Macro-micro hybrid simulation method for electrical equipment insulation defect analysis

Abstract

The invention discloses a macro-micro hybrid simulation method for electrical equipment insulation defect analysis, which comprises the following steps: S1, establishing a gas defect discharge process simulation model, and describing microscopic particle state change and electric field distribution in a gas discharge process through a particle transport equation and a Poisson equation; the method comprises the steps of S1, establishing an electromagnetic propagation process simulation model, and describing the propagation process of an electromagnetic signal generated by gas discharge through a Maxwell equation set, and S3, performing joint simulation on the model, taking equivalent current of gas discharge as an excitation source, simulating propagation of the electromagnetic signal in power equipment through a finite time domain difference method FDTD, and recording a signal value of a receiver. According to the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment, the insulation defects of the power equipment are accurately analyzed and monitored, important support is provided for state monitoring and maintenance of the power equipment, and digital transformation of the monitoring technology of the power equipment is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of macro-micro hybrid simulation, in particular to a macro-micro hybrid simulation method for analyzing insulation defects of power equipment. BACKGROUND

[0002] Partial discharge (PD) is the main cause of insulation failure of electrical equipment. Accurate positioning of partial discharge can provide important information for state monitoring and maintenance of electrical equipment. When the discharge process occurs, ultrasonic, visible light, high-frequency current and electromagnetic waves and other physical parameters are generated. Detection of these physical parameters is an important basis for determining whether discharge occurs. Among them, electromagnetic wave signals are widely used in discharge detection due to their long transmission distance and high anti-interference characteristics.

[0003] At present, the common partial discharge signal propagation analysis often uses a Gaussian source as the excitation source of electromagnetic simulation, and some researches also gradually use empirical waveforms obtained through experiments to replace the excitation source. Using a Gaussian pulse is conducive to observing the response characteristics of the simulation model to wideband signals, but the parameters of the Gaussian pulse are often preset, so they cannot better fit the actual discharge signal. For example, different insulation media and different gas environments often cause changes in various micro-reaction rates during gas discharge, which in turn affect the current source results of partial discharge. This influence is difficult to reflect from the parameters such as pulse width of the Gaussian function. In addition, the amplitude of the Gaussian source is often taken as a unit value, which makes it difficult to analyze the discharge amount and other factors from the observed signals. Therefore, the electromagnetic process simulation using the Gaussian source is often applied to sensor characteristic analysis, selection of signal monitoring position on the equipment, and analysis of signal differences at different positions on the equipment, etc. There are still deficiencies in applying to internal defect state analysis. Using empirical discharge waveforms in laboratory environment can better analyze specific defect models, but still cannot adapt to changing discharge conditions. Therefore, establishing a more intuitive connection between micro-processes and observed signals is the focus of this section, and is also an important model basis for the digital transformation of power equipment monitoring. SUMMARY

[0004] The purpose of the present application is to provide a macro-micro hybrid simulation method for analyzing insulation defects of power equipment, which realizes accurate analysis and monitoring of insulation defects of power equipment, not only provides important support for state monitoring and maintenance of power equipment, but also promotes the digital transformation of power equipment monitoring technology.

[0005] The present application provides a macro-micro hybrid simulation method for analyzing insulation defects of power equipment, comprising the following steps:

[0006] S1, a gas defect discharge process simulation model is established, the micro-particle state change and electric field distribution in the gas discharge process are described through a particle transport equation and a Poisson equation, the particle transport equation is:

[0007]

[0008] wherein, N e , N p , N n respectively represent the number of free electrons, positive ions and negative ions; α is a collision ionization coefficient; η is an adsorption coefficient; β represents a recombination coefficient; D represents a diffusion coefficient; v e , v p , v n respectively represent the migration speed of free electrons, positive ions and negative ions; t represents time;

[0009] The Poisson equation is:

[0010]

[0011] wherein, is the spatial potential distribution; ε is the relative dielectric constant of the medium; q is the unit charge amount;

[0012] S2, an electromagnetic propagation process simulation model is established, the electromagnetic signal propagation process generated by the gas discharge is described through a Maxwell equation set, the Maxwell equation set is:

[0013]

[0014] wherein, B and E respectively represent the magnetic field and the electric field; J represents the excitation current;

[0015] S3, the joint simulation model, the equivalent current of the gas discharge is taken as the excitation source, the electromagnetic signal propagation in the power equipment is simulated through a finite time domain difference method (FDTD), and the signal value of the receiver is recorded.

[0016] Preferably, in step S1, the charge accumulation at the interface of the dielectric plate is considered in the gas defect discharge process simulation model, only the accumulation amount of free electrons is calculated, and the relationship between the surface charge density and the volume charge density is represented through the following formula:

[0017] σΔS=ρΔV;

[0018] Wherein, σ is the surface charge density; ρ is the volume charge density.

