Transformer fault inversion location method and device based on acoustic thermal field model

Abstract

The present invention provides a transformer fault inversion location method and device based on an acoustic thermal field model, which can be applied to the technical field of power fault diagnosis and location. The fault inversion location method includes: in response to detecting a partial discharge signal of a target transformer, obtaining the time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer; determining the target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal of each predetermined area; wherein each simulated alarm signal is obtained by simulating the partial discharge ultrasonic field of each predetermined area under normal operating power using the acoustic thermal field model; taking each position in the target fault area as a particle, using the acoustic thermal field model to perform iterative simulation of the partial discharge ultrasonic field in the target fault area under normal operating power, and optimizing the particle position to determine the target fault position.

Description

Technical Field

[0001] The present invention relates to the technical field of power fault diagnosis and positioning, and in particular to a transformer fault inversion positioning method and device based on an acoustic thermal field model. Background Art

[0002] Partial discharge (PD) is a phenomenon of local electrical breakdown in the insulation system, which often occurs at defects in the insulation material or in areas where the electric field is concentrated between dielectrics. Accurate PD positioning helps to deeply assess the degree of insulation damage and provides key information for formulating effective maintenance strategies, preventing equipment failures, and ensuring the safety and efficiency of the power system.

[0003] At present, in related examples, PD simulation positioning is usually performed according to a predetermined oil temperature and ultrasonic propagation speed. Due to the large difference between the simulation environment and the actual environment, the positioning accuracy of PD in the actual environment based on the simulation results is poor. Summary of the invention

[0004] In view of the above problems, the present invention provides a transformer fault inversion location method, device, equipment, medium and program product based on an acoustic thermal field model.

[0005] According to a first aspect of the present invention, there is provided a transformer fault inversion location method based on an acoustic-thermal field model, comprising: in response to detecting a local discharge signal of a target transformer, obtaining time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer; determining a target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal in each predetermined area; wherein each simulated alarm signal is obtained by simulating a local discharge ultrasonic field in each predetermined area under normal operating power using an acoustic-thermal field model; and taking each position in the target fault area as a particle, performing iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power using an acoustic-thermal field model, and optimizing the particle position to determine the target fault position.

[0006] According to an embodiment of the present invention, a target fault area is determined by comparing each alarm signal delay information with each simulated alarm signal delay information of a predetermined area, including: based on the least squares method, processing each alarm signal delay information and each simulated alarm signal delay information of each predetermined area to obtain a matching degree between each alarm signal delay information and each simulated alarm signal delay information; and based on the matching degree, determining the target fault area from each predetermined area.

[0007] According to an embodiment of the present invention, the acoustic thermal field model includes: a coupling model of the transformer and multi-physical fields, a local discharge model and a pressure acoustic field control model; wherein the multi-physical fields include a magnetic field, a thermal field and a flow field; the method also includes: based on the natural oil circulation heat dissipation mechanism of the transformer under normal operating conditions, respectively constructing a magnetic field control model, a thermal field control model and a flow field control model; based on the mechanism of transient pressure acoustic simulation of local discharge ultrasonic signals, constructing a local discharge model; and based on the propagation path of ultrasonic waves inside the transformer, constructing a pressure acoustic field control model.

[0008] According to an embodiment of the present invention, each simulated alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model, including: constructing a transformer geometric model according to the geometric parameters of the target transformer; the transformer geometric model includes multiple predetermined areas; and taking the center of each predetermined area as the local discharge source, and inputting the normal operating power of the target transformer under each working condition and the local discharge intensity of each predetermined area into the acoustic thermal field model to obtain each simulated alarm signal.

[0009] According to an embodiment of the present invention, each position in a target fault area is taken as a particle, and an acoustic thermal field model is used to perform iterative simulation of a local discharge ultrasonic field in the target fault area under normal operating power, and the particle position is optimized to determine the target fault position, including: based on a predetermined population size, a plurality of initial positions are randomly determined from the target fault area; with the plurality of initial positions as local discharge sources, the acoustic thermal field model is used to perform local discharge ultrasonic field simulation under normal operating power, and the delay information of each simulation alarm signal corresponding to each initial position is obtained; based on a target fitness function, the fitness of each initial position is obtained according to the delay information of each simulation alarm signal corresponding to each initial position and the delay information of each alarm signal; the particle movement information is obtained according to a predetermined convergence factor and a random number calculation; based on the particle movement information and the fitness of each initial position, each initial position is updated to obtain a plurality of updated positions; and based on each updated position, the operation of performing local discharge ultrasonic field simulation under normal operating power using the acoustic thermal field model is returned to be executed until the fitness of the target updated position meets a predetermined threshold value, so as to determine the target updated position as the target fault position.

[0010] According to an embodiment of the present invention, based on particle movement information and the fitness of each initial position, each initial position is updated to obtain multiple updated positions, including: determining the influence of each particle in the population based on the fitness of each initial position; and based on the influence of each particle in the population, updating each initial position according to the particle movement information to obtain multiple updated positions.

[0011] Another aspect of the present invention provides a transformer fault inversion location device based on an acoustic thermal field model, comprising: an acquisition module, a comparison module and a determination module.

[0012] The acquisition module is used to obtain the time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer in response to the detection of the partial discharge signal of the target transformer. The comparison module is used to determine the target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal in each predetermined area; wherein each simulated alarm signal is obtained by simulating the partial discharge ultrasonic field of each predetermined area under normal operating power using the acoustic thermal field model. The determination module is used to use each position in the target fault area as a particle, use the acoustic thermal field model to iteratively simulate the partial discharge ultrasonic field in the target fault area under normal operating power, and optimize the particle position to determine the target fault position.

[0013] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above method when executed by a processor.

