CN114429069B - Modeling and fault early warning method and system for dry-type reactor - Google Patents
Modeling and fault early warning method and system for dry-type reactor Download PDFInfo
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Abstract
The invention provides a modeling and fault early warning method and a modeling and fault early warning system of a dry-type reactor, wherein the modeling and fault early warning method comprises the following steps: establishing an electromagnetic-circuit two-dimensional analysis model of the dry reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model; calculating the temperature field and flow field distribution of the reactor by taking the encapsulation loss as a heat source of a three-dimensional fluid-temperature field, and establishing a three-dimensional fluid-temperature field finite element model by adopting a cyclic iteration algorithm to perform multi-physical field model fusion; then, adopting a fusion algorithm of a PSO-SVM to reduce the order of the three-dimensional fluid-temperature field finite element model; and on the basis of the model establishment, various turn-to-turn short circuit fault simulation is carried out to realize fault identification and early warning discrimination. The modeling and fault early warning method improves early warning accuracy, and can be widely applied to the fields of design and early warning of dry-type reactor equipment.
Description
Technical Field
The invention relates to the field of a dry-type reactor modeling and fault early warning method based on a multi-physical field coupling technology, in particular to a dry-type reactor modeling and fault early warning method and a dry-type reactor modeling and fault early warning system.
Background
The dry reactor is used as main auxiliary equipment of a remote alternating current transmission system, plays roles in compensating capacitive current, maintaining system voltage level, improving line transmission capacity and the like in the system, and promotes the development of a power grid to a certain extent. The dry type air-core reactor has a plurality of excellent performances and is widely used at present, however, due to long-term operation, various problems are difficult to avoid in the dry type air-core reactor, fire burning can be caused more seriously, the reactor cannot work normally, and great threat is caused to the safety of a power grid. In addition, since the hot spot temperature of the reactor and the service life of the reactor are directly related, accurate calculation of the temperature field distribution of the dry reactor plays a vital role for designers and on-site operation and maintenance personnel.
At present, students at home and abroad have more researches on the temperature field of the dry-type reactor, and mainly comprise an average temperature rise calculation method, a convection heat transfer coefficient method and a fluid temperature field coupling method. The average temperature rise cannot reflect the temperature rise distribution conditions of different positions of the reactor, and most of the average temperature rise cannot be used for engineering inspection of the thermal performance of the reactor. The temperature change on the winding height of the reactor is also given by using a finite element method and giving different convection heat transfer coefficients to the surface of the reactor, but the method still depends on an empirical formula in the determination of the convection heat transfer coefficients.
In addition, the current research also has an example of establishing a three-dimensional reactor flow field-temperature field calculation model by utilizing a multi-field coupling finite element theory, but the model is not fused with a magnetic circuit model, is not simplified and reduced, and has high calculation complexity. In addition, researches on a dry-type reactor fault early warning system based on a multi-physical field coupling model are still rarely reported.
In view of this, the present invention has been made.
Disclosure of Invention
In view of the above, the invention discloses a modeling and fault early warning method for dry reactor equipment and a modeling and fault early warning system corresponding to the modeling and fault early warning method, wherein a two-dimensional electromagnetic-circuit analysis model of a dry reactor is established by adopting Maxwell, a three-dimensional fluid-temperature field finite element model is established for the dry reactor by adopting COMSOL finite element simulation software, multi-field fusion is carried out by adopting a joint simulation technology, and a temperature field model reduction processing method based on an improved particle swarm optimization support vector machine is introduced for solving the problem of model simplification. Finally, based on the model, a dry-type reactor turn-to-turn short circuit fault early warning method based on variable fitting is provided to realize early warning and alarm of turn-to-turn short circuit faults, the whole method improves early warning accuracy, and the method can be widely applied to the fields of design, early warning, fault diagnosis direction and the like of dry-type reactor equipment.
Specifically, the invention is realized by the following technical scheme:
In a first aspect, the invention discloses a modeling and fault early warning method of a dry-type reactor, which comprises the following steps:
Establishing an electromagnetic-circuit two-dimensional analysis model of the dry reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
Calculating the temperature field and flow field distribution of the reactor by taking the encapsulation loss as a heat source of a three-dimensional fluid-temperature field, and establishing a three-dimensional fluid-temperature field finite element model by adopting a cyclic iteration algorithm to perform multi-physical field model fusion;
then, adopting a fusion algorithm of a PSO-SVM to reduce the order of the three-dimensional fluid-temperature field finite element model;
And on the basis of the model establishment, various turn-to-turn short circuit fault simulation is carried out to realize fault identification and early warning discrimination.
