CN116882341A - Simulation method and system for surface defect partial discharge electromagnetic signals - Google Patents
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Abstract
The invention relates to a simulation method and a system of an electromagnetic signal of partial discharge of a surface defect, wherein the method comprises the following steps: constructing a creeping discharge fluid-chemical simulation model; simulating the microscopic process of partial discharge of the surface defects by using a fluid-chemical simulation model to obtain the motion condition of charged particles in the surface discharge process, and calculating the surface discharge current value; constructing an electromagnetic signal simulation model based on a finite integration method; and using the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the local discharge of the creeping defect by using an electromagnetic signal simulation model. Compared with the prior art, the invention has the advantages of establishing the connection between the macroscopic electromagnetic signal and the microscopic defect, more accurate partial discharge simulation signal and the like.
Description
Technical Field
The invention relates to the field of partial discharge signal simulation, in particular to a simulation method and a simulation system of an edge defect partial discharge electromagnetic signal.
Background
The gas insulated switchgear (Gas Insulated Switchgear, GIS) is an important device that is widely used in electrical power systems. As the demand for safe and reliable operation of the power grid increases, GIS devices are increasingly being used in power grids of various voltage classes. Epoxy resin is an important insulating material in GIS equipment, and basin-type insulators, post insulators and the like which are formed by pouring the epoxy resin are important insulating devices of the GIS equipment. The surface of the gas-solid interface is the part with the weakest insulating strength of the epoxy resin insulating part, and the creeping discharge of the gas-solid interface occurs more times in the GIS, thereby seriously damaging the key fault of the equipment safety. Partial discharge detection is a current general method for detecting insulation defects, defect positioning and state evaluation can be realized by observing partial discharge signals, and maintenance personnel can take further measures in time.
Partial discharge signal simulation is an effective means for researching partial discharge, and partial discharge signals are important basis for evaluating the insulation state of equipment in field detection. The creeping discharge microscopic process involves a complex particle reaction process, and the influence of the insulating medium on the discharge process is also not negligible. Starting from the creeping discharge microscopic process, a simulation connection between the partial discharge microscopic process and the partial discharge signal is established, so that not only can a more accurate discharge signal be obtained than in the current research, but also the influence of the change of the discharge condition on the discharge signal can be further explored on the basis, and the more accurate assessment of the insulation state of the equipment is realized.
However, in the current discharge signal simulation, most of the discharge microscopic processes are ignored, and gaussian pulses are directly used as signal excitation sources. The gaussian pulse is quite different from the actual discharge current, which results in that the signal simulation result cannot accurately reflect the defect state.
Disclosure of Invention
The invention aims to provide a simulation method and a simulation system for a partial discharge electromagnetic signal of a surface defect, which can establish the connection between a macroscopic electromagnetic signal and a microscopic defect from a microscopic discharge mechanism to obtain a more accurate partial discharge simulation signal, and can obtain the influence of different discharge conditions on the macroscopic electromagnetic signal by adjusting microscopic simulation parameters.
The aim of the invention can be achieved by the following technical scheme:
a simulation method of an electromagnetic signal of partial discharge of a surface defect comprises the following steps:
step 1) constructing a creeping discharge fluid-chemical simulation model;
step 2) simulating the microscopic process of the partial discharge of the surface defects by using a fluid-chemical simulation model to obtain the motion condition of charged particles in the surface discharge process, and calculating the surface discharge current value;
step 3) constructing an electromagnetic signal simulation model based on a finite integration method;
and 4) taking the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the partial discharge of the creeping defect by using an electromagnetic signal simulation model.
The fluid-chemical simulation model is formed by coupling four equations, namely an electron number density transport equation, an electron energy density transport equation, a heavy particle multicomponent diffusion equation and an electric field poisson equation, which describe the gas discharge process under the action of voltage in the creeping discharge process.