[0015] The fifth aspect of the present invention also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0016] According to an embodiment of the present invention, a transformer acoustic thermal field model is constructed, and local discharge ultrasonic field simulation is performed in each predetermined area under normal operating power. The obtained simulated alarm signals are compared with the alarm signal delay information detected in real time in the target transformer to preliminarily determine the fault area. In the process of preliminary fault determination, the influence of thermodynamics and acoustic effects on the alarm delay in actual application scenarios is considered, thereby improving the accuracy of fault location. After the fault location is preliminarily determined, each position in the target fault area is taken as a particle, and the acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, thereby optimizing the particle position to determine the global optimal target fault position, thereby improving the speed, precision and accuracy of local discharge fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0018] Figure 1 A graph showing the relationship between transformer operating temperature and sound pressure propagation is shown;

[0019] Figure 2The simulated sensor signal field strength for partial discharge faults at different locations in the transformer is shown;

[0020] Figure 3 An application scenario diagram of a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown;

[0021] Figure 4 A flow chart of a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown;

[0022] Figure 5 A flow chart showing a method for constructing an acoustic thermal field model according to an embodiment of the present invention is shown;

[0023] Figure 6 A flow chart of determining a target fault location according to a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown;

[0024] Figure 7 A structural block diagram of a transformer fault inversion location device based on an acoustic thermal field model according to an embodiment of the present invention is shown;

[0025] Figure 8 A block diagram of an electronic device suitable for a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "include", "comprises", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0028] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] Partial discharge (PD) is a phenomenon of local electrical breakdown in the insulation system. It often occurs at defects in the insulation material or in areas where the electric field is concentrated between dielectrics. This discharge does not form a complete electrical breakdown path throughout the insulation material, but is limited to a specific area. Partial discharge usually occurs in places with high electric field strength, such as defects, bubbles or inclusions in the insulation material. This phenomenon generates high-frequency electromagnetic and acoustic signals, which can be captured and analyzed by professional equipment. Accurate PD positioning helps to deeply assess the degree of insulation damage and provide key information for formulating effective equipment maintenance strategies. By monitoring and locating partial discharge, the aging of the insulation material and the severity of the defects can be understood in a timely manner, thereby predicting the potential risk of insulation breakdown. The above key information helps prevent equipment failures and ensure the safety and efficiency of the power system. Therefore, the careful monitoring and precise positioning of partial discharge play a vital role in the daily maintenance of the power system and the safety of equipment.

[0031] Traditional PD positioning methods mainly use the arrival time estimation method, that is, the location of the discharge source is determined by measuring the time difference of ultrasonic signals between different sensors, which requires solving the TDOA (Time Difference of Arrival) positioning equation. However, due to the complex internal conditions and severe interference during transformer operation, it is necessary to consider the complex error effects when solving the TDOA equation, and the robustness of actual scene applications is poor. Building a transformer empirical model with multi-physical field coupling instead of TDOA solution is an efficient solution. However, in the field of transformer modeling and positioning, in order to reduce calculation time and cost, the geometric structure and physical field model are generally simplified, and the influence of temperature gradient and internal structure during transformer operation is not considered.

[0032] However, in actual operation scenarios, temperature has a significant impact on ultrasonic signal propagation. The impact of temperature on ultrasonic propagation is analyzed below through a specific embodiment:

[0033] A sealed container was used as the measuring pool, with dimensions of 50 cm in length, 30 cm in width, and 20 cm in height, to ensure that the insulating oil was not affected by the outside world during the measurement process. The insulating oil was set at 30°C as the starting point, and then gradually heated up to 90°C at intervals of 30°C. After reaching each temperature point, it was maintained for at least 5 minutes to ensure that the oil temperature was uniform. Then, at least 5 repeated measurements were performed at each temperature point using a sound velocity meter to reduce accidental errors. Each measurement was performed at an interval of 1 minute, and finally the sound velocity data and oil temperature data at each temperature point were recorded. The relationship between the sound velocity and temperature of the insulating oil obtained through experimental fitting is shown in the following formula (1):

[0034] (1)

[0035] Wherein, T is the fluid temperature, the unit is K; v is the speed of sound, the unit is m / s.

[0036] Figure 1 A graph showing the transformer operating temperature versus sound pressure propagation is shown.

[0037] For the oil tank model under four oil temperature gradients, the influence of oil flow temperature on the propagation speed of ultrasonic signals is analyzed, such as Figure 1 As shown, as the oil temperature rises, the fluid density decreases, further affecting the acoustic impedance of the oil flow, thereby affecting the time it takes for the sound pressure sensor to reach the sound pressure threshold and affecting the positioning accuracy.

[0038] By counting the triggering time (when the sound pressure intensity is greater than 0.1Pa) of the partial discharge signal transmitted to the ultrasonic sensor at a distance of 3.2m, the influence of different oil flow temperatures on the ultrasonic sound velocity is obtained. The results are shown in the following table:

[0039] Table 1 shows the signal arrival time and ultrasonic sound velocity at different oil flow temperatures:

[0040]

[0041] Through Figure 1 From the analysis of Table 1, it is found that temperature has a significant effect on the propagation speed of ultrasonic signals and the signal arrival time. When the oil temperature rises by 10°C, the ultrasonic speed will increase by about 40 m / s. This change in sound speed will cause errors in partial discharge ultrasonic positioning.

[0042] In actual application scenarios, when the transformer operates under different working conditions, the change in its heating power will directly affect the oil temperature. In addition, the temperature inside the transformer shows a gradient distribution with high temperature at the top and low temperature at the bottom, rather than a uniform constant temperature distribution. This is because during the operation of the transformer, iron loss, copper loss and additional loss will cause the insulating oil temperature to gradually increase, thereby causing the insulating oil volume to expand and the density to decrease. In this case, the insulating oil with higher temperature and lower density will rise, while the cold oil with higher density will sink. In addition, the oil circulation heat dissipation causes the hot oil to flow out from the top of the transformer and the cold oil to flow in from the bottom, eventually forming a nonlinear temperature gradient field inside the transformer, resulting in nonlinear propagation of ultrasonic signals. Especially in large transformers, due to the large volume of the oil tank and the high operating temperature, as well as the large flow rate and pressure of the liquid during the circulation process, the influence of oil temperature on ultrasonic positioning is more significant.

[0043] In combination with the above embodiments and actual transformer operating conditions, since temperature has a significant impact on the propagation of sound speed, oil circulation causes the oil temperature to be divided into various gradients. Therefore, considering the different sound speeds of oil flows at different temperatures under oil circulation, simulation will be more in line with actual application scenarios.

[0044] Figure 2 The simulated sensor signal field strength for partial discharge faults at different locations in the transformer is shown.