In a second aspect, the present invention discloses a modeling and fault warning system for a dry reactor device, comprising:
establishing a two-dimensional analysis model module: the method comprises the steps of establishing an electromagnetic-circuit two-dimensional analysis model of the dry-type reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
Establishing a three-dimensional analysis model module: the encapsulation loss is used as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor, and a cyclic iteration algorithm is adopted to perform multi-physical field model fusion to establish a three-dimensional fluid-temperature field finite element model;
And the reduced order analysis module: the fusion algorithm is used for reducing the three-dimensional fluid-temperature field finite element model by adopting a PSO-SVM;
And a fault analysis module: the method is used for carrying out multiple turn-to-turn short circuit fault simulation based on the model establishment to realize fault identification and early warning discrimination.
In a third aspect, the present invention discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the modeling and fault warning method according to the first aspect.
In a fourth aspect, the present invention discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the modeling and fault warning method according to the first aspect when said program is executed.
In a word, the scheme of the invention adopts Maxwell to establish a magnetic circuit analysis model of the dry reactor, encapsulation loss is obtained through model calculation, and the encapsulation loss is used as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor; and then, a three-dimensional fluid-temperature field finite element model is established for the dry reactor by adopting COMSOL finite element simulation software, and multi-field coupling joint simulation is carried out.
The multi-physical field coupling model in the prior art can realize the characteristic description of dry reactor equipment, but has complex structure and slow calculation, and cannot meet the requirement of system instantaneity. In order to ensure the calculation precision of multiple physical fields, simultaneously meet the calculation speed, fully consider the influences of heat transfer, heat dissipation and the like of a reactor, and solve the problems of complex calculation flow and no real-time performance of a temperature field, a proxy model method for improving a particle swarm optimization support vector machine (PSO-SVM) is introduced to perform reduced-order processing on a temperature field model; finally, the modeling of the dry reactor based on the multi-physical field coupling is completed. Based on the model, a method for early warning the turn-to-turn short circuit fault of the dry-type reactor is provided. Firstly, setting faults with different degrees of turn-to-turn short circuits at different points of a simulation model; secondly, carrying out simulation analysis on the distribution of the space magnetic field along the central axial lead and the central transverse lead to obtain the relationship between turn-to-turn short circuit faults and the space magnetic field; and finally, respectively taking the magnetic field variation of the observation point and the fault degree as fitting variables to establish a fault degree early warning function and verifying. The result shows that the absolute error between the result of the simulation function and the actual value is very small, and the relative error is within the allowable error range, thereby proving the effectiveness of the method for early warning faults.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a general block diagram of a dry reactor modeling algorithm based on a multi-physical field coupling technique;
FIG. 2 is a flow chart of a multi-physical field fusion process;
FIG. 3 is a flow chart of an improved PSO optimized SVM;
fig. 4 is a basic structural diagram of a reactor;
FIG. 5 is a diagram of an electromagnetic-circuit analysis model coupling of a dry air reactor;
(a) A two-dimensional finite element model (b) an external constraint circuit;
FIG. 6 is a magnetic field distribution diagram of a reactor model simulation;
(a) An upper magnetic field profile (b) a lower magnetic field profile;
fig. 7 is a graph of the temperature field profile after steady state of the dry air reactor;
FIG. 8 is a flow velocity profile of an air flow field around a dry air reactor;
FIG. 9 is a graph of predicted outcome indicators for the improved PSO-SVM;
(a) Mean square error indicator graph (b) square error indicator graph;
FIG. 10 is a graph of the response surface of the improved PSO-SVM proxy model;
FIG. 11 shows a graph of magnetic field distribution along the ATC direction and fault level early warning variation;
(a)s＝0.28％(b)s＝16.78％(c)s＝33.29％；
FIG. 12 shows a graph of magnetic field distribution along LTC direction versus fault level warning;
(a)s＝0.28％(b)s＝16.78％(c)s＝33.29％；
FIG. 13 is a fitted curve of the fault extent function;
(a) A C point fitting curve (b) an E point fitting curve;
FIG. 14 is a schematic diagram of a prediction system according to an embodiment of the present invention;
fig. 15 is a schematic flow chart of a computer device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" depending on the context.
The invention discloses a modeling and fault early warning method of a dry reactor based on a multi-physical field coupling technology, which comprises the following steps:
Establishing an electromagnetic-circuit two-dimensional analysis model of the dry reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
taking the encapsulation loss as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor, and adopting a cyclic iteration algorithm to perform multi-physical field model fusion to establish a three-dimensional fluid-temperature field finite element model;
then, adopting a fusion algorithm of a PSO-SVM to reduce the order of the three-dimensional fluid-temperature field finite element model;
And on the basis of the model establishment, various turn-to-turn short circuit fault simulation is carried out to realize fault identification and early warning discrimination.
