CN107122538A - The temperature rise optimization method and system of a kind of UHVDC converter station tab terminal - Google Patents

The temperature rise optimization method and system of a kind of UHVDC converter station tab terminal Download PDF

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Publication number
CN107122538A
CN107122538A CN201710270151.2A CN201710270151A CN107122538A CN 107122538 A CN107122538 A CN 107122538A CN 201710270151 A CN201710270151 A CN 201710270151A CN 107122538 A CN107122538 A CN 107122538A
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mrow
tab terminal
design parameter
temperature rise
msub
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CN107122538B (en
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朱宽军
宋胜利
张雪松
司佳钧
李冬青
杜晓磊
肖鲲
孙娜
牛海军
李军辉
刘操兰
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides a kind of temperature rise optimization method of UHVDC converter station tab terminal, the tab terminal after design parameter optimization is set to avoid or mitigate the loss that causes of power network disaster accident caused by tab terminal generates heat, while it also avoid that butt joint terminal size design is excessive and the economic waste that causes.Methods described is：Design parameter is chosen, corresponding tip side daughter board overlap maximum temperature rise T is drawnmax, and Tmax≤TuWhen, functional relation between maximum temperature rise and design parameter is drawn, tab terminal reliability R value is drawn by Monte Carlo methods with reference to tab terminal plate limit state equation, as R >=RuWhen, choose initial designs point, and different parameters near initial designs point are combined, calculate corresponding overlap maximum temperature rise value and then draw reliability, draw the functional relation between design parameter and reliability, using tab terminal cost as optimal conditions sets target function, the optimal design parameter of tab terminal is obtained using reliability as constraints.

Description

The temperature rise optimization method and system of a kind of UHVDC converter station tab terminal
Technical field
The invention belongs to UHVDC converter station power network prevention technique field, and it is extra-high more particularly, to one kind Press the temperature rise optimization method and system of DC converter station tab terminal.
Background technology
With the development of national economy, the scale of UHVDC converter station engineering expands rapidly, and in its station through-flow loop Tab terminal overheating defect can cause current conversion station device structure, material etc. to produce fatigue damage, shorten the service life of equipment, shadow The safe and stable operation of power network is rung.The through-flow return contacts terminal fever phenomenon of UHVDC converter station having now been found that, it is several All UHVDC converter stations at present in fortune are contained, the scope involved is big, the extent of injury that causes is high, if made Into catastrophe failure, the general number of its economic loss is huge, be difficult to accurate estimation.Extra-high voltage project is used as the most crucial composition of power network Part, its security and stability requires higher, and the economic loss caused that breaks down is huger, therefore to the extra-high voltage direct-current change of current The tab terminal temperature rise design for engineering of standing proposes higher requirement.
The content of the invention
According to an aspect of the invention, it is proposed that a kind of temperature rise optimization method of UHVDC converter station tab terminal, bag Include：
Step 1, the design parameter of multigroup tab terminal is obtained, and tab terminal overlap is simulated by FEM model Temperature rise process, record tab terminal overlap maximum temperature rise Tmax, by TmaxWith temperature rise limit value TuIt is compared, chooses Tmax≤TuDesign parameter retained, wherein the design parameter includes：Tab terminal length X1, tab terminal width X2、 Tab terminal thickness X3, tab terminal overlapping part resistance X4The electric current X loaded with tab terminal5；
Step 2, the design parameter of one group of reservation is randomly selected, T is drawn by neural network modelmaxWith the design of reservation Temperature rise functional relation between parameter, and carry out Monte Carlo (MCS) calculate obtain corresponding tab terminal reliability R0Value, By current R0With reliability limiting value RuIt is compared, if R0≥Ru, then retain current design parameter, otherwise, choose and protect again The one group of design parameter stayed so that reliability R0≥Ru, and retain current design parameter；
Step 3, reliability R >=R currently to retainuDesign parameter as initial designs point, obtain with initial designs Point is combined for the different designs parameter in the finite neighborhood of origin, is simulated by FEM model and draws setting after combination Count the corresponding maximum temperature rise T of parametermax, and then the limit state equation of tip side daughter board is combined, pass through Monte Carlo simulation approach (MCS) corresponding tab terminal reliability R value is calculated, the function between R and design parameter is drawn by neural network model Relation；
Step 4, according to the functional relation between R and design parameter, using the cost of tab terminal as optimal conditions, mesh is designed Scalar functions f (X)=C=X1·X2·X3ρ ω, wherein C are the cost of tab terminal,The density of tab terminal, ω is unit The cost of volume tab terminal, using the reliability of tab terminal as constraints, obtains the optimal design parameter of tab terminal For minf (x)=E (X1·X2·X3·ρ·ω)st.R≥Ru, wherein X1、X2And X3Value be optimization after design ginseng Number.