The electron number density transport equation is:
wherein n is e Is electron number density, mu e E is the electric field strength, D e Is the electron diffusion coefficient; the right side of the equation is an electron number density source term, x j To the mole fraction of the substances participating in reaction j, k j For the rate coefficient of reaction j, N n For the neutral particle number density in the reaction domain,an operator is calculated for the gradient.
The electron energy density transport equation is:
wherein n is ε Is electron energy density, mu ε And D ε Respectively representing electron energy mobility and diffusion coefficient; to the right of the equation is the energy term, Δε, that electrons lose or gain in inelastic collisions j Representing the energy loss of reaction j.
The heavy particle multicomponent diffusion equation is:
wherein ρ is the density of the mixture, ω k The mass fraction of the substance k, u is the total average fluid velocity, j k Is the diffusion flux vector of substance k, R k Is the concentration change rate expression of substance k; the heavy particle multicomponent diffusion equation describes the course of motion of non-electronic particles of larger mass during discharge.
The electric field poisson equation is:
wherein ε 0 Represents the vacuum dielectric constant, ε r Representing the relative permittivity of the medium; ρ V The space charge density in the reaction domain is represented and calculated from the density of the positive and negative particles.
The calculation method of the creeping discharge current value comprises the following steps:
wherein V is 0 For simulating the voltage value applied by the medium-high voltage electrode, e is the meta-charge, n + 、n - Respectively represent the number density, mu, of each positive and negative ion in the reaction domain + 、μ - Respectively represent positive and negative ion mobility, D + 、D - Respectively represent positive and negative ion mobility, n e Is electron number density, mu e E is the electric field strength, E is the electron mobility L Representing the laplace electric field in the reaction domain, V representing the volume of the simulated computation region,an operator is calculated for the gradient.
When an electromagnetic signal simulation model is utilized to simulate an electromagnetic signal generated by partial discharge of a surface defect, a calculation area is firstly divided into grid units with proper precision for space dispersion, wherein the grid units comprise two sets of grids which are mutually orthogonal and nested and are respectively expressed as a base grid G and a dual grid GThe electric field and the magnetic field are respectively in the basic grid G and the dual grid +.>The discretization calculation is performed in the following formula:
wherein C is equal toDiscrete rotation operators in the base grid and the dual grid respectively, S and +.>Discrete divergence operators in the base grid and the dual grid, e 1 Is the voltage of one side of the base grid, h 1 To the magnetic pressure of a certain side of the dual grid, j 2 B for current density in dual grid 2 For specifying the magnetic flux on the surface surrounded by the base grid, d 2 For the electric flux on the surface surrounded by the specified dual grid, q is the charge in the dual grid.
When an electromagnetic signal simulation model is utilized to simulate an electromagnetic signal generated by partial discharge of a surface defect, a frog-leaping method is adopted to perform time dispersion on the basis of space dispersion, voltage e and magnetic flux b are alternately sampled at intervals of half time steps, so that calculation of time integration is stable, and the selection of time step delta t meets the following conditions:
where c is the speed of light in vacuum, and Δx, Δy, and Δz are the grid lengths in the x, y, and z directions, respectively.
A simulation system for partial discharge electromagnetic signals along a surface defect, for implementing the method as described above, comprising:
the surface discharge fluid-chemical simulation model construction and simulation module simulates a surface defect partial discharge microscopic process through the fluid-chemical simulation model to obtain a charged particle motion condition in the surface discharge process, and calculates a surface discharge current value;
the electromagnetic signal simulation model construction and simulation module is used for constructing an electromagnetic signal simulation model based on a limited integration method, taking the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the local discharge of the creeping defect by using the electromagnetic signal simulation model.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention provides a simulation method of a surface defect partial discharge electromagnetic signal, which establishes the connection between a macroscopic electromagnetic signal and a microscopic defect and can obtain a more accurate partial discharge simulation signal based on a microscopic discharge simulation result.
