CN108521114A - A kind of Optimal Configuration Method of transformer neutral point capacitance blocking device - Google Patents
A kind of Optimal Configuration Method of transformer neutral point capacitance blocking device Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
- H02H7/04—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for transformers
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Abstract
The invention discloses a kind of Optimal Configuration Methods of transformer neutral point capacitance blocking device.Its feature is based on the coupling model of equivalent field road, and by being grounded transformer neutral point DC current value in Finite element arithmetic power grid, result of calculation is more accurate and close to measured value；Convergence speed of the algorithm and computational accuracy are improved using the non-linear reduction Inertia Weight improvement particle cluster algorithm for considering cosine adjustment factor with multiple target discrete particle cluster algorithm；The Studying factors of multiple target discrete particle cluster algorithm are controlled with Inertia Weight and regulatory factor, can equilibrium particle ability of searching optimum and local development ability；Particle more new strategy is introduced into particle cluster algorithm, improves particle update quality and algorithm computational efficiency.This method can distribute capacitance blocking device rationally from global angle, reduce the cost for administering D.C. magnetic biasing, and regulation effect is apparent, and carried optimization algorithm computational accuracy is high, fast convergence rate, be suitable for the solution of practical problem.
Description
Technical field
The invention belongs to high-voltage dc transmission electrical domain, more particularly to a kind of optimization of transformer neutral point capacitance blocking device
Configuration method.
Background technology
D.C. high voltage transmission (high voltage direct current, HVDC) technology has increasingly extensive in China
Application, but it is run in monopole ground return circuit or when asymmetric operation, underground is injected in the grounded pole of earth current, changes big
Ground potential is distributed, and the transformer of neutral ground may be caused to generate different degrees of DC magnetic bias phenomena, make transformer temperature
It increases, loss increase, vibrate aggravation, seriously threaten the safe and stable operation of electric system.
The DC current of transformer neutral point can thoroughly be obstructed by installing capacitance blocking device at grounding transformer neutral point additional,
Inhibit transformer DC magnetic bias phenomenon.However theory and practice shows that the installation of capacitance blocking device can cause direct current in ground
The redistribution of stream, for needing the power grid of improvement D.C. magnetic biasing, if transformer neutral point only more serious in D.C. magnetic biasing
Place installs capacitance blocking device additional, and other transformer neutral point DC currents in power grid may be caused out-of-limit, cannot be reached ideal
Inhibition.Therefore be directed to AC network of the earthing pole near region containing more neutral grounded transformers, should from global angle,
Distributing rationally for capacitance blocking device is carried out, to inhibit the DC magnetic bias phenomena of transformer in power grid.
For the optimization allocation of capacitance blocking device, existing method mostly uses the optimization algorithms such as genetic algorithm and asks this
The problems such as topic is solved, but there are transformer neutral point DC currents to calculate inaccuracy, and algorithm is easily absorbed in local convergence, it is difficult to
Acquire the allocation optimum scheme of capacitance blocking device.
Invention content
The purpose of the present invention is the deficiency for existing method, a kind of the excellent of transformer neutral point capacitance blocking device is provided
Change configuration method.This method by the way of field circuit method by Finite element arithmetic transformer neutral point DC current, simultaneously
Using multiple target discrete particle cluster algorithm, particle is avoided to be absorbed in local convergence, is filled in the hope of earthing pole near region power grid capacitance blocking
The allocation optimum scheme set.
The purpose of the present invention is realized by following technical measures：
Step 1：According to topological structure of electric, ground power grid is equivalent to resistor network, builds ground wire frame model；
Step 2：Earthing pole near region soil is divided into multilayer, and every layer of electric resistance of soil is determined with reference to the geological model of soil
Rate builds soil layering model；
Step 3：Ground wire frame model and soil layering model are coupled, equivalent field road coupling model is built, by having
The first method of limit calculates the DC current values of grounding transformer neutral point in the power grid of earthing pole near region, and uses inversion method, adjusts and becomes
Soil resistivity near the earth point of power station compares the calculated value of model with measured value, correction model parameter, it is ensured that etc.