According to the method described in claim 1, it is characterised in that the utilization FEM model realizes the temperature of tab terminal Rising process simulation is：
When tab terminal flows through electric current, its current density should meet Current continuity equation：
γ is the conductance of tab terminal in formula,For the current potential at tab terminal any point；
Tab terminal is in the case where having endogenous pyrogen and Unsteady Heat Transfer, and the differential equation for describing its temperature field is：
T is the thermodynamic temperature of arbitrfary point in formula,The respectively density and specific heat capacity of tab terminal, t is the time, and V is Field domain；
The energy that tab terminal is used to raise temperature is E=qw, the heat q that tab terminal endogenous pyrogen is producedw, according to its Electric current field distribution is obtained：
Q=qw-qs-qe (3)
In formulaFor the heat produced by tab terminal internal heat resource, Ex、Ey、EzFor tab terminal Electric-field intensity；qs=αconA0(Tf-T0) it is tab terminal to surrounding medium institute dispersed heat, αconFor heat loss through convection coefficient Unit is W/ (m2·℃)；A0It is m for heat loss through convection square measure2, TfFor heater temperature unit for DEG C, T0For environment temperature list Position for DEG C；Pass through heat radiation dispersed heat, ε for tab terminaliFor emissivity, 0.92 is taken；σ is Stefan-Boltzmann constant, takes 5.67 × 10-8W/m2.K4；AiFor radiating surface i area；FijFor by radiating surface i to radiation Face j form factor, typically takes Fij=1；
Simultaneous formula (1) (2) (3) builds the FEM model of tab terminal, and carries out gridding point with simplified model, true Determine after initial temperature rise and boundary condition, the Electric Field Distribution of each node of tab terminal is obtained using formula (1), then with formula (3) The energy Q of tab terminal absorption is obtained, last applying equation (2) solves the Distribution of temperature rise situation obtained on tab terminal, Zhi Houchong It is new to determine resistivity γ, then alternately current field and the calculating of temperature rise, so as to realize that current field and the simultaneous of temperature rise are asked Solution, the simulation for the joint terminal temperature rise process that achieves a butt joint.
Preferably, wherein the temperature rise limit value TuSpan be：60K~200K.
Preferably, wherein the neural network model used in the step 2 is three-layer neural network structure, wherein described set Count parameter X1、X2、X3、X4And X5For input parameter, the number of hidden layer neuron is 12, TmaxFor output parameter, hidden layer Neuron activation functions are Sigmoid type activation primitives, and output layer activation primitive is linear Purelin types transmission function, initially Weights choose the random number between (- 1,1), and the selection range of learning rate is between 0.01~0.8, factor of momentum selection range Between 0.9~0.95, the condition of convergence is network overall error E≤1.0e-12.
Preferably, wherein the limit state equation of the tab terminal plate is：
Z=g (X)=g (X1,X2,X3,X4,X5)=Tu-Tmax(X1,X2,X3,X4,X5)=0.
Preferably, wherein the neural network model used in the step 3 is three-layer neural network structure, wherein design ginseng Number X1、X2、X3、X4And X5For input parameter, the number of hidden layer neuron is 9, and R is output parameter, hidden layer neuron Activation primitive and output layer activation primitive are Sigmoid type activation primitives, random between initial weight selection (- 1,1) Number, the selection range of learning rate is between 0.01~0.9, and factor of momentum selection range restrains bar between 0.85~0.98 Part is network overall error E≤1.0e-12.
Preferably, wherein the functional relation between the R and design parameter is：
Wherein, wji, vkjAnd b1j, b2kThe connection weight and threshold value of neutral net are represented respectively, and δ is transmission function.
Preferably, wherein the value that corresponding tab terminal reliability R is calculated by Monte Carlo simulation approach (MCS) When, the number realization of selection is 5000 times.
Preferably, wherein when calculating corresponding tab terminal reliability R, design parameter can be normalized for：
Optimize system there is provided a kind of temperature rise of UHVDC converter station tab terminal according to another aspect of the present invention, Including：
Design parameter is obtained and temperature rise analogue unit, the design parameter X for obtaining one group of tab terminal1、X2、X3、X4With And X5, and by the temperature rise process of FEM model simulation tab terminal overlap, record tab terminal overlap is most Big temperature rise Tmax, by TmaxWith temperature rise limit value TuIt is compared, works as Tmax≤TuWhen, send data to the first reliability calculating list Member proceeds to calculate, and design parameter is otherwise chosen again and carries out temperature rise simulation；
First reliability calculating unit, T is drawn by neural network modelmaxMultiple functions between design parameter are closed System, takes the limit state equation of one of which relation combination tip side daughter board, calculates corresponding by Monte Carlo simulation approach (MCS) Tab terminal reliability R value, by current R and reliability limiting value RuIt is compared, if R >=Ru, retain current design ginseng Number, otherwise, chooses one group of design parameter of reservation so that reliability R again0≥Ru, and retain current design parameter；
Second reliability calculating unit, with the reliability R >=R currently retaineduDesign parameter as initial designs point, obtain Take by the different designs parameter in the finite neighborhood of origin of initial designs point and be combined, simulated and obtained by FEM model The corresponding maximum temperature rise T of design parameter gone out after combinationmax, and then the limit state equation of tip side daughter board is combined, it is special by covering Monte Carlo Simulation of Ions Inside method (MCS) calculates corresponding tab terminal reliability R value, and R and design parameter are drawn by neural network model Between functional relation；
Optimize unit, using the cost of tab terminal as optimal conditions, design object function f (X)=C=X1·X2·X3. ρ ω, wherein C are the cost of tab terminal,The density of tab terminal, ω is the cost of unit volume tab terminal, to connect The reliability of joint terminal is as constraints, and the optimal design parameter for obtaining tab terminal is minf (x)=E (X1·X2·X3· ρ·ω)st.R≥Ru, wherein X1、X2And X3, value be optimization after design parameter.
