CN105005675A - Composite insulator electric field optimization method based on multi-target genetic algorithm - Google Patents
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Abstract
The invention discloses a composite insulator electric field optimization method based on a multi-target genetic algorithm. The method comprises steps as follows: 1) solving an electrostatic field open domain problem; 2) establishing a composite insulator geometric model; 3) performing numerical computation on the composite insulator electric field intensity in Maxwell software; 4) in order to break through the limitation of a Maxwell software optimization module, adopting the VB (visual basic) script of the Maxwell software to realize data connection of the Maxwell software and MATLAB software; 5) in the MATLAB software, optimizing shed radiuses with the multi-target genetic algorithm; 6) obtaining optimized composite insulator electric field distribution. According to the method, the field intensity Range_E minimization and the field intensity gradient Max_GradE minimization are taken as multiple optimization targets, and large, medium and small shed radiuses of a composite insulator are optimized, so that the composite insulator electric field distribution is more uniform.
Description
Technical field
The present invention relates to a kind of composite insulator electric Field Optimization method based on multi-objective genetic algorithm, belong to composite insulator Electric Field Distribution optimisation technique field.
Background technology
Transmission line composite insulator is the line support device widely used in electric system, due to be combined into insulator have antifouling property good, light, be convenient to the advantages such as maintenance, therefore be widely used in the electric system of China, guarantee safe operation of power system and promotion China electric power industry development have played remarkable effect.
But composite insulator also runs into some problems in practice, under such as core brittle fracture, icing, electric property seriously reduces, Electric Field Distribution is seriously uneven etc.Wherein Electric Field Distribution is seriously uneven is that composite insulator uses a faced pressing problem on super extra high voltage line.Composite insulator, due to the low conductivity of its profile feature, hardware fitting structure and silastic material, makes Potential distribution rapid decay from high-pressure side, and such Potential distribution makes to create higher electric field at contiguous high-pressure side and earth terminal place.If surface electric field of insulator has exceeded corona inception field strength (0.45kV/mm), corona discharge will be produced.
Electric Field Distribution is an important content in the research of transmission line of electricity external insulation.The chance that highfield causes insulator chain and gold utensil surface to produce corona discharge increases, and can run cause serious threat to electric power safety.For UHV (ultra-high voltage) and extra high voltage line, its electric pressure is high, electric field intensity is large, composite insulator Electric Field Distribution is more uneven compared with conventional line, therefore improves the gordian technique that composite insulator Electric Field Distribution is the development of ultra-high/extra-high voltage engineering external insulation equipment and safe operation thereof.
The method of current research composite insulator Electric Field Distribution optimization has much, but adopts the method for multiple-objection optimization electric field not have by changing full skirt radius.In the analysis of electric field of insulator, two objective functions (field intensity extreme difference Range_E and maximum field strength gradient Max_GradE) are contradiction, its minimum value does not obtain at same place, the optimization of simple target is difficult to meet more uniform electric field requirement, and it is significant for therefore proposing Multipurpose Optimal Method.The present invention is minimum minimum as two optimization aim with magnetic field gradient maximal value Max_GradE using field intensity extreme difference Range_E, by constantly changing large, medium and small full skirt radius, asks for the value of its optimum.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of method changing full skirt radius and optimize composite insulator Electric Field Distribution, minimum minimum as many optimization aim with magnetic field gradient maximal value Max_GradE using field intensity extreme difference Range_E, the large, medium and small full skirt radius of composite insulator is optimized, makes composite insulator Electric Field Distribution more even.
For solving the problems of the technologies described above, the invention discloses a kind of composite insulator electric Field Optimization method based on multi-objective genetic algorithm, it is characterized in that, comprise the following steps:
1) open domain problem of composite insulator is solved:
Unbounded open domain is converted into bounded closed domain, adopts far field unit to simulate the method for Unbounded Domains, zoning is decided to be 200%.
