CN107122538B - Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal - Google Patents
Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal Download PDFInfo
- Publication number
- CN107122538B CN107122538B CN201710270151.2A CN201710270151A CN107122538B CN 107122538 B CN107122538 B CN 107122538B CN 201710270151 A CN201710270151 A CN 201710270151A CN 107122538 B CN107122538 B CN 107122538B
- Authority
- CN
- China
- Prior art keywords
- terminal
- temperature rise
- joint
- reliability
- design parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 238000013461 design Methods 0.000 claims abstract description 128
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 30
- 238000010438 heat treatment Methods 0.000 claims abstract description 7
- 230000006870 function Effects 0.000 claims description 51
- 238000003062 neural network model Methods 0.000 claims description 26
- 230000004913 activation Effects 0.000 claims description 24
- 210000002569 neuron Anatomy 0.000 claims description 24
- 238000004088 simulation Methods 0.000 claims description 15
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 10
- 230000005684 electric field Effects 0.000 claims description 8
- 230000017525 heat dissipation Effects 0.000 claims description 6
- 239000010979 ruby Substances 0.000 claims description 6
- 229910001750 ruby Inorganic materials 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 230000005855 radiation Effects 0.000 claims description 3
- 239000002699 waste material Substances 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 17
- 238000004458 analytical method Methods 0.000 description 8
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 230000008878 coupling Effects 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 239000010949 copper Substances 0.000 description 3
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 2
- 239000000956 alloy Substances 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 2
- 239000003990 capacitor Substances 0.000 description 2
- 239000000306 component Substances 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 229910052802 copper Inorganic materials 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 239000004519 grease Substances 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 229920000742 Cotton Polymers 0.000 description 1
- 229910000881 Cu alloy Inorganic materials 0.000 description 1
- 244000137852 Petrea volubilis Species 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a temperature rise optimization method for a connector terminal of an extra-high voltage direct current converter station, which can prevent or reduce loss caused by power grid disaster accidents due to heating of the connector terminal after design parameters are optimized, and simultaneously avoid economic waste caused by oversize design of the connector terminal. The method comprises the following steps: selecting design parameters to obtain the maximum temperature rise T of the lap joint part of the corresponding joint terminal platemaxAnd T ismax≤TuObtaining a function relation between the maximum temperature rise and design parameters, obtaining the value of the reliability R of the joint terminal by combining the extreme state equation of the joint terminal board through a Monte Carlo method, and obtaining the reliability R of the joint terminal when the reliability R is larger than or equal to RuAnd then selecting an initial design point, combining different parameters near the initial design point, calculating the maximum temperature rise value of the corresponding lap joint part to obtain reliability, obtaining a functional relation between the design parameters and the reliability, setting a target function by taking the cost of the joint terminal as an optimization condition, and obtaining the optimized design parameters of the joint terminal by taking the reliability as a constraint condition.
Description
Technical Field
The invention belongs to the technical field of disaster prevention of an extra-high voltage direct current converter station power grid, and particularly relates to a temperature rise optimization method and system for a connector terminal of an extra-high voltage direct current converter station.
Background
Along with the development of national economy, the scale of the extra-high voltage direct current converter station engineering is rapidly enlarged, and the overheat defect of the joint terminal of the through-flow loop in the station can cause the fatigue damage of the structure, materials and the like of converter station equipment, shorten the service life of the equipment and influence the safe and stable operation of a power grid. The current discovered heating phenomenon of the connector terminal of the through-flow loop of the extra-high voltage direct current converter station almost comprises all the extra-high voltage direct current converter stations which are in operation at present, the swept range is large, the caused harm degree is high, and if serious faults are caused, the economic loss is large in general amount and difficult to accurately estimate. The extra-high voltage engineering is used as the most core component of a power grid, the requirement on safety and stability is higher, and the economic loss caused by the fault is more huge, so that the extra-high voltage direct current converter station engineering has higher requirements on the temperature rise design of a joint terminal of the extra-high voltage direct current converter station engineering.
Disclosure of Invention
According to one aspect of the invention, the temperature rise optimization method for the connector terminal of the extra-high voltage direct current converter station is provided, and comprises the following steps:
The method of claim 1, wherein the simulating the temperature rise of the tab terminal using the finite element model is:
when a connector terminal flows current, the current density of the connector terminal meets the current continuity equation:
where gamma is the electrical conductivity of the tab terminal,
the potential at any point of the tab terminal;
under the condition that the joint terminal has an internal heat source and unsteady heat conduction, a differential equation for describing a temperature field of the joint terminal is as follows:
wherein T is the thermodynamic temperature at any point,
respectively the density and specific heat capacity of the joint terminal, t is time, and V is field;
the energy of the joint terminal for raising the temperature is E-qwHeat q generated by heat source in terminalwThe current field distribution according to it is given by:
Q＝qw-qs-qe (3)
in the formula
For heat generated by a heat source inside the tab terminal, Ex、Ey、EzThe electric field strength of the tab terminal; q. q.ss＝αconA0(Tf-T0) Heat dissipated from the terminals of the terminals to the surrounding medium, alphaconThe unit of the convective heat dissipation coefficient is W/(m)2·℃)；A0Is the unit of convection heat dissipation area is m2，TfIs the temperature unit of the heating body is DEG C0In units of ambient temperature;
the heat dissipated by the thermal radiation for the tab terminal,itaking emissivity as 0.92; sigma is Stefan-Boltzmann constant, and is 5.67 × 10-8W/m2.K4；AiIs the area of the radiating plane i; fijFor the form factor from plane i to plane j, generally take Fij＝1；
The method comprises the steps of (1), (2) and (3) constructing a finite element model of the joint terminal, carrying out gridding to simplify the model, obtaining electric field distribution of each node of the joint terminal by using the formula (1) after determining initial temperature rise and boundary conditions, then solving energy Q absorbed by the joint terminal by using the formula (3), finally solving the temperature rise distribution condition on the joint terminal by using the formula (2), then re-determining resistivity gamma, and alternately calculating a current field and a temperature rise field, thereby realizing simultaneous solving of the current field and the temperature rise field and realizing simulation of the temperature rise process of the joint terminal.