(2) Because the artificial intelligence technology such as deep learning is also widely applied to the fields of partial discharge data analysis and the like, the artificial intelligence technology such as deep learning generally needs a large number of partial discharge data samples, and the defect or fault state samples under the experimental and field operation conditions are relatively lacking.
(3) Because the digital twin technology is greatly focused in the field of monitoring the state of the power equipment, a state-based simulation technology is needed for constructing the digital twin model for the state of the equipment, the partial discharge signal simulation method can simulate the influence of different defect states on the discharge signal by adjusting microscopic simulation parameters, and can provide a simulation method foundation for the digital twin technology for the state of the equipment.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a fluid-chemical geometric model in one embodiment.
Fig. 3 is a geometric model of a discharge signal simulation in one embodiment.
FIG. 4 is a schematic diagram of a spatial discretization method based on a finite integration method in one embodiment.
FIG. 5 is a graph of time domain simulation results of y-direction electric field signals at the position of a signal probe in one embodiment.
FIG. 6 is a time domain simulation waveform of the z-direction electric field signal of the signal probe position at different voltages in one embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
The embodiment provides a simulation method of an electromagnetic signal of partial discharge of an edge defect, as shown in fig. 1, comprising the following steps:
step 1) constructing a creeping discharge fluid-chemical simulation model.
The fluid-chemical simulation model takes into account specific particle reaction conditions in the gas environment and is well descriptive of the interaction between the insulating medium and the discharge. Compared with the traditional streamer discharge fluid model, microscopic simulation based on the fluid-chemical simulation model can not only obtain the change condition of specific substances, but also can perfectly represent the processes of secondary electron emission, surface charge accumulation and the like on the surface of an insulating medium.
The fluid-chemical simulation model is formed by coupling four equations, namely an electron number density transport equation, an electron energy density transport equation, a heavy particle multicomponent diffusion equation and an electric field poisson equation, which describe the gas discharge process under the action of voltage in the creeping discharge process.
The electron number density transport equation is:
wherein n is e Is electron number density, mu e E is the electric field strength, D e Is the electron diffusion coefficient; the right side of the equation is an electron number density source term, x j To the mole fraction of the substances participating in reaction j, k j For the rate coefficient of reaction j, N n For the neutral particle number density in the reaction domain,an operator is calculated for the gradient.
The electron energy density transport equation is:
wherein n is ε Is electron energy density, mu ε And D ε Respectively representing electron energy mobility and diffusion coefficient; to the right of the equation is the energy term, Δε, that electrons lose or gain in inelastic collisions j Representing the energy loss of reaction j.
The heavy particle multicomponent diffusion equation is:
wherein ρ is the density of the mixture, ω k The mass fraction of the substance k, u is the total average fluid velocity, j k Is the diffusion flux vector of substance k, R k Is the concentration change rate expression of substance k; the heavy particle multicomponent diffusion equation describes the course of motion of non-electronic particles of larger mass during discharge.
The electric field poisson equation is:
wherein ε 0 Represents the vacuum dielectric constant, ε r Representing the relative permittivity of the medium; ρ V Representing the space charge density in the reaction domain, which can be calculated from the density of the positive and negative particles.
In the fluid-chemical simulation model, it is necessary to define the particle reaction existing in the gas environment, and in this embodiment, the discharge signal simulation in the SF6 gas environment is taken as an example for explanation. The primary particles present in SF6 gas react as follows:
R1：e+SF 6 ＝>2e+SF 6 +
R2：e+SF 6 ＝>SF 6 -
R3：e+SF 6 ＝>SF 5 - +F
R4：e+SF 6 ＝>SF 4 - +F 2
R5：e+SF 6 ＝>e+SF 6
R6-R9：e+SF 6 ＝>e+SF 6 *
R10-R12：M - +SF 6 + ＝>SF 6 +M
wherein R1 is ionization reaction, R2-R4 are three electron attachment reactions considered in simulation in the embodiment, R5 is elastic collision of electrons and neutral molecules, R6-R9 are excitation reaction of neutral molecules, and R10-R12 represent positive and negative ion recombination reaction in a reaction domain, wherein M - Representing three anions generated by the reactions R2-R4. The reaction rate of reactions R1-R9 is controlled by a rate coefficient, which is based on raw cross-sectional data and solved by the Boltzmann equation using two expansion solutions, and the result is expressed as a function form related to the electron average energy.