[0045] like Figure 2 As shown in Figure 3, the partial discharge ultrasonic signals of partial discharge faults occurring at different locations in the transformer are constantly changing when propagating in the transformer. Therefore, it is necessary to introduce acoustic and thermal effects as boundary conditions when constructing the acoustic-thermal field model.

[0046] However, few current positioning methods have considered the impact of thermodynamic and acoustic effects on signal propagation, including the impact of temperature gradients and internal structure changes on ultrasonic propagation speed, resulting in poor positioning accuracy.

[0047] In view of the above problems, an embodiment of the present invention provides a transformer fault inversion location method based on an acoustic thermal field model, the method comprising constructing an acoustic thermal field model of the transformer, performing local discharge ultrasonic field simulation in each predetermined area under normal operating power, comparing each simulated alarm signal obtained with the alarm signal delay information detected in real time in the target transformer, and preliminarily determining the fault area. In the process of preliminary fault determination, the influence of thermodynamics and acoustic effects on the alarm delay in actual application scenarios is considered, thereby improving the accuracy of fault location. After the fault location is preliminarily determined, each position in the target fault area is taken as a particle, and the acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, thereby optimizing the particle position to determine the global optimal target fault position, thereby improving the speed, precision and accuracy of local discharge fault location.

[0048] Figure 3 A diagram showing an application scenario of a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention.

[0049] like Figure 3 As shown, the application scenario 300 according to this embodiment may include a first terminal device 301 , a network 302 , and a server 303 .

[0050] The network 302 is used to provide a medium for a communication link between the first terminal device 301 and the server 303. The network 302 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0051] The user can use the first terminal device 301 to interact with the server 303 through the network 302 to receive or send messages, etc. For example, the user can input the power of the normally operating transformer and the local discharge intensity information of the simulated fault area into the first terminal device 301, and transmit it to the server 303 through the network 302. By constructing the transformer fault inversion positioning method based on the acoustic thermal field model, the accurate location of the transformer local discharge fault can be calculated by the server 303.

[0052] Various communication client applications may be installed on the first terminal device 301, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example). The first terminal device 301 may be various electronic devices with a display screen and supporting web browsing, including but not limited to tablet computers, laptop portable computers, and desktop computers, etc.

[0053] The server 303 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by the user using the first terminal device 301. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0054] It should be noted that the transformer fault inversion location method based on the acoustic thermal field model provided in the embodiment of the present invention can generally be executed by the server 303. Accordingly, the transformer fault inversion location device based on the acoustic thermal field model provided in the embodiment of the present invention can generally be set in the server 303. The transformer fault inversion location method based on the acoustic thermal field model provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 303 and can communicate with the first terminal device 301 and / or the server 303. Correspondingly, the transformer fault inversion location device based on the acoustic thermal field model provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 303 and can communicate with the first terminal device 301 and / or the server 303.

[0055] Alternatively, the transformer fault inversion location method based on the acoustic thermal field model provided in the embodiment of the present invention may also be executed by the first terminal device 301, or may also be executed by other terminal devices different from the first terminal device 301. Accordingly, the transformer fault inversion location device based on the acoustic thermal field model provided in the embodiment of the present invention may also be provided in the first terminal device 301, or may be provided in other terminal devices different from the first terminal device 301.

[0056] It should be understood that Figure 3 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0057] The following will be based on Figure 3 The scene described by Figure 4~Figure 6 The transformer fault inversion location method based on the acoustic thermal field model according to an embodiment of the present invention is described in detail.

[0058] Figure 4 A flow chart of a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown.

[0059] like Figure 4 As shown, the transformer fault inversion location method 400 based on the acoustic thermal field model of this embodiment includes operations S410 to S430.

[0060] In operation S410, in response to detecting a partial discharge signal of a target transformer, time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer is obtained.

[0061] In operation S420, the target fault area is determined by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal of each predetermined area.

[0062] In operation S430, each position in the target fault area is taken as a particle, and an acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, and the particle position is optimized to determine the target fault position.

[0063] According to an embodiment of the present invention, the ultrasonic sensors are arranged around the transformer oil tank according to circumstances.

[0064] According to an embodiment of the present invention, in order to accelerate the positioning speed and improve the convergence of the positioning solution, the TDOA algorithm is used to construct a transformer fault inversion database to achieve the preliminary positioning of the target fault area. When a real partial discharge occurs, the sound pressure data acquired by the real ultrasonic sensor in real time is processed by wavelet denoising, and the delay difference of each ultrasonic sensor reaching the alarm threshold is extracted, that is, the alarm signal delay information.

[0065] The time delay information of each simulated alarm signal of each predetermined area is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model.

[0066] Figure 5 A flow chart of a method for constructing an acoustic thermal field model according to an embodiment of the present invention is shown.

[0067] like Figure 5 As shown, the acoustic thermal field model 511 includes a transformer and multi-physical field coupling model 506, a partial discharge model 508 and a pressure acoustic field control model 510; wherein the multi-physical field 502 includes a magnetic field, a thermal field and a flow field.

[0068] During the operation of the natural oil circulation air-cooled transformer, the transformer oil absorbs the heat generated by the windings. A small part of the heat is directly transferred to the air through the transformer shell by convection heat transfer. The remaining heat is transferred to the heat sink through the oil pipe connected to the upper part of the oil tank along with the transformer oil, and finally to the air. The oil cooled by the heat sink is then returned to the oil tank through the oil pipe connected to the lower part of the oil tank to realize oil circulation.

[0069] Combined with the natural oil circulation heat dissipation mechanism, the calculation model under normal operation of the transformer can be equivalent to a magnetic-thermal-fluid multi-physical field coupling model of the transformer and its environment. Therefore, based on the natural oil circulation heat dissipation mechanism 501 of the transformer under normal operating conditions, a magnetic field control model 503, a thermal field control model 504 and a flow field control model 505 are constructed respectively.

[0070] The magnetic field control equation constructed in the magnetic field control model 503 is shown in the following formula (2):

[0071] (2)

[0072] in, is a vector differential operator; is the magnetic field strength, in units of ; is the current density vector, in units of ; B is the magnetic flux density, unit is T; A is the magnetic vector potential, unit is ; is the material conductivity, in units of ; is the external injection current density, in units of ; E is the electric field strength, unit is .

