method based on temperature-permeability coupling effect, which comprises: obtaining current leakage magnetic field measured data, current temperature data and current winding terminal voltage data of a transformer; inputting the current temperature data into a permeability correction model to calculate the corresponding time-varying relative permeability; inputting the time-varying relative permeability into an inductance correction model to calculate the corresponding time-varying winding inductance, and inputting the current temperature data into a resistance correction model to calculate the corresponding time-varying winding resistance; inputting the time-varying winding inductance, the time-varying winding resistance and the current winding terminal voltage data into a winding current time-varying response model to calculate the corresponding time-varying winding current; determining the current leakage magnetic field theoretical data of the transformer; the current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type, the steady-state leakage magnetic field is determined based on the time-varying relative permeability and the time-varying winding current, and the fault disturbance field corresponding to each preset fault type is determined based on the current temperature data and the steady-state leakage magnetic field; Calculating a magnetic balance tensor based on the current leakage magnetic field theoretical data, and calculating a deformation positioning tensor based on the deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field; When the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion, a fault alarm is executed.

[0006] In one embodiment, the step of inputting the current temperature data into the magnetic permeability correction model to calculate the corresponding time-varying relative magnetic permeability includes: According to the formula: ; ; in, is the time-varying relative permeability; for t Current temperature data at the moment; is the time-varying absolute magnetic permeability; , is the reference temperature The absolute magnetic permeability under is the vacuum permeability; , is the first-order temperature coefficient of absolute permeability, , is the second-order temperature coefficient of absolute permeability; Determine the time-varying relative magnetic permeability corresponding to the current temperature data.

[0007] In one embodiment, the step of inputting the time-varying relative permeability into an inductance correction model to calculate the corresponding time-varying winding inductance includes: According to the formula: ; ; ; ; in, is the high voltage winding self-inductance; is the self-inductance of the low voltage winding; is the number of turns of the high voltage winding; is the number of turns of the low voltage winding; It is the ratio of the effective cross-sectional area of ​​the core to the length of the magnetic path; is the leakage permeance; is the mutual inductance of high and low voltage windings; is the reference temperature Rated coupling coefficient under ; is the temperature attenuation coefficient; Determining a time-varying winding inductance corresponding to the time-varying relative magnetic permeability; the time-varying winding inductance includes the high-voltage winding self-inductance, the low-voltage winding self-inductance, and the high- and low-voltage winding mutual inductance; And / or, the step of inputting the current temperature data into a resistance correction model to calculate the corresponding time-varying winding resistance includes: According to the formula: ; ; in, is the high voltage winding resistance; is the low voltage winding resistance; is the reference temperature The DC resistance of the high voltage winding under ; is the reference temperature The DC resistance of the low voltage winding under is the temperature coefficient of resistance; Determine a time-varying winding resistance corresponding to the current temperature data; the time-varying winding resistance includes the high-voltage winding resistance and the low-voltage winding resistance.

[0008] In one embodiment, the step of inputting the time-varying winding inductance, the time-varying winding resistance, and the current winding terminal voltage data into a winding current time-varying response model to calculate the corresponding time-varying winding current includes: According to the formula: ; ; ; in, is the time-varying winding current; is the rate of change of winding current; is the current winding terminal voltage data; is the load equivalent inductance; is the load equivalent resistance; A corresponding time-varying winding current is determined; the time-varying winding current is obtained by integrating the winding current change rate.

[0009] In one embodiment, the step of determining the current leakage magnetic field theoretical data includes: The current leakage magnetic field theoretical data is determined according to the following formula:

[0010] in, is the current leakage magnetic field theoretical data; is the steady-state leakage magnetic field; For then a fault disturbance field; n is a positive integer, representing the number of preset fault types; N is the total number of preset fault types.

[0011] In one embodiment, the step of determining the steady-state leakage magnetic field based on the time-varying relative permeability and the time-varying winding current comprises: determining the steady-state leakage magnetic field according to the following formula:

[0012] wherein, is the number of winding turns; is the average length of the leakage path; And / or, based on the current temperature data, the steady-state leakage magnetic field, the step of determining the fault disturbance field corresponding to each preset fault type comprises: determining the fault disturbance field according to the following formula:

[0013] wherein, is the Laplace operator; is the speed of electromagnetic waves in the medium; is the fault current density; is the time-varying conductivity; is the time-varying dielectric constant; is the vacuum dielectric constant; is the local electric field of the fault point; is the movement speed of the plasma; is the reference conductivity; is the temperature coefficient; is the activation energy; is the Boltzmann constant.

[0014] In one embodiment, the step of calculating the magnetic equilibrium tensor according to the current leakage magnetic field theoretical data comprises: calculating the magnetic equilibrium tensor according to the following formula: ; wherein, is the magnetic equilibrium tensor; is the current leakage magnetic field theoretical data; V is the fault sensitive domain of the transformer; And / or, the step of calculating the deformation positioning tensor according to the deviation of the current leakage magnetic field measured data and the steady-state leakage magnetic field comprises: calculating the deformation positioning tensor according to the following formula: ; wherein, is the deformation locating tensor; is the current leakage magnetic field measured data; z 1 、z 2are the Z-direction boundary values of the transformer fault sensitive domain respectively.