In this embodiment, a two-dimensional axisymmetric model of the insulating air gap defect is built using the Comsol software, as shown in fig. 2. Wherein, the length of cylinder sets up to 1cm in the emulation, and the radius sets up to 0.1cm, has set up oval protruding in high-voltage electrode department to can produce the creeping discharge in electric field strength distortion department, oval bellied long minor axis's ratio is 5:1, the protrusion distance is 0.05cm, and the total length of the distance of the creeping discharge is 0.95cm.
Step 2) simulating the partial discharge microscopic process of the surface defect by using a fluid-chemical simulation model to obtain the movement condition of charged particles in the surface discharge process, and calculating the surface discharge current value based on a formula (5):
wherein V is 0 Is imitated byThe voltage value applied by the true middle-high voltage electrode, e is the meta-charge, n + 、n - Respectively represent the number density, mu, of each positive and negative ion in the reaction domain + 、μ - Respectively represent positive and negative ion mobility, D + 、D - Respectively represent positive and negative ion mobility, E L Representing the laplace electric field in the reaction domain, and V represents the volume of the simulated computation region.
And 3) constructing an electromagnetic signal simulation model based on a limited integration method.
And 4) taking the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the partial discharge of the creeping defect by using an electromagnetic signal simulation model.
Fig. 3 shows a schematic diagram of the position between the electromagnetic signal excitation source and the detection probe constructed in the electromagnetic signal simulation in this embodiment, and the probe coordinates are (0.5,0.5,0.5).
In the embodiment, the microscopic discharge simulation current result obtained in the step 2) is used as a signal excitation source, and the discharge electromagnetic signal is obtained based on the finite integration method simulation. The finite integration method is a method for discretizing and solving a maxwell Wei Jifen equation set. When using this method for simulation, it is first necessary to divide the calculation region into grid cells with appropriate accuracy and perform spatial dispersion. Wherein the grid unit comprises two sets of grids which are mutually orthogonal and nested and respectively expressed as a base grid G and a dual gridAs shown in fig. 4. Further, the electric field and the magnetic field are respectively in the basic grid G and the dual grid->The discretization calculation is performed in the following formula:
wherein C is equal toDiscrete rotation operators in the base grid and the dual grid respectively, S and +.>Discrete divergence operators in the base grid and the dual grid, e 1 Is the voltage of one side of the base grid, h 1 To the magnetic pressure of a certain side of the dual grid, j 2 B for current density in dual grid 2 For specifying the magnetic flux on the surface surrounded by the base grid, d 2 For the electric flux on the surface surrounded by the specified dual grid, q is the charge in the dual grid.
On a spatially discrete basis, further time-discrete is required for computation. In this embodiment, the frog-leaping method is adopted to perform time dispersion, voltage e and magnetic flux b are alternately sampled every half time step, and in order to stabilize the calculation of time integral, the selection of time step Δt needs to satisfy the following conditions:
where c is the speed of light in vacuum, and Δx, Δy, and Δz are the grid lengths in the x, y, and z directions, respectively.
Based on the above steps, the waveform of the electromagnetic signal of the partial discharge of the surface defect can be obtained. FIG. 5 shows a simulated signal result, i.e., a waveform of the partial discharge electric field signal along a surface defect, generated by the partial discharge signal simulation method of the present invention.