Fig. 1 is a general operation algorithm diagram of a modeling and fault early warning method according to an embodiment of the present invention, and is shown in fig. 1, and specifically includes the following steps:
A. modeling a two-dimensional electromagnetic-circuit model:
An electromagnetic-circuit two-dimensional analysis model of the dry reactor is established by using Maxwell, the model comprises a finite element magnetic field model and a circuit model, the dry reactor is generally symmetrical about a central axis, and therefore, according to the actual size of the dry reactor, a two-dimensional finite element model comprising air, an insulating medium and a coil can be established. And designing a dry reactor model by using finite element electromagnetic analysis software Maxwell. And determining a solving domain, namely determining and selecting 1/12 circumference of the two-dimensional model to represent according to the requirement of high calculation efficiency and combining the magnetic field distribution characteristics. Since triangular mesh division has the advantage of slight smoothness for the description of the two-dimensional model, this type of mesh is used when dividing each part of the dry reactor. Meanwhile, in order to avoid that the error of each discrete unit influences the calculation precision during calculation, the grid density is not suitable to be too large. And (3) connecting the equivalent circuits of the coils of all layers in parallel, applying independent voltage sources to form a circuit model, and respectively coupling the equivalent circuits of the coils with corresponding coil units to obtain an electromagnetic-circuit analysis model. Further, the encapsulation loss is calculated by the analysis model by using the formula (1), and the temperature field and flow field distribution of the reactor are calculated by using the encapsulation loss as a heat source of the three-dimensional fluid-temperature field.
The j-th layer loss of the P j winding mainly comprises resistive loss P aj and eddy current loss P bj, and gamma is the conductivity of an aluminum wire; omega is the angular frequency at which the excitation is applied; d j is the wire diameter of the jth turn of wire; 1 j is the radius of each turn of wire; b j is the magnetic induction intensity at the center of the jth turn of the wire.
B. Three-dimensional fluid-temperature field model modeling
In the invention, COMSOL finite element simulation software is adopted to establish a three-dimensional fluid-temperature field finite element model for the dry reactor, and numerical boundary conditions of the model are established by analyzing the structural characteristics and heat dissipation characteristics of the dry reactor.
(1) Calculation theoretical model
Firstly, the cooling mode of the dry reactor is mostly natural convection, the heat exchange between the reactor and air meets the basic equation of heat transfer, and the air flow and the heat transfer follow the physical conservation law, namely mass conservation, momentum conservation and energy conservation. Under natural convection conditions, the air surrounding the dry air reactor is regarded as incompressible fluid, and a steady-state fluid general control equation under rectangular coordinates is as follows:
Wherein ρ is the fluid density; u is the fluid flow velocity vector; Is a fluid universal variable; /(I) Is a generalized diffusion coefficient; Is a generalized source term.
Based on the basic law of heat transfer, a steady-state three-dimensional temperature field equation and boundary conditions of the dry reactor are shown in a formula (3):
Wherein, ts and T f are respectively the thermodynamic temperatures of the solid and the fluid; lambda x、λy、λz is the heat conductivity coefficient of the solid domain material in the directions of x axis, y axis and z axis; q is the sum of the bulk heat source densities of the solution domains; Γ 1 is a class 1 boundary condition; t W is the known wall temperature; Γ 2 is a class 2 boundary condition; lambda n is the normal thermal conductivity of boundary gamma 2; q 0 is the heat flux density of the boundary; Γ 3 is a class 3 boundary condition; h is the convective heat transfer coefficient of the solid surface.
(2) Finite element model building
In building a three-dimensional fluid-temperature field coupling calculation model, the following assumptions are made for the model:
1) The computing model has axisymmetry, and the symmetrical surface of the encapsulation, the rain cover and the star-shaped support can be regarded as a heat insulation surface.
2) The temperature difference between the envelopes is small, the radiation heat transfer proportion is small, and the heat exchange inside the reactor is mainly convection and conduction. The heat transfer between the outer surface of the rain-proof cover and the external cooling air considers convection and radiation, and the emissivity of the surface of the rain-proof cover is 0.9. 3) The encapsulation is considered as a material homopolar entity, the rain cover and the star-shaped support are considered as linear materials, and the nonlinear relationship between material parameters and temperature is not considered. 4) The air domain of the calculation model is the air between the reactor rain cover, the outermost layer encapsulation and the lower star-shaped support.
The boundary conditions of the finite element model should satisfy, in addition to the control equation (2):
1) Slip-free boundary conditions, V x＝Vy＝Vz =0, were specified on the calculated domain boundary Γ 1, reactor solid and air interface Γ 3, with a surface emissivity of 0.9. 2) A constant temperature boundary condition, i.e., t| Γ1＝T0, is specified on the calculation domain boundary Γ 1, and the reactor ambient temperature is set to 293.15K. 3) The gravitational acceleration was set to 9.81m/s2, the direction being the negative of the z-axis. 4) The heat source within the winding envelope is determined by the calculated winding losses of the electromagnetic field.