2) composite insulator geometric model is set up:
Employ the simplified model of composite insulator.Cut down model along coordinate plane, domain is defined as: X-axis forward 50%, X-axis negative sense 0%; Y-axis forward 50%, Y-axis negative sense 50%; Z axis forward 10%, Z axis negative sense 10%.
3) in Maxwell software, numerical evaluation is carried out to composite insulator electric field intensity;
4) Maxwell software and MATLAB software associative is realized:
Adopt the method for Calling MATLAB com component in Maxwell VBS (script of Microsoft Visual Basic language simplifies version VBScript).
5) in MATLAB software, multi-objective Optimization Genetic Algorithm is adopted to be optimized full skirt radius;
6) the composite insulator Electric Field Distribution of optimization is drawn.
Above-mentioned steps 3) in, in Maxwell software, numerical evaluation is carried out to composite insulator electric field intensity, specifically refers to, based on finite element theory, numerical evaluation is carried out to insulator electric field.Comprise the following steps:
31): by whole zoning discretize;
32): Area Node and unit are numbered in order;
33) local excitation matrix and the local matrix of coefficients of this finite element: successively Local treatment is carried out to each finite element, is tried to achieve;
34): local coefficient's matrix of each unit and local excitation matrix are added in the right-hand vector of overall coefficient matrix and system of equations;
35): consider imposed boundary con ditions, the right-hand vector of overall coefficient matrix and system of equations is revised;
36) the field intensity numerical solution of each node: solving equations, is drawn.
Above-mentioned steps 4) in realize Maxwell software and MATLAB software associative, adopt the method for Calling MATLAB com component in MaxwellVBS.
Above-mentioned steps 5) in, in MATLAB software, adopt multi-objective Optimization Genetic Algorithm to be optimized full skirt radius, the concrete GADST module that adopts is carried out, and comprises the following steps:
51): determine multiple optimization aim.Be specially determine the minimum and magnetic field gradient maximal value Max_GradE of field intensity extreme difference Range_E minimum be two optimization aim.
52): the constrained type determining full skirt radius optimization problem;
53): produce initial population;
54): make an Evolution of Population generation;
55): judge whether finishing iteration, iteration termination condition be reach before the maximum iteration time of setting or experimental error be less than before the minimum error values of setting, if not finishing iteration, then return 53).
56): export full skirt radius Pareto optimal solution (Pareto optimal solution).
Above-mentioned steps 54) in, an Evolution of Population generation is comprised the following steps:
541) championship way selection is used.Specifically carry out selection operation based on sequence value and crowding distance, for two individualities, when sequence value is different, regardless of its crowding distance, sequence is worth little individuality by selected; When sequence value is identical, the large individuality of crowding distance is by selected.
542) intersect, make a variation.
543) sub-population is created.
544) father and son population is merged.
545) sequence value is calculated.
546) non-dominated ranking.Each stepping 1 from 1, often taking turns in r circulation, comparing the individual p be not sorted in population and all the other all individual q be not sorted successively, checking whether individual q arranges individual p.If do not arrange, then individual p is endowed when preorder value; Otherwise, because individual p arranges by individual q.If therefore the sequence value of individual p is higher than when preorder value, should participate in next round sequence.
547) crowding distance is calculated
548) population is pruned.Along with the merging of father and son population, the new population size obtained is original twice, therefore needs in the individuality doubling Population Size, prune out the individuality that number equals Population Size.
549) mean distance is calculated.