[0015] In one of the embodiments, the preset fault criterion includes a turn-to-turn short circuit fault criterion and a winding deformation fault criterion; The turn-to-turn short circuit fault criterion includes:

[0016] wherein, characterizes the trace of the magnetic balance tensor, is a reference magnetic field tensor; is a first calibration coefficient; characterizes the Frobenius norm of the magnetic balance tensor; is a second calibration coefficient; is a phase angle corresponding to the maximum eigenvalue of the magnetic balance tensor; is a reference phase angle; is a phase offset threshold; The method further includes: if the magnetic balance tensor satisfies any one of the turn-to-turn short circuit fault criterion, a turn-to-turn short circuit fault alarm is executed; and / or, the winding deformation fault criterion includes: ; wherein, is a fault critical threshold.

[0017] In one of the embodiments, the current temperature data is obtained by weighted fusion of the oil tank medium temperature and the winding conductor temperature of the transformer.

[0018] In a second aspect, the present application also provides a transformer fault detection device based on temperature-permeability coupling effect, the device includes: an acquisition module, configured to acquire current leakage magnetic field measured data, current temperature data and current winding terminal voltage data of a transformer; a permeability correction module, configured to input the current temperature data into a permeability correction model to calculate a corresponding time-varying relative permeability; a circuit parameter correction module, configured to input the time-varying relative permeability into an inductance correction model to calculate a corresponding time-varying winding inductance, and input the current temperature data into a resistance correction model to calculate a corresponding time-varying winding resistance; a current calculation module configured to input the time-varying winding inductance, the time-varying winding resistance and the current winding terminal voltage data into a winding current time-varying response model to calculate a corresponding time-varying winding current; a leakage magnetic field theoretical calculation module configured to determine current leakage magnetic field theoretical data of the transformer; the current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type, the steady-state leakage magnetic field is determined based on the time-varying relative magnetic permeability and the time-varying winding current, and the fault disturbance field corresponding to each preset fault type is determined based on the current temperature data and the steady-state leakage magnetic field; a feature calculation module configured to calculate a magnetic balance tensor based on the current leakage magnetic field theoretical data and a deformation positioning tensor based on a deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field; a judgment module configured to execute fault alarm when the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion.

[0019] One of the above technical solutions has the following advantages or beneficial effects: the present application aims at the detection of transformer winding deformation and turn-to-turn short circuit fault, acquires current leakage magnetic field measured data, current temperature data and current winding terminal voltage data of the transformer, corrects the magnetic permeability by using the current temperature data, constructs a temperature and magnetic permeability coupling model, effectively reduces the leakage magnetic field calculation error under high temperature working condition, corrects the circuit parameters and the magnetic permeability by using the current temperature data, ensures that the leakage magnetic field theoretical data always tracks the actual working condition of the transformer, avoids the misjudgment caused by temperature drift of the traditional static model, combines the leakage magnetic field theoretical data and the leakage magnetic field measured data to solve the magnetic balance tensor and the deformation positioning tensor, facilitates the accurate separation of fault signals and background noise, effectively solves the problems of poor anti-interference ability and low precision in current transformer fault recognition, and realizes the rapid and accurate detection of transformer winding deformation and turn-to-turn short circuit fault. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 a flowchart of the transformer fault detection method in one embodiment; Figure 2 an application scenario diagram of the transformer fault detection method in one embodiment; Figure 3 a structural block diagram of the transformer fault detection device in one embodiment. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0022] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to one of skill in the art, embodiments described herein can be combinable with other embodiments.

[0023] The application provides a transformer fault detection method based on temperature-magnetic permeability coupling effect, and the main idea of the application is that: by constructing a time-varying leakage magnetic field analytical model of temperature-magnetic permeability coupling, measuring the leakage magnetic vector gradient by using a three-dimensional fiber sensor array, and constructing a multi-criterion fusion protection logic based on magnetic balance tensor analysis to identify the defect type and degree. Through the four-layer closed loop of perception, modeling, features, and criteria, the application can realize accurate evaluation of inter-turn insulation defects under complex working conditions and break the limitations of traditional constant parameter models.

[0024] As shown in Figure 1 The application provides a transformer fault detection method based on temperature-magnetic permeability coupling effect, and the main idea of the application is that: by constructing a time-varying leakage magnetic field analytical model of temperature-magnetic permeability coupling, measuring the leakage magnetic vector gradient by using a three-dimensional fiber sensor array, and constructing a multi-criterion fusion protection logic based on magnetic balance tensor analysis to identify the defect type and degree. Through the four-layer closed loop of perception, modeling, features, and criteria, the application can realize accurate evaluation of inter-turn insulation defects under complex working conditions and break the limitations of traditional constant parameter models. S102, obtaining current leakage magnetic field measured data, current temperature data, and current winding terminal voltage data of the transformer.

[0025] The current leakage magnetic field measured data can be obtained according to the leakage magnetic field data collected by the fiber grating magneto-optical sensor arranged on the transformer. Specifically, the fiber grating magneto-optical sensor at each installation point can collect the radial, tangential, and axial components of the leakage magnetic field, and the current leakage magnetic field measured data can be obtained through signal processing, demodulation, filtering, and coordinate conversion.