[0019] Preferably, in step S2, the different relative dielectric constants of the air medium and the insulating material medium are considered in the electromagnetic propagation process simulation model, and the potential distribution satisfies the continuity equation:

[0020]

[0021] Wherein, the subscripts 1 and 2 represent air medium and insulating material medium respectively; n represents the normal component perpendicular to the interface.

[0022] Preferably, in step S3, the calculation formula of the equivalent current in the joint simulation model is:

[0023]

[0024] Wherein, I represents the current; Ua represents the applied voltage.

[0025] Preferably, the simulation model is applicable to a straight cylinder type gas switch device, the geometric structure of which includes an insulator, an aluminum shell and an internal copper wire, and the insulator material is epoxy resin.

[0026] Therefore, the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment is adopted, so that the accurate analysis and monitoring of the insulation defects of the power equipment are realized, important support is provided for the state monitoring and maintenance of the power equipment, and the digital transformation of the power equipment monitoring technology is promoted.

[0027] The technical solutions of the present application will be further described in detail below with the aid of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 It is a schematic diagram of various reactions between charged particles in the air gap discharge process of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0029] Figure 2 It is a GIS simulation structure schematic diagram of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0030] Figure 3 It is a full-time sequence simulation flowchart of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0031] Figure 4 It is a comparison diagram of experimental measurement results and simulation results of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0032] Figure 5 (a) in the figure is a schematic diagram of signal intensity distribution inside the device at different times of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0033] Figure 5 (b) in the figure is a schematic diagram of signal intensity distribution inside the device at t=20ms of the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment of the present application;

[0034] Figure 5 Figure (c) in the above is a signal intensity distribution diagram of the device interior at t=30 ms of the macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to the present application;

[0035] Figure 5 Figure (d) in the above is a signal intensity distribution diagram of the device interior at t=50 ms of the macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to the present application;

[0036] Figure 5 Figure (e) in the above is a signal intensity distribution diagram of the device interior at t=80 ms of the macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to the present application;

[0037] Figure 6 Figure (f) in the above is a time-domain signal waveform diagram at the sensor of the macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to the present application. DETAILED DESCRIPTION

[0038] The technical solutions of the present application are further described below by means of the accompanying drawings and examples.

[0039] Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the usual meanings understood by those with ordinary skills in the art to which the present application pertains.

[0040] The terms "first", "second", and similar terms used in the present application do not indicate any order, number, or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the elements or objects appearing before the terms encompass the elements or objects listed after the terms and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like only indicate relative positional relationships, which can change when the absolute positions of the described objects change.

[0041] Example 1

[0042] As shown in Figures 1-6 , the macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to the present application comprises the following steps:

[0043] The gas discharge causes the change of the charged particle state, including collision ionization, recombination, adsorption, diffusion and particle migration, and other chain reactions affecting the particle state distribution. However, unlike the gas gap discharge between the metal plates, the gas gap discharge occurs in the insulating medium, and the biggest difference between the insulating medium and the metal electrode is that the insulating medium almost does not contain free-moving charges, so the breakdown of the gas gap between the electrodes often leads to the short circuit between the electrodes, and the presence of the insulating medium prevents the further development of the breakdown streamer, so the breakdown between the gas gap often automatically extinguishes. Figure 1 The various reactions between the charged particles in the gas gap discharge process are shown. In the gas gap discharge process, the electric field between the gas gap needs to consider the joint influence of the space charge distribution of the streamer and the accumulated charge on the medium surface, because the charged particles will accumulate at the insulating medium. When the space charge at the medium plate further accumulates, the space electric field generated by the space charge further strengthens, leading to the weakening of the applied field strength between the gas gap, and the discharge process gradually weakens until it is eventually extinguished.

[0044] S1, a gas defect discharge process simulation model is established, the micro-particle state change and the electric field distribution in the gas discharge process are described by a particle transport equation and a Poisson equation, and the particle transport equation is:

[0045]

[0046] wherein N e , N p , N n respectively represent the number of free electrons, positive ions and negative ions; α is the collision ionization coefficient; η is the adsorption coefficient; β represents the recombination coefficient; D represents the diffusion coefficient; v e , v p , v n respectively represent the migration speed of free electrons, positive ions and negative ions; t represents time;

[0047] The Poisson equation is:

[0048]

[0049] wherein, is the space potential distribution, ε is the relative dielectric constant of the medium, and q is the unit charge amount;

[0050] In step S1, the charge accumulation at the interface of the medium plate is considered in the gas defect discharge process simulation model, only the accumulation amount of free electrons is calculated, and the relationship between the surface charge density and the volume charge density is represented by the following formula:

[0051] σΔS=ρΔV;

[0052] wherein σ is the surface charge density; and ρ is the volume charge density.