[0073] The thermal field control equation constructed in the thermal field control model 504 is shown in the following formula (3):

[0074] (3)

[0075] in, is the fluid density in units of ; is the constant pressure heat capacity, in units of ; T is temperature, unit is K; u is velocity field, unit is ; Q is the heat source, unit is ; k is the thermal conductivity, unit is .

[0076] The flow field control equation constructed in the flow field control model 505 is shown in the following formula (4):

[0077] (4)

[0078] Among them, p is the fluid pressure, the unit is Pa; I is the unit matrix; F refers to the pressure vector, the unit is N.

[0079] The magnetic field control model 503 , thermal field control model 504 and flow field control model 505 together constitute a transformer and multi-physical field coupling model 506 .

[0080] Based on the mechanism 507 of transient pressure acoustic simulation of partial discharge ultrasonic signal, a partial discharge model 508 is constructed. Through transient pressure acoustic simulation of partial discharge ultrasonic signal, the partial discharge signal adopts a double exponential decay model, and the periodic pulse with an interval of 33us is used as the partial discharge source. The single pulse expression is shown in the following formula (5):

[0081] (5)

[0082] in, is the attenuation constant; A is the normalized amplitude.

[0083] The propagation paths of sound waves are divided into three categories: (1) internal refraction propagation: the path deflection caused by the different ultrasonic velocities in different media; (2) internal diffraction propagation: when the ultrasonic wavelength is close to the width of the shelter, the ultrasonic wave will change direction and propagate around the shelter; (3) structural propagation: when the ultrasonic wave contacts the structural carrier of the power transformer, it continues to propagate inside the structural carrier in the form of shear waves. However, since the attenuation of ultrasonic waves in the structural carrier is generally severe, it can be filtered by setting an appropriate threshold.

[0084] Based on the propagation path 509 of the ultrasonic wave inside the transformer, a pressure acoustic field control model 510 is constructed. The pressure acoustic field control model 510 is based on acoustic characteristics and takes into account the fluid density and the acoustic field boundary conditions to construct a pressure acoustic field control equation, as shown in the following formula (6):

[0085] (6)

[0086] Where c is the speed of sound, in units of ; Represents the dipole moment in units of ; Represents the total sound pressure in units of ; represents the mechanical quality factor; Represents acoustic impedance in units of .

[0087] According to the embodiments of the present invention, the magnetic field control model, thermal field control model, and flow field control model are constructed by combining the transformer operation temperature rise principle and the oil circulation cooling mechanism, realizing the accurate simulation of the temperature of each part during the operation of the transformer, and establishing a high-fidelity acoustic transformer operation model; combining the propagation path of ultrasound inside the transformer, the influence of temperature on the wave velocity, and the mechanism of transient pressure acoustic simulation of partial discharge ultrasonic signals, a partial discharge model and a pressure acoustic field control model are constructed for the transformer model, realizing high-precision simulation of partial discharge signal propagation. Taking into account the thermodynamic and acoustic effects in actual application scenarios, the constructed acoustic thermal field model is closer to the actual operating conditions, thereby improving the accuracy of fault location.

[0088] By comparing the delay information of each alarm signal with the simulated alarm signal delay information of each predetermined area, determining the target fault area may include comparing the difference between the alarm signal delay information and the simulated alarm signal delay information or comparing the ratio of the alarm signal delay information to the simulated alarm signal delay information and a threshold value, and then determining the target fault area with the smallest numerical difference between the two.

[0089] The transformer is divided into several blocks based on the shape and size of the transformer oil tank and numbered. The target fault area is the block where partial discharge occurs in the transformer that is preliminarily determined.

[0090] The acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power. Optimizing the particle position to determine the target fault position can be achieved through algorithms such as the grey wolf optimization algorithm, the particle swarm optimization algorithm, and the ant colony optimization algorithm.

[0091] According to an embodiment of the present invention, by constructing a transformer acoustic thermal field model, local discharge ultrasonic field simulation is performed in each predetermined area under normal operating power, and each simulated alarm signal obtained is compared with the alarm signal delay information detected in real time in the target transformer to preliminarily determine the fault area. In the process of preliminary fault determination, the influence of thermodynamics and acoustic effects on the alarm delay in actual application scenarios is considered, thereby improving the accuracy of fault location. After the fault location is preliminarily determined, each position in the target fault area is taken as a particle, and the acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, thereby optimizing the particle position to determine the global optimal target fault position, thereby improving the speed, precision and accuracy of local discharge fault location.

[0092] According to an embodiment of the present invention, a target fault area is determined by comparing each alarm signal delay information with each simulated alarm signal delay information of a predetermined area, including: based on the least squares method, processing each alarm signal delay information and each simulated alarm signal delay information of each predetermined area to obtain a matching degree between each alarm signal delay information and each simulated alarm signal delay information; and based on the matching degree, determining the target fault area from each predetermined area.

[0093] The matching degree is calculated based on the least square method, wherein the matching degree can be the approximation obtained by performing a difference calculation between the time delay information of each alarm signal and the time delay information of each simulated alarm signal of each predetermined area, and the calculation method is shown in the following formula (7):

[0094] (7)

[0095] in, , N is the total number of blocks divided by the transformer model; n is the total number of sensors set; L i is the matching approximation of the corresponding blocks, i.e., the matching degree; To simulate the alarm signal delay, is the alarm signal delay difference. For example, in simulation, the delay difference of the jth sensor corresponding to the i-th block is , the delay difference corresponding to the jth sensor of the PD signal in the actual test is .

[0096] Based on the least squares method, the matching degree of each block is calculated using the above formula (7), and each predetermined area corresponding to the minimum matching degree is determined as the target fault area.

[0097] According to an embodiment of the present invention, the least square method is used to process the delay information of each alarm signal and the delay information of each simulated alarm signal in each predetermined area to obtain the matching degree between the two. Based on the matching degree, the preliminary positioning of the target fault area is achieved, the number of iterations in data calculation is reduced, the calculation speed is improved, and the time required for a single iteration is reduced, which can greatly shorten the calculation time and calculation cost.