The method not only can obtain a signal simulation result which is more consistent with the actual situation, but also can reflect the change of the microscopic discharge condition on the simulation result of the signal. Fig. 6 shows simulated time domain waveforms of z-direction electric field signals of the signal probe positions under three groups of different voltages. The electric field signal of discharge excitation appears earlier with increasing voltage and has a larger peak.
The embodiment obtains the surface defect partial discharge electromagnetic signal based on the simulation result of the surface discharge microscopic process. In the simulation model, the change condition of the discharge electromagnetic signal can be obtained by adjusting the parameter setting of the microscopic simulation model, so that the influence rule of different discharge conditions on the discharge signal is obtained.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. The simulation method of the surface defect partial discharge electromagnetic signal is characterized by comprising the following steps of:
step 1) constructing a creeping discharge fluid-chemical simulation model;
step 2) simulating the microscopic process of the partial discharge of the surface defects by using a fluid-chemical simulation model to obtain the motion condition of charged particles in the surface discharge process, and calculating the surface discharge current value;
step 3) constructing an electromagnetic signal simulation model based on a finite integration method;
and 4) taking the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the partial discharge of the creeping defect by using an electromagnetic signal simulation model.
2. The simulation method of the partial discharge electromagnetic signal of the surface defect according to claim 1, wherein the fluid-chemical simulation model is formed by coupling four equations of an electron number density transport equation, an electron energy density transport equation, a heavy particle multicomponent diffusion equation and an electric field poisson equation, wherein the four equations describe the gas discharge process under the action of voltage in the surface discharge process.
3. The simulation method of the partial discharge electromagnetic signal of the surface defect according to claim 2, wherein the electron number density transport equation is:
wherein n is e Is electron number density, mu e E is the electric field strength, D e Is the electron diffusion coefficient; the right side of the equation is an electron number density source term, x j To the mole fraction of the substances participating in reaction j, k j For the rate coefficient of reaction j, N n For the neutral particle number density in the reaction domain,an operator is calculated for the gradient.
4. A simulation method of an electromagnetic signal of partial discharge of an edge defect according to claim 3, wherein the electron energy density transport equation is:
wherein n is ε Is electron energy density, mu ε And D ε Respectively representing electron energy mobility and diffusion coefficient; to the right of the equation is the energy term, Δε, that electrons lose or gain in inelastic collisions j Representing the energy loss of reaction j.
5. The method for simulating an electromagnetic signal for partial discharge of an edge defect of claim 4, wherein the heavy particle multicomponent diffusion equation is:
wherein ρ is the density of the mixture, ω k The mass fraction of the substance k, u is the total average fluid velocity, j k Is the diffusion flux vector of substance k, R k Is the concentration change rate expression of substance k; the heavy particle multicomponent diffusion equation describes the course of motion of non-electronic particles of larger mass during discharge.
6. The simulation method of the partial discharge electromagnetic signal of the surface defect according to claim 5, wherein the poisson equation of the electric field is:
wherein ε 0 Represents the vacuum dielectric constant, ε r Representing the relative permittivity of the medium; ρ V The space charge density in the reaction domain is represented and calculated from the density of the positive and negative particles.
7. The simulation method of partial discharge electromagnetic signals of a surface defect according to claim 1, wherein the calculation method of the surface discharge current value is as follows:
wherein V is 0 For simulating the voltage value applied by the medium-high voltage electrode, e is the meta-charge, n + 、n - Respectively represent the number density, mu, of each positive and negative ion in the reaction domain + 、μ - Respectively represent positive and negative ion mobility, D + 、D - Respectively represent positive and negative ion mobility, n e Is electron number density, mu e E is the electric field strength, E is the electron mobility L Representing the laplace electric field in the reaction domain, V representing the volume of the simulated computation region,an operator is calculated for the gradient.