[0026] The current temperature data can be obtained according to the temperature data collected by the temperature sensor arranged on the transformer. Alternatively, the current temperature data can be understood as a fusion temperature, which can be obtained by weighting and fusing the oil tank medium temperature and the winding conductor temperature of the transformer. For example, the oil tank medium temperature can be collected by a fiber grating temperature sensor in real time, and the temperature can reflect the magnetic circuit environmental temperature and affect the core magnetic permeability. In addition, the conductor temperature of the winding wire cake can be measured in real time by a winding embedded temperature sensor PT100, and the temperature can reflect the circuit loss thermal effect and affect the winding resistance. By weighting and fusing the two, the fusion temperature can be obtained, and of course the weighting coefficients can also be changed according to actual needs, which are only examples here.

[0027] A specific embodiment is as follows Figure 2As shown, fiber Bragg grating magneto-optical sensors can be arrayed along the transformer's cylindrical coordinate system (x, y, z): N measurement layers are arranged along the winding axis (z-axis), with M sensors evenly distributed along the circumference (Z=0) of each layer. Mounting methods include magnetic attachment to the inner wall of the oil tank and the oil gap outside the winding, though other methods are also possible and are not limited here. Furthermore, PT100 sensors can be embedded in every ten coil layers, positioned in the center of the winding. The number of temperature sensors can be adjusted as needed.

[0028] S104: Input the current temperature data into the magnetic permeability correction model to calculate the corresponding time-varying relative magnetic permeability.

[0029] The Curie-Weiss law gives the macroscopic relationship between the magnetic permeability and temperature of ferromagnetic materials (such as silicon steel sheets in transformer cores):

[0030] Where C is the Curie constant, T c is the Curie temperature, based on which the mathematical modeling of the magnetic permeability correction model can be carried out.

[0031] A specific implementation method is to perform a Taylor expansion on the permeability function near a reference temperature T0, ignoring high-order small terms to obtain a function of absolute permeability. Based on the function of absolute permeability and the vacuum permeability, a function of relative permeability can be obtained as the final permeability correction model. This model can be understood as a temperature-permeability coupling model, using the current temperature data T(t) as the model input and the time-varying relative permeability as the model output, to achieve a close correlation between temperature and permeability. Furthermore, through the Taylor expansion of the Curie-Weiss law, the nonlinear attenuation effect of the core permeability with temperature can be quantified, providing a basis for circuit parameter correction in S106.

[0032] S106 , inputting the time-varying relative permeability into the inductance correction model to calculate the corresponding time-varying winding inductance, and inputting the current temperature data into the resistance correction model to calculate the corresponding time-varying winding resistance.

[0033] S108 , inputting the time-varying winding inductance, the time-varying winding resistance, and the current winding terminal voltage data into the winding current time-varying response model to calculate the corresponding time-varying winding current.

[0034] The time-varying winding inductance may include the high-voltage winding self-inductance, the low-voltage winding self-inductance, and the mutual inductance of the high and low voltage windings. The time-varying winding resistance may include the high-voltage winding resistance and the low-voltage winding resistance. The time-varying winding current may include the high-voltage winding current and the low-voltage winding current. The time-varying winding current can be understood as a winding current vector composed of the high-voltage winding current and the low-voltage winding current.

[0035] The current winding terminal voltage data can be understood as a port voltage vector composed of the high-voltage winding voltage and the low-voltage winding voltage of the transformer.

[0036] The winding current time-varying response model can be understood as a time-varying state space equation used to solve the corresponding time-varying winding current according to the time-varying winding inductance, the time-varying winding resistance and the current winding terminal voltage data.

[0037] In S110, current leakage magnetic field theoretical data inside the transformer is determined. The current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type. The steady-state leakage magnetic field is determined based on the time-varying relative permeability and the time-varying winding current. The fault disturbance field corresponding to each preset fault type is determined based on the current temperature data and the steady-state leakage magnetic field.

[0038] The steady-state leakage magnetic field can be understood as the leakage magnetic field data of the transformer in a normal working condition without faults. The steady-state leakage magnetic field can be determined based on the Ampere loop theorem and the magnetic circuit Ohm's law, as well as the time-varying relative permeability and the time-varying winding current. The fault disturbance field can be understood as a distortion field of the transformer in a defective working condition. When the transformer fails, there will be a deviation between the time-varying winding current and the normal current. The current deviation will cause a distortion of the magnetic motive force, so the leakage magnetic field will deviate from the normal distribution.

[0039] In S112, a magnetic balance tensor is calculated according to the current leakage magnetic field theoretical data, and a deformation positioning tensor is calculated according to the deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field.

[0040] The magnetic balance tensor can be used to quantify the symmetry of the magnetic field energy distribution, and the deformation positioning tensor can be used to locate the magnetic field distortion gradient.

[0041] In S114, fault alarm is performed when the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion.