The invention provides a kind of temperature rise optimization method of UHVDC converter station tab terminal, make design parameter optimization Tab terminal afterwards can avoid or mitigate the loss that power network disaster accident is caused caused by tab terminal generates heat, while also keeping away Exempt from that butt joint terminal size design is excessive and the economic waste that causes.
Brief description of the drawings
By reference to the following drawings, the illustrative embodiments of the present invention can be more fully understood by：
Fig. 1 is the temperature rise optimization method flow chart of the tab terminal according to the preferred embodiment of the present invention；
Fig. 2 is that tab terminal temperature limit analyzes experimental circuit；
Fig. 3 is the T according to the preferred embodiment of the present inventionmaxNeural network model is fitted with design parameter；
Fig. 4 is the R and design parameter fitting neural network model according to the preferred embodiment of the present invention；And
Fig. 5 is the structural representation for optimizing system according to the temperature rise of the tab terminal of the preferred embodiment of the present invention.
Embodiment
The illustrative embodiments of the present invention are introduced with reference now to accompanying drawing, however, the present invention can use many different shapes Formula is implemented, and it is to disclose at large and fully there is provided these embodiments to be not limited to embodiment described herein The present invention, and fully pass on the scope of the present invention to person of ordinary skill in the field.For showing for being illustrated in the accompanying drawings Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements are attached using identical Icon is remembered.
Unless otherwise indicated, term (including scientific and technical terminology) used herein has to person of ordinary skill in the field It is common to understand implication.Further it will be understood that the term limited with usually used dictionary, is appreciated that and it The linguistic context of association area has consistent implication, and is not construed as Utopian or excessively formal meaning.
Fig. 1 is the temperature rise optimization method flow chart of the tab terminal of the preferred embodiment of the present invention.As shown in figure 1, tip side The temperature rise optimization method 100 of son chooses multigroup design parameter first, show that current design parameter is corresponding by FEM model Street corner terminal board overlap maximum temperature rise value, and when current maximum temperature rise is less than or equal to temperature rise limit value, pass through nerve Network model draws the functional relation between maximum temperature rise and design parameter, and it is logical to combine the limit state equation of tip side daughter board The value that MonteCarlo methods draw the reliability R of tab terminal is crossed, when R is more than or equal to reliability limiting value, selection is initially set Enumeration, and the different designs parameter obtained near initial designs point is combined, and calculates corresponding tip side daughter board overlap Maximum temperature rise value so that draw reliability, and show that function between design parameter and reliability is closed by neural network model System, finally using tab terminal cost as optimal conditions sets target function, tip side is obtained using reliability as constraints The optimal design parameter of son.
As shown in figure 1, method 100 is since step 101.In a step 101, the design parameter of multigroup tab terminal is obtained X1、X2、X3、X4And X5.Preferably, design parameter is respectively tab terminal length X1, tab terminal width X2, tab terminal it is thick Spend X3, tab terminal overlapping part resistance X4, the electric current X of tab terminal loading5。
In a step 102, the temperature rise process of tab terminal overlap is simulated by FEM model, tab terminal is recorded The maximum temperature rise T of overlapmax.Preferably, the utilization FEM model realizes that the temperature rise process simulation of tab terminal is：
When tab terminal flows through electric current, its current density should meet Current continuity equation：
γ is the conductance of tab terminal in formula,For the current potential at tab terminal any point；
Tab terminal is in the case where having endogenous pyrogen and Unsteady Heat Transfer, and the differential equation for describing its temperature field is：
T is the thermodynamic temperature of arbitrfary point in formula,The respectively density and specific heat capacity of tab terminal, t is the time, and V is Field domain；
The energy that tab terminal is used to raise temperature is E=qw, the heat q that tab terminal endogenous pyrogen is producedw, according to its Electric current field distribution is obtained：
Q=qw-qs-qe (3)
In formulaFor the heat produced by tab terminal internal heat resource, Ex、Ey、EzFor tab terminal Electric-field intensity；qs=αconA0(Tf-T0) it is tab terminal to surrounding medium institute dispersed heat, αconFor heat loss through convection coefficient Unit is W/ (m2·℃)；A0It is m for heat loss through convection square measure2, TfFor heater temperature unit for DEG C, T0For environment temperature list Position for DEG C；Pass through heat radiation dispersed heat, ε for tab terminaliFor emissivity, 0.92 is taken；σ is Stefan-Boltzmann constant, takes 5.67 × 10-8W/m2.K4；AiFor radiating surface i area；FijFor by radiating surface i to radiation Face j form factor, typically takes Fij=1；
Simultaneous formula (1) (2) (3) builds the FEM model of tab terminal, when carrying out the solution of FEM model, first Model is simplified, and carries out gridding point and forms junior unit.It is determined that after initial temperature rise and boundary condition, utilizing formula (1) Electric Field Distribution of each node of tab terminal is obtained, the energy Q of tab terminal absorption is then obtained with formula (3), is finally applied Formula (2) solves the Distribution of temperature rise situation obtained on tab terminal, and resistivity γ is redefined afterwards, is further continued for solving point of electric field Cloth.In the calculating of current field alternately and temperature rise, the simultaneous solution of current field and temperature rise is realized, so as to realize pair The simulation of tab terminal temperature rise process.
