CN108984978A - Tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm - Google Patents
Tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm Download PDFInfo
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Abstract
The tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm that the invention discloses a kind of, includes the following steps: the electric field finite element analysis model that three support insulators are established in Three-dimensional CAD Software；Determine the structural parameters to be optimized and constraint condition of each key position of three support insulators；Establish the electric property evaluation index of three post insulator minor structures；Construct the objective function of three support insulator Optimal Structure Designings；Using particle swarm algorithm, the structure of three support insulators is optimized.The present invention has comprehensively considered three support insulator key position electricity function indexs, it can be the Optimal Structure Designing providing method and approach of complex-shaped three support insulator, and design cycle and the cost of three support insulators is effectively reduced, improve the electric property of three support insulators, there is good practicability and economy.
Description
Technical field
The invention belongs to Electric Power Equipment Insulation structure-design technique fields, in particular to the GIL tri- based on particle swarm algorithm
Support insulator electric property optimum design method.
Background technique
Three support insulators are gas-insulated metal closed power transmission line (Gas-insulated Metal-enclosed
Transmission Line, GIL) critical component, play a part of that conductor and electric insulation, structure is supported to set in GIL
Meter needs to fully consider the electric property of its different parts, in order to avoid the generation of failure of insulation.At present for three support insulators
Design optimizes mainly by computer numerical value calculation means for the electrical property of a certain position of three support insulators,
And while a certain position electric property of three support insulators is promoted, often the performance at other positions will be declined, tool
There is significant limitation.
The Optimal Structure Designing of three support insulators is related to insulator abdomen shape, insulator supporting leg shape and inserts
Multiple variables such as surface shape, performance requirement include insulator surface tangentially and formate field intensity, multiple mesh such as insert surface field strength
Mark, belongs to the optimization problem scope of multivariable multi-objective restriction.Three support insulators are complex-shaped, and different parts are different
Feature, need to determine a kind of structure compared with multi-parameter, be difficult to meet total optimization structure using Experience Design.
Summary of the invention
To solve problems of the prior art, the purpose of the present invention is to provide a kind of based on particle swarm algorithm
Tri- support insulator electric property optimum design method of GIL, can satisfy GIL with three support insulators and is carrying out piece electrical
When can optimize, need to consider simultaneously the requirement of different parts different performance index, to improve the whole of three support insulators comprehensively
Body electric property.
In order to achieve the above objectives, the present invention is achieved by using following technical scheme:
Tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm, includes the following steps:
1) the electric field finite element analysis model for establishing three support insulators determines the electric field finite element fraction of three support insulators
Analyse the structural parameters to be optimized of model；
2) it determines the constraint condition of three support insulators structural parameters to be optimized, while choosing the electricity of three support insulators
Gas Performance Evaluating Indexes；
3) according to the structure feature of three support insulators, three support insulators structural parameters to be optimized are divided into several
A subset, and determine the optimization sequence of subset；
4) optimization object function of three support insulator Optimal Structure Designings is constructed according to electric property evaluation index；
5) particle swarm algorithm is used, according to the constraint condition of the optimization of subset sequence and structural parameters to be optimized, is solved
Optimum structure parameter under corresponding optimization object function.
In step 1), in the electric field finite element analysis model of three support insulators, according to three support insulator shapes
Structure feature chooses structural parameters to be optimized.
According to the structure feature of the supporting leg shape of three support insulators and the structure feature of metal insert shape, choose to
The structural parameters of optimization.
Choose the horizontal seat put in length of embedment, diameter and the surface radius of corner and supporting leg spline curve of metal insert
It is denoted as structural parameters to be optimized, wherein the points that curvature larger part is chosen in supporting leg spline curve are smaller compared to curvature
It is more to locate the points chosen.
In step 2), using the value range of structural parameters to be optimized as constraint condition；Choose three post insulator sublists
Face formate field intensity maximum value E '1, three support insulator surface tangential fields maximum value E'2And metal insert surface synthesizes field
Strong maximum value E '3As electric property evaluation index.