C. multi-physical field model fusion
The fusion method of the dry-type reactor multi-physical field coupling adopts a load transfer loop iteration algorithm until convergence conditions are met: since the conductor resistivity is temperature dependent, it is necessary to first establish constraints of a two-dimensional magnetic circuit-circuit analysis model-calculate the current and loss in each envelope of the reactor at the initial temperature in the electromagnetic field and apply boundary conditions, perform magnetic field analysis, and simulate the current and loss of each winding; and then, reading winding loss, transmitting the winding loss to a three-dimensional fluid-temperature field model, applying load and flow field temperature field boundary conditions, then performing three-dimensional fluid-temperature field finite element model simulation to obtain temperature field distribution and encapsulation temperature, finally judging whether convergence conditions are reached, comparing adjacent two temperature differences to be smaller than a set value or reaching cycle times, and stopping iteration if the convergence conditions are reached, so as to complete the multi-physical field fusion process. The whole fusion flow is shown in fig. 2.
D. model reduction of improved PSO-SVM
The finite element method is used for calculating the fluid-temperature field model, and the method is more accurate and visual, but has the defects of complex construction flow and slow calculation.
In order to solve the problem that a high-order complex model can meet the requirement of real-time performance while ensuring the calculation precision of multiple physical fields, the invention introduces a PSO-SVM fusion algorithm to realize model order reduction, and the algorithm has the advantages of PSO optimizing accuracy and SVM small sample prediction. However, PSO algorithms tend to fall into locally optimal solutions, resulting in frequent local convergence problems. Therefore, the invention provides an improved algorithm, namely, the problem is solved by adopting the information entropy and the variation strategy in the differential variation, and the defect that the population diversity of the PSO algorithm is rapidly reduced in later iteration can be effectively overcome. The improved PSO optimization process is shown in FIG. 3.
As can be seen from fig. 3, after the information entropy is introduced, the information entropy of the population is calculated and compared with the set threshold value, so that the population can automatically determine whether the differential variation is required, thereby avoiding the repeated variation of the population, improving the calculation efficiency and ensuring that the particle swarm algorithm falls into a locally optimal solution. Wherein the information entropy mathematical expression is
Where U is a source symbol and p i is the probability corresponding to a different source symbol. After the variation is determined, the DE/best/1/bin variation strategy is adopted in the early iteration stage, and the DE/rand/1/bin strategy is adopted in the later iteration stage, so that the algorithm can be more focused on global searching in the early stage and focused on local refined searching in the later stage. In addition, the improvement measure also corrects the inertia weight factor omega, since c 1 has better effect from 2.05 to 0.5, and c 2 has better effect from 0.5 to 2.05, the inertia weight factor is corrected by adopting an adaptive formula. The specific correction formula is as follows:
Wherein: c 1max and c 2max are each 2.05, and c 1min and c 2min are each 0.5; t is the current iteration number; t max is the maximum number of iterations; omega max is the maximum inertial weight factor and omega min is the minimum inertial weight factor.
In addition, the traditional agent model is mostly oriented to structural optimization design, input variables are mostly structural parameters, and the agent model constructed based on the parameters can improve the optimization design speed but cannot be fused with data calculated by the system model. Therefore, in the invention, the current and the loss of the reactor are taken as input parameters, the inter-turn short circuit fault which is easy to be broken down of the dry reactor equipment is taken as a research object, and the average temperature of the winding is selected as a predicted value. When the whole model is started, the agent reduced-order model can predict the target value according to the operation parameters and the actual working conditions of the dry-type reactor calculated by the joint simulation.
E. variable fitting dry-type reactor turn-to-turn short circuit fault early warning
When the test fault test is carried out on the dry-type reactor entity, the test data are difficult to measure, and certain economic loss and other problems are caused, so that the destructive test on the entity can be effectively avoided by carrying out various turn-to-turn short circuit fault simulation on the multi-physical field coupling model, the economic loss is reduced, and more complete fault data types and early warning grade division are obtained.
And setting turn-to-turn short circuit faults with different degrees at different positions of the multi-physical field coupling model, adding fault current excitation into the fault model, and analyzing the spatial distribution conditions of magnetic fields along the direction of a central axial lead (ATC) and the direction of a central transverse Lead (LTC) under different faults. When a short circuit fault occurs among turns of the k layers of coils, the turn-to-turn short circuit fault degree s is defined as follows:
s＝hs/Hk*100％ (6)
Where H k is the height of the kth coil and hs is the height of the shorting ring. Because the turn-to-turn short circuit fault is gradually developed from a simple fault, the value range of s is generally 0-35%.
The change percentage of the magnetic field is adoptedIs defined as follows:
B 0 and B s represent the magnetic field strength at the same point in normal operation and inter-turn fault operation, respectively.