Preferably, the FEM model used in the present invention sets up joint by the modeling method of ANSYS parametric programs The three-dimensional finite element model of terminal, and temperature field and current field are carried out by the SOLID227 unit butt joints terminal in ANSYS Carry out interleaved computation.The unit has ten nodes, and each node has 4 frees degree, can peer end daughter board carry out transient state and steady State crack preventing.SOLID227 coupled field material properties must be provided with resistivity, pyroconductivity, mass density, specific heat capacity, Dielectric coefficient.The present invention uses free mesh by model partition for tetrahedron element, recycles ANSYS smart dimensions control Technology processed, to realize the control of size and the density distribution to grid.Due to being influenced each other between electric field and temperature field, Bu Nengdan Solely some field is solved, therefore must be solved using direct-coupled method.Before solution, specify divide first Analysis type is transient analysis, opening time integrating effect；Secondly the time according to needed for tip side daughter board reaches steady operation, fixed Adopted load step and the time terminated, patent of the present invention takes the time of load the end of the step to be 4 hours, then defines load current, carries Lotus variation pattern is step load, opens automatic time step-length function.The initial temperature for setting tip side daughter board is 20 DEG C of room temperature Homogeneous temperature field, the temperature at tab terminal two ends approximately maintains 20 DEG C.During loading, selected on certain end face of tab terminal Take all node loading currents vector of tip side daughter board, direction perpendicular end surface, while the voltage perseverance for defining node on the face is Zero；And the node coupled voltages free degree all on another end face.
In step 103, by TmaxWith temperature rise limit value TuIt is compared.Preferably, the temperature rise limit value span For：60K~200K.Temperature rise limit value is analyzed experiment by the temperature limit of tip side daughter board and drawn, specific experimental procedure will Illustrate in greater detail below.
At step 104, T is chosenmax≤TuDesign parameter retained, T is drawn by neural network modelmaxWith setting Count the functional relation between parameter.Preferably, the neural network model used in step 104 is three-layer neural network structure, its Middle design parameter X1、X2、X3、X4And X5It is 5 for the number of input parameter, i.e. input layer, hidden layer neuron Number is 12, TmaxIt it is 1 for output parameter, the i.e. number of output layer neuron, hidden layer neuron activation function is Sigmoid type activation primitives, output layer activation primitive be linear Purelin types transmission function, initial weight choose (- 1,1) it Between random number, the selection range of learning rate between 0.01~0.8, factor of momentum selection range between 0.9~0.95, The condition of convergence is network overall error E≤1.0e-12.
In step 105, the design parameter of one group of reservation is randomly selected, T is drawn by neural network modelmaxWith reservation Design parameter between temperature rise functional relation, carry out Monte Carlo simulation approach (MCS) calculate obtain corresponding tab terminal can Value by spending R.Preferably, tab terminal temperature rise reliability calculating is firstly the need of the temperature rise limit state side for setting up tab terminal Journey, according to tip side daughter board temperature rise analysis of Influential Factors, influence tab terminal, which does temperature rise major influence factors, is, tip side daughter board Width, the thickness of tip side daughter board, the length of tip side daughter board, the resistance of tip side daughter board and tip side daughter board are added carries Electric current.The limit condition design normally used according to tip side daughter board, the limit state equation of tip side daughter board is：
Z=g (X)=g (X1,X2,X3,X4,X5)=Tu-Tmax(X1,X2,X3,X4,X5)=0.
Because the maximum temperature rise Tmax of tab terminal overlapping part functional relation formula can not use explicit expression, therefore connect The limit state equation analytic expression of head end daughter board is difficult to obtain, so the present invention makes T using neural network modelmaxJoin with design Several functional relations approaches the limit state equation of tip side daughter board as far as possible.
During butt joint terminal temperature rise optimization design, tab terminal reliability as constraints can be calculated tab terminal Reliability：R=∫G (x) ＞ 0fx(X) dX=P { g (X) ＞ 0 } >=Ru, wherein RuFor reliability limiting value, solving this formula needs to know Reliable probability constraint is converted into certainty constraint, application by the joint probability density function of tip side daughter board temperature rising state function Theoretical method, the joint probability density function of function of state is difficult to determine, and this formula is stealthy functional relation, it is impossible to directly anti- The functional relation of design parameter and reliability is mirrored, therefore the present invention is carried out at random using Monte Carlo methods to sample data Sampling, and probability analysis is carried out to data, the average and standard deviation of limit state function value are obtained, so as to try to achieve tab terminal temperature Rise RELIABILITY INDEX.Preferably, corresponding tab terminal reliability R is calculated by Monte Carlo simulation approach0Value when, selection Number realization is 5000 times.