In step 3), according to each position of three support insulators structural parameters to be optimized to three support insulator electrical resistances
The difference of energy evaluation index influence degree determines the optimization sequence of subset.
The influence degree κ of structural parameters to be optimized to three support insulator electric property evaluation indexesiBy following formula
It determines:
Wherein, X1With X2Respectively represent value different under the same structural parameters to be optimized, Ei1' and Ei2' respectively represent with
The corresponding electric property evaluation index value of structural parameters to be optimized.
In step 3), three support insulators structural parameters to be optimized are divided into two subsets, one of subset packet
The structural parameters of the shape containing supporting leg, another subset include the structural parameters of metal insert shape.
In step 4), after the normalization of electric property evaluation index, then analytic hierarchy process (AHP) is used, construct three support insulators
The optimization object function of Optimal Structure Designing.
Detailed process is as follows for step 5):
Step 5.1, primary population is created, each population includes structural parameters to be optimized and local optimal result；
Step 5.2, reinitialize the position of each particle, calculates the fitness under each particle, using fitness as just
The local optimum of beginning as a result, fitness optimal using under all particles as initial total optimization result, wherein the position of particle
It sets and represents structural parameters to be optimized, the calculated value of fitness representing optimized objective function；
Step 5.3, particle position is updated；
Step 5.4, the fitness after updating under each new particle is calculated, and records total optimization result and local optimum
As a result；
Step 5.5, the above-mentioned steps that iterate 5.3 and step 5.4 until fitness reaches in setting range, then terminate
Iteration.
Compared with prior art, the invention has the following beneficial effects:
Tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm of the invention, first establishes three
The electric field finite element analysis model of support insulator, and the electric field finite element analysis model of determining three support insulators is to be optimized
Structural parameters；The constraint condition of three support insulators structural parameters to be optimized is determined again, while choosing three support insulators
Electric property evaluation index；Further according to the structure feature of three support insulators, three support insulators structure to be optimized is joined
Number is divided into several subsets, and determines the optimization sequence of subset；Three post insulators are constructed further according to electric property evaluation index
The optimization object function of minor structure optimization design；Particle swarm algorithm is finally used, it is sequentially and to be optimized according to the optimization of subset
The constraint condition of structural parameters solves the optimum structure parameter under corresponding optimization object function；To sum up, the present invention comprehensively considers
Three support insulator key position electricity function indexs can be the Optimal Structure Designing of complex-shaped three support insulator
Providing method and approach can effectively improve the efficiency of three support insulator design and optimizations, three support insulators are effectively reduced
Design cycle and cost, sufficiently improve three support insulators electric property reach overall performance optimization.Using the present invention
Method can satisfy GIL with three support insulators carry out piece electrical performance optimization when, need to consider different parts simultaneously
The requirement of different performance index, to improve the piece electrical performance of three support insulators comprehensively.Further, it is choosing to excellent
When the structural parameters of change, according to the structure feature and features of shape of three support insulators, three pillars are described using spline curve
Insulator supporting leg shape chooses spline control points as structural parameters to be optimized, sufficiently lowers and exist using modern optimization method
The complexity of model is constructed in optimization process.The present invention chooses limited but can fully describe three support insulator geometries
Parameter three support insulators are optimized, reduce structural parameters to be optimized as far as possible under the premise of meeting effect of optimization
Quantity；Electric property evaluation index limited but that three support insulator electric properties can be fully described is chosen, to optimizing
Three support insulator electric properties in journey are evaluated, and are reduced electric property as far as possible under the premise of meeting effect of optimization and are commented
The quantity of valence index.The convergence and arithmetic speed for improving optimization process, have saved time cost and operation cost.
Further, the present invention specifies structural parameters to the influence degree of three support insulator electric property evaluation indexes
Evaluation method, three support insulator electric properties are evaluated according to each position of three support insulators structural parameters to be optimized
The influence degree of index is different, and selected all structural parameters are divided into different subsets, are optimized in sequence, drops
The low complexity of optimization process.