Imitate the accuracy of field circuit method model；
Step 4：To put into the minimum number and transformer neutral point DC current absolute content summation of capacitance blocking device
Minimum optimization aim establishes multiple target discrete particle cluster Optimized model；
Step 5：Initialize particle matrix and particle populations speed, set algorithm parameter, including Inertia Weight, study because
Son, maximum iteration, dimension and solution space range；
Step 6：It calculates the adaptive value of particle and it is evaluated, record the optimal location of particle individual and group respectively
And optimal adaptation value；
Step 7：More new particle simultaneously randomly selects individual into row variation processing, while particle more new strategy being used to avoid non-peace
Wholegrain repeats；
Step 8：It solves the forward positions Pareto and optimal solution is obtained according to the decision strategy of policymaker, more new individual and group
Optimal location and optimal adaptation angle value；
Step 9：Judge whether result of calculation meets end condition, calculating is terminated if meeting, if being unsatisfactory for returning to step
Rapid 7 calculate again, until meeting end condition.
The supplementary explanation and important technology of the Optimal Configuration Method require as follows：
A. in multiple target discrete particle cluster algorithm, Inertia Weight is using the non-linear reduction plan for considering cosine adjustment factor
Slightly, value is the difference of Inertia Weight maximum value and the very poor same iteration change rate product of μ times of Inertia Weight, and wherein μ is adjusted for cosine
Coefficient, iteration change rate are the ratio of current iteration number and maximum iteration under radical sign.The rule of Inertia Weight is as follows：
In formula, w represents inertia weight, wmaxAnd wminThe respectively maximum value and minimum value of inertia weight, t and tmaxRespectively
It is current iteration number and maximum iteration, μ is cosine adjustment factor, and r is regulatory factor, by test of many times, the value of r
Ranging from 0.7~1.In entire iterative process, with the increase of iterations, the value of inertia weight w is by wmaxIt is gradually reduced
For wmin.In iteration early period, inertia weight w values are larger, and search capability is stronger, are conducive to particle and carry out global search；Repeatedly
For the later stage, inertia weight w values are smaller, are conducive to local fine search, search result is made to converge to globally optimal solution.W's is non-
Linear change characteristic keeps the reduction amount at its initial stage larger, and particle can be made to enter local search as early as possible, and the reduction amount in later stage is smaller,
Improve the convergence precision of algorithm.
B. in multiple target discrete particle cluster algorithm, using the Inertia Weight Schistosomiasis control factor, enhancing Inertia Weight and
The interaction between the factor is practised, with the global search of equilibrium particle and local development ability.Studying factors c1And c2Size determine
The ability of self-teaching and team learning ability of particle are determined, rule is as follows：
In formula, c1sAnd c2sRespectively c1And c2Initial value, c1eAnd c2eRespectively c1And c2Stop value, r be adjust because
Son, value range are 0.7~1.It is arranged in iteration starting stage c1More than c2, the ability of self-teaching of particle is relatively strong at this time and group
Body learning ability is weaker, is easy to implement global search；In iteration later stage c1Less than c2, the ability of self-teaching of particle is weaker at this time
And team learning ability is stronger, and sufficient information exchange can be carried out between particle, is conducive to accelerating algorithm convergence.Studying factors
Variation is influenced by Inertia Weight, has nonlinear characteristic, considers the ability of searching optimum of particle and local development ability,
Introduce regulatory factor, enhance the interaction between Inertia Weight and Studying factors, can boosting algorithm performance, keep algorithm more suitable
Solution for practical problem.
C. in the updated particle matrix of multiple target discrete particle cluster algorithm, it is understood that there may be the fitness of certain particles is discontented
Sufficient constraints, i.e. non-security particles.Hastily not only result of calculation can be made invalid, also for further calculating on these particles
Efficiency of algorithm can be reduced, therefore the present invention proposes a kind of particle more new strategy：Safety is set in Discrete Particle Swarm Optimization Algorithm
Particle collection and non-security particles collection, fitness in particle matrix after update is unsatisfactory for constraints particle be recorded in it is non-security
Particle is concentrated, and updates corresponding particle again, while security particles are recorded in security particles and are concentrated, more for further solving
Objective optimisation problems.The particle δ that security particles are concentrated is represented by：
The particle η that non-security particles is concentrated is represented by：
In addition, in particle renewal process, the historical record that updated particle need to be concentrated with non-security particles is compared
Compared with if particle records identical with the particle that non-security particles is concentrated, particle need to update again, until being concentrated with non-security particles
Historical record it is different.