In step 106, by current R0With reliability limiting value RuIt is compared, if R0≥Ru, then current design is retained Parameter, otherwise re-executes step 105.Preferably, reliability limiting value is set according to actual conditions, can be 99% or 92% Etc. satisfactory numerical value.
In step 107, tab terminal reliability R is chosen0≥RuWhen one group of design parameter as initial designs point, obtain Take by the different designs parameter in the finite neighborhood of origin of initial designs point and be combined, simulated and obtained by FEM model The corresponding maximum temperature rise T of design parameter gone out after combinationmax.Preferably, the computational methods of the maximum temperature rise value in step 107 with Step 102 is identical, herein without repeating.
In step 108, with reference to the limit state equation of tip side daughter board, calculated by Monte Carlo simulation approach (MCS) Corresponding tab terminal reliability R value, the functional relation between R and design parameter is drawn by neural network model.It is preferred that The method that reliability is calculated in ground, step 108 is identical with step 105, herein without repeating.Preferably, used in step 108 Neural network model be three-layer neural network structure, wherein design parameter X1、X2、X3、X4And X5For input parameter, that is, input The number of layer neuron is 5, and the number of hidden layer neuron is 9, and R is output parameter, i.e. the number of output layer neuron For 1, hidden layer neuron activation function and output layer activation primitive are Sigmoid type activation primitives, initial weight choosing The random number between (- 1,1) is taken, the selection range of learning rate is between 0.01~0.9, and factor of momentum selection range is 0.85 Between~0.98, the condition of convergence is network overall error E≤1.0e-12.
In step 109, according to the functional relation between R and design parameter, using the cost of tab terminal as optimal conditions, Design object function f (X)=C=X1·X2·X3ρ ω, wherein C are the cost of tab terminal,The density of tab terminal, ω For the cost of unit volume tab terminal, using the reliability of tab terminal as constraints, the optimization for obtaining tab terminal is set Meter parameter is minf (x)=E (X1·X2·X3·ρ·ω)st.R≥Ru, wherein X1、X2And X3Value be optimization after setting Count parameter.
In embodiments of the present invention, verified by terminal board temperature rise test the foundation FEM model whether Correctly.For UHVDC converter station tab terminal material, carry out respectively aluminium sheet-aluminium sheet, copper coin-copper sheet material electric current- Temperature rise test.Specifically test method is：Terminal board two ends are tightened with copper conductor connection blending bolt, then it is by copper conductor and electric greatly The output end connection of testing equipment is flowed, so that electric current flows through terminal board from power supply box by copper conductor；Overlapped apart from terminal board Position 20 is to installation voltage tester line at 25cm, and test thread end connects the input of voltage digital multimeter, and is read with table Test position magnitude of voltage；Its current direction is by console control, by test sample forward direction, backward voltage and electric current respectively, and Measure the resistance value of terminal board；Resistance test system is removed, 100KVA transformers and soft company are coupled in series with copper conductor Fishplate bar, and respectively add n temperature sensor on terminal board both sides；And transformer is controlled by console and output electricity is improved Stream, reaches stabling current after required value, is changed by computer monitoring terminal board real time temperature.Fig. 2 limits for tab terminal temperature rise Value analysis experimental circuit.Wherein, 201 be 220V AC powers, and 202 be pressure regulator, and 203 be step-down transformer, and 204 be protection Resistance, 205 be coupled capacitor device, and 206 be laboratory sample tab terminal, and 207 be temperature sensor, and 208 be temperature polling instrument. In practical operation, usually using the temperature of tab terminal temperature limit analysis experiment test tab terminal overlapping part, deformation and The situation of change of applied electric force compounded grease, to determine the tip side of unlike material, different current density design points and different operating modes The temperature limit of son, direct basis is provided for terminal board optimization design.Because current conversion station tab terminal is generally fine copper and aluminium alloy Laboratory sample tab terminal can be using aluminum alloy materials manufacture in material, therefore this experiment.Step is as follows：
(1) surface oxide layer is removed using the polishing of 200#, 400# fine sandpaper successively before installing, buffed surface is cleaned with acetone simultaneously With clean byssus or toilet paper wiped clean；And the electric force compounded grease of the overlapping part painting 0.2mm in test specimen；
(2) adapting electric voltage of test specimen overlapping part is determined before testing；
(3) power-up flow to 1500A, and through-flow 4 hours observe the temperature variations of test specimen overlapping part, if temperature stabilization 10 hour observation test specimen situations of change of heating are then continued for, if the temperature rise of test specimen test specimen occurs significantly after heating 10 hours Flex point, then this there is the temperature rise as temperature limit of flex point.If temperature rise do not occur obvious for test specimen test specimen after heating 10 hours Flex point, will move back electric current to continuation power-up after 0 and flow to 2000A, 2500A, and repeat experiment above step, until tip side daughter board Untill there is obvious flex point in temperature rise.The obvious flex point is temperature rise limit value.