Preferably, wherein said limit value of temperature rise TuThe value range is as follows: 60K to 200K.
Preferably, the neural network model used in step 2 is a three-layer neural network structure, wherein the device is a three-layer neural networkCounting parameter X1、X2、X3、X4And X5For the input parameters, the number of hidden layer neurons is 12, TmaxFor output parameters, the hidden layer neuron activation function is a Sigmoid type activation function, the output layer activation function is a linear Purelin type transfer function, an initial weight value selects a random number between (-1,1), the selection range of the learning rate is between 0.01 and 0.8, the selection range of the momentum factor is between 0.9 and 0.95, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
Preferably, wherein the terminal board limit state equation is:
Z＝g(X)＝g(X1,X2,X3,X4,X5)＝Tu-Tmax(X1,X2,X3,X4,X5)＝0。
preferably, the neural network model used in step 3 is a three-layer neural network structure, wherein the parameter X is designed1、X2、X3、X4And X5The number of the hidden layer neurons is 9 for input parameters, R is an output parameter, the hidden layer neuron activation function and the output layer activation function are both Sigmoid type activation functions, the initial weight value selects a random number between (-1,1), the learning rate selection range is 0.01-0.9, the momentum factor selection range is 0.85-0.98, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
Preferably, wherein the functional relationship between R and the design parameter is:
wherein, wji，vkjAnd b1j，b2kRespectively representing the connection weight value and the threshold value of the neural network, and being transfer functions.
Preferably, the number of simulations selected when calculating the value of the reliability R of the corresponding joint terminal by the monte carlo simulation Method (MCS) is 5000.
Preferably, wherein the corresponding joint terminal reliability R is calculated, the design parameters may be normalized as:
according to another aspect of the invention, a temperature rise optimization system for a connector terminal of an extra-high voltage direct current converter station is provided, which comprises:
a design parameter acquisition and temperature rise simulation unit for acquiring design parameters X of a set of tab terminals1、X2、X3、X4And X5And simulating the temperature rise process of the joint terminal lap joint part through a finite element model, and recording the maximum temperature rise T of the joint terminal lap joint partmaxWill TmaxWith limit value of temperature rise TuMaking a comparison when Tmax≤TuIf so, sending the data to a first reliability calculation unit for calculation, otherwise, reselecting design parameters for temperature rise simulation;
a first reliability calculation unit for obtaining T through the neural network modelmaxTaking one group of the relations and combining the limit state equation of the terminal board, calculating the corresponding value of the reliability R of the terminal board by a Monte Carlo simulation Method (MCS), and comparing the current R with the reliability limit value RuBy comparison, if R is not less than RuIf not, reselecting a reserved set of design parameters to ensure a reliability R0≥RuAnd keeping the current design parameters;
a second reliability calculating unit for calculating the current reserved reliability R ≧ RuThe design parameters are used as initial design points, different design parameters in a limited neighborhood taking the initial design points as original points are obtained and combined, and the maximum temperature rise T corresponding to the combined design parameters is obtained through simulation of a finite element modelmaxAnd further combining the extreme state equation of the joint terminal board, calculating the corresponding value of the reliability R of the joint terminal through a Monte Carlo simulation Method (MCS), and obtaining R and the setting through a neural network modelCalculating functional relation between parameters;
an optimization unit for designing an objective function f (X) C (X) by using the cost of the terminal as an optimization condition1·X2·X3Rho · ω, where C is the cost of the tab terminal,
the density of the tab terminal, ω, is the cost per unit volume of the tab terminal, and the reliability of the tab terminal is taken as a constraint condition to obtain the optimum design parameter of the tab terminal, minf (X) ═ E (X)1·X2·X3·ρ·ω)st.R≥RuWherein X is1、X2And X3And the value of (1) is the optimized design parameter.
The invention provides a temperature rise optimization method for a connector terminal of an extra-high voltage direct current converter station, which can prevent or reduce loss caused by power grid disaster accidents due to heating of the connector terminal after design parameters are optimized, and simultaneously avoid economic waste caused by oversize design of the connector terminal.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a flowchart of a temperature rise optimizing method of a tab terminal according to a preferred embodiment of the present invention;
FIG. 2 is a circuit diagram of an analysis experiment of temperature rise limit of a connector terminal;
FIG. 3 is T according to a preferred embodiment of the present inventionmaxFitting a neural network model with the design parameters;
FIG. 4 is a diagram of a neural network model fitting R to design parameters in accordance with a preferred embodiment of the present invention; and
fig. 5 is a schematic structural view of a temperature rise optimizing system of a tab terminal according to a preferred embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flowchart of a temperature rise optimizing method of a tab terminal according to a preferred embodiment of the present invention. As shown in FIG. 1, a temperature rise optimization method 100 of a terminal block comprises the steps of firstly selecting a plurality of sets of design parameters, obtaining a maximum temperature rise value of a lap joint part of a terminal block corresponding to the current design parameters through a finite element model, obtaining a functional relation between the maximum temperature rise and the design parameters through a neural network model when the current maximum temperature rise is less than or equal to a temperature rise limit value, obtaining a reliability value R of the terminal block through a MonteCarlo method by combining a limit state equation of the terminal block, selecting an initial design point when the R is greater than or equal to the reliability limit value, obtaining different design parameters near the initial design point for combination, calculating the maximum temperature rise value of the lap joint part of the corresponding terminal block to obtain the reliability, obtaining a functional relation between the design parameters and the reliability through the neural network model, and finally setting a target function by taking the cost of the terminal block as an optimization condition, and obtaining the optimized design parameters of the joint terminal by taking the reliability as a constraint condition.