In order to quantitatively describe the relationship between the fault degree and the magnetic field change, the magnetic field change quantity and the fault degree of an observation point are respectively used as fitting variables to establish a fitting function. When a fault occurs, the fault degree s is set as an independent variable, and the magnetic field variation is setSet as a dependent variable. Simplifying the fault degree early warning function into solving/>And s, a fitted function of the degree of function failure and the magnetic rate of change, as shown in the following equation:
In the formula, d A is taken along the distance of the ATC direction, d L is taken along the distance of the LTC direction, and curve fitting functions of magnetic field changes and fault degrees of different measuring points are simulated, wherein A, B, C, D is a undetermined fitting coefficient.
And finally, realizing fault identification and early warning discrimination through the fault degree early warning function.
The fusion method of the multi-physical field coupling adopts a cyclic iteration algorithm of load transfer until convergence conditions are met. Firstly, establishing constraint conditions (package initial current and loss setting) of a two-dimensional magnetic circuit analysis model, applying boundary conditions, performing magnetic field analysis, and performing simulation calculation on the current and loss of each winding; and reading winding loss, transmitting the winding loss to a three-dimensional fluid-temperature field model, applying load and flow field temperature field boundary conditions, then performing three-dimensional fluid-temperature field finite element model simulation to obtain temperature field distribution and encapsulation temperature, and finally judging whether convergence conditions (the temperature difference between two adjacent times is smaller than a set value or the number of times of circulation is reached) are reached, and stopping iteration if the conditions are reached, thereby completing multi-field fusion.
A specific practical case is given below for a specific scenario:
A. Two-dimensional electromagnetic-circuit model modeling
The embodiment of the invention takes a 35-volt dry air-core reactor as an example to carry out algorithm description. The main parameters of the reactor used are shown in table 1 below. A star-shaped bracket is arranged at the upper end and the lower end of the reactor respectively and is used as an incoming line and outgoing line converging line, and mechanical clamping force is provided for encapsulation. A plurality of tiny polyester struts are arranged between the reactor envelopes in the radial direction, and air passages between the struts form heat dissipation channels between the envelopes. The winding in the encapsulation adopts a plurality of layers of parallel coils, each layer of coil is formed by winding a plurality of strands of round aluminum wires in parallel, a polyester film and a non-woven fabric impregnated with epoxy glue are wound on the surface of each coil, and the insulating material on the encapsulation surface is glass fiber reinforced epoxy resin. The basic structure is shown in fig. 4.
Table 1 main parameters of dry reactor
Maxwell is introduced according to the actual model and size of the dry reactor, and a two-dimensional finite element model including air, an insulating medium, and a coil is built as shown in FIG. 5 (a). Further, the equivalent circuits of the coils of each layer are connected in parallel, an independent voltage source is applied to form a circuit model, and as shown in fig. 5 (b), the equivalent circuits of the coils are respectively coupled with corresponding coil units to obtain an electromagnetic-circuit analysis model. Fig. 5 (b) is a schematic diagram of an external constraint circuit of the reactor, rn is the resistance of the n-th layer coil, wn is the finite element module of the n-th layer coil, and the wires in the coil layers are tightly wound, which can be equivalently the two-dimensional rectangle of fig. 5 (a). The magnetic field distribution diagram of the reactor obtained by performing simulation calculation on the magnetic field of the dry air-core reactor during operation according to the model established in fig. 5 is shown in fig. 6 (a) and (b).
Then, the encapsulation loss is calculated by the analysis model and by using the formula (1), and the temperature field and flow field distribution of the reactor are calculated by using the encapsulation loss as a heat source of the three-dimensional fluid-temperature field.
B. Three-dimensional fluid-temperature field model modeling
And establishing a three-dimensional fluid-temperature field finite element model for the dry type air-core reactor by adopting COMSOL finite element simulation software. After meshing and boundary condition application, solving a temperature field distribution diagram after reaching a steady state is shown in fig. 7, and a flow velocity distribution diagram of an air flow field around the reactor is shown in fig. 8. From the graph, the temperature of the reactor encapsulation is in a descending trend from top to bottom, the bottom air is accelerated to rise under the natural convection effect after absorbing the heat of the bottom of the encapsulation, the air enters the air duct at a high speed, the convection heat dissipation effect of the air after entering is weakened, and the air begins to be stable after the speed is accelerated to a certain value.
C. multi-physical field model fusion
And carrying out fusion of multiple physical coupling fields of the dry reactor by adopting a load transfer loop iteration algorithm: setting the iteration times k=100, setting the percentage of the temperature difference value between two adjacent times to be 5%, and completing the multi-physical field fusion process.