Fig. 3 is the T according to the preferred embodiment of the present inventionmaxNeural network model is fitted with design parameter.From the figure 3, it may be seen that refreshing It is three-layer neural network structure, wherein design parameter X through network model 3001、X2、X3、X4And X5For input parameter, that is, input The number of layer neuron is 5, and the number of hidden layer neuron is 12, TmaxFor of output parameter, i.e. output layer neuron Number is 1, and hidden layer neuron activation function is Sigmoid type activation primitives, and output layer activation primitive is linear Purelin types Transmission function, initial weight chooses the random number between (- 1,1), and the selection range of learning rate is moved between 0.01~0.8 Predictor selection scope is measured between 0.9~0.95, the condition of convergence is network overall error E≤1.0e-12.
Fig. 4 is the R and design parameter fitting neural network model according to the preferred embodiment of the present invention.As shown in Figure 4, it is neural Network model 400 is three-layer neural network structure, wherein design parameter X1、X2、X3、X4And X5For input parameter, i.e. input layer The number of neuron is 5, and the number of hidden layer neuron is 9, and R is output parameter, i.e., the number of output layer neuron is 1 Individual, hidden layer neuron activation function and output layer activation primitive are Sigmoid type activation primitives, initial weight choose (- 1,1) random number between, the selection range of learning rate between 0.01~0.9, factor of momentum selection range 0.85~ Between 0.98, the condition of convergence is network overall error E≤1.0e-12.
Fig. 5 is the structural representation for optimizing system according to the temperature rise of the tab terminal of the preferred embodiment of the present invention.Such as Fig. 5 institutes Show, the temperature rise optimization system 500 of tab terminal includes design parameter and obtained and temperature rise analogue unit 501, the first reliability calculating Unit 502, the second reliability calculating unit 503 and optimization unit 504.
Preferably, design parameter is obtained and temperature rise analogue unit 501 obtains the design parameter of multigroup tab terminal, and is passed through FEM model simulates the temperature rise process of tab terminal overlap, records the maximum temperature rise T of tab terminal overlapmax, will TmaxWith temperature rise limit value TuIt is compared, chooses Tmax≤TuDesign parameter retained, wherein the design parameter includes： Tab terminal length X1, tab terminal width X2, tab terminal thickness X3, tab terminal overlapping part resistance X4And tab terminal The electric current X of loading5。
Preferably, the first reliability calculating unit 502 randomly selects the design parameter of one group of reservation, passes through neutral net mould Type draws TmaxTemperature rise functional relation between the design parameter of reservation, and carry out Monte Carlo (MCS) and calculate to obtain corresponding Tab terminal reliability R0Value, by current R0With reliability limiting value RuIt is compared, if R0≥Ru, then current design is retained Parameter, otherwise, chooses one group of design parameter of reservation so that reliability R again0≥Ru, and retain current design parameter；
Preferably, reliability R >=R of the second reliability calculating unit 503 currently to retainuDesign parameter as initial Design point, acquisition is combined by the different designs parameter in the finite neighborhood of origin of initial designs point, passes through finite element mould Pattern is intended and draws the corresponding maximum temperature rise T of design parameter after combinationmax, and then combine the limiting condition side of tip side daughter board Journey, corresponding tab terminal reliability R value is calculated by Monte Carlo simulation approach (MCS), R is drawn by neural network model Functional relation between design parameter；
Preferably, optimization unit 504 is used for according to the functional relation between R and design parameter, with the cost of tab terminal For optimal conditions, design object function f (X)=C=X1·X2·X3ρ ω, wherein C are the cost of tab terminal,Tip side The density of son, ω is the cost of unit volume tab terminal, and the reliability using tab terminal obtains tip side as constraints The optimal design parameter of son is minf (x)=E (X1·X2·X3·ρ·ω)st.R≥Ru, wherein X1、X2And X3, value be For the design parameter after optimization.
Another of the temperature rise optimization system 500 of a preferred embodiment of the present invention tab terminal and the present invention are preferred real The temperature rise optimization method 100 for applying a tab terminal is corresponded, herein without being described in detail.
The present invention is described by reference to a small amount of embodiment.However, it is known in those skilled in the art, as What subsidiary Patent right requirement was limited, except the present invention other embodiments disclosed above equally fall the present invention's In the range of.
Normally, all terms used in the claims are all solved according to them in the usual implication of technical field Release, unless clearly defined in addition wherein.All references " one/described/be somebody's turn to do [device, component etc.] " are all opened ground At least one example in described device, component etc. is construed to, unless otherwise expressly specified.Any method disclosed herein Step need not all be run with disclosed accurate order, unless explicitly stated otherwise.