[0053] S2, an electromagnetic propagation process simulation model is established, and electromagnetic signal propagation process generated by gas discharge is described by Maxwell equations, and the Maxwell equations are as follows:

[0054]

[0055] The Maxwell equations are usually solved by using a finite time domain difference method algorithm, and the core is to use a central difference method to solve the spinor equation set in the Maxwell equation set to obtain an approximate solution in the space distribution, and then calculate the change process of the electromagnetic field with time. In the formula, B and E represent the magnetic field and the electric field respectively, and J represents the excitation current. In the model, the above discharge simulation model is used for solving.

[0056] In step S2, the electromagnetic propagation process simulation model considers the different relative dielectric constants of air medium and insulating material medium, and the potential distribution satisfies the continuity equation:

[0057]

[0058] Wherein, the subscripts 1 and 2 represent the air medium and the insulating material medium respectively, and n represents the normal component perpendicular to the interface.

[0059] S3, joint simulation model, the equivalent current of gas discharge is used as an excitation source, the propagation of electromagnetic signal in the power equipment is simulated by finite time domain difference method (FDTD), and the signal value of the receiver is recorded.

[0060] In step S3, the calculation formula of the equivalent current in the joint simulation model is:

[0061]

[0062] Wherein, I represents the current, and Ua represents the applied voltage.

[0063] The simulation model is suitable for straight cylinder type gas switch equipment, and the geometric structure includes insulator, aluminum shell and internal copper wire, and the insulator material is epoxy resin.

[0064] The geometric structure adopts a common straight cylinder type gas switch equipment (GIS), which is composed of an insulator, an aluminum shell and an internal copper wire. The insulator material is epoxy resin, and the relative dielectric constant is the same as that of the dielectric plate in the gas gap discharge. The dielectric plate is 1 cm thick. The aluminum shell has an outer diameter of 6 cm, an inner diameter of 5 cm and a wall thickness of 1 cm. The total length of the GIS is 50 cm, the length of the copper wire is 50 cm, and the radius is 1 cm.

[0065] The joint simulation model firstly solves the equivalent current of the air gap discharge, and then uses the time-domain waveform of the equivalent current as an excitation source to obtain the spatial electromagnetic field distribution and time-domain signal waveform. Figure 3 The simulation flowchart is shown, and the detailed simulation steps are as follows:

[0066] Discharge process simulation

[0067] The fluid model is used to solve the state of charged particles at each time step, and the steps are as follows:

[0068] Electric field distribution calculation; under the driving of the applied electric field, electrons move towards the anode, resulting in a large number of electrons gathering at the head of the anode side and leaving a large number of positive charges. This uneven distribution of space charge will cause distortion of the electric field. According to the Poisson equation, the electric field distribution is calculated according to the free charge density in space. Coefficient calculation; most of the coefficients used in the fluid model are affected by the size of the electric field, and the model parameters of different discharge regions are updated according to the spatial distribution of the electric field in step 1. In addition, the migration speed of charged particles is also positively correlated with the electric field strength, and the particle migration speed is calculated according to the different particle mobilities. State update of charged particles; based on the coefficients obtained in step 2, the change term of charged particles is calculated, including collision ionization term, recombination term, diffusion term, photoionization term and convection term caused by particle migration, etc. Based on the particle balance equation, the state distribution of electrons, positive ions and negative ions after a variety of micro-reactions is updated.

[0069] Equivalent current calculation; based on the state of charged particles obtained in step a, the discharge current is calculated as follows: discharge current calculation. The discharge current is determined by the applied electric field, drift velocity and the number of corresponding electrons, positive ions and negative ions. For each time step, the equivalent current of the air gap is calculated. Boundary conditions. When the charges reach the surface of the dielectric material, they cannot be absorbed by the dielectric, so the surface charge density of the dielectric will increase. Calculate the boundary conditions of the electric field distribution affected by the surface charge to obtain the potential at both ends of the air gap affected by the charge accumulation. After step b, the change of the state of charged particles also causes the change of the distribution of space charge, which further causes the change of the electric field distribution. Therefore, return the simulation process to step a, continue to calculate the state change in the new time step, until the simulation calculation is completed.

[0070] Electromagnetic wave radiation simulation; According to the simulation results of steps a and b, record the equivalent current value of each time step in the simulation time. Then, the simulation obtained pulse current is used as the excitation source of electromagnetic wave. The FDTD method is used to simulate the propagation of the excited electromagnetic wave, as follows. Excitation source setting: adjust the current step of the fluid model to the same as the time step of the FDTD method using the interpolation algorithm, and the simulation time is 20 ns. According to the discharge current waveform recorded by the fluid model in the simulation time, the discharge current value of each time step is set at the excitation source position during the FDTD simulation process. Electromagnetic wave propagation: the current value in the excitation source causes the change of electric field and magnetic field in the simulation area. The propagation of electromagnetic field fluctuation is calculated by difference equation, and the signal value of the receiver is recorded in the simulation time. As can be seen from the response to the electric field, the equivalent sensor here is similar to a monopole antenna to receive EM waves. In essence, this process is a method of equivalent antenna to pure resistance circuit in order to evaluate the potential from the electric field, thus avoiding the requirement of bandwidth and impedance matching when using monopole antenna.