D. model reduction of improved PSO-SVM
The average temperature of the winding is used as one of key indexes for measuring the working state of the reactor, and the trend of the temperature rise can intuitively reflect the running condition of equipment, so that the average temperature of the winding is used as an output variable. The current and loss of the reactor are used as input parameters. In order to verify and improve the PSO-SVM proxy model prediction precision, simulation deduction is carried out under different working conditions based on a multi-physical field model and combined with the dry type air core reactor operation working condition parameters, 90 groups of data capable of effectively reflecting the input-output relationship characteristics are obtained, 75 groups of data are selected as the reduced order model training data, 15 groups of data are used as the test data, the constructed reduced order model is tested, the error analysis mode adopts the mode of mean square error MSE and variance R 2 to judge, the value of R 2 is closer to 1.0 when the MSE is closer to 0, and the accuracy is higher. The test results are shown in fig. 9.
It can be seen from the figure that the predicted value of the winding average temperature is very close to the calculated value of the model. MSE is 0.0021101 and R 2 is 0.994 in training set results; MSE was 8.783e-5 and R 2 was 0.99977 in the test set results. Therefore, the reduced order model established by the invention has higher confidence. And finally, selecting a proper kernel function for the trained reduced order model, solving SVM coefficients, constructing a corresponding regression function, and constructing a response surface of the input-output relationship according to the regression function. The response surface form of the improved PSO-SVM reduced order model is shown in FIG. 10.
E. variable fitting dry-type reactor turn-to-turn short circuit fault early warning
In this example, as known from a multi-physical field coupling model of the dry air-core reactor, the magnetic field strength near the radial third encapsulation is close to 0, and the magnetic field strength is smaller as the radial direction is farther from the center point, so that the dry air-core reactor is generally selected as a fault locating point at the center of the third encapsulation, a point C (1 m along the ATC center) and a point E (3 m along the LTC center) are selected as measuring points, and three fault degrees are set: the degrees of failure corresponding to the light failure, the moderate failure and the heavy failure are 0.28%, 16.78% and 33.29% respectively. When the same position of the reactor fails, the change of the magnetic field increases with the increase of the failure degree; at the same degree of failure, the failure of the innermost coil has the greatest effect on the magnetic field, while the failure of the outermost coil has the least effect on the magnetic field. The distribution diagrams of the magnetic field change curve along ATC and LTC when a fault occurs are shown in fig. 11 and 12, respectively, wherein 1/3 of the upper part of the reactor is defined as "upper part", 1/3 of the middle part of the reactor is defined as "middle part", 1/3 of the bottom of the reactor is defined as "bottom", the distance along ATC direction is dA, and the distance along LTC direction is dL.
As can be seen from fig. 11, the maximum value of the magnetic field variation curve along the ATC direction at the three fault levels appears at about da=0.25 meters, and when dA is greater than 1m, the magnetic field variation of the "middle" of the reactor is greater than 0, the magnetic field variations of the "upper" and "lower" are smaller than 0, and the magnetic field variation of the "upper" is smaller than the magnetic field variation of the "lower". As shown in fig. 12, when s=0.28%, the magnetic field changes more in the close inner wall (dl=1m) than in the outer wall (dl=3m) of the dry air-core reactor on the change curve of the magnetic field in the LTC direction. As the degree of failure increases, the difference in the amount of change in the inner and outer walls gradually decreases. Under the two fault degrees of s=16.78% and s= 33.29%, the magnetic field change of the near inner wall is smaller than the magnetic field change of the end outer wall, and the change trend of the magnetic fields at the two ends is basically consistent. When the same position of the reactor fails, the change of the magnetic field increases with the increase of the failure degree; at the same degree of failure, the failure of the innermost coil has the greatest effect on the magnetic field, while the failure of the outermost coil has the least effect on the magnetic field.
From the above analysis we have obtained the relationship of inter-turn short circuit fault to the spatial magnetic field. Solving a fitting function of the fault degree and the magnetic change rate according to a formula (8) to obtain the following formula:
The curve fitting of the magnetic field changes at points C and E to the extent of the fault was simulated as shown in fig. 13. The result of verifying the fitting error is shown in table 2, and as can be seen from table 2, the absolute error between the S early warning level calculated by the fault function and the actual S early warning level is smaller than 1, and the relative error is within the allowable error range (0-10%), so that the accuracy of the fault degree early warning function is verified, and a theoretical basis is provided for detecting the turn-to-turn short circuit fault.
TABLE 2 fitting error results
As shown in fig. 14, the present invention further provides a modeling and fault early warning system of a modeling and fault early warning method, including:
Establishing a two-dimensional analysis model module 101: the method comprises the steps of establishing an electromagnetic-circuit two-dimensional analysis model of the dry-type reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
Establishing a three-dimensional analysis model module 102: the encapsulation loss is used as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor, and a cyclic iteration algorithm is adopted to perform multi-physical field model fusion to establish a three-dimensional fluid-temperature field finite element model;
The reduced order analysis module 103: the fusion algorithm is used for reducing the three-dimensional fluid-temperature field finite element model by adopting a PSO-SVM;
The failure analysis module 104: the method is used for carrying out multiple turn-to-turn short circuit fault simulation based on the model establishment to realize fault identification and early warning discrimination.