[0098] According to an embodiment of the present invention, each simulated alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model, including: constructing a transformer geometric model according to the geometric parameters of the target transformer; the transformer geometric model includes multiple predetermined areas; and taking the center of each predetermined area as the local discharge source, and inputting the normal operating power of the target transformer under each working condition and the local discharge intensity of each predetermined area into the acoustic thermal field model to obtain each simulated alarm signal.

[0099] According to an embodiment of the present invention, the plurality of predetermined areas are obtained by dividing the transformer model into a plurality of blocks and numbering the blocks based on the shape and size of the transformer oil tank. For example, the transformer may be divided into 50 predetermined fault areas.

[0100] Multiple data points are used as simulated ultrasonic sensors and arranged around the transformer tank according to specific conditions. The normal operating power of the target transformer under various working conditions and the local discharge intensity of each predetermined area are known parameters. After inputting the acoustic thermal field model, the transformer operation simulation can be performed.

[0101] During the simulation, the moment when the partial discharge signal is first received is recorded, and the partial discharge fault modules corresponding to the predetermined areas with different numbers are respectively enabled to simulate the partial discharge ultrasonic field, and the corresponding numbers of the predetermined fault areas, the ultrasonic simulation results and the alarm moments of each ultrasonic sensor are recorded to obtain each simulation alarm signal and construct a partial discharge fault inversion database. After the fault occurs, the partial discharge fault inversion database can be directly called to preliminarily determine the fault location and realize rapid positioning.

[0102] According to an embodiment of the present invention, the transformer model is divided into several blocks and numbered, the spatial position of the fault source is changed and large-scale simulation is carried out for each number, the arrival time of the ultrasonic signal at several observation points on the surface of the transformer model is obtained, that is, each simulation alarm signal is obtained, and a high-precision acoustic thermal field simulation model containing PD source position and observation point information is constructed, which is used for preliminary positioning of the local discharge fault position and for iterative simulation of the local discharge ultrasonic field in the target fault area, and then the particle position is optimized to determine the global optimal target fault position, so as to achieve fast and accurate localization of the local discharge fault.

[0103] Figure 6 A flow chart of determining a target fault location by a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown.

[0104] like Figure 6 As shown, the method 600 of determining a target fault location by using an acoustic-thermal field model-based transformer fault inversion location method of this embodiment includes operations S641 to S649.

[0105] In operation S641 , a plurality of initial positions are randomly determined from within a target fault area based on a predetermined population size.

[0106] In operation S642, multiple initial positions are used as local discharge sources, and a local discharge ultrasonic field simulation is performed under normal operating power using an acoustic thermal field model to obtain time delay information of each simulation alarm signal corresponding to each initial position.

[0107] In operation S643, based on the target fitness function, the fitness of each initial position is obtained according to each simulation alarm signal delay information and each alarm signal delay information corresponding to each initial position.

[0108] In operation S644, particle movement information is calculated based on a predetermined convergence factor and a random number.

[0109] In operation S645, each initial position is updated based on the particle movement information and the fitness of each initial position to obtain a plurality of updated positions.

[0110] In operation S646, based on each updated position, a partial discharge ultrasonic field simulation operation is performed under normal operating power using an acoustic thermal field model.

[0111] In operation S647, it is determined whether the fitness of the updated position of the target meets a predetermined threshold. If it does not meet the predetermined threshold, operation S648 is performed. If it meets the predetermined threshold, operation S649 is performed.

[0112] In operation S648, the location information is updated.

[0113] In operation S649, the target updated position is determined as the target fault position.

[0114] According to an embodiment of the present invention, within the fault block initially located, a high-precision acoustic thermal field simulation model is constructed, and iterative simulation is performed under Comsol multiphysics and Matlab, and the gray wolf optimization algorithm is combined to perform simulation optimization. The gray wolf optimization algorithm demonstrates excellent global search capabilities by simulating the social behavior of gray wolf groups. Compared with traditional optimization methods, it can more effectively avoid falling into the dilemma of local optimality. By simulating the hunting strategy of gray wolf groups, that is, , and Wolf's target level and The wolf's following behavior enables comprehensive exploration in a multi-dimensional search space. This multi-level, multi-strategy search mechanism not only enhances the algorithm's global vision, but also improves its adaptive adjustment capabilities, enabling it to find better solutions in complex and multi-peak optimization problems.

[0115] The gray wolf optimization algorithm of the present invention has five steps, namely, initialization stage, simulation of gray wolf social behavior, updating position, evaluating fitness, and updating optimal solution.

[0116] First, we randomly select N points of the population size in the target fault area, set the corresponding coordinates of these points as the partial discharge source of the transformer model for simulation, and set the obtained ultrasonic sensor time difference as the position of the particle. Then, we compare and calculate the fitness of these particle positions, and set the wolf according to the size of the fitness. , , and As the initial position of the wolf , , and Different from the current method of using the TDOA algorithm to obtain a nonlinear equation group and directly solving it using the PSO algorithm, the present invention uses the three-dimensional positions of N particles in the Grey Wolf Optimization Algorithm as the local discharge source position for fault simulation, and compares the fitness of the simulation results with the real local discharge signal, and finally determines the target fault position.

[0117] The target fitness function is an important component of the present invention, which is used to evaluate the quality of the solution of each particle and serves as a criterion in the iterative process of the algorithm. The fitness function expression of the i-th particle is shown in the following formula (8):

[0118] (8)

[0119] Where i=1,2,...,N, n is the number of ultrasonic sensor channels; t tj The delay information of each simulation alarm signal corresponding to each initial position, t t It is the time delay information of each alarm signal.

[0120] The particle movement information is obtained according to the predetermined convergence factor and the random number. The particle movement information includes the direction and distance of particle movement, as shown in the following formula (9):

[0121] (9)

[0122] Where a is a predetermined convergence factor, which usually decreases linearly from 2 to 0 and is used to control the convergence speed. r is a random number between [0,1]. Vectors A and C represent the moving direction and distance of the updated particle.

[0123] Based on the particle movement information and the fitness of each initial position, each initial position is updated to obtain multiple updated positions. The following is the mathematical expression of the position update process:

[0124] First, the distance vector between ω wolf and α, β and δ wolves is calculated, as shown in the following formula (10):

[0125] (10)

[0126] Among them, , , are the positions of α, β and δ wolves in the current iteration, is the position of ω wolf in the current iteration.