[0042] The technical chain of the above method is mainly temperature sensing → permeability correction → time-varying circuit parameters → leakage magnetic field analysis → fault feature extraction → three-dimensional criterion. Specifically, by obtaining the current leakage magnetic field measured data, the current temperature data and the current winding terminal voltage data of the transformer, the temperature and the permeability coupling model is constructed by correcting the permeability using the current temperature data, which effectively reduces the leakage magnetic calculation error under high temperature working condition. By correcting the circuit parameters and the permeability using the current temperature data, it is ensured that the leakage magnetic field theoretical data always tracks the actual working condition of the transformer, avoiding the misjudgment caused by temperature drift of the traditional static model. The leakage magnetic field theoretical data is combined with the leakage magnetic field measured data to solve the magnetic balance tensor and the deformation positioning tensor, which facilitates accurate separation of fault signals and background noise, effectively solves the problems of poor anti-interference ability and low precision in current transformer fault recognition, and realizes rapid and accurate detection of transformer winding deformation and turn-to-turn short circuit fault.

[0043] In one embodiment, S104 can specifically include: determining the time-varying relative permeability corresponding to the current temperature data according to a formula, which is specifically: ; ; wherein, is the time-varying relative permeability; is the current temperature data at the moment t; t is the time-varying absolute permeability; is the absolute permeability at the reference temperature is the vacuum permeability; is the first-order temperature coefficient of the absolute permeability, is the second-order temperature coefficient of the absolute permeability. In this embodiment, the relative permeability is the ratio of the absolute permeability to the vacuum permeability.

[0044] In one embodiment, S106 can specifically include: determining the time-varying winding inductance corresponding to the time-varying relative permeability according to a formula, which is:

[0045] ; ; ; ; ; wherein, is the high-voltage winding self-inductance; is the low-voltage winding self-inductance; is the high-voltage winding number of turns; is the low-voltage winding number of turns; is the ratio of the effective cross-sectional area of the core to the length of the magnetic circuit; is the leakage permeance; is the high-low voltage winding mutual inductance; is the rated coupling coefficient at the reference temperature is the temperature attenuation coefficient. In one embodiment, S106 can specifically further include: according to the formula:

[0046] ; ; wherein, is the high-voltage winding resistance; is the low-voltage winding resistance; is the reference temperature is the temperature attenuation coefficient. ​high-voltage winding direct-current resistance under the current temperature data; reference temperature low-voltage winding direct-current resistance under the current temperature data; resistance temperature coefficient determining a time-varying winding resistance corresponding to the current temperature data; the time-varying winding resistance includes the high-voltage winding resistance and the low-voltage winding resistance.

[0047] In one of the embodiments, S108 can specifically include: determining the corresponding time-varying winding current according to a formula as follows: wherein, time-varying winding current winding current change rate current winding terminal voltage data load equivalent inductance load equivalent resistance. The final time-varying winding current can be obtained by integrating the winding current change rate.

[0048] By taking the time-varying relative permeability, the time-varying winding inductance and the time-varying winding resistance as inputs of the winding current time-varying response model, the calculation error of the traditional fixed parameter model under the nonlinear working conditions such as high temperature and overload is avoided. The inductance and resistance matrix can be updated in real time with temperature, which can quickly capture the electrical response in the thermal transient process, such as transformer short-circuit fault. By using temperature to drive the magnetic circuit parameter correction in real time, a high-fidelity time-varying state space model is constructed, and dynamic coupling of multiple physical fields is realized.

[0049] In one of the embodiments, S110 can specifically include: determining the current leakage magnetic field theoretical data according to a formula as follows:

[0050] wherein, current leakage magnetic field theoretical data steady-state leakage magnetic field the first n order fault disturbance field n positive integer, representing the number of preset fault types N total number of preset fault types

[0051] It can be understood that the internal leakage magnetic induction intensity of the transformer can be decomposed into a steady-state component and a fault disturbance component, which can be specifically described in a cylindrical coordinate system (such as shown in FIG. 1) : x radial, y circumferential, and z axial) to embody the axial symmetry characteristics of the transformer. Figure 2 n ​​​​It can be further understood as the mode number of the fault magnetic field, which is used to distinguish different faults, such as n = 1 for turn-to-turn short circuit, n = 2 for winding deformation, and the like, preferably, N It is preferable to be 3-5 orders, so as to cover most of the fault characteristics and reduce the data processing amount of the computer.

[0052] In one of the embodiments, the step of determining the steady-state leakage magnetic field based on the time-varying relative permeability and the time-varying winding current in S110 can specifically include determining the steady-state leakage magnetic field according to the following formula:

[0053] wherein, is the number of turns of the winding; is the average length of the leakage magnetic path; the steady-state component satisfies the Laplace equation: It can be understood that the leakage magnetic field under normal working conditions is only determined by the time-varying relative permeability and the time-varying winding current after temperature correction.

[0054] In one of the embodiments, the step of determining the fault disturbance field corresponding to each preset fault type based on the current temperature data and the steady-state leakage magnetic field in S110 can specifically include determining the fault disturbance field according to the following formula:

[0055] wherein, is the Laplace operator, which can be used to describe the spatial distribution change of the magnetic field; is the speed of electromagnetic wave in medium; is the fault current density; is the time-varying conductivity; is the time-varying dielectric constant; is the vacuum dielectric constant; is the local electric field of the fault point; is the movement speed of the plasma; is the reference conductivity; is the temperature coefficient; is the activation energy; is the Boltzmann constant.