The beneficial effect that the present invention reaches:
First, instant invention overcomes and use the single optimization module of Maxwell software inhouse and the defect that causes.The Maxwell software inhouse that electric field composite insulator Electric Field Numerical Calculation adopts optimizes module with Parametric Analysis, but the optimized algorithm mode of inside modules is more single, the optimization task of complex condition cannot be adapted to, and map data and display mode limited.And MATLAB is as current classic computational science software, in algorithm development, data visualization, data analysis, numerical evaluation etc., have original performance, the encapsulation scale of its algoritic module, diversity and extensibility are that other programming languages are difficult to reach.Therefore, for optimizing full skirt size further, to obtain more excellent field strength distribution, the present invention uses the multi-objective genetic algorithm in outside MATLAB software to eliminate the limitation of internal module.The core of genetic algorithm optimization is the setting of fitness function, and fitness cannot be simulated with MATLAB, must be calculated by Maxwell, the present invention, by adopting the method for Calling MATLAB com component in Maxwell VBS (script of Microsoft Visual Basic language simplifies version VBScript), successfully achieves Maxwell software and MATLAB software associative.Because all external algorithm of the present invention realize the tool box all using MATLAB and maturation thereof, this ensure that the correctness of algorithm, decrease the scramble time.
In addition, in the analysis of electric field of insulator, two objective functions (field intensity extreme difference Range_E and maximum field strength gradient Max_GradE) are contradiction, its minimum value does not obtain at same place, the optimization of simple target is difficult to meet the demands, therefore the present invention proposes Multipurpose Optimal Method, and minimum with the minimum and magnetic field gradient maximal value Max_GradE of field intensity extreme difference Range_E is two optimization aim, asks for the optimum large, medium and small full skirt radius of insulator.The method of multiple-objection optimization of the present invention, each side of can making overall plans factor, strengthen effect of optimization, make insulator electric field evenly.
Accompanying drawing explanation
Fig. 1 system chart of the present invention;
Fig. 2 multi-objective genetic algorithm GADST module optimizes full skirt radius process flow diagram;
Fig. 3 Evolution of Population process flow diagram;
The field strength distribution that Fig. 4 use GADST to carry out optimized parameter that multi-objective genetic algorithm iteration obtains is corresponding;
The field strength distribution that Fig. 5 use GADST to carry out optimized parameter that multi-objective genetic algorithm iteration obtains is corresponding.
Embodiment
Below in conjunction with accompanying drawing, the present invention is further described.
System chart of the present invention as shown in Figure 1.Based on a composite insulator electric Field Optimization method for multi-objective genetic algorithm, comprise altogether six major parts: solve electrostatic field open domain problem; Set up composite insulator geometric model; In Maxwell software, numerical evaluation is carried out to composite insulator electric field intensity; Realize Maxwell software and MATLAB software associative; In MATLAB software, multi-objective Optimization Genetic Algorithm is adopted to be optimized full skirt radius; Draw the composite insulator Electric Field Distribution of optimization.
Step 1 solves electrostatic field open domain problem: be converted into bounded closed domain by unbounded open domain, adopts far field unit to simulate the method for Unbounded Domains, zoning is decided to be 200%.
Step 2 sets up composite insulator geometric model: the simplified model employing composite insulator.Cut down model along coordinate plane, domain is defined as: X-axis forward 50%, X-axis negative sense 0%; Y-axis forward 50%, Y-axis negative sense 50%; Z axis forward 10%, Z axis negative sense 10%.
Step 3, in Maxwell software, carries out numerical evaluation to composite insulator electric field intensity: specifically carry out numerical evaluation based on finite element theory to insulator electric field.Specifically be divided into six steps:
1) by whole zoning discretize;
2) Area Node and unit are numbered in order;
3) successively Local treatment is carried out to each finite element, try to achieve local excitation matrix and the local matrix of coefficients of this finite element;
4) local coefficient's matrix of each unit and local excitation matrix are added in the right-hand vector of overall coefficient matrix and system of equations;
5) consider imposed boundary con ditions, revise the right-hand vector of overall coefficient matrix and system of equations;
6) solving equations, draws the field intensity numerical solution of each node.
Step 4 realizes Maxwell software and MATLAB software associative.Specifically adopt the method for Calling MATLAB com component in MaxwellVBS (script of Microsoft Visual Basic language simplifies version VBScript).
Step 5, in MATLAB software, adopts multi-objective Optimization Genetic Algorithm to be optimized full skirt radius.In MATLAB software, adopt multi-objective Optimization Genetic Algorithm to be optimized full skirt radius, the concrete GADST module that adopts is carried out.