As shown in fig. 1, method 100 begins at step 101. In step 101, design parameters X of a plurality of sets of tab terminals are obtained1、X2、X3、X4And X5. Preferably, the design parameters are respectively the tab terminal length X1Width X of the tab terminal2Thickness X of the tab terminal3The joint terminal lap joint partResistance X4Current X loaded at the terminals of the terminals5。
In step 102, the temperature rise process of the joint terminal lap part is simulated through a finite element model, and the maximum temperature rise T of the joint terminal lap part is recordedmax. Preferably, the simulation of the temperature rise process of the joint terminal by using the finite element model is as follows:
when a connector terminal flows current, the current density of the connector terminal meets the current continuity equation:
where gamma is the electrical conductivity of the tab terminal,
the potential at any point of the tab terminal;
under the condition that the joint terminal has an internal heat source and unsteady heat conduction, a differential equation for describing a temperature field of the joint terminal is as follows:
wherein T is the thermodynamic temperature at any point,
respectively the density and specific heat capacity of the joint terminal, t is time, and V is field;
the energy of the joint terminal for raising the temperature is E-qwHeat q generated by heat source in terminalwThe current field distribution according to it is given by:
Q＝qw-qs-qe (3)
in the formula
For heat generated by a heat source inside the tab terminal, Ex、Ey、EzThe electric field strength of the tab terminal; q. q.ss＝αconA0(Tf-T0) Heat dissipated from the terminals of the terminals to the surrounding medium, alphaconThe unit of the convective heat dissipation coefficient is W/(m)2·℃)；A0Is the unit of convection heat dissipation area is m2，TfIs the temperature unit of the heating body is DEG C0In units of ambient temperature;
the heat dissipated by the thermal radiation for the tab terminal,itaking emissivity as 0.92; sigma is Stefan-Boltzmann constant, and is 5.67 × 10-8W/m2.K4；AiIs the area of the radiating plane i; fijFor the form factor from plane i to plane j, generally take Fij＝1；
The joint terminal is characterized in that a finite element model of the joint terminal is built in a joint type (1), (2) and (3), and when the finite element model is solved, the model is simplified and is divided into small units in a gridding mode. After the initial temperature rise and the boundary condition are determined, the electric field distribution of each node of the joint terminal is obtained by using the formula (1), then the energy Q absorbed by the joint terminal is solved by using the formula (3), finally the temperature rise distribution condition on the joint terminal is solved by using the formula (2), then the resistivity gamma is determined again, and then the electric field distribution is solved continuously. In the calculation of the current field and the temperature rise field which are alternately carried out, the simultaneous solution of the current field and the temperature rise field is realized, so that the simulation of the temperature rise process of the joint terminal is realized.
Preferably, the finite element model used in the present invention is a three-dimensional finite element model of the terminal connector established by a modeling method of ANSYS parameterization program, and the temperature field and the current field of the terminal connector are alternately calculated by the SOLID227 unit in ANSYS. The unit has ten nodes, each node has 4 degrees of freedom, and transient and steady-state thermoelectric coupling analysis can be carried out on the terminal board. SOLID227 coupling field material properties must be set to resistivity, thermal conductivity, mass density, specific heat capacity, dielectric coefficient. The invention divides the model into tetrahedral units by adopting free grid division and then realizes the control of the size and density distribution of the grid by using the intelligent size control technology of ANSYS. Because the electric field and the temperature field are mutually influenced, a certain field cannot be solved independently, and therefore a direct coupling method must be adopted for solving. Before solving, firstly, designating the analysis type as transient analysis and opening a time integration effect; secondly, defining the load step and the ending time according to the time required by the connector terminal board to achieve stable work, wherein the ending time of the load step is 4 hours, then defining the load current, the load change mode is step load, and starting the automatic time step function. A uniform temperature field was set in which the initial temperature of the terminal block was 20 c at room temperature, and the temperature at both ends of the terminal block was maintained at approximately 20 c. During loading, all node loading current vectors of a joint terminal board are selected on one end face of a joint terminal, the direction of the current vectors is vertical to the end face, and meanwhile, the voltage of the node on the end face is defined to be constant zero; while all nodes on the other end face couple voltage degrees of freedom.
In step 103, T is addedmaxWith limit value of temperature rise TuA comparison is made. Preferably, the temperature rise limit value range is as follows: 60K to 200K. The temperature rise limit is obtained by an analytical test of the temperature rise limit of the terminal block, and the specific experimental procedure will be described in detail below.