Further, the present invention determines optimization object function according to the importance of different electric property evaluation indexes, sufficiently
Consider that the importance of different parts electricity function index, reasonable distribution weight enable the final result of optimization to expire on the whole
Optimal requirement in sufficient engineering.
Detailed description of the invention
Fig. 1 is that the three post insulator minor structures of GIL and electric property in the present invention based on particle swarm algorithm (PSO) optimize
Design method flow diagram.
Fig. 2 is particle swarm algorithm flow diagram of the present invention.
Fig. 3 is that three support insulators are installed on the axial arrangement schematic diagram after metal shell.
Fig. 4 is the longitudinal sectional view of Fig. 3.
Fig. 5 is that three support insulators are installed on the schematic perspective view after metal shell.
Fig. 6 is the schematic diagram that method of the invention chooses Optimal Parameters to three support insulators.
In figure, tri- support insulator of 1-, 2- center conductor, the embedding key of 3- metal, 4- particle collector, 5- metal shell, 6-
Supporting leg.
Specific embodiment
The present invention is described in more detail with reference to the accompanying drawings and examples.
Embodiment
As depicted in figs. 1 and 2, the GIL based on particle swarm algorithm (PSO) of the present embodiment is optimized with three support insulators and is set
Meter method, includes the following steps:
Step 1): the electric field finite element analysis model of three support insulators, three post insulators are established using Three-dimensional CAD Software
The electric field finite element analysis model of son is the geometrical model about three support insulators, determines that three support insulator electric fields of description have
The structural parameters of finite element analysis model, structural parameters include the supporting leg shape of three support insulators structural parameters and metal it is embedding
The structural parameters of part shape, wherein inserts length of embedment, inserts diameter and inserts table including being used to describe metal insert shape
The abscissa of face radius of corner and the point P1- point P9 as shown in FIG. 6 for being used to determine supporting leg spline curve, it is bent on supporting leg
The points that rate larger part is chosen are more compared to the points that curvature smaller part is chosen.Using above-mentioned 12 structural parameters as to be optimized
Structural parameters；
Step 2): the value range of three support insulators structural parameters to be optimized is determined, and using value range as constraint
Condition.The structural parameters of metal insert have inserts length of embedment, inserts diameter and insert surface radius of corner.Extremely referring to Fig. 3
Fig. 6, the supporting leg of three support insulators are rotary body；As shown in fig. 6, supporting leg shape is by one by 9 passes in two-dimensional surface
Key point (P1~P9) spline curve fitting, which is xoy plane, with the center of three support insulators for xoy plane
Coordinate origin, using direction horizontally to the right as x-axis forward direction, using direction straight up as y-axis forward direction, wherein point P1~P9It is corresponding
Coordinate is respectively (x1, 410), (x2, 400), (x3, 370), (x4, 332), (x5, 320), (x6, 260), (x7, 190), (x8,
And (x 190)9, 180).The structural parameters of supporting leg shape by spline curve 9 key point (P1~P9) abscissa (x1~x9)
It determines, such as P in Fig. 61~P9It is shown.By inserts length of embedment, inserts diameter, insert surface radius of corner and point P1~P9's
Abscissa x1~x9This 12 parameters are successively denoted as X ' as structural parameters to be optimized, by this 12 parameters1、X'2、……X′12,
Corresponding value range are as follows:
In formula, X '1、X'2、X′3、X'4、X′5、X'6、X'7、X′8、X′9、X′10、X′11With X '12For structural parameters to be optimized,
X′1min、X′1max、X'2min、X'2max、X′3min、X′3max、X'4min、X'4max、X′5min、X′5max、X′6min、X′6max、X′7min、
X′7max、X′8min、X′8max、X′9min、X′9max、X′10min、X′10max、X′11min、X′11max、X′12minWith X '12maxFor knot to be optimized
The variation range of structure parameter.