[0056] In one of the embodiments, the step of calculating the magnetic equilibrium tensor according to the current leakage magnetic field theoretical data in S112 can specifically include calculating the magnetic equilibrium tensor according to the following formula: ; wherein, is the magnetic equilibrium tensor; is the current leakage magnetic field theoretical data; VThe fault sensitive domain of the transformer; the magnetic equilibrium tensor is used to quantify the unbalance degree of the magnetic field energy distribution, which will surge when the transformer fails, so that the failure can be identified and judged through this phenomenon.

[0057] In one embodiment, the step of calculating the deformation positioning tensor in S112 according to the deviation of the current leakage magnetic field measured data and the steady-state leakage magnetic field can specifically include: calculating the deformation positioning tensor according to the following formula: ; Wherein, is the deformation positioning tensor; is the current leakage magnetic field measured data; z 1 、z 2are the Z-direction boundary values of the fault sensitive domain of the transformer.

[0058] Wherein, can be used to reflect the difference between the magnetic field after the fault and the normal state, can represent the current leakage magnetic field measured data, is the steady-state leakage magnetic field, that is, the reference leakage magnetic field after temperature correction under normal working condition, no fault, this formula can calculate the magnetic field distortion caused by the fault, and exclude the steady-state field interference. For example, when the transformer occurs inter-turn short circuit, the magnetic field at the short circuit point will be enhanced, the radial will increase. can be used to describe the spatial variation rate of the magnetic field deviation, for example, at the radial bulge, the variation rate of x in the radial direction, that is, will increase.

[0059] In one embodiment, the preset fault criterion can include an inter-turn short circuit fault criterion and a winding deformation fault criterion; the inter-turn short circuit fault criterion includes:

[0060] Wherein, characterized as the trace of the magnetic equilibrium tensor, can be understood as the trace of the 3x3 matrix, reflecting the sum of the magnetic field in three orthogonal directions, which will be suddenly changed when a fault occurs, for example, when an inter-turn short circuit occurs, the current density at the fault point increases sharply, and the local magnetic field rotation will increase, eventually leading to significantly deviating from the normal value ; is the reference magnetic field tensor; is the first calibration coefficient, which can be 0.3; characterized as the Frobenius norm of the magnetic equilibrium tensor, which can represent the global distortion of the magnetic field distribution, when the winding is deformed or short-circuited, the magnetic field symmetry will be destroyed, the off-diagonal elements will increase, and eventually the norm will significantly increase; is a second calibration coefficient, and specifically can be 0.5; is a maximum eigenvalue of the magnetic balance tensor, corresponding to a principal component of the magnetic field energy; is a phase angle corresponding to the maximum eigenvalue of the magnetic balance tensor, and can be used to reflect the phase of the principal energy direction. The main direction of the normal magnetic field is consistent with the winding axial direction, and when an inter-turn short circuit occurs, a radial component is introduced, causing the phase to deviate; is a reference phase angle; is a phase deviation threshold. According to the above criterion, by checking whether the trace norm of the magnetic balance tensor and the eigenvalue phase are significantly abnormal, it can be determined whether the transformer has an axial deformation or a short circuit fault.

[0061] At this time, S114 can specifically include: if the magnetic balance tensor satisfies any one of the inter-turn short circuit fault criteria, an inter-turn short circuit fault alarm is performed.

[0062] In one of the embodiments, the winding deformation fault criterion can include: ; wherein, is a fault critical threshold, and can be If the deformation positioning tensor of a certain monitoring point exceeds the fault critical threshold, it can be determined that a fault occurs at this position. In combination with the above inter-turn short circuit fault criterion and winding deformation fault criterion, the fault type and fault position of the transformer can be determined, and the fault identification and positioning accuracy can be effectively improved.

[0063] The defects of the above scheme and the proposed solutions are the results of the inventors after practice and careful study. Therefore, the discovery process of the above problems and the solutions proposed by the present disclosure to solve the above problems should be the contribution of the inventors to the present disclosure in the process of the present disclosure.

[0064] It should be understood that, for the foregoing method embodiments, although each step in the flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise explicitly stated herein, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart of the method embodiments can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0065] Based on the same inventive concept, the embodiments of the present application also provide a transformer fault detection device based on temperature-magnetic permeability coupling effect for implementing the above-mentioned transformer fault detection method based on temperature-magnetic permeability coupling effect. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more transformer fault detection device embodiments based on temperature-magnetic permeability coupling effect provided below can be referred to the limitations of the transformer fault detection method based on temperature-magnetic permeability coupling effect in the above, which will not be repeated here.