The invention has the advantages that：
The present invention is based on the coupling model of equivalent field road, with being grounded transformer neutral point in Finite element arithmetic power grid
DC current values, result of calculation are more accurate and close to measured value；Based on multiple target discrete particle cluster algorithm, using consideration cosine
The non-linear reduction Inertia Weight of adjustment factor improves particle cluster algorithm, improves convergence speed of the algorithm and computational accuracy；It adopts
Particle cluster algorithm is improved with the Studying factors controlled by Inertia Weight, regulatory factor is introduced, is capable of the global search of equilibrium particle
Ability and local development ability, increase the general applicability of algorithm, algorithm are made to be more suitable for the solution of practical problem；In particle
Particle more new strategy is introduced in group's algorithm, improves particle update quality and algorithm computational efficiency.
Description of the drawings
Fig. 1 is capacitance blocking installation optimization configuration method flow chart.
Fig. 2 is certain direct current grounding pole near region electric network composition schematic diagram.
Fig. 3 is transformer equivalent circuit.
Fig. 4 is equivalent field road coupling model schematic diagram.
Fig. 5 is each plant stand transformer neutral point DC current value.
Fig. 6 is that w changes scatter plot with iterations.
Fig. 7 is c1And c2Change scatter plot with iterations.
Fig. 8 is particle more new strategy schematic diagram.
Fig. 9 is each transformer neutral point DC current value after installation blocking device.
Multiple target Discrete Particle Swarm Optimization Algorithm result of calculation when being 5A that Figure 10 is limit value.
Figure 11 is that the capacitance blocking device number of units of three kinds of configuration methods installation compares.
Specific implementation mode
The present invention will be described in detail with reference to the accompanying drawings and examples.It is necessarily pointed out that the present embodiment is only
It is used to further illustrate the present invention, should not be understood as limiting the scope of the invention, which is skilled in technique
Personnel can make some nonessential modifications and adaptations according to the content of foregoing invention.
Embodiment：
The present embodiment builds equivalent field road coupling model, using Finite element arithmetic power grid by taking certain direct current grounding pole as an example
In each transformer neutral point DC current values, and capacitance blocking device is optimized using multiple target discrete particle cluster algorithm
Configuration inhibits transformer DC magnetic bias phenomenon, capacitance blocking installation optimization configuration method flow chart such as Fig. 1 institutes from global angle
Show.
1, certain direct current grounding pole near region electric network model
Certain direct current grounding pole near region power grid include 3,500kV power plant, 6,500kV substations, DC converter station 2, ±
800kV extra-high voltage direct-currents Transmission Corridor two, transmission power is respectively 6400MW and 8000MW.By above-mentioned power station number consecutively
It is 1~11, electric network composition schematic diagram is as shown in Figure 2.Wherein, current conversion station 4 and 5 shares a direct current grounding pole, and and earthing pole
Between air line distance be respectively 72.86km and 77.11km.
If to be grounded extremely origin, direction is X-axis positive direction from west to east, and direction is Y-axis positive direction from south to north, by ground
Table is vertically that Z axis positive direction establishes coordinate system to the earth's core direction.Website is positioned according to the GPS longitudes and latitudes of each website, can be calculated
The geographic coordinate values of each website.The geographical position coordinates of System for HVDC System Earth Pole and each plant stand of near region AC network are such as
Shown in table 1.