Meanwhile choosing three support insulator surface formate field intensity maximum value E '1, three support insulator surface tangential fields are most
Big value E'2And metal insert surface formate field intensity maximum value E '3As electric property evaluation index.
Step 3): structural parameters to be optimized are divided into two subsets, one of subset includes the embedding key-shaped shape of metal
Structural parameters, another subset include the structural parameters of supporting leg shape.Structural parameters to be optimized are to electric property evaluation index
Influence degree κiIt is determined by following formula:
Wherein, X1With X2Respectively represent value different under the same structural parameters to be optimized, Ei1' and Ei2' respectively represent with
The corresponding electric property evaluation index value of structural parameters to be optimized；
By calculating κiAfter obtain, the structural parameters of metal insert are affected to metal insert surface field strength, but to three
The tangential and formate field intensity on support insulator surface influences smaller；Three support insulator supporting leg shapes are to three support insulator surfaces
Tangential field is affected with metal insert surface formate field intensity, and influences on three support insulator surface formate field intensities smaller.
Therefore, first optimize the shape of three support insulator supporting legs, then determine metal insert structure again.
Step 4) constructs evaluation function:
Electric property evaluation index is normalized, process is as follows:
In formula, E '1c、E′2cWith E '3cThe respectively controlling value of three support insulator surface formate field intensities, three support insulators
The controlling value of surface tangential field and the controlling value of metal insert surface formate field intensity.
In structure optimization, since metal insert surface formate field intensity and insulator surface formate field intensity nargin are lower, because
Metal insert surface formate field intensity and three support insulator surface formate field intensities are considered as by this influences the main of electric property
Factor.It is larger accordingly, due to insulator surface tangential field nargin, therefore its importance is lower than other two index.Root
According to the importance of electric property evaluation index, application level analytic approach constructs judgment matrix are as follows:
The weight coefficient of electric property evaluation index after determining three normalization is respectively ω1、ω2And ω3, construction it is excellent
Change objective function are as follows:
φ1(x)=ω1f1(x)+ω2f2(x)+ω3f3(x)；
Step 5) uses particle swarm algorithm optimizing.As shown in Fig. 2, for particle swarm algorithm flow chart in the present invention.It is to be optimized
Structural parameters are expressed as the position of particle, and the calculated value of optimization object function is expressed as the fitness of particle.Specific steps include:
A 20 primary populations) are created, each population includes structural parameters to be optimized and local optimal result；
B) reinitialize the position of each particle, the fitness under each particle is calculated, using fitness as initial office
Portion's optimal result.Meanwhile the optimal result of fitness under all particles is searched out, using optimal fitness as initial entirety
Optimal result；
C particle position) is updated, specially；
The position of each particle is updated by speed more new formula, the speed of particle swarm algorithm more new formula is as follows:
Wherein C1And C2For accelerated factor；r1And r1For random number, value range is [0,1], pidIndicate that each particle exists
The position that local optimum is taken off, gidIndicate the position that population total optimization is taken off, xid(k) particle of last iteration is indicated
Position, vid(k) speed of last iteration is indicated；The position of particle is updated using speed more new formula；
D the fitness after updating under each new particle) is calculated, and records total optimization result and local optimum result；
E) iterate above-mentioned steps C) and step D), until fitness reaches in setting range, then terminate iteration.
The present invention has comprehensively considered three support insulator key position electricity function indexs, can be complex-shaped three
The Optimal Structure Designing providing method and approach of column insulator, and design cycle and the cost of three support insulators is effectively reduced,
Improve the electric property of three support insulators, there is good practicability and economy.