Claims (10)

1. a kind of temperature rise optimization method of UHVDC converter station tab terminal, including：
Step 1, the design parameter of multigroup tab terminal is obtained, and passes through the temperature of FEM model simulation tab terminal overlap The process of liter, records the maximum temperature rise T of tab terminal overlapmax, by TmaxWith temperature rise limit value TuIt is compared, chooses Tmax ≤TuDesign parameter retained, wherein the design parameter includes：Tab terminal length X1, tab terminal width X2, joint Terminal thickness X3, tab terminal overlapping part resistance X4The electric current X loaded with tab terminal5；
Step 2, the design parameter of one group of reservation is randomly selected, T is drawn by neural network modelmaxWith the design parameter of reservation Between temperature rise functional relation, and carry out Monte Carlo (MCS) calculate obtain corresponding tab terminal reliability R0Value, ought Preceding R0With reliability limiting value RuIt is compared, if R0≥Ru, then retain current design parameter, otherwise, choose what is retained again One group of design parameter so that reliability R0≥Ru, and retain current design parameter；
Step 3, reliability R >=R currently to retainuDesign parameter as initial designs point, obtain using initial designs point to be former Different designs parameter in the finite neighborhood of point is combined, and is simulated by FEM model and is drawn the design parameter after combination Corresponding maximum temperature rise Tmax, and then the limit state equation of tip side daughter board is combined, counted by Monte Carlo simulation approach (MCS) Corresponding tab terminal reliability R value is calculated, the functional relation between R and design parameter is drawn by neural network model；
Step 4, according to the functional relation between R and design parameter, using the cost of tab terminal as optimal conditions, design object letter Number f (X)=C=X1·X2·X3ρ ω, wherein C are the cost of tab terminal, and the density of ρ tab terminals, ω is unit volume The cost of tab terminal, using the reliability of tab terminal as constraints, the optimal design parameter for obtaining tab terminal is Minf (x)=E (X1·X2·X3·ρ·ω)st.R≥Ru, wherein X1、X2And X3Value be optimization after design parameter.
2. according to the method described in claim 1, it is characterised in that the utilization FEM model realizes the temperature rise of tab terminal Process simulation is：
When tab terminal flows through electric current, its current density should meet Current continuity equation：
γ is the conductance of tab terminal in formula,For the current potential at tab terminal any point；
Tab terminal is in the case where having endogenous pyrogen and Unsteady Heat Transfer, and the differential equation for describing its temperature field is：
<mrow> <mfrac> <mo>&amp;part;</mo> <mrow> <mo>&amp;part;</mo> <mi>x</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mi>x</mi> </msub> <mfrac> <mrow> <mo>&amp;part;</mo> <mi>T</mi> </mrow> <mrow> <mo>&amp;part;</mo> <mi>x</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mo>&amp;part;</mo> <mrow> <mo>&amp;part;</mo> <mi>y</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mi>y</mi> </msub> <mfrac> <mrow> <mo>&amp;part;</mo> <mi>T</mi> </mrow> <mrow> <mo>&amp;part;</mo> <mi>y</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mo>&amp;part;</mo> <mrow> <mo>&amp;part;</mo> <mi>z</mi> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mi>z</mi> </msub> <mfrac> <mrow> <mo>&amp;part;</mo> <mi>T</mi> </mrow> <mrow> <mo>&amp;part;</mo> <mi>z</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>Q</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&amp;rho;</mi> <mi>c</mi> <mfrac> <mrow> <mo>&amp;part;</mo> <mi>T</mi> </mrow> <mrow> <mo>&amp;part;</mo> <mi>t</mi> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo> </mrow> <mo>&amp;Element;</mo> <mi>V</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
T is the thermodynamic temperature of arbitrfary point in formula, and ρ, c is respectively the density and specific heat capacity of tab terminal, and t is the time, and V is field Domain；
The energy that tab terminal is used to raise temperature is E=qw, the heat q that tab terminal endogenous pyrogen is producedw, according to its electric current Field distribution is obtained：
Q=qw-qs-qe (3)
In formulaFor the heat produced by tab terminal internal heat resource, Ex、Ey、EzFor the electricity of tab terminal Field intensity；qs=αconA0(Tf-T0) it is tab terminal to surrounding medium institute dispersed heat, αconFor heat loss through convection coefficient unit For W/ (m2·℃)；A0It is m for heat loss through convection square measure2, TfFor heater temperature unit for DEG C, T0It is for environment temperature unit ℃；Pass through heat radiation dispersed heat, ε for tab terminaliFor emissivity, 0.92 is taken；σ is this base of a fruit Sweet smell-Boltzmann constant, takes 5.67 × 10-8W/m2.K4；AiFor radiating surface i area；FijFor by radiating surface i to radiating surface j Form factor, typically take Fij=1；
Simultaneous formula (1) (2) (3) builds the FEM model of tab terminal, and carries out gridding point with simplified model, it is determined that just After beginning temperature rise and boundary condition, the Electric Field Distribution of each node of tab terminal is obtained using formula (1), is then obtained with formula (3) The energy Q that tab terminal absorbs, last applying equation (2) solves the Distribution of temperature rise situation obtained on tab terminal, again true afterwards Determine resistivity γ, then alternately current field and the calculating of temperature rise, so that the simultaneous solution of current field and temperature rise is realized, it is real The simulation of existing butt joint terminal temperature rise process.