[0127] The wolf's position update process is obtained by the following formula (11):

[0128] (11)

[0129] in, , , , They are the new positions of α, β and δ wolves after one iteration.

[0130] According to an embodiment of the present invention, based on particle movement information and the fitness of each initial position, each initial position is updated to obtain multiple updated positions, including: determining the influence of each particle in the population based on the fitness of each initial position; and based on the influence of each particle in the population, updating each initial position according to the particle movement information to obtain multiple updated positions.

[0131] It divides the particles into , , as well as ,in The first three solutions are the most influential. , and The value of is the best, and the rest Wolves belong to the pack and are used to update , and Data corresponding to wolves.

[0132] According to an embodiment of the present invention, a high-precision acoustic thermal field simulation model is constructed, and the target fault location is determined by combining the Gray Wolf optimization algorithm for simulation optimization. This method can more effectively avoid falling into the dilemma of local optimality and realize comprehensive exploration in a multi-dimensional search space. At the same time, the global vision of the algorithm is enhanced, and its adaptive adjustment ability is improved, so that it can find a better solution in complex and multi-peak optimization problems, and can significantly improve the search accuracy of the model and the accuracy of the global optimal solution. In addition, the Gray Wolf optimization algorithm has a faster convergence speed. When solving high-dimensional problems, it can quickly find the global optimal solution, greatly improve the calculation speed, and achieve fast and accurate local discharge fault location.

[0133] A partial discharge experiment is simulated and implemented in a specific embodiment to verify the effectiveness and accuracy of the transformer fault inversion location method based on the acoustic thermal field model proposed in the present invention.

[0134] A 3D geometric model is established based on the size of a 120 MVA / 220 kV oil-immersed three-phase five-column transformer. The shell size of the model can be 7.6m×2.8m×2.7m. There are four sets of heat sinks on each side of the oil tank, which are connected to the top and bottom of the oil tank through oil pipes.

[0135] The generation and propagation modes of partial discharge signals are set for the transformer model by using formula (5) and formula (6), so as to simulate the propagation phenomenon of partial discharge ultrasonic signals in the transformer.

[0136] According to the structure of the transformer, the transformer is divided into 60 fault blocks. The central position of each fault block is activated to simulate partial discharge. The average trigger time of each simulated sensor after the partial discharge fault occurs is recorded as the feature of the block, that is, the simulated alarm signal delay information, and the acoustic thermal field model database is constructed based on this.

[0137] For example, a sampling frequency of 10MHz is used to collect signals from a five-channel ultrasonic sensor array. The five ultrasonic sensors are placed on the top, sides, front and back (except the bottom) of the transformer tank shell.

[0138] In the field experiment, thermocouples were used to simulate the oil temperature gradient during transformer operation, and the PD defect model was used to simulate the actual situation. The PD defect model was immersed in different positions in the experimental transformer oil tank, and several thermocouples were placed at the transformer winding position to simulate the heating of the winding. The defect model was pressurized to 12KV to cause continuous partial discharge and collect data from each sensor. A total of 14 groups of fault data were collected, including 4 groups of core faults, 4 groups of winding faults, and 6 groups of transformer oil faults, and the target fault area was preliminarily determined.

[0139] The target fault location is accurately located by the Gray Wolf Optimization Algorithm. When designing and implementing the optimization algorithm, reasonable parameter settings are crucial to the performance and effect of the algorithm. In order to ensure that the positioning method used can effectively find the global optimal solution, we configured and optimized the relevant parameters of the Gray Wolf Optimization Algorithm. The sensitivity analysis results are as follows:

[0140] Table 2 shows the results of sensitivity analysis:

[0141]

[0142] Through sensitivity analysis, the population size, maximum number of iterations and convergence factor are reasonably set. The population size is set to a moderate number to ensure the diversity of the search, reduce the risk of falling into a local optimal solution, and avoid excessive computational burden. The maximum number of iterations is set to provide enough time for the algorithm to converge, so as to ensure the optimization effect without wasting computing resources. The convergence factor is set to a larger value to promote the algorithm's exploration ability in the early stage and maintain the ability to converge to known high-quality areas in the later stage. The relevant parameters of the specific positioning method are set as: population size N=20, maximum number of iterations T=80, convergence factor is 2.

[0143] In order to evaluate the accuracy of the transformer fault inversion location method based on the acoustic thermal field model proposed in the present invention, the location errors of the fault source location based on the CHAN algorithm, the PSO algorithm and the fault inversion location method based on the present invention are compared. By analyzing the collected waveform data, the fault occurrence event is measured using a UHF sensor, and the time difference of the pulse signal received by each sensor is calculated one by one for the fault location algorithm. The results are shown in Table 3:

[0144] Table 3 shows the positioning results of the CHAN algorithm, PSO algorithm and fault inversion method:

[0145]

[0146] Comparing the performance of the three methods in this experiment, the average positioning errors obtained by the CHAN method and the PSO method were 0.40dm and 0.29dm respectively, and the average positioning error obtained by the fault inversion method proposed in the present invention was reduced to 0.09dm. Taking into account some factors such as sensor detection radius, environmental noise, and position measurement error, these positioning results are within a reasonable range of error. The error calculated by the fault inversion positioning method proposed in the present invention is significantly lower than the positioning errors of the other two methods, demonstrating its advantage in accuracy.

[0147] In addition, among the total of 14 data sets, 6 data sets are affected by the ultrasonic propagation characteristics, and the signal propagation path is seriously distorted. When the CHAN algorithm and PSO algorithm are used for positioning, the positioning effect of these 6 data sets is extremely poor or even fails to converge. Since the CHAN algorithm and the PSO algorithm are based on conventional TDOA solutions, it is necessary to set such influences as errors and solve them together. However, accurate error settings require a lot of experiments and experience to obtain, and the internal structure of the transformer is complex. Facing fault sources in different parts, it is difficult to find a unified error setting method. The method proposed in the present invention still has accurate positioning effects for the 6 data sets. Since the fusion of ultrasonic propagation characteristics and fault positioning is realized through finite element modeling, the errors caused by solving the TDOA equation and the internal structure of the transformer are avoided, showing its advantages in practicality and robustness.