1 each plant stand geographical position coordinates of table
According to topological structure of electric, ground wire frame model is built in conjunction with each plant stand transformer parameter.Only counted due to model and
DC current, therefore ground power grid can be equivalent to resistance network model.For 500kV substations, transformer is self coupling
Transformer, Equivalent DC model is as shown in figure 3, indicate input capacitance blocking device when the switch in Fig. 3 disconnects.For exchange
Transmission line of electricity can be carried out equivalent according to topological structure of electric using circuit DC resistance, i.e., alternating current circuit is equivalent to electricity
Resistance, calculation formula are：
In formula, ρ is line material resistivity, and L is line length, and s is the sectional area of single lines, and n is conducting wire division number,
C is that circuit returns number.In the case where considering running temperature, line resistance value can be converted as the following formula：
RT=RL[1+α(T-20)] (8)
In formula, α is temperature coefficient, and T is temperature.Uncertain, the R in temperatureTCan be approximately RL1.05~1.1
Times.For DC power transmission line, its equivalent method is consistent with transmission line of alternation current.
According to earthing pole near region soil parameters, soil layering model is built.Since DC transmission system earth current is main
Flowing through the earth's crust and outer mantle top layer portion, and the earth's crust includes humic soil layer and pristine formation, electrical resistivity range is respectively 10~
1000 Ω m and 1000~20000 Ω m, earth mantle resistivity are less than crustal parts resistivity, thus can be in be grounded extremely
The heart, length and width are that 4 layers of soil layering model are established in the range of 400km, simulate practical soil regime, the electricity of every layer of soil model
Resistance rate and thickness are as shown in table 2.
The resistivity and thickness of 2 soil model of table
Since the resistivity of every layer of soil is different, thus on the interface of Two layer soil, by electric scalar potential functionIt indicates
Convergence condition be：
In formula,WithThe electric scalar potential of Two layer soil, γ are indicated respectively1And γ2The conductance of Two layer soil is indicated respectively
Rate.According to soil model known point potential valueIt is as follows that the boundary condition write on boundary face S can be arranged：
Using the method for field circuit method, ground wire frame model and soil layering model are coupled, build equivalent field road coupling
Molding type, as shown in Figure 4.Ground potential is set to couple as the degree of freedom of FEM calculation, and using coupling unit direct solution
The interaction of field is distributed using ANSYS finite element analysis softwares using the ground potential in finite element model for solving field domain, into
And the DC current values of grounding transformer neutral point in power grid are calculated.
To ensure the accuracy of equivalent field road coupling model, using inversion method, by adjusting near substation grounding point
Soil resistivity, make equivalent field road coupling model calculate gained each transforming plant main transformer neutral point direct current value approach actual measurement
Value, and then verify the accuracy of model.The soil resistivity for changing zonule near substation, can correct due to ground resistance
Error caused by rate further increases the accuracy of model.
When earthing pole earth current is 5000A, the plant stand transformer neutral point DC current value of the earthing pole near region 11
Simulation calculation value and measured value it is as shown in table 3, it can be seen that the equivalent field road coupling model built can reflect this area hand over
The practical operation situation of straight-flow system demonstrates the accuracy of model.When earthing pole earth current is 5000A, the earthing pole
The plant stand transformer neutral point DC current value of near region 11 is as shown in Figure 5
3 equivalent field road coupling model simulation value of table is compared with measured value
2, multiple target discrete particle cluster algorithm
The capacitance blocking device of the earthing pole near region power grid is optimized using multiple target Discrete Particle Swarm Optimization Algorithm
Configuration, optimization aim is to make the minimum number and transformer neutral point DC current absolute content summation of input capacitance blocking device
It is minimum.Since discrete particle cluster algorithm is to indicate particle on the basis of particle swarm optimization algorithm using binary variable, pass through
Particle movement, thus speed and position using multiple target discrete particle cluster algorithm to particle are realized in the 0-1 transformation of binary variable
It sets when being updated, speed update rule is represented by：
vi=wvi+c1r1(pi-xi)+c2r2(pg-xi) (13)
Wherein xiIndicate the position of particle, viIndicate the change rate of particle position, w is Inertia Weight, c1And c2For study because
Son, piAnd pgThe personal best particle and group's optimal location of particle, and x are indicated respectivelyi、piAnd pgIt all can only be 0 and 1.At this time
The more new formula of particle position is：
Wherein, sig (vi) function is a restricted function, by xiEach component be limited in the range of [0,1],
Rand () indicates a random number in [0,1] section.The particle position of discrete particle cluster algorithm is updated by viSize determine
It is fixed, viValue closer to 1, particle position xiBe transformed to 1 probability it is bigger, if viValue closer to 0, then particle position xiTransformation
It is bigger for 0 probability.