[0071] Simulation results:

[0072] Figure 4 The simulation results of the air gap discharge current are given, and compared with the measured values. As can be seen from the figure, the simulation values can well reflect the characteristics of the discharge current at different time nodes, including: the initial stage of discharge, the exponential increasing stage of current, the slowing down stage of current growth under the influence of accumulated charge, the stage of current decline caused by further increase of accumulated charge and the final stage of discharge extinction. Considering the great randomness of gas discharge process, such as initial discharge voltage and seed electron position, etc., therefore the related difference between simulation value and measured value is within the allowable error range.

[0073] The current source waveform obtained in the last section is used as the excitation source of the gas discharge signal, and the propagation process in the electrical equipment is observed as shown in Figure 5 The signal intensity distribution at different times changes with the change of current, and its propagation process is also affected by the geometry of the equipment.

[0074] Figure 6 The electromagnetic signal waveforms measured by different sensors are shown. For the electromagnetic propagation process in open space, it satisfies the Maxwell equations. As can be seen from the equation set, when the electric field does not change at the initial time, a time-varying magnetic field is excited in space under the influence of pulse current, the magnetic field curl is proportional to the current value, the time-varying magnetic field excites time-varying electric field, and then the propagation of electromagnetic field is completed. And the sensor completes signal monitoring by inductive antenna potential difference, so its electric field size is positively related to space. Therefore, by comparing the waveform difference between the monitoring signal and the excitation source signal, the differential relationship between the monitoring signal and the excitation source can be seen.

[0075] Therefore, the application adopts the macro-micro hybrid simulation method for analyzing the insulation defects of the power equipment to realize accurate analysis and monitoring of the insulation defects of the power equipment, thereby providing important support for state monitoring and maintenance of the power equipment and promoting digital transformation of the power equipment monitoring technology.

[0076] The above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can still be modified or replaced equivalently, and these modifications or equivalent replacements should not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A macro-micro hybrid simulation method for power equipment insulation defect analysis, characterized in that, The method comprises the following steps: S1, a gas defect discharge process simulation model is established, the micro-particle state change and electric field distribution in the gas discharge process are described through a particle transport equation and a Poisson equation, the particle transport equation is: where N e , N p , N n represent the number of free electrons, positive ions and negative ions, respectively; a is the collision ionization coefficient; η is the adsorption coefficient; β represents the recombination coefficient; D represents the diffusion coefficient; v e , v p , v n represent the migration speed of free electrons, positive ions and negative ions, respectively; t represents time; and the Poisson equation is: wherein is the spatial potential distribution; ε is the relative permittivity of the medium; q is the unit charge quantity; S2, an electromagnetic propagation process simulation model is established, the electromagnetic signal propagation process generated by the gas discharge is described through a Maxwell equation set, the Maxwell equation set is: Wherein, B and E represent magnetic field and electric field respectively; J represents excitation current; S3, a joint simulation model is established, the equivalent current of the gas discharge is taken as an excitation source, the electromagnetic signal propagation in the power equipment is simulated through a finite time domain difference method (FDTD), and the signal value of a receiver is recorded.

2. The macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to claim 1, characterized in that, In step S1, the charge accumulation at the interface of the dielectric plate is considered in the gas defect discharge process simulation model, only the accumulation of free electrons is calculated, and the relationship between the surface charge density and the volume charge density is represented through the following formula: σΔS = ρΔV; Wherein, σ is the surface charge density; ρ is the volume charge density.

3. The macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to claim 1, characterized in that, In step S2, the different relative dielectric constants of the air medium and the insulating material medium are considered in the electromagnetic propagation process simulation model, the potential distribution satisfies a continuity equation: Wherein, the subscripts 1 and 2 represent the air medium and the insulating material medium respectively; n represents the normal component perpendicular to the interface.

4. The macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to claim 1, characterized in that, In step S3, the calculation formula of the equivalent current in the joint simulation model is: Wherein, I represents the current; Ua represents the applied voltage.

5. The macro-micro hybrid simulation method for analyzing insulation defects of power equipment according to claim 1, characterized in that, The simulation model is suitable for a straight cylinder type gas switch equipment, the geometric structure of which comprises an insulator, an aluminum shell and a copper wire inside, and the insulator material is epoxy resin.

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