[0148] In addition, in order to explore the significance of considering temperature gradient for localization of partial discharge faults, the experimental data without thermocouple heating and the effects of various localization methods are compared. The results are shown in Table 4.

[0149] Table 4 compares the positioning results of different positioning methods when considering the influence of temperature on positioning:

[0150]

[0151] As shown in Table 4, under different transformer maximum oil temperatures, as the oil temperature gradient continues to increase, the error of methods such as CHAN and PSO for solving the TDOA equation will gradually increase. This is because they ignore the effect of temperature rise on ultrasonic sound velocity. From formula (1), it can be seen that when the oil temperature rises by 10°C, the ultrasonic velocity will increase by about 40 m / s, which in turn affects the arrival time difference of the signal and leads to a decrease in positioning accuracy. The method proposed in the present invention successfully simulates the change of temperature field by adjusting the heating parameters of the model, effectively considers the effect of temperature on ultrasonic sound velocity, makes the positioning accuracy basically unaffected, and significantly reduces the error. This method not only improves the accuracy and stability of positioning, but also enhances the adaptability of the system in different temperature environments. In addition, with the help of this method, the reliability of the positioning system in practical applications has been significantly improved, and it can maintain high efficiency in complex and dynamic environments, providing a more reliable solution for practical industrial applications.

[0152] In order to evaluate the effect of fast calculation, the initial positioning is compared with that without initial positioning. Considering that the TDOA equation solved by the initial positioning is simple and the solution time is very short, the time spent on the initial positioning is not considered. The final results are shown in Table 5:

[0153] Table 5 compares the impact of preliminary positioning on fast calculation:

[0154]

[0155] From the above results, we can see that initial positioning can accelerate the calculation, which not only greatly reduces the number of iterations, but also reduces the time required for a single iteration, which can greatly shorten the calculation time and cost, and is more suitable for localization of partial discharge faults during transformer operation. The process of initial positioning enables the algorithm to obtain an initial position closer to the actual fault point in the initial stage, thereby reducing the number of searches in unnecessary space. Through this preliminary positioning method, the algorithm can converge to the global optimal solution or a position close to the optimal solution more quickly, significantly improving the calculation efficiency. In addition, due to the reduction in the amount of calculation for each iteration, the overall calculation time is also shortened, which is particularly important for power system monitoring and fault location with high real-time requirements. During the operation of the transformer, this acceleration effect enables the system to respond to partial discharge faults more quickly, take maintenance measures in a timely manner, and improve the reliability and safety of the power system.

[0156] Based on the above transformer fault inversion location method based on acoustic thermal field model, the present invention also provides a transformer fault inversion location device based on acoustic thermal field model. Figure 7 The device is described in detail.

[0157] Figure 7 A structural block diagram of a transformer fault inversion location device based on an acoustic thermal field model according to an embodiment of the present invention is shown.

[0158] like Figure 7 As shown, the transformer fault inversion location device 700 based on the acoustic thermal field model of this embodiment includes an acquisition module 710 , a comparison module 720 and a determination module 730 .

[0159] The acquisition module 710 is used to acquire the time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer in response to detecting the partial discharge signal of the target transformer. In one embodiment, the acquisition module 710 can be used to perform the operation S410 described above, which will not be repeated here.

[0160] The comparison module 720 is used to determine the target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal of each predetermined area; wherein each simulated alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model. In one embodiment, the comparison module 720 can be used to perform the operation S420 described above, which will not be repeated here.

[0161] The determination module 730 is used to use each position in the target fault area as a particle, use the acoustic thermal field model to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, and optimize the particle position to determine the target fault position. In one embodiment, the determination module 730 can be used to perform the operation S430 described above, which will not be repeated here.

[0162] According to an embodiment of the present invention, the comparison module further includes a delay information processing submodule and a target fault area determination submodule.

[0163] The delay information processing submodule is used to process the delay information of each alarm signal and the delay information of each simulated alarm signal in each predetermined area based on the least square method to obtain the matching degree between the delay information of each alarm signal and the delay information of each simulated alarm signal. The target fault area determination submodule is used to determine the target fault area from each predetermined area based on the matching degree.

[0164] According to an embodiment of the present invention, the acoustic thermal field model includes: a transformer and multi-physical field coupling model, a partial discharge model and a pressure acoustic field control model; wherein the multi-physical field includes a magnetic field, a thermal field and a flow field. The above-mentioned device also includes: a control model construction submodule, a partial discharge model construction submodule, and a pressure acoustic field control model construction submodule.

[0165] The control model construction submodule is used to construct the magnetic field control model, thermal field control model and flow field control model respectively based on the natural oil circulation heat dissipation mechanism of the transformer under normal operating conditions. The partial discharge model construction submodule is used to construct the partial discharge model based on the mechanism of transient pressure acoustic simulation of partial discharge ultrasonic signals. The pressure acoustic field control model construction submodule is used to construct the pressure acoustic field control model based on the propagation path of ultrasonic waves inside the transformer.

[0166] According to an embodiment of the present invention, the comparison module further includes: a geometric model building submodule and an input submodule.

[0167] The geometric model construction submodule is used to construct a transformer geometric model according to the geometric parameters of the target transformer; the transformer geometric model includes multiple predetermined areas. The input submodule uses the center of each predetermined area as the local discharge source, and inputs the normal operating power of the target transformer under each working condition and the local discharge intensity of each predetermined area into the acoustic thermal field model to obtain various simulation alarm signals.

[0168] According to an embodiment of the present invention, the determination module includes: an initial position determination submodule, a simulation alarm signal delay information acquisition submodule, a fitness determination submodule, a particle movement information calculation submodule, an update submodule, and a target fault position determination submodule.

[0169] The initial position determination submodule is used to randomly determine multiple initial positions from the target fault area based on a predetermined population size. The simulation alarm signal delay information acquisition submodule is used to use multiple initial positions as local discharge sources, use the acoustic thermal field model to perform local discharge ultrasonic field simulation under normal operating power, and obtain the simulation alarm signal delay information corresponding to each initial position. The fitness determination submodule is used to obtain the fitness of each initial position based on the target fitness function according to the simulation alarm signal delay information and each alarm signal delay information corresponding to each initial position. The particle movement information calculation submodule is used to calculate and obtain particle movement information based on a predetermined convergence factor and a random number. The update submodule is used to update each initial position based on the particle movement information and the fitness of each initial position to obtain multiple updated positions. The target fault position determination submodule is used to return to perform the local discharge ultrasonic field simulation operation using the acoustic thermal field model under normal operating power based on each updated position until the fitness of the target updated position meets the predetermined threshold, so as to determine the target updated position as the target fault position.