In step 104, T is selectedmax≤TuThe design parameters of (a) are reserved, and T is obtained through a neural network modelmaxAnd design parameters. Preferably, the neural network model used in step 104 is a three-layer neural network structure in which the parameter X is designed1、X2、X3、X4And X5For the input parameters, i.e. the number of input layer neurons is 5, the number of hidden layer neurons is 12, TmaxThe number of output layer neurons is 1, the hidden layer neuron activation function is a Sigmoid type activation function, the output layer activation function is a linear Purelin type transfer function, an initial weight value selects a random number between (-1,1), the selection range of the learning rate is 0.01-0.8, the selection range of the momentum factor is 0.9-0.95, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
In step 105, a set of design parameters is randomly selected and T is derived from the neural network modelmaxAnd the temperature rise function relation between the temperature rise function relation and the reserved design parameters is calculated by a Monte Carlo simulation Method (MCS) to obtain the corresponding value of the reliability R of the joint terminal. Preferably, the calculation of the reliability of the temperature rise of the joint terminal requires establishing a temperature rise limit state equation of the joint terminal, and according to the analysis of the temperature rise influence factors of the joint terminal plate, the main influence factors influencing the temperature rise of the joint terminal are the width of the joint terminal plate, the thickness of the joint terminal plate, the length of the joint terminal plate, the resistance of the joint terminal plate and the current loaded by the joint terminal plate. The terminal board is designed according to the limit state of normal use of the terminal board, and the limit state equation of the terminal board is as follows:
Z＝g(X)＝g(X1,X2,X3,X4,X5)＝Tu-Tmax(X1,X2,X3,X4,X5)＝0。
because the functional relation formula of the maximum temperature rise Tmax of the joint terminal overlapping part can not be expressed explicitly, the analytical formula of the extreme state equation of the joint terminal board is difficult to calculate, and the invention uses a neural network model to ensure that T is increasedmaxThe functional relationship with the design parameters approximates the extreme equation of state of the terminal block as closely as possible.
When the temperature rise of the joint terminal is optimally designed, the reliability of the joint terminal can be calculated by taking the reliability of the joint terminal as a constraint condition: r ═ n-g(x)＞0fx(X)dX＝P{g(X)＞0}≥RuWherein R isuFor the limit value of the reliability, solving the formula needs to know that the joint probability density function of the temperature rise state function of the joint terminal plate converts the reliability probability constraint into the certainty constraint, a theoretical method is applied, the joint probability density function of the state function is difficult to determine, and the formula is an invisible function relation and cannot directly reflect the function relation of the design parameters and the reliability, so that the Monte Carlo method is used for randomly sampling sample data, probability analysis is carried out on the data, the mean value and the standard deviation of the function value of the limit state are obtained, and the index of the temperature rise reliability of the joint terminal is obtained. Preference is given toCalculating the reliability R of the corresponding joint terminal by Monte Carlo simulation method0The number of simulation times selected is 5000 times.
In step 106, the current R is set0And a reliability limit value RuBy comparison, if R0≥RuThen the current design parameters are retained, otherwise step 105 is re-executed. Preferably, the reliability limit value is set according to actual conditions, and can be a satisfactory value such as 99% or 92%.
In step 107, a tab terminal reliability R is selected0≥RuTaking a group of design parameters as an initial design point, obtaining different design parameters in a limited neighborhood taking the initial design point as an origin point for combination, and simulating and obtaining the maximum temperature rise T corresponding to the combined design parameters through a finite element modelmax. Preferably, the method for calculating the maximum temperature rise value in step 107 is the same as that in step 102, and is not described herein again.
In step 108, the extreme state equation of the terminal block is combined, the corresponding reliability value R of the terminal block is calculated by Monte Carlo Simulation (MCS), and the functional relation between R and the design parameter is obtained by the neural network model. Preferably, the method for calculating the reliability in step 108 is the same as that in step 105, and will not be described herein. Preferably, the neural network model used in step 108 is a three-layer neural network structure in which the parameter X is designed1、X2、X3、X4And X5The method comprises the steps of inputting parameters, namely the number of input layer neurons is 5, the number of hidden layer neurons is 9, R is output parameters, namely the number of output layer neurons is 1, hidden layer neuron activation functions and output layer activation functions are Sigmoid activation functions, selecting random numbers between (-1,1) as initial weights, selecting a learning rate within the range of 0.01-0.9, selecting a momentum factor within the range of 0.85-0.98, and converging the conditions that the total error E of a network is less than or equal to 1.0E-12.
In step 109, an objective function f (X) ═ C ═ X is designed based on the functional relationship between R and the design parameter, with the cost of the tab terminal as an optimization condition1·X2·X3Rho · ω, where C is the cost of the tab terminal,
the density of the tab terminal, ω, is the cost per unit volume of the tab terminal, and the reliability of the tab terminal is taken as a constraint condition to obtain the optimum design parameter of the tab terminal, minf (X) ═ E (X)1·X2·X3·ρ·ω)st.R≥RuWherein X is1、X2And X3The value of (c) is the optimized design parameter.