Claims (10)
1. the tri- support insulator electric property optimum design method of GIL based on particle swarm algorithm, which is characterized in that including as follows
Step:
1) the electric field finite element analysis model for establishing three support insulators determines the electric field finite element analysis mould of three support insulators
The structural parameters to be optimized of type；
2) it determines the constraint condition of three support insulators structural parameters to be optimized, while choosing the electrical resistance of three support insulators
It can evaluation index；
3) according to the structure feature of three support insulators, three support insulators structural parameters to be optimized are divided into several height
Collection, and determine the optimization sequence of subset；
4) optimization object function of three support insulator Optimal Structure Designings is constructed according to electric property evaluation index；
5) particle swarm algorithm is used, according to the constraint condition of the optimization of subset sequence and structural parameters to be optimized, is solved right
Answer the optimum structure parameter under optimization object function.
2. the GIL tri- support insulator electric property optimum design method according to claim 1 based on particle swarm algorithm,
It is characterized in that, in step 1), in the electric field finite element analysis model of three support insulators, according to three support insulator shapes
Structure feature, choose structural parameters to be optimized.
3. the GIL tri- support insulator electric property optimum design method according to claim 2 based on particle swarm algorithm,
It is characterized in that, according to the structure feature of the supporting leg shape of three support insulators and the structure feature of metal insert shape, choosing
Take structural parameters to be optimized.
4. the GIL tri- support insulator electric property optimum design method according to claim 3 based on particle swarm algorithm,
It is characterized in that, the cross put in length of embedment, diameter and the surface radius of corner and supporting leg spline curve of selection metal insert
Coordinate is as structural parameters to be optimized.
5. the GIL tri- support insulator electric property optimum design method according to claim 1 based on particle swarm algorithm,
It is characterized in that, in step 2), using the value range of structural parameters to be optimized as constraint condition；Choose three support insulators
Surface formate field intensity maximum value E '1, three support insulator surface tangential fields maximum value E '2And metal insert surface synthesis
Field strength maximum value E '3As electric property evaluation index.
6. the GIL tri- support insulator electric property optimum design method according to claim 1 based on particle swarm algorithm,
It is characterized in that, in step 3), according to each position of three support insulators structural parameters to be optimized to three support insulators electricity
The difference of gas Performance Evaluating Indexes influence degree determines the optimization sequence of subset.
7. the GIL tri- support insulator electric property optimum design method according to claim 6 based on particle swarm algorithm,
It is characterized in that, the influence degree κ of structural parameters to be optimized to three support insulator electric property evaluation indexesiBy following public affairs
Formula determines:
Wherein, X1With X2Respectively represent value different under the same structural parameters to be optimized, Ei1' and Ei2' respectively represent with to excellent
Change the corresponding electric property evaluation index value of structural parameters.
8. the GIL tri- support insulator electric property optimum design method according to claim 1 based on particle swarm algorithm,
It is characterized in that, three support insulators structural parameters to be optimized are divided into two subsets, one of subset in step 3)
Structural parameters comprising supporting leg shape, another subset include the structural parameters of metal insert shape.
9. the GIL tri- support insulator electric property optimum design method according to claim 1 based on particle swarm algorithm,
It is characterized in that, after the normalization of electric property evaluation index, then using analytic hierarchy process (AHP), three pillars of construction are exhausted in step 4)
The optimization object function of edge minor structure optimization design.
10. the tri- support insulator electric property optimization design side GIL according to claim 1 based on particle swarm algorithm
Method, which is characterized in that detailed process is as follows for step 5):
Step 5.1, primary population is created, each population includes structural parameters to be optimized and local optimal result；
Step 5.2, reinitialize the position of each particle, calculates the fitness under each particle, using fitness as initial
Local optimum as a result, fitness optimal using under all particles as initial total optimization result, wherein the position generation of particle
Table structural parameters to be optimized, the calculated value of fitness representing optimized objective function；
Step 5.3, particle position is updated；
Step 5.4, the fitness after updating under each new particle is calculated, and records total optimization result and local optimum knot
Fruit；
Step 5.5, the above-mentioned steps that iterate 5.3 and step 5.4 until fitness reaches in setting range, then terminate iteration.
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