In multiple target discrete particle cluster algorithm, Inertia Weight is using the non-linear reduction plan for considering cosine adjustment factor
Slightly, and utilize the Inertia Weight Schistosomiasis control factor, while introducing particle more new strategy, with the ability of searching optimum of equilibrium particle and
Local development ability enhances convergence speed of the algorithm and computational accuracy.
It is 30 that population, which is arranged, and maximum iteration is 60 times, c1s=c2e=2.5, c1e=c2s=1.5, wminIt is 0.3,
wmaxIt is 0.9, Inertia Weight and Studying factors, w, c is calculated according to formula (1) to formula (4)1And c2With iterations variation scatter plot point
Not not as shown in Figure 6 and Figure 7.Wherein particle more new strategy schematic diagram is as shown in Figure 8.
3, result of calculation is distributed rationally
The decision strategy of optimal solution is obtained as the premise for the value that do not transfinite in each plant stand neutral point of main transformer DC current values of guarantee
Under, the installation number of capacitance blocking device is reduced as far as possible, reduces input cost, if identical in the presence of installation blocking device quantity,
But the different situation of plant stand is installed, the installation for making each transformer neutral point DC current absolute content summation minimum should be therefrom chosen
Scheme is as optimal solution.
Assuming that the earthing pole near region transformer neutral point DC current limit value is 5A, capacitance blocking is carried out using optimization algorithm
Installation optimization, which is matched, postpones each plant stand neutral point of main transformer DC current size as shown in figure 9, optimization algorithm optimizing output result is as schemed
Shown in 10.As it can be seen that when transformer neutral point DC current limit value be 5A when, the optimal installation website of capacitance blocking device be 1,3,
9 and No. 11 websites.
By the optimum results of Figure 10 it is found that distributing capacitance blocking dress rationally using multiple target Discrete Particle Swarm Optimization Algorithm
During setting, result of calculation can restrain fast and reliablely.The algorithm can solve the Pareto of multi-objective optimization question
Forward position, and obtain optimal solution according to decision strategy.Required prioritization scheme can preferentially make the direct current grounding pole near region power grid input
Capacitance blocking device minimum number, and the sum of each plant stand neutral point of main transformer DC current absolute value minimum can be made, embodied
The validity of algorithm simultaneously plans that there are nargin for the power grid later stage.
Consider that the actual demand of power network development passes through multiple target it is now assumed that direct current limit value is respectively 3A, 5A, 7A, 9A, 10A
Discrete Particle Swarm Optimization Algorithm distributes capacitance blocking device rationally, distributes that the results are shown in Table 4 rationally.Each plant stand transformer at this time
Neutral point direct current value is as shown in table 5.
Table 4 distributes result rationally
5 each plant stand transformer neutral point DC current value/A of table
To verify the superiority of put forward algorithm, the unoptimizable algorithm pair of genetic algorithm and " out-of-limit to install " is now respectively adopted
The capacitance blocking device of the earthing pole near region power grid optimizes configuration.The chromosome length that genetic algorithm is arranged is 11, initially
Population Size is 30, crossover probability 0.9, mutation probability 0.2, and maximum evolutionary generation was 60 generations.By multiple target discrete particle
The configuration result of colony optimization algorithm is compared with the configuration result of genetic algorithm and unoptimizable algorithm, when transformer neutral point is straight
When galvanic electricity restriction value is identical, the capacitance blocking device number of units of three kinds of configuration methods installation is as shown in figure 11.
Embodiment shows method proposed by the invention, by improve particle cluster algorithm can from global angle consider every
It the number of units of straight device and layouts, improves the convergence precision and speed of algorithm, and capacitance blocking dress is reduced by iteration optimizing
The total number of units of installation set reduces the cost for administering D.C. magnetic biasing.