In an embodiment of the invention, the established finite element model is verified to be correct by a terminal plate temperature rise test. And respectively carrying out current-temperature rise tests on aluminum plate-aluminum plate and copper plate-copper plate materials aiming at the terminal material of the joint of the extra-high voltage direct current converter station. The specific test method comprises the following steps: connecting two ends of the terminal board by using a copper wire and screwing the two ends by using bolts, and then connecting the copper wire with the output end of the high-current test equipment, so that current flows through the terminal board from the power box through the copper wire; installing a voltage test line at a position 20-25 cm away from the lap joint part of the terminal board, connecting the end part of the test line with the input end of a voltage digital multimeter, and reading the voltage value of the test part by using the meter; the current flow direction is controlled by a control console, and the resistance value of the terminal board is measured by respectively testing the forward voltage, the reverse voltage and the current of the sample; removing the resistance testing system, connecting the 100KVA transformer and the soft connecting plate in series by using a copper wire, and adding n temperature sensors on two sides of the terminal plate respectively; and controlling the transformer and improving the output current through the console, stabilizing the current after the required value is reached, and monitoring the real-time temperature change of the terminal board through a computer. Fig. 2 is a circuit diagram of an analysis experiment of temperature rise limit of a joint terminal. The device comprises a voltage regulator 201, a voltage reduction transformer 203, a protective resistor 204, a coupling capacitor 205, an experimental sample joint terminal 206, a temperature sensor 207 and a temperature polling instrument 208, wherein the voltage regulator 201 is a 220V alternating current power supply, the voltage regulator 202 is a voltage reduction transformer, the temperature sensor 207 is a coupling capacitor, and the temperature polling instrument 208 is a temperature polling instrument. In actual operation, the temperature rise limit value of the joint terminal is analyzed and tested to test the temperature and deformation of the joint part of the joint terminal and the change condition of the coated electric composite grease, so that the temperature rise limit values of the joint terminal with different materials, different current density design values and different working conditions are determined, and a direct basis is provided for the optimized design of the terminal board. Because the converter station joint terminal is mostly pure copper and aluminum alloy material, therefore experimental sample joint terminal can adopt the aluminum alloy material to make in this experiment. The method comprises the following steps:
(1) before installation, polishing with 200# and 400# fine sand paper to remove surface oxide layer, cleaning the polished surface with acetone, and wiping with clean white cotton cloth or toilet paper; coating 0.2mm of electric composite grease on the lap joint part of the test piece;
(2) measuring the joint resistance of the lap joint part of the test piece before the test;
(3) and (3) adding current to 1500A, carrying out through-flow for 4 hours to observe the temperature change condition of the lap joint part of the test piece, if the temperature is stable, continuously heating for 10 hours to observe the temperature change condition of the test piece, and if the temperature of the test piece after being heated for 10 hours has an obvious inflection point, the temperature rise with the inflection point is the temperature rise limit value. If the test piece does not have an obvious inflection point of temperature rise after being heated for 10 hours, continuing to add the current to 2000A and 2500A after the current is withdrawn to 0, and repeating the above test steps until the obvious inflection point of the temperature rise of the connector terminal plate occurs. The obvious inflection point is the temperature rise limit value.
FIG. 3 is T according to a preferred embodiment of the present inventionmaxAnd fitting the neural network model with the design parameters. As can be seen from FIG. 3, the neural network model 300 is a three-layer neural network structure, in which the design parameter X1、X2、X3、X4And X5For the input parameters, i.e. the number of input layer neurons is 5, the number of hidden layer neurons is 12, TmaxThe number of output layer neurons is 1, the hidden layer neuron activation function is a Sigmoid type activation function, the output layer activation function is a linear Purelin type transfer function, an initial weight value selects a random number between (-1,1), the selection range of the learning rate is 0.01-0.8, the selection range of the momentum factor is 0.9-0.95, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
FIG. 4 is a diagram of a neural network model fitting R to design parameters in accordance with a preferred embodiment of the present invention. As can be seen from FIG. 4, the neural network model 400 has three layersNeural network structure in which the parameter X is designed1、X2、X3、X4And X5The method comprises the steps of inputting parameters, namely the number of input layer neurons is 5, the number of hidden layer neurons is 9, R is output parameters, namely the number of output layer neurons is 1, hidden layer neuron activation functions and output layer activation functions are Sigmoid activation functions, selecting random numbers between (-1,1) as initial weights, selecting a learning rate within the range of 0.01-0.9, selecting a momentum factor within the range of 0.85-0.98, and converging the conditions that the total error E of a network is less than or equal to 1.0E-12.
Fig. 5 is a schematic structural view of a temperature rise optimizing system of a tab terminal according to a preferred embodiment of the present invention. As shown in fig. 5, the temperature rise optimizing system 500 of the tab terminal includes a design parameter obtaining and temperature rise simulating unit 501, a first reliability calculating unit 502, a second reliability calculating unit 503, and an optimizing unit 504.
Preferably, the design parameter obtaining and temperature rise simulating unit 501 obtains design parameters of a plurality of sets of joint terminals, simulates a temperature rise process of the joint terminal overlapping portion through a finite element model, and records the maximum temperature rise T of the joint terminal overlapping portionmaxWill TmaxWith limit value of temperature rise TuComparing and selecting Tmax≤TuWherein the design parameters include: length X of terminal1Width X of the tab terminal2Thickness X of the tab terminal3Resistance X of joint terminal lap joint part4And current X loaded by the terminal of the terminal5。
Preferably, the first reliability calculation unit 502 randomly selects a set of reserved design parameters, and derives T through a neural network modelmaxTemperature rise function relation with reserved design parameters, and Monte Carlo (MCS) calculation is carried out to obtain corresponding joint terminal reliability R0Will be present as R0And a reliability limit value RuBy comparison, if R0≥RuIf not, reselecting the reserved set of design parameters to make the reliability R0≥RuAnd keep currentThe design parameters of (1);
preferably, the second reliability calculation unit 503 calculates the reliability R ≧ R with the currently retained reliabilityuThe design parameters are used as initial design points, different design parameters in a limited neighborhood taking the initial design points as original points are obtained and combined, and the maximum temperature rise T corresponding to the combined design parameters is obtained through simulation of a finite element modelmaxThen, in combination with the extreme state equation of the joint terminal board, calculating the corresponding value of the reliability R of the joint terminal through a Monte Carlo simulation Method (MCS), and obtaining the functional relation between R and the design parameter through a neural network model;
preferably, the optimization unit 504 is configured to design the objective function f (X) ═ C ═ X according to a functional relationship between R and the design parameter, with the cost of the tab terminal as an optimization condition1·X2·X3Rho · ω, where C is the cost of the tab terminal,
the density of the tab terminal, ω, is the cost per unit volume of the tab terminal, and the reliability of the tab terminal is taken as a constraint condition to obtain the optimum design parameter of the tab terminal, minf (X) ═ E (X)1·X2·X3·ρ·ω)st.R≥RuWherein X is1、X2And X3And the value of (1) is the optimized design parameter.