Fig. 15 is a schematic structural diagram of a computer device according to the present disclosure. Referring to fig. 15, the computer apparatus 400 includes at least a memory 402 and a processor 401; the memory 402 is connected to the processor through a communication bus 403, and is configured to store computer instructions executable by the processor 401, and the processor 301 is configured to read the computer instructions from the memory 402 to implement the steps of the modeling and fault warning method according to any of the foregoing embodiments.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal magnetic disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Finally, it should be noted that: while this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings are not necessarily required to be in the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The foregoing description of the preferred embodiments of the present disclosure is not intended to limit the disclosure, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present disclosure.
Claims (5)
1. The modeling and fault early warning method for the dry-type reactor is characterized by comprising the following steps of:
Establishing an electromagnetic-circuit two-dimensional analysis model of the dry reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
taking the encapsulation loss as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor, and adopting a cyclic iteration algorithm to perform multi-physical field model fusion to establish a three-dimensional fluid-temperature field finite element model;
then, adopting a fusion algorithm of a PSO-SVM to reduce the order of the three-dimensional fluid-temperature field finite element model;
Performing multiple turn-to-turn short circuit fault simulation on the basis of the establishment of the three-dimensional fluid-temperature field finite element model after the order reduction to realize fault identification and early warning discrimination;
the method for establishing the electromagnetic-circuit two-dimensional analysis model of the dry reactor by using the Maxwell comprises the following steps of:
Establishing a finite element magnetic field model of air, an insulating medium and a coil according to the actual size of the dry reactor, and designing by using Maxwell;
Parallel connection is carried out on equivalent circuits of coils of all layers of the dry-type reactor, and an independent power supply is applied to form a circuit model;
Respectively coupling the equivalent circuits of the coils with corresponding coil units to obtain the electromagnetic-circuit two-dimensional analysis model;
The method for calculating the encapsulation loss through the electromagnetic-circuit two-dimensional analysis model comprises the following steps:
the temperature field and flow field distribution of the reactor is calculated using the following formula to calculate the encapsulation loss as the heat source of the three-dimensional fluid-temperature field:
Wherein, P j represents the j-th layer loss of the winding, P aj is resistive loss, P bj is eddy current loss, and gamma is the conductivity of the aluminum wire; omega is the angular frequency at which the excitation is applied; d j is the wire diameter of the jth turn of wire; i j is the current flowing in the jth turn of wire; r j is the resistance of the jth turn of wire; 1 j is the radius of each turn of wire; b j is the magnetic induction intensity at the center of the jth turn of wire;
the method for establishing the three-dimensional fluid-temperature field finite element model comprises the following steps:
Firstly, establishing a calculation theoretical model: the air surrounding the dry reactor is regarded as incompressible fluid, and a general control equation of steady-state fluid under rectangular coordinates is as follows:
In the above formula, ρ is the fluid density; u is the fluid flow velocity vector; Is a fluid universal variable; /(I) Is a generalized diffusion coefficient; /(I)Is a generalized source item;
based on the basic law of heat transfer, a steady-state three-dimensional temperature field equation and boundary conditions of the dry reactor are shown as the following formula:
Wherein, ts is the thermodynamic temperature of the solid; lambda x、λy、λz is the heat conductivity coefficient of the solid domain material in the directions of x axis, y axis and z axis; q is the sum of the bulk heat source densities of the solution domains; Γ 1 is a class 1 boundary condition; t W is the known wall temperature; Γ 2 is a class 2 boundary condition; lambda n is the normal thermal conductivity of boundary gamma 2; q 0 is the heat flux density of the boundary; Γ 3 is a class 3 boundary condition; h is the convective heat transfer coefficient of the solid surface;
then establishing a finite element model based on the condition that the following conditions are met:
Specifying a slip-free boundary condition, i.e., V x＝Vy＝Vz =0, at the calculated domain boundary Γ 1, reactor solid and air interface Γ 3, the surface emissivity taking 0.9; specifying a constant temperature boundary condition, namely T| Γ1＝T0, on a calculation domain boundary gamma 1, and setting the ambient temperature of the reactor to be 293.15K; setting the gravity acceleration to be 9.81m/s 2, wherein the direction is the negative direction of the z axis; the heat source in the winding package is determined by the winding loss obtained by calculation of the electromagnetic field;
in the process of establishing a three-dimensional fluid-temperature field finite element model, the following assumption needs to be made on the model itself:
The calculation theoretical model has axisymmetry, and the symmetry plane of the encapsulation, the rain cover and the star-shaped support can be regarded as a heat insulation surface; the temperature difference between the envelopes is small, the radiation heat transfer proportion is small, and the heat exchange inside the reactor is mainly convection and conduction; the heat transfer between the outer surface of the rain-proof cover and the external cooling air considers convection and radiation, and the emissivity of the surface of the rain-proof cover is 0.9; the encapsulation is regarded as the material homopolar whole, the rain cover and the star-shaped support are regarded as linear materials, and the nonlinear relation between the material parameters and the temperature is not considered; the air domain of the calculation model is the air between the rain cover, the outermost encapsulation and the lower star-shaped support of the reactor.