3. according to the method described in claim 1, it is characterised in that the temperature rise limit value TuSpan be：60K~ 200K。
4. according to the method described in claim 1, it is characterised in that the neural network model used in the step 2 is three layers Neural network structure, wherein the design parameter X1、X2、X3、X4And X5For input parameter, the number of hidden layer neuron is 12 It is individual, TmaxFor output parameter, hidden layer neuron activation function is Sigmoid type activation primitives, and output layer activation primitive is linear Purelin type transmission functions, initial weight chooses the random number between (- 1,1), the selection range of learning rate 0.01~ Between 0.8, factor of momentum selection range is between 0.9~0.95, and the condition of convergence is network overall error E≤1.0e-12.
5. according to the method described in claim 1, it is characterised in that the limit state equation of the tip side daughter board is：
Z=g (X)=g (X1,X2,X3,X4,X5)=Tu-Tmax(X1,X2,X3,X4,X5)=0.
6. according to the method described in claim 1, it is characterised in that the neural network model used in the step 3 is three layers Neural network structure, wherein design parameter X1、X2、X3、X4And X5For input parameter, the number of hidden layer neuron is 9, R For output parameter, hidden layer neuron activation function and output layer activation primitive are Sigmoid type activation primitives, initial power Value chooses the random number between (- 1,1), and the selection range of learning rate is between 0.01~0.9, and factor of momentum selection range exists Between 0.85~0.98, the condition of convergence is network overall error E≤1.0e-12.
7. according to the method described in claim 1, it is characterised in that the functional relation between the R and design parameter is：
<mrow> <mi>R</mi> <mo>=</mo> <mi>&amp;delta;</mi> <mo>&amp;lsqb;</mo> <mi>b</mi> <msub> <mn>2</mn> <mi>k</mi> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>9</mn> </munderover> <msub> <mi>v</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> <mi>&amp;delta;</mi> <mrow> <mo>(</mo> <mi>b</mi> <msub> <mn>1</mn> <mi>j</mi> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>5</mn> </munderover> <msub> <mi>w</mi> <mrow> <mi>j</mi> <mi>i</mi> </mrow> </msub> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>,</mo> <mrow> <mo>(</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
Wherein, wji, vkjAnd b1j, b2kThe connection weight and threshold value of neutral net are represented respectively, and δ is transmission function.
8. according to the method described in claim 1, it is characterised in that described to calculate corresponding by Monte Carlo simulation approach (MCS) Tab terminal reliability R value when, the number realization of selection is 5000 times.
9. according to the method described in claim 1, it is characterised in that when calculating corresponding tab terminal reliability R, can be to design Parameter be normalized for：
<mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>0.5</mn> <mo>&amp;lsqb;</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mn>0.5</mn> <mo>&amp;lsqb;</mo> <mi>max</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mi>min</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>...</mo> <mn>5</mn> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
10. a kind of temperature rise optimization system of UHVDC converter station tab terminal, including：
Design parameter is obtained and temperature rise analogue unit, the design parameter for obtaining multigroup tab terminal, and passes through finite element mould Pattern intends the temperature rise process of tab terminal overlap, records the maximum temperature rise T of tab terminal overlapmax, by TmaxWith temperature Rise limiting value TuIt is compared, chooses Tmax≤TuDesign parameter retained, wherein the design parameter includes：Tab terminal Length X1, tab terminal width X2, tab terminal thickness X3, tab terminal overlapping part resistance X4The electricity loaded with tab terminal Flow X5；
First reliability calculating unit, randomly selects the design parameter of one group of reservation, T is drawn by neural network modelmaxWith guarantor Temperature rise functional relation between the design parameter stayed, and carry out Monte Carlo (MCS) and calculate that to obtain corresponding tab terminal reliable Spend R0Value, by current R0With reliability limiting value RuIt is compared, if R0≥Ru, then retain current design parameter, otherwise, weight It is new to choose the one group of design parameter retained so that reliability R0≥Ru, and retain current design parameter；
Second reliability calculating unit, with the reliability R >=R currently retaineduDesign parameter as initial designs point, obtain with Initial designs point is combined for the different designs parameter in the finite neighborhood of origin, is simulated by FEM model and draws group The corresponding maximum temperature rise T of design parameter after conjunctionmax, and then the limit state equation of tip side daughter board is combined, pass through Monte Carlo Simulation (MCS) calculates corresponding tab terminal reliability R value, is drawn by neural network model between R and design parameter Functional relation；
Optimize unit, for according to the functional relation between R and design parameter, using the cost of tab terminal as optimal conditions, if Count object function f (X)=C=X1·X2·X3ρ ω, wherein C are the cost of tab terminal, the density of ρ tab terminals, and ω is The cost of unit volume tab terminal, using the reliability of tab terminal as constraints, obtains the optimization design of tab terminal Parameter is minf (x)=E (X1·X2·X3·ρ·ω)st.R≥Ru, wherein X1、X2And X3, value be optimization after setting Count parameter.
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