The temperature rise optimization system 500 of the joint terminal according to one preferred embodiment of the present invention corresponds to the temperature rise optimization method 100 of the joint terminal according to another preferred embodiment of the present invention, and detailed description thereof is omitted.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
Claims (10)
1. A temperature rise optimization method for a connector terminal of an extra-high voltage direct current converter station comprises the following steps:
step 1, obtaining design parameters of a plurality of groups of joint terminals, simulating the temperature rise process of the lap joint part of the joint terminals through a finite element model, and recording the maximum temperature rise T of the lap joint part of the joint terminalsmaxWill TmaxWith limit value of temperature rise TuComparing and selecting Tmax≤TuWherein the design parameters include: length X of terminal1Width X of the tab terminal2Thickness X of the tab terminal3Resistance X of joint terminal lap joint part4And current X loaded by the terminal of the terminal5；
Step 2, randomly selecting a group of reserved design parameters, and obtaining T through a neural network modelmaxAnd the temperature rise function relation between the design parameters and the reserved design parameters is carried out, and the corresponding joint terminal reliability R is obtained by calculating the MCS by the Monte Carlo simulation method0Will be present as R0And a reliability limit value RuBy comparison, if R0≥RuIf not, reselecting the reserved set of design parameters to make the reliability R0≥RuAnd keeping the current design parameters;
step 3, using the currently reserved reliability R to be more than or equal to RuThe design parameters are used as initial design points, different design parameters in a limited neighborhood taking the initial design points as original points are obtained and combined, and the maximum temperature rise T corresponding to the combined design parameters is obtained through simulation of a finite element modelmaxThen, in combination with the extreme state equation of the joint terminal board, calculating the corresponding value of the reliability R of the joint terminal through a Monte Carlo simulation method MCS, and obtaining the functional relation between R and the design parameter through a neural network model;
and 4, designing an objective function f (X) ═ C ═ X according to the functional relation between R and the design parameters and by taking the cost of the joint terminal as an optimization condition1·X2·X3ρ · ω, where C is the cost of the tab terminal,
the density of the joint terminal, ω, is the cost of the joint terminal per unit volume, and the reliability of the joint terminal is taken as a constraint condition, and the optimal design parameter of the joint terminal is obtained as min f (X) E (X)1·X2·X3·ρ·ω)st.R≥RuWherein X is1、X2And X3The value of (c) is the optimized design parameter.
2. The method of claim 1, wherein said simulating the temperature rise of the joint terminal overlap portion by a finite element model is:
when a connector terminal flows current, the current density of the connector terminal meets the current continuity equation:
where gamma is the electrical conductivity of the tab terminal,
the potential at any point of the tab terminal;
under the condition that the joint terminal has an internal heat source and unsteady heat conduction, a differential equation for describing a temperature field of the joint terminal is as follows:
wherein T is the thermodynamic temperature at any point,
c is the density and specific heat capacity of the joint terminal respectively, t is time, and V is a field;
the energy of the joint terminal for raising the temperature is E-qwHeat q generated by heat source in terminalwThe current field distribution according to it is given by:
Q＝qw-qs-qe (3)
in the formula
For heat generated by a heat source inside the tab terminal, Ex、Ey、EzThe electric field strength of the tab terminal; q. q.ss＝αconA0(Tf-T0) Heat dissipated from the terminals of the terminals to the surrounding medium, alphaconThe unit of the convective heat dissipation coefficient is W/(m)2·℃)；A0Is the unit of convection heat dissipation area is m2，TfIs the temperature unit of the heating body is DEG C0In units of ambient temperature;
the heat dissipated by the thermal radiation for the tab terminal,itaking emissivity as 0.92; sigma is Stefan-Boltzmann constant, and is 5.67 × 10-8W/m2.K4；AiIs the area of the radiating plane i; fijFor the shape factor from plane i to plane j, take Fij＝1；
The method comprises the steps of (1), (2) and (3) constructing a finite element model of the joint terminal, carrying out gridding to simplify the model, obtaining electric field distribution of each node of the joint terminal by using the formula (1) after determining initial temperature rise and boundary conditions, then solving energy Q absorbed by the joint terminal by using the formula (3), finally solving the temperature rise distribution condition on the joint terminal by using the formula (2), then re-determining resistivity gamma, and alternately calculating a current field and a temperature rise field, thereby realizing simultaneous solving of the current field and the temperature rise field and realizing simulation of the temperature rise process of the joint terminal.
3. Method according to claim 1, characterised in that said limit temperature rise T isuThe value range is as follows: 60K to 200K.