[0170] According to an embodiment of the present invention, the update submodule includes an update unit for determining the influence of each particle in the population based on the fitness of each initial position; and based on the influence of each particle in the population, updating each initial position according to particle movement information to obtain multiple updated positions.

[0171] Figure 8 A block diagram of an electronic device suitable for a transformer fault inversion location method based on an acoustic thermal field model according to an embodiment of the present invention is shown.

[0172] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present invention includes a processor 801, which can perform various appropriate actions and processes according to the program stored in the ROM 802 or the program loaded from the storage part 808 to the RAM 803. The processor 801 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0173] In RAM803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM802 and RAM803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to an embodiment of the present invention by executing the programs in ROM802 and / or RAM803. It should be noted that the program can also be stored in one or more memories other than ROM802 and RAM803. Processor 801 can also perform various operations of the method flow according to an embodiment of the present invention by executing the programs stored in one or more memories.

[0174] According to an embodiment of the present invention, the electronic device 800 may further include an I / O interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that the computer program read therefrom is installed into the storage portion 808 as needed.

[0175] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0176] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM802 and / or RAM803 described above and / or one or more memories other than ROM802 and RAM803.

[0177] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the transformer fault inversion location method based on the acoustic thermal field model provided by the embodiment of the present invention.

[0178] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when the processor 801 executes the computer program. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0179] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination. The scope of the present invention is defined by the attached claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A transformer fault inversion location method based on acoustic thermal field model, characterized in that: The acoustic thermal field model includes: a transformer and multi-physical field coupling model, a partial discharge model and a pressure acoustic field control model; wherein the multi-physical field includes a magnetic field, a thermal field and a flow field; the method includes: Based on the natural oil circulation heat dissipation mechanism of the transformer under normal operating conditions, the magnetic field control model, thermal field control model and flow field control model are constructed respectively; Based on the mechanism of transient pressure acoustics simulation of partial discharge ultrasonic signals, a partial discharge model is constructed; Based on the propagation path of the ultrasonic wave inside the transformer, constructing the pressure acoustic field control model; In response to detecting a partial discharge signal of a target transformer, acquiring time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer; Determine the target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal of each predetermined area; wherein each simulated alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model; and Taking each position in the target fault area as a particle, the acoustic thermal field model is used to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, and the particle position is optimized to determine the target fault position.

2. The method according to claim 1, characterized in that The step of determining a target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal in a predetermined area comprises: Based on the least square method, processing the time delay information of each alarm signal and the time delay information of each simulated alarm signal of each predetermined area to obtain the matching degree between the time delay information of each alarm signal and the time delay information of each simulated alarm signal; and Based on the matching degree, the target fault area is determined from the predetermined areas.

3. The method according to claim 1, characterized in that Each simulation alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model, and includes: According to the geometric parameters of the target transformer, a transformer geometric model is constructed; the transformer geometric model includes a plurality of predetermined areas; and The center of each predetermined area is used as a local discharge source, and the normal operating power of the target transformer under each working condition and the local discharge intensity of each predetermined area are respectively input into the acoustic thermal field model to obtain the simulation alarm signals.

4. The method according to claim 1, characterized in that: The method of taking each position in the target fault area as a particle, using the acoustic thermal field model to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, and optimizing the particle position to determine the target fault position includes: Based on a predetermined population size, randomly determining a plurality of initial locations from within the target fault area; Taking multiple initial positions as local discharge sources, using the acoustic thermal field model to perform local discharge ultrasonic field simulation under normal operating power, and obtaining time delay information of each simulation alarm signal corresponding to each initial position; Based on the target fitness function, according to the delay information of each simulation alarm signal corresponding to each initial position and the delay information of each alarm signal, the fitness of each initial position is obtained; The particle movement information is obtained according to the predetermined convergence factor and the random number calculation; Based on the particle movement information and the fitness of each initial position, updating each initial position to obtain a plurality of updated positions; and Based on each updated position, return to execute the partial discharge ultrasonic field simulation operation under normal operating power using the acoustic thermal field model until the fitness of the target updated position meets a predetermined threshold, so as to determine the target updated position as the target fault position.

5. The method according to claim 4, characterized in that The updating of each initial position based on the particle movement information and the fitness of each initial position to obtain a plurality of updated positions includes: Determine the influence of each particle in the population based on the fitness of each initial position; and Based on the influence of each particle in the population, each initial position is updated according to the particle movement information to obtain a plurality of updated positions.

6. A transformer fault inversion location device based on acoustic thermal field model, characterized in that: The acoustic thermal field model includes: a transformer and multi-physical field coupling model, a partial discharge model and a pressure acoustic field control model; wherein the multi-physical field includes a magnetic field, a thermal field and a flow field; the device includes: The control model building submodule is used to build a magnetic field control model, a thermal field control model and a flow field control model based on the natural oil circulation heat dissipation mechanism of the transformer under normal operating conditions; The partial discharge model building submodule is used to build a partial discharge model based on the mechanism of transient pressure acoustics simulation of partial discharge ultrasonic signals; A pressure acoustic field control model building submodule is used to build a pressure acoustic field control model based on the propagation path of ultrasonic waves inside the transformer; An acquisition module, configured to acquire, in response to detecting a partial discharge signal of a target transformer, time delay information of each alarm signal of each ultrasonic sensor deployed in the target transformer; A comparison module, used to determine the target fault area by comparing the time delay information of each alarm signal with the time delay information of each simulated alarm signal of each predetermined area; wherein each simulated alarm signal is obtained by simulating the local discharge ultrasonic field of each predetermined area under normal operating power using an acoustic thermal field model; and The determination module is used to use each position in the target fault area as a particle, use the acoustic thermal field model to perform iterative simulation of the local discharge ultrasonic field in the target fault area under normal operating power, and optimize the particle position to determine the target fault position.

7. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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