[0066] In one embodiment, as shown in Figure 3 A transformer fault detection device 300 based on temperature-magnetic permeability coupling effect is provided, comprising: an acquisition module 301, a magnetic permeability correction module 302, a circuit parameter correction module 303, a current calculation module 304, a leakage magnetic field theoretical calculation module 305, a feature calculation module 306 and a judgment module 307, wherein: The acquisition module 301 is configured to acquire current leakage magnetic field measured data, current temperature data and current winding terminal voltage data of the transformer; The magnetic permeability correction module 302 is configured to input the current temperature data into a magnetic permeability correction model to calculate a corresponding time-varying relative magnetic permeability; The circuit parameter correction module 303 is configured to input the time-varying relative magnetic permeability into an inductance correction model to calculate a corresponding time-varying winding inductance, and input the current temperature data into a resistance correction model to calculate a corresponding time-varying winding resistance; The current calculation module 304 is configured to input the time-varying winding inductance, the time-varying winding resistance and the current winding terminal voltage data into a winding current time-varying response model to calculate a corresponding time-varying winding current; The leakage magnetic field theoretical calculation module 305 is configured to determine current leakage magnetic field theoretical data inside the transformer; the current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type, the steady-state leakage magnetic field is determined based on the time-varying relative magnetic permeability and the time-varying winding current, and the fault disturbance field corresponding to each preset fault type is determined based on the current temperature data and the steady-state leakage magnetic field; The feature calculation module 306 is configured to calculate a magnetic balance tensor according to the current leakage magnetic field theoretical data, and calculate a deformation positioning tensor according to the deviation of the current leakage magnetic field measured data and the steady-state leakage magnetic field; The judgment module 307 is configured to execute fault alarm in the case that the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion.

[0067] In one embodiment, the magnetic permeability correction module 302 is specifically configured to calculate the time-varying relative magnetic permeability according to the formula: ; ; wherein, is the time-varying relative permeability; is the current temperature data at the moment; t is the time-varying absolute permeability; is the absolute permeability at the reference temperature is the vacuum permeability; is the first-order temperature coefficient of the absolute permeability, is the second-order temperature coefficient of the absolute permeability; determines the time-varying relative permeability corresponding to the current temperature data. In one embodiment, the circuit parameter correction module 303 is specifically configured to determine the time-varying relative permeability according to the formula:

[0068] ; ; ; ; ; wherein, is the high-voltage winding self-inductance; is the low-voltage winding self-inductance; is the high-voltage winding number of turns; is the low-voltage winding number of turns; is the ratio of the effective cross-sectional area of the core to the length of the magnetic circuit; is the leakage permeance; is the high-low voltage winding mutual inductance; is the rated coupling coefficient at the reference temperature is the temperature attenuation coefficient; determines the time-varying winding inductance corresponding to the time-varying relative permeability; the time-varying winding inductance includes the high-voltage winding self-inductance, the low-voltage winding self-inductance, and the high-low voltage winding mutual inductance. In one embodiment, the circuit parameter correction module 303 is specifically configured to determine the time-varying winding inductance according to the formula:

[0069] ; ; wherein, is the high-voltage winding resistance; is the low-voltage winding resistance; is the high-voltage winding direct-current resistance at the reference temperature is the low-voltage winding direct-current resistance at the reference temperature is the low-voltage winding direct-current resistance at the reference temperature is the low-voltage winding direct-current resistance at the reference temperature is the low-voltage winding direct-current resistance at the reference temperature ​temperature coefficient of resistance; determining a time-varying winding resistance corresponding to the current temperature data; the time-varying winding resistance comprises a high-voltage winding resistance and a low-voltage winding resistance.

[0070] In one embodiment, the current calculation module 304 is specifically configured to: according to the formula: ; ; ; wherein, is a time-varying winding current; is a winding current change rate; is current winding terminal voltage data; is a load equivalent inductance; is a load equivalent resistance; determining a corresponding time-varying winding current; the time-varying winding current is obtained by integrating the winding current change rate.

[0071] In one embodiment, the leakage magnetic field theoretical calculation module 305 is specifically configured to determine the current leakage magnetic field theoretical data according to the following formula:

[0072] wherein, is the current leakage magnetic field theoretical data; is a steady-state leakage magnetic field; is a first n order fault disturbance field; n is a positive integer, representing the number of a preset fault type; N is the total number of preset fault types.

[0073] In one embodiment, the leakage magnetic field theoretical calculation module 305 is further specifically configured to: determine the steady-state leakage magnetic field according to the following formula:

[0074] wherein, is a winding number of turns; is an average length of a leakage magnetic path.

[0075] In one embodiment, the leakage magnetic field theoretical calculation module 305 is further specifically configured to: determine the fault disturbance field according to the following formula:

[0076] wherein, is a Laplace operator; is a speed of an electromagnetic wave in a medium; is a fault current density; a time-varying conductivity; a time-varying permittivity; a vacuum permittivity; a local electric field at a fault point; a movement speed of the plasma; a reference conductivity; a temperature coefficient; an activation energy; a Boltzmann constant.

[0077] In one embodiment, the feature calculation module 306 is specifically configured to calculate the magnetic balance tensor according to the following formula: ; wherein, is the magnetic balance tensor; is the current leakage field theoretical data; V is a fault sensitive domain of the transformer.

[0078] In one embodiment, the feature calculation module 306 is specifically further configured to calculate the deformation positioning tensor according to the following formula: ; wherein, is the deformation positioning tensor; is the current leakage field measured data; z 1 、z 2 are Z-direction boundary values of the fault sensitive domain of the transformer.