2. The modeling and fault pre-warning method according to claim 1, wherein the method for performing multi-physical field model fusion by using a loop iteration algorithm comprises:
Firstly, establishing constraint conditions of a two-dimensional magnetic circuit-circuit analysis model, namely calculating current and loss in each package of the reactor at an initial temperature in an electromagnetic field, applying boundary conditions, performing magnetic field analysis, and performing simulation calculation on the current and loss of each winding;
Reading winding loss, transmitting the winding loss to a three-dimensional fluid-temperature field finite element model, and applying load and flow field temperature field boundary conditions;
performing three-dimensional fluid-temperature field finite element model simulation to obtain temperature field distribution and encapsulation temperature;
Judging whether a convergence condition is reached, comparing the adjacent two temperature differences to be smaller than a set value or reaching the circulation times, and stopping iteration if the convergence condition is reached.
3. The modeling and fault pre-warning method according to any one of claims 1 to 2, wherein the method for reducing the three-dimensional fluid-temperature field finite element model by using a PSO-SVM fusion algorithm comprises:
firstly, calculating information entropy of a population, and comparing and setting a threshold, wherein the mathematical expression of the information entropy is as follows:
Wherein U is an information source symbol, and p i is the probability corresponding to different information source symbols; after the variation is determined, a DE/best/1/bin variation strategy is adopted in the early iteration stage, and a DE/rand/1/bin strategy is adopted in the later iteration stage;
and then the inertia weight factor omega is corrected by adopting an adaptive formula, wherein the specific correction formula is as follows:
Wherein: c 1max and c 2max are each 2.05, and c 1min and c 2min are each 0.5; t is the current iteration number; t max is the maximum number of iterations; omega max is the maximum inertial weight factor, omega min is the minimum inertial weight factor;
the current and loss of the reactor are used as input parameters, inter-turn short circuit faults which are easy to break down of the dry reactor equipment are used as research objects, the average temperature of windings is selected as a predicted value, and a target value is predicted according to the operation parameters and the actual working conditions of the dry reactor calculated by joint simulation.
4. The modeling and fault early warning method according to any one of claims 1 to 3, wherein the method for realizing the identification and early warning discrimination of the faults by simulating the multiple turn-to-turn short circuit faults comprises the following steps:
Respectively taking the magnetic field variation of the observation point and the fault degree as fitting variables to establish a fitting function; when a short circuit fault occurs among turns of the k layers of coils, the turn-to-turn short circuit fault degree s is defined as follows:
s＝hs/Hk*100％；
in the above formula, H k is the height of the kth coil, and hs is the height of the short-circuit ring;
The change percentage of the magnetic field is adopted Is defined as follows:
B 0 and B s in the above formula respectively represent the magnetic field intensity at the same point during normal operation and turn-to-turn fault operation;
When a fault occurs, the fault degree s is set as an independent variable, and the magnetic field variation is set Setting the fault degree early warning function as a dependent variable, and simplifying the fault degree early warning function into solution/>And s, a fitted function of the degree of function failure and the magnetic rate of change, as shown in the following equation:
Taking d A along the ATC direction and d L along the LTC direction, and simulating curve fitting functions of magnetic field changes and fault degrees of different measuring points, wherein A, B, C, D is a undetermined fitting coefficient;
And finally, realizing fault identification and early warning discrimination through the fitting function.
5. A system employing the modeling and fault warning method of any of claims 1-4, comprising:
establishing a two-dimensional analysis model module: the method comprises the steps of establishing an electromagnetic-circuit two-dimensional analysis model of the dry-type reactor by using Maxwell, wherein the model comprises a finite element magnetic field model and a circuit model, and calculating to obtain encapsulation loss through the model;
Establishing a three-dimensional analysis model module: the encapsulation loss is used as a heat source of a three-dimensional fluid-temperature field to calculate the temperature field and flow field distribution of the reactor, and a cyclic iteration algorithm is adopted to perform multi-physical field model fusion to establish a three-dimensional fluid-temperature field finite element model;
And the reduced order analysis module: the fusion algorithm is used for reducing the three-dimensional fluid-temperature field finite element model by adopting a PSO-SVM;
And a fault analysis module: the method is used for carrying out multiple turn-to-turn short circuit fault simulation based on the model establishment to realize fault identification and early warning discrimination.
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