Claims (4)
1. a kind of Optimal Configuration Method of transformer neutral point capacitance blocking device, it is characterised in that this method includes following step
Suddenly：
Step 1：According to topological structure of electric, ground power grid is equivalent to resistor network, builds ground wire frame model；
Step 2：Earthing pole near region soil is divided into multilayer, and every layer of soil resistivity is determined with reference to the geological model of soil,
Build soil layering model；
Step 3：Ground wire frame model and soil layering model are coupled, equivalent field road coupling model is built, passes through finite element
Method calculates the DC current values of grounding transformer neutral point in the power grid of earthing pole near region, and uses inversion method, adjusts substation
Soil resistivity near earth point compares the calculated value of model with measured value, correction model parameter, it is ensured that equivalent field
The accuracy of road coupling model；
Step 4：It is minimum with the minimum number and transformer neutral point DC current absolute content summation that put into capacitance blocking device
For optimization aim, multiple target discrete particle cluster Optimized model is established；
Step 5：Initialize particle matrix and particle populations speed, set algorithm parameter, including Inertia Weight, Studying factors, most
Big iterations, dimension and solution space range；
Step 6：The adaptive value for calculating particle simultaneously evaluates it, records the optimal location and most of particle individual and group respectively
Good adaptive value；
Step 7：More new particle simultaneously randomly selects individual into row variation processing, while particle more new strategy being used to avoid non-security grain
Son repeats；
Step 8：Solve the forward positions Pareto and optimal solution obtained according to the decision strategy of policymaker, more new individual and group it is optimal
Position and optimal adaptation angle value；
Step 9：Judge whether result of calculation meets end condition, calculating is terminated if meeting, if being unsatisfactory for return to step 7 again
Secondary calculating, until meeting end condition.
2. a kind of Optimal Configuration Method of transformer neutral point capacitance blocking device as described in claim 1, it is characterised in that
Configuration is optimized using multiple target discrete particle cluster algorithm, the Inertia Weight of algorithm is using the non-thread of consideration cosine adjustment factor
Property reduce strategy, value is the difference of Inertia Weight maximum value and the very poor same iteration change rate product of μ times of Inertia Weight, and wherein μ is
Cosine adjustment factor, iteration change rate are the ratio of current iteration number and maximum iteration under radical sign.The rule of Inertia Weight
It is then as follows：
In formula, w represents inertia weight, wmaxAnd wminThe respectively maximum, minimum value of inertia weight, t and tmaxIt is current change respectively
Generation number and maximum iteration, μ are cosine adjustment factor, and r is regulatory factor, and by test of many times, the value range of r is
0.7~1.
3. a kind of Optimal Configuration Method of transformer neutral point capacitance blocking device as described in claim 1, it is characterised in that
Studying factors in multiple target discrete particle cluster algorithm introduce regulatory factor, and value is controlled by Inertia Weight, and use is non-linear
Reduce strategy, by Inertia Weight and the reduction degree of the regulatory factor Schistosomiasis control factor, rule is as follows：
In formula, c1And c2Represent Studying factors, c1sAnd c2sRespectively c1And c2Initial value, c1eAnd c2eRespectively c1And c2End
Only it is worth, r is regulatory factor, and value range is 0.7~1.
4. a kind of Optimal Configuration Method of transformer neutral point capacitance blocking device as described in claim 1, it is characterised in that
Particle more new strategy is introduced in multiple target discrete particle cluster algorithm, particle more new strategy is set in Discrete Particle Swarm Optimization Algorithm
Security particles collection and non-security particles collection are set, the particle that fitness in particle matrix after update is unsatisfactory for constraints is recorded in
Non-security particles is concentrated, and updates corresponding particle again, while security particles are recorded in security particles and are concentrated, for further
Solve multi-objective optimization question.In particle renewal process, updated particle need to be with the historical record of non-security particles concentration
It is compared, if particle records identical with the particle that non-security particles is concentrated, particle need to update again, until with non-security grain
Historical record in subset is different.The particle δ that security particles are concentrated is represented by：
The particle η that non-security particles is concentrated is represented by：
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| CN115189480B (en) * | 2022-09-08 | 2022-12-09 | 国网江西省电力有限公司电力科学研究院 | Transformer self-adaptive direct-current magnetic bias adjusting system and method based on multi-source coordination |
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