8. The simulation method of partial discharge electromagnetic signals of an edge surface defect according to claim 1, wherein when simulating the electromagnetic signals generated by partial discharge of the edge surface defect by using an electromagnetic signal simulation model, a calculation area is firstly divided into grid units with proper precision for space dispersion, wherein the grid units comprise two sets of mutually orthogonal and nested grids which are respectively expressed as a base grid G and a dual grid GThe electric field and the magnetic field are respectively in the basic grid G and the dual grid +.>The discretization calculation is performed in the following formula:
wherein C is equal toDiscrete rotation operators in the base grid and the dual grid respectively, S and +.>Discrete divergence operators in the base grid and the dual grid, e 1 Is the voltage of one side of the base grid, h 1 To the magnetic pressure of a certain side of the dual grid, j 2 B for current density in dual grid 2 For specifying the magnetic flux on the surface surrounded by the base grid, d 2 For the electric flux on the surface surrounded by the specified dual grid, q is the charge in the dual grid.
9. The simulation method of the electromagnetic signal of the partial discharge of the surface defect according to claim 8, wherein when the electromagnetic signal simulation model is used for simulating the electromagnetic signal generated by the partial discharge of the surface defect, the frog-leaping method is adopted to perform time dispersion on the basis of space dispersion, the voltage e and the magnetic flux b are alternately sampled every half time step, and the time step deltat is selected to satisfy the following conditions for stabilizing the calculation of the time integral:
where c is the speed of light in vacuum, and Δx, Δy, and Δz are the grid lengths in the x, y, and z directions, respectively.
10. A simulation system of an electromagnetic signal of a partial discharge of an edge defect, characterized in that it is adapted to implement a method according to any one of claims 1-9, comprising:
the surface discharge fluid-chemical simulation model construction and simulation module simulates a surface defect partial discharge microscopic process through the fluid-chemical simulation model to obtain a charged particle motion condition in the surface discharge process, and calculates a surface discharge current value;
the electromagnetic signal simulation model construction and simulation module is used for constructing an electromagnetic signal simulation model based on a limited integration method, taking the creeping discharge current value as an excitation signal, and simulating an electromagnetic signal generated by the local discharge of the creeping defect by using the electromagnetic signal simulation model.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104729595A (en) * | 2015-02-12 | 2015-06-24 | 西安交通大学 | Intra-tube phase-separated-type two-phase fluid electromagnetic flow meter measuring device and method |
| CN111445959A (en) * | 2020-01-16 | 2020-07-24 | 华中科技大学 | Method for constructing liquid metal battery simulation model |
| US20210048557A1 (en) * | 2019-08-15 | 2021-02-18 | Tsinghua University | Method and device for simulating discharge, and computer device |
| CN113095019A (en) * | 2021-04-02 | 2021-07-09 | 上海交通大学 | Partial discharge signal simulation method and system |
-
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- 2023-06-01 CN CN202310644717.9A patent/CN116882341B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104729595A (en) * | 2015-02-12 | 2015-06-24 | 西安交通大学 | Intra-tube phase-separated-type two-phase fluid electromagnetic flow meter measuring device and method |
| US20210048557A1 (en) * | 2019-08-15 | 2021-02-18 | Tsinghua University | Method and device for simulating discharge, and computer device |
| CN111445959A (en) * | 2020-01-16 | 2020-07-24 | 华中科技大学 | Method for constructing liquid metal battery simulation model |
| CN113095019A (en) * | 2021-04-02 | 2021-07-09 | 上海交通大学 | Partial discharge signal simulation method and system |
Non-Patent Citations (2)
| Title |
|---|
| WENXIA SIMA 等: "Plasma model of discharge along a dielectric surface in N2/O2 mixtures", 《PHYSICS OF PLASMAS》, vol. 23, no. 6, 31 December 2016 (2016-12-31), pages 063508 - 1 * |
| 董国静 等: "脉冲电应力下空气-聚酰亚胺绝缘沿面放电过程数值模拟", 《电工技术学报》, vol. 35, no. 9, 10 May 2020 (2020-05-10), pages 2006 - 2019 * |
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