Step 6 draws the composite insulator Electric Field Distribution of optimization.
Wherein, in steps of 5, GADST module is adopted to carry out.As shown in Figure 2.Specifically be divided into six steps:
1) multiple optimization aim is determined.Be specially determine the minimum and magnetic field gradient maximal value Max_GradE of field intensity extreme difference Range_E minimum be two optimization aim.
2) constrained type of full skirt radius optimization problem is determined.
3) initial population is produced.
4) an Evolution of Population generation is made.
5) judge whether finishing iteration, iteration termination condition be reach before the maximum iteration time of setting or experimental error be less than before the minimum error values of setting, if not finishing iteration, then return 3).
6) full skirt radius Pareto optimal solution (Pareto optimal solution) is exported.
Wherein make an Evolution of Population generation, as shown in Figure 3, be specifically divided into nine steps:
1) championship way selection is used.Specifically carry out selection operation based on sequence value and crowding distance, for two individualities, when sequence value is different, regardless of its crowding distance, sequence is worth little individuality by selected; When sequence value is identical, the large individuality of crowding distance is by selected.
2) intersect, make a variation.
3) sub-population is created.
4) father and son population is merged.
5) sequence value is calculated.
6) non-dominated ranking.Each stepping 1 from 1, often taking turns in r circulation, comparing the individual p be not sorted in population and all the other all individual q be not sorted successively, checking whether individual q arranges individual p.If do not arrange, then individual p is endowed when preorder value; Otherwise, because individual p arranges by individual q.If therefore the sequence value of individual p is higher than when preorder value, should participate in next round sequence.
7) crowding distance is calculated.
8) population is pruned.Along with the merging of father and son population, the new population size obtained is original twice, therefore needs in the individuality doubling Population Size, prune out the individuality that number equals Population Size.
9) mean distance is calculated.
Embodiment:
Multiple-objection optimization uses the multi-objective genetic algorithm module GADST in matlab software to realize.For multiple-objection optimization, fitness function rreturn value is different.Function returns column vector, deposits the value of multiple output variable.Due to function interface restriction in use, return column vector here, deposit the simulation result of output variable Range_E and Max_GradE respectively.
Optimizer settings setting value is as shown in table 1.
Table 1 multiple-objection optimization programming value
With large, medium and small full skirt radius 0.1025,0.0825,0.0625 as initial parameter.To Range_E and Max_GradE two targets, be optimized by multi-objective genetic algorithm parameter, the optimum solution that result obtains is respectively: 0.0955, and 0.0933,0.0585.Field strength distribution corresponding to optimized parameter as shown in Figure 4 and Figure 5.Under initial parameter and under optimized parameter, the target function value of model is shown in Table 2 respectively.The strong extreme difference value Range_E that optimized parameter obtains is 2.94 × 10
7, magnetic field gradient maximal value Max_GradE is 2.95 × 10
10.Compared with initial value initial value, multi-objective genetic algorithm makes field intensity extreme difference value Range_E optimize 7.1%, makes magnetic field gradient maximal value Max_GradE optimize 14.0%.
The multiple objective function value of front and back optimized by table 2
| Max_GradE | Range_E | |
| Under initial parameter | 34289038360 | 31638868 |
| Under optimized parameter | 2.95×10 10 | 2.94×10 7 |
The method overcomes the shortcoming that objective optimization module in maxwell software can only realize single object optimization, execution time longer, very flexible.The method uses multi-objective genetic algorithm to be optimized electric field intensity in matlab software, decreases size of code, has saved computing time, and enhances its adaptability.The method not only can be used for full skirt radius, can also apply to asking in process of different other structural parameters optimal case of electric pressure composite insulator.