[0079] In one embodiment, the preset fault criterion includes a turn-to-turn short circuit fault criterion and a winding deformation fault criterion; wherein, the turn-to-turn short circuit fault criterion includes:

[0080] wherein, characterizes a trace of the magnetic balance tensor, is a reference magnetic field tensor; is a first calibration coefficient; characterizes a Frobenius norm of the magnetic balance tensor; is a second calibration coefficient; is a phase angle corresponding to a maximum eigenvalue of the magnetic balance tensor; is a reference phase angle; is a phase shift threshold; The judgment module 307 is specifically configured to execute the turn-to-turn short circuit fault alarm if the magnetic balance tensor satisfies any one of the turn-to-turn short circuit fault criterion.

[0081] In one embodiment, the winding deformation fault criterion includes: ; wherein, is a fault critical threshold.

[0082] In one embodiment, the current temperature data is obtained by weighted fusion of the oil tank medium temperature and the winding conductor temperature of the transformer.

[0083] The specific limitations of the transformer fault detection device based on the temperature-permeability coupling effect can be seen in the limitations of the transformer fault detection method based on the temperature-permeability coupling effect in the foregoing, which will not be repeated here. Each module in the above transformer fault detection device based on the temperature-permeability coupling effect can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each of the above modules.

[0084] In addition, in the above example embodiment of the transformer fault detection device based on the temperature-permeability coupling effect, the logical division of each program module is only illustrative. In actual applications, the above function allocation can be completed by different program modules according to needs, for example, for the configuration requirements of the corresponding hardware or the convenience of software implementation. The internal structure of the transformer fault detection device based on the temperature-permeability coupling effect is divided into different program modules to complete all or part of the functions described above.

[0085] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0086] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered as the scope of the present application. In the above embodiments, the description of each embodiment is focused on, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0087] The terms "include", "includes" and "including" as used herein are meant to be analogous to "comprise", "comprises" and "comprising" and are inclusive or open-ended and do not exclude additional, unrecited elements or method steps. The terms "comprise", "comprises" and "comprising" as used herein are meant to be synonymous with "include", "includes" and "including" and are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0088] As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. "Or" means "and / or" and "and" means "and / or" unless the context clearly dictates otherwise. Stated another way, the use of "or" or "and" means "and / or" unless expressly stated otherwise. As used herein, the term "and / or" means "and" or "or", or both, and the term "and / or" shall be interpreted as such and not as a limitation of alternatives. As used herein, "multiple" means two or more. "And / or", describing the relationship between associated objects, means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B exist at the same time, and B alone. The character " / " generally represents that the associated objects before and after are a "or" relationship.

[0089] As used herein, "first" and "second" are merely used to distinguish similar objects, and do not represent a specific order for the objects. It can be understood that "first" and "second" can be interchanged in a specific order or sequence as appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged as appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0090] The above-described embodiments are merely several embodiments of the present application, which are described in detail and specifically, but should not be construed as limiting the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A transformer fault detection method based on temperature-permeability coupling effect, comprising: Obtain the transformer's current leakage magnetic field measured data, current temperature data, and current winding terminal voltage data; Inputting the current temperature data into the magnetic permeability correction model to calculate the corresponding time-varying relative magnetic permeability; Inputting the time-varying relative permeability into an inductance correction model to calculate a corresponding time-varying winding inductance, and inputting the current temperature data into a resistance correction model to calculate a corresponding time-varying winding resistance; Inputting the time-varying winding inductance, the time-varying winding resistance and the current winding terminal voltage data into a winding current time-varying response model to calculate the corresponding time-varying winding current; Determining current theoretical data of a leakage magnetic field inside the transformer; the current theoretical data of the leakage magnetic field is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type, the steady-state leakage magnetic field being determined based on the time-varying relative permeability and the time-varying winding current, and the fault disturbance field corresponding to each preset fault type being determined based on the current temperature data and the steady-state leakage magnetic field; Calculating a magnetic balance tensor based on the current leakage magnetic field theoretical data, and calculating a deformation positioning tensor based on the deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field; When the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion, a fault alarm is executed.

2. The method according to claim 1, characterized in that The step of inputting the current temperature data into the magnetic permeability correction model to calculate the corresponding time-varying relative magnetic permeability includes: According to the formula: ； ； in, is the time-varying relative permeability; for t Current temperature data at the moment; is the time-varying absolute magnetic permeability; , is the reference temperature The absolute magnetic permeability under is the vacuum permeability; , is the first-order temperature coefficient of absolute permeability, , is the second-order temperature coefficient of absolute permeability; Determine the time-varying relative magnetic permeability corresponding to the current temperature data.

3. The method according to claim 2, characterized in that The step of inputting the time-varying relative permeability into the inductance correction model to calculate the corresponding time-varying winding inductance includes: According to the formula: ； ； ； ； in, is the high voltage winding self-inductance; is the self-inductance of the low voltage winding; is the number of turns of the high voltage winding; is the number of turns of the low voltage winding; It is the ratio of the effective cross-sectional area of ​​the core to the length of the magnetic path; is the leakage permeance; is the mutual inductance of high and low voltage windings; is the reference temperature Rated coupling coefficient under ; is the temperature attenuation coefficient; Determining a time-varying winding inductance corresponding to the time-varying relative magnetic permeability; the time-varying winding inductance includes the high-voltage winding self-inductance, the low-voltage winding self-inductance, and the high- and low-voltage winding mutual inductance; And / or, the step of inputting the current temperature data into a resistance correction model to calculate the corresponding time-varying winding resistance includes: According to the formula: ； ； in, is the high voltage winding resistance; is the low voltage winding resistance; is the reference temperature The DC resistance of the high voltage winding under ; is the reference temperature The DC resistance of the low voltage winding under is the temperature coefficient of resistance; Determine a time-varying winding resistance corresponding to the current temperature data; the time-varying winding resistance includes the high-voltage winding resistance and the low-voltage winding resistance.