4. The method of claim 1, wherein the neural network model used in step 2 is a three-layer neural network structure, wherein the design parameter X1、X2、X3、X4And X5For the input parameters, the number of hidden layer neurons is 12, TmaxFor output parameters, the hidden layer neuron activation function is a Sigmoid type activation function, the output layer activation function is a linear Purelin type transfer function, an initial weight value selects a random number between (-1,1), the selection range of the learning rate is between 0.01 and 0.8, the selection range of the momentum factor is between 0.9 and 0.95, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
5. The method according to claim 1, wherein the terminal board extreme state equation is:
Z＝g(X)＝g(X1,X2,X3,X4,X5)＝Tu-Tmax(X1,X2,X3,X4,X5)＝0。
6. the method of claim 1, wherein the neural network model used in step 3 is a three-layer neural network structure, wherein the design parameter X is1、X2、X3、X4And X5The number of the hidden layer neurons is 9 for input parameters, R is an output parameter, the hidden layer neuron activation function and the output layer activation function are both Sigmoid type activation functions, the initial weight value selects a random number between (-1,1), the learning rate selection range is 0.01-0.9, the momentum factor selection range is 0.85-0.98, and the convergence condition is that the total error E of the network is less than or equal to 1.0E-12.
7. The method of claim 1, wherein the functional relationship between R and design parameters is:
wherein, wji，vkjAnd b1j，b2kRespectively representing the connection weight value and the threshold value of the neural network, and being transfer functions.
8. The method of claim 1, wherein the number of simulations selected when calculating the corresponding value of the reliability R of the joint terminal by means of Monte Carlo Simulation (MCS) is 5000.
9. The method of claim 1, wherein in calculating the respective terminal reliability R, the design parameters are normalized by:
10. a temperature rise optimization system of a connector terminal of an extra-high voltage direct current converter station comprises:
a design parameter acquisition and temperature rise simulation unit for acquiring design parameters of multiple groups of joint terminals, simulating the temperature rise process of the joint terminal lap joint part through a finite element model, and recording the maximum temperature rise T of the joint terminal lap joint partmaxWill TmaxWith limit value of temperature rise TuComparing and selecting Tmax≤TuWherein the design parameters include: length X of terminal1Width X of the tab terminal2Thickness X of the tab terminal3Resistance X of joint terminal lap joint part4And current X loaded by the terminal of the terminal5；
A first reliability calculation unit for randomly selecting a reserved set of design parametersDeriving T from the network modelmaxAnd the temperature rise function relation between the design parameters and the reserved design parameters is carried out, and the corresponding joint terminal reliability R is obtained by calculating the MCS by the Monte Carlo simulation method0Will be present as R0And a reliability limit value RuBy comparison, if R0≥RuIf not, reselecting the reserved set of design parameters to make the reliability R0≥RuAnd keeping the current design parameters;
a second reliability calculating unit for calculating the current reserved reliability R ≧ RuThe design parameters are used as initial design points, different design parameters in a limited neighborhood taking the initial design points as original points are obtained and combined, and the maximum temperature rise T corresponding to the combined design parameters is obtained through simulation of a finite element modelmaxThen, in combination with the extreme state equation of the joint terminal board, calculating the corresponding value of the reliability R of the joint terminal through a Monte Carlo simulation method MCS, and obtaining the functional relation between R and the design parameter through a neural network model;
an optimization unit for designing an objective function f (X) ═ C ═ X according to a functional relationship between R and the design parameter, with the cost of the tab terminal as an optimization condition1·X2·X3Rho · ω, where C is the cost of the tab terminal,
the density of the joint terminal, ω, is the cost of the joint terminal per unit volume, and the reliability of the joint terminal is taken as a constraint condition, and the optimal design parameter of the joint terminal is obtained as min f (X) E (X)1·X2·X3·ρ·ω)st.R≥RuWherein X is1、X2And X3And the value of (1) is the optimized design parameter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710270151.2A CN107122538B (en) | 2017-04-24 | 2017-04-24 | Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710270151.2A CN107122538B (en) | 2017-04-24 | 2017-04-24 | Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107122538A CN107122538A (en) | 2017-09-01 |
| CN107122538B true CN107122538B (en) | 2020-11-17 |
Family
ID=59725829
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710270151.2A Active CN107122538B (en) | 2017-04-24 | 2017-04-24 | Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107122538B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107490441B (en) * | 2017-10-16 | 2020-12-04 | 广州贯行电能技术有限公司 | Method and device for judging reasonable temperature rise of electric cabinet caused by current |
| CN109858074B (en) * | 2018-12-13 | 2023-06-13 | 云南电网有限责任公司电力科学研究院 | Transformer oil flow surge simulation method based on finite volume method |
| CN112765861B (en) * | 2020-12-30 | 2023-06-20 | 广东电网有限责任公司电力科学研究院 | Temperature characteristic curve acquisition method and system for overheat defect of high-voltage switch equipment |
| CN113158383B (en) * | 2021-02-24 | 2022-10-28 | 西安交通大学 | Method for evaluating actual field intensity of direct-current cable joint by using coaxial simplified model |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2377688Y (en) * | 1999-05-07 | 2000-05-10 | 杨泰和 | Linear adjustable voltage limit circuit arrangement for charging battery |
| JP2005012082A (en) * | 2003-06-20 | 2005-01-13 | Matsushita Electric Ind Co Ltd | Metallized film capacitor |