Claims (7)
1., based on a composite insulator electric Field Optimization method for multi-objective genetic algorithm, comprise the following steps:
(1) electrostatic field open domain problem is solved;
(2) composite insulator geometric model is set up;
(3) in Maxwell software, numerical evaluation is carried out to composite insulator electric field intensity;
(4) Maxwell software and MATLAB software associative is realized;
(5) in MATLAB software, multi-objective Optimization Genetic Algorithm is adopted to be optimized full skirt radius;
(6) the composite insulator Electric Field Distribution of optimization is drawn.
2. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 1, it is characterized in that: in described step (1), solve composite insulator open domain problem, bounded closed domain is converted into by unbounded open domain, adopt far field unit to simulate the method for Unbounded Domains, zoning is decided to be 200%.
3. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 1, is characterized in that: described step sets up composite insulator geometric model in (2), employs the simplified model of composite insulator,
Cut down model along coordinate plane, domain is defined as: X-axis forward 50%, X-axis negative sense 0%; Y-axis forward 50%, Y-axis negative sense 50%; Z axis forward 10%, Z axis negative sense 10%.
4. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 1, it is characterized in that: carrying out numerical evaluation to composite insulator electric field intensity in described step (3) is carry out numerical evaluation based on finite element theory to insulator electric field, and concrete steps are as follows:
(4a), by whole zoning discretize;
(4b), Area Node and unit are numbered in order;
(4c), to each finite element carry out Local treatment successively, try to achieve local excitation matrix and the local matrix of coefficients of this finite element;
(4d), local coefficient's matrix of each unit and local excitation matrix are added in the right-hand vector of overall coefficient matrix and system of equations;
(4e), consider imposed boundary con ditions, revise the right-hand vector of overall coefficient matrix and system of equations;
(4f), solving equations, draw the field intensity numerical solution of each node.
5. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 1, it is characterized in that: realize Maxwell software and MATLAB software associative in described step (4), adopt the method for Calling MATLAB com component in Maxwell VBS.
6. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 1, it is characterized in that: realize multi-objective genetic algorithm described in described step (5) and be optimized full skirt radius and adopt GADST module to carry out, concrete step is as follows:
(6a), determine multiple optimization aim, be specially determine the minimum and magnetic field gradient maximal value Max_GradE of field intensity extreme difference Range_E minimum be two optimization aim;
(6b) constrained type of full skirt radius optimization problem, is determined;
(6c), initial population is produced;
(6d) an Evolution of Population generation, is made;
(6e), judge whether finishing iteration, iteration termination condition be reach before the maximum iteration time of setting or experimental error be less than before the minimum error values of setting, if not finishing iteration, then return (6c);
(6f) full skirt radius Pareto optimal solution, is exported.
7. the composite insulator electric Field Optimization method based on multi-objective genetic algorithm according to claim 6, is characterized in that: described step (6d) makes an Evolution of Population generation, specifically refers to:
(7a) use championship way selection, specifically carry out selection operation based on sequence value and crowding distance, for two individualities, when sequence value is different, regardless of its crowding distance, sequence is worth little individuality by selected; When sequence value is identical, the large individuality of crowding distance is by selected;
(7b) intersect, make a variation;
(7c) sub-population is created;
(7d) father and son population is merged;
(7e) sequence value is calculated;
(7f) non-dominated ranking; Each stepping 1 from 1, often taking turns in r circulation, compared by the individual p be not sorted in population and all the other all individual q be not sorted successively, check whether individual q arranges individual p, if do not arrange, then individual p is endowed when preorder value; Otherwise, because individual p arranges by individual q, if therefore the sequence value of individual p higher than when preorder value, next round sequence should be participated in;
(7g) crowding distance is calculated;
(7h) population is pruned; Along with the merging of father and son population, the new population size obtained is original twice, therefore needs in the individuality doubling Population Size, prune out the individuality that number equals Population Size;
(7i) mean distance is calculated.
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| CN105574266A (en) * | 2015-12-16 | 2016-05-11 | 西安交通大学 | Multi-population genetic algorithm based comprehensive optimization design method for electrical and mechanical performance of basin-type insulator |
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