4. The method according to claim 3, characterized in that The step of inputting the time-varying winding inductance, the time-varying winding resistance, and the current winding terminal voltage data into the winding current time-varying response model to calculate the corresponding time-varying winding current includes: According to the formula: ； ； ； in, is the time-varying winding current; is the rate of change of winding current; is the current winding terminal voltage data; is the load equivalent inductance; is the load equivalent resistance; A corresponding time-varying winding current is determined; the time-varying winding current is obtained by integrating the winding current change rate.

5. The method according to claim 4, characterized in that The step of determining the current leakage magnetic field theoretical data includes: The current leakage magnetic field theoretical data is determined according to the following formula: in, is the current leakage magnetic field theoretical data; is the steady-state leakage magnetic field; For the n order fault disturbance field; n is a positive integer representing the number of the preset fault type; N is the total number of preset fault types.

6. The method according to claim 5, characterized in that The step of determining the steady-state leakage magnetic field based on the time-varying relative permeability and the time-varying winding current comprises: The steady-state leakage magnetic field is determined according to the following formula: in, is the number of winding turns; is the average length of the leakage magnetic path; And / or, the step of determining a fault disturbance field corresponding to each preset fault type based on the current temperature data and the steady-state leakage magnetic field includes: The fault disturbance field is determined according to the following formula: in, is the Laplace operator; is the speed of electromagnetic waves in the medium; is the fault current density; is the time-varying conductivity; is the time-varying dielectric constant; is the dielectric constant of vacuum; is the local electric field at the fault point; is the velocity of plasma; is the reference conductivity; is the temperature coefficient; is the activation energy; is the Boltzmann constant.

7. The method according to claim 6, characterized in that The step of calculating the magnetic balance tensor according to the current leakage magnetic field theoretical data includes: The magnetic balance tensor is calculated according to the following formula: ； in, is the magnetic equilibrium tensor; is the current leakage magnetic field theoretical data; V is the fault sensitive domain of the transformer; And / or, the step of calculating the deformation positioning tensor based on the deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field includes: The deformation positioning tensor is calculated according to the following formula: ； in, locating a tensor for the deformation; is the current leakage magnetic field measured data; z 1 、z 2 are the Z-direction boundary values ​​of the fault sensitive region of the transformer.

8. The method according to claim 7, characterized in that The preset fault criteria include a turn-to-turn short circuit fault criterion and a winding deformation fault criterion; The turn-to-turn short circuit fault criterion includes: in, Characterized as the trace of the magnetic equilibrium tensor, is the reference magnetic field tensor; is the first calibration coefficient; Characterized by the Frobenius norm of the magnetic equilibrium tensor; is the second calibration coefficient; is the phase angle corresponding to the maximum eigenvalue of the magnetic equilibrium tensor; is the reference phase angle; is the phase shift threshold; The method further comprises: If the magnetic balance tensor satisfies any one of the inter-turn short circuit fault criteria, executing an inter-turn short circuit fault alarm; And / or, the winding deformation fault criterion includes: ； in, is the critical fault threshold.

9. The method according to any one of claims 1 to 8, characterized in that The current temperature data is obtained by weighted fusion of the oil tank medium temperature and the winding conductor temperature of the transformer.

10. A transformer fault detection device based on temperature-permeability coupling effect, characterized in that: The device comprises: An acquisition module is used to obtain the transformer's current leakage magnetic field measured data, current temperature data, and current winding terminal voltage data; A magnetic permeability correction module, configured to input the current temperature data into a magnetic permeability correction model to calculate the corresponding time-varying relative magnetic permeability; a circuit parameter correction module, configured to input the time-varying relative permeability into an inductance correction model to calculate a corresponding time-varying winding inductance, and input the current temperature data into a resistance correction model to calculate a corresponding time-varying winding resistance; a current calculation module, configured to input the time-varying winding inductance, the time-varying winding resistance, and the current winding terminal voltage data into a winding current time-varying response model to calculate a corresponding time-varying winding current; a leakage magnetic field theoretical calculation module, configured to determine current leakage magnetic field theoretical data within the transformer; the current leakage magnetic field theoretical data is obtained by synthesizing a steady-state leakage magnetic field and a fault disturbance field corresponding to each preset fault type, the steady-state leakage magnetic field being determined based on the time-varying relative permeability and the time-varying winding current, and the fault disturbance field corresponding to each preset fault type being determined based on the current temperature data and the steady-state leakage magnetic field; a feature calculation module, configured to calculate a magnetic balance tensor based on the current leakage magnetic field theoretical data, and to calculate a deformation positioning tensor based on a deviation between the current leakage magnetic field measured data and the steady-state leakage magnetic field; The judgment module is used to execute a fault alarm when the magnetic balance tensor or the deformation positioning tensor meets a preset fault criterion.

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