| CN101126929A (en) * | 2007-09-05 | 2008-02-20 | 东北大学 | Continuous miner remote real-time failure forecast and diagnosis method and device |
| CN103344839A (en) * | 2013-07-01 | 2013-10-09 | 江苏大学 | Wireless detection method and device for contact resistance of busbar joint |
| CN105095962A (en) * | 2015-07-27 | 2015-11-25 | 中国汽车工程研究院股份有限公司 | Method for predicting dynamic mechanical property of material based on BP artificial neural network |
| CN106021676A (en) * | 2016-05-13 | 2016-10-12 | 国网上海市电力公司 | Multi-circle cable steady-state temperature rise acquiring method based on transfer matrix |
| CN106096116A (en) * | 2016-06-06 | 2016-11-09 | 中国电力科学研究院 | A kind of method and system for setting up temperature prediction model for the terminal board of coating electric force compounded grease |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090142257A1 (en) * | 1997-07-22 | 2009-06-04 | Blacklight Power, Inc. | Inorganic hydrogen compounds and applications thereof |
-
2017
- 2017-04-24 CN CN201710270151.2A patent/CN107122538B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2377688Y (en) * | 1999-05-07 | 2000-05-10 | 杨泰和 | Linear adjustable voltage limit circuit arrangement for charging battery |
| JP2005012082A (en) * | 2003-06-20 | 2005-01-13 | Matsushita Electric Ind Co Ltd | Metallized film capacitor |
| CN101126929A (en) * | 2007-09-05 | 2008-02-20 | 东北大学 | Continuous miner remote real-time failure forecast and diagnosis method and device |
| CN103344839A (en) * | 2013-07-01 | 2013-10-09 | 江苏大学 | Wireless detection method and device for contact resistance of busbar joint |
| CN105095962A (en) * | 2015-07-27 | 2015-11-25 | 中国汽车工程研究院股份有限公司 | Method for predicting dynamic mechanical property of material based on BP artificial neural network |
| CN106021676A (en) * | 2016-05-13 | 2016-10-12 | 国网上海市电力公司 | Multi-circle cable steady-state temperature rise acquiring method based on transfer matrix |
| CN106096116A (en) * | 2016-06-06 | 2016-11-09 | 中国电力科学研究院 | A kind of method and system for setting up temperature prediction model for the terminal board of coating electric force compounded grease |
Non-Patent Citations (3)
| Title |
|---|
| Analytical solutions of the equations for the transient temperature field in the three-fluid parallel-channel heat exchanger with three thermal communications;Leszek Malinowski .ect;《International Journal of Heat and Mass Transfer》;20160123;164-170 * |
| Neural diagnostic system for transformer thermal overload protection;V.Galdi .ect;《IEE Proceedings - Electric Power Applications》;20000930;第147卷(第2期);415-421 * |
| 高压电力电缆温度场和载流量评估研究动态;梁永春;《高电压技术》;20160430;第42卷(第4期);1142-1150 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107122538A (en) | 2017-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107122538B (en) | Temperature rise optimization method and system for extra-high voltage direct current converter station joint terminal | |
| CN103323703B (en) | Cable connector fault diagnosing method | |
| CN104764984B (en) | The improved method of transformer oil paper insulation dielectric response Equivalent Circuit Parameter identification | |
| CN108627545A (en) | Method and system for nondestructive testing | |
| CN110543717B (en) | Method and device for constructing GIS equipment temperature rise characteristic simulation model | |
| Kopsidas et al. | Power network reliability framework for integrating cable design and ageing | |
| CN112115628A (en) | Hot spot temperature detection method based on distribution calculation of temperature field of oil-immersed transformer | |
| CN104502768A (en) | Current-induced-heat-type defect evaluation method used for power equipment connecting part | |
| CN103970947A (en) | Real-time temperature rise measurement method of electromagnetic system of low-voltage apparatus based on finite element analysis | |
| Shiravand et al. | Prediction of transformer fault in cooling system using combining advanced thermal model and thermography | |
| CN106096116B (en) | Method and system for establishing temperature rise prediction model for terminal board coated with electric power compound grease | |
| CN104142455A (en) | Grounding grid corrosion fault diagnosis method based on Tellegen theorem | |
| Dubyago et al. | Estimation of Insulating Materials Depreciation and Forecasting the Residual cable Resource considering the current core Temperature | |
| CN108920742A (en) | A kind of cable internal flaw characterizing method based on surface temperature probability density distribution | |
| Jauregui-Rivera et al. | Improving reliability assessment of transformer thermal top-oil model parameters estimated from measured data | |
| CN112097982B (en) | Method for measuring interface pressure of cable intermediate joint under action of electric field and temperature difference | |
| CN110781589B (en) | Method for detecting overheating fault of lap joint of gas insulated metal closed power transmission line | |
| CN117192307A (en) | Assessment method for risk state of insulating layer of dry distribution transformer in cold-hot alternating environment | |
| CN111239486A (en) | Electric energy monitoring method and system | |
| CN117168639A (en) | Cable temperature field distribution measuring device, method and equipment | |
| Bragatto et al. | Assessing thermal behavior of medium voltage cable joints through simulations and measurements | |
| CN106130141A (en) | A kind of charge control method, charging system and electronic equipment | |
| Luo et al. | A method for hot spot temperature monitoring of oil-immersed transformers combining physical simulation and intelligent neural network | |
| CN101697291B (en) | Method for calculating OPGW optical cable short circuit current heat effect by using improved synthetic method | |
| Bragatto et al. | Measuring yearly variation of temperature of medium voltage cable joints |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20171012 Address after: 100192 Beijing city Haidian District Qinghe small Camp Road No. 15 Applicant after: China Electric Power Research Institute Applicant after: State Grid Corporation of China Applicant after: State Grid Henan Electric Power Company Address before: 100192 Beijing city Haidian District Qinghe small Camp Road No. 15 Applicant before: China Electric Power Research Institute Applicant before: State Grid Corporation of China |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |