CN109245110A - A kind of distribution network voltage rich in small power station optimizes and revises method - Google Patents
A kind of distribution network voltage rich in small power station optimizes and revises method Download PDFInfo
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- CN109245110A CN109245110A CN201811321085.8A CN201811321085A CN109245110A CN 109245110 A CN109245110 A CN 109245110A CN 201811321085 A CN201811321085 A CN 201811321085A CN 109245110 A CN109245110 A CN 109245110A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
A kind of distribution network voltage rich in small power station disclosed by the invention optimizes and revises method, comprising: step 1 enumerates the adjusting pressure measure that can improve quality of voltage, analyzes influence of the different adjusting pressure measures to voltage；Practical power distribution network is built simulation model, and simulates voltage problem existing for the power distribution network by step 2；The influence of the model built to step 2 of adjusting pressure measure cited by step 3, analytical procedure 1, determines effective adjusting pressure measure；Step 4, after step 3, using voltage optimization control algolithm, obtain optimal adjusting pressure measure combination, realize and adjustment is controlled to the voltage optimization of power distribution network.Voltage optimization method of the invention is able to achieve to the voltage optimization effect for being rich in small power station's power distribution network.
Description
Technical field
The invention belongs to voltage optimization adjustment and Treatment process field, be related to rich in small power station's distribution network voltage quality compared with
A kind of optimization method of difference, and in particular to a kind of that electric system network voltage is optimized based on the innovatory algorithm of genetic algorithm
Adjusted Option.
Background technique
Voltage optimization adjustment is the basic measures for guaranteeing grid voltage quality and the weight for realizing systematic economy operation with improvement
Want means.In Operation of Electric Systems, due to the difference of each substation's load level and load character, results in part substation and go out
Existing reactive capability is insufficient and the idle surplus of another part, be easy to cause the brownout or excessively high of system, influences electric system
Rate of qualified voltage and economy, stable operation.When electric system operates normally, reactive power output and the load of system
Reactive power consumption and network reactive loss balance each other.If vacancy occurs for the reactive power compensation capacity of system, load side electricity
Pressure is forced to reduce, and after voltage drop is as low as some critical value, voltage value continuously declines and cannot restore, to cause electricity
Pressure collapse, collapse of voltage will make all load rejections of this area, be forced to have a power failure, in some instances it may even be possible to expand as between several parts of system
Lose synchronization, lead to asynchronous oscillation, cause system-wide accident, lose more loads.Therefore, it is necessary to adjust transformer
Tap gear and compensation capacitor group, generator terminal voltage run power system stability, set in existing reactive compensation
It is standby cannot meet the requirements in the case where, increase reactive-load compensation equipment in certain nodes, to the whole network it is idle optimize adjustment with
It administers, realizes the smallest investment of added compensation capacity, obtain maximum rate of qualified voltage.
Currently, having had already appeared many pertinent literatures: (1) determining that reactive power optimization of power system is multiple target, include
Function loss minimization, voltage deviation are minimum and voltage stability index is minimum.Fuzzy control theory and mould are introduced in particle swarm algorithm
Quasi- annealing theory, improved algorithm avoid the problem of falling into local optimum.(2) establishing subregion reactive balance degree is mesh
Target model, on the basis of local measurement information, the actual no power input of capacitor connects with the idle power output of its ideal expectation
Short range degree is exactly reactive balance degree.The utilization rate of low-voltage reactive compensator is improved, and the active loss of power distribution network also subtracts
It is few.(3) determine optimization aim be it is diversified, the investment including active loss minimization, reactive-load compensation equipment is minimum, voltage water
It is flat most high, the optimal compensation node of ac and dc systems is obtained using prediction correction prim al- dual interior point m ethod, and corresponding optimal
Compensation capacity.(4) the multiple agent immune model of the GA for reactive power optimization problem comprising distributed generation resource is established, is utilized
Multiple agent immune algorithm realizes idle work optimization, which is based on clustering and competition clone's mechanism is improved.(5) fixed compensation is determined
The optimization problem is solved using differential evolution algorithm with the minimum target of year total expenditure expense with dynamic compensation capacity.Difference is calculated
Application of the method in this field is more rare.(6) with electric system active loss minimum and the minimum target of voltage out-of-limit degree,
With the multi-objective particle swarm algorithm Solve problems with mutation.In the algorithm due to non-dominated ranking and crowding distance
It uses, the diversity of search improves, and convergence speed of the algorithm also improves.
Above-mentioned document establishes different mathematical models, adjusts combinatorial problem with different algorithms to solve voltage optimization, real
Existing different optimization aim.But it is all not involved with using newly-increased reactive compensation capacity minimum as objective function.
Summary of the invention
The purpose of the present invention is to provide a kind of distribution network voltages rich in small power station to optimize and revise method, and this method can change
The quality of voltage of kind power distribution network, is realized to the comprehensive solution for being rich in small power station's distribution network voltage problem.
The purpose of the present invention is by the following technical programs to solve:
A kind of distribution network voltage rich in small power station optimizes and revises method, comprising the following steps:
Step 1 is directed to rich in small power station's distribution network voltage, and acquisition can improve the adjusting pressure measure of quality of voltage, and analysis is not
Influence with adjusting pressure measure to voltage；
Practical power distribution network is built simulation model, and simulates voltage problem existing for the power distribution network by step 2；
The influence of adjusting pressure measure model built to step 2 in step 3, analytical procedure 1, determines that effective pressure regulation is arranged
It applies；
Step 4, after step 3, using voltage immune genetic algorithm program, obtain optimal adjusting pressure measure combination, realize
Adjustment is controlled to the voltage optimization of power distribution network.
Preferably, in step 2: in known geographical wiring diagram, needing that the figure is simplified and modified, remove to matching
Network voltage influences the little factor, is simplified geographical wiring diagram, then draws letter further according to simplified geographical wiring diagram
Change isopleth map, Matlab/Simulink simulation model is finally built according to simplified isopleth map.
Preferably, in step 3: the adjusting pressure measure that step 1 is previously mentioned is the factor influential on network voltage, and different
The problems of the electricity distribution network model is surely solved, so screening can not play influence to electricity distribution network model and make by step 3
Adjusting pressure measure.
Preferably, step 4 specifically includes:
Power system voltage is optimized and revised and is expressed as the nonlinear model containing constraint condition with governing problem by step 4.1；
It includes Load Flow Program part and immune genetic algorithm that step 4.2, power system voltage, which are optimized and revised with administering method,
Program part, Load Flow Program use the Newton method program under polar coordinates；
Immune genetic algorithm program part, by generator voltage VGi, capacitor switching gear DCi, load tap changer
Adjustable gear BiThree classes control variable is encoded to antibody, wherein VGiFor real coding, DCi、BiFor integer coding, transformer voltage ratio
KiIt is mapped as adjustable gear Bi, reactive compensation capacity QCiIt is mapped as capacitor switching gear DCi；Control variable is contaminated
Colour solid coding, is randomly generated initial population, carries out Load flow calculation, the fitness function value and concentration of each individual is calculated, with row
Sequence method carrys out selected section parents' individual, and supplements the new individual in part；If being not met by voltage requirement, new supplement is formulated
Node is repaid, reactive-load compensation equipment is increased newly in certain nodes, a control variable is encoded again, carries out Load flow calculation, until meeting
It is required that.
Preferably, in step 4.1, the establishment step of the nonlinear model containing constraint condition includes:
Step 4.1.1, the establishment of objective function: voltage optimization adjustment with administer target be whole network voltage qualification rate most
It is minimum to increase candidate compensation buses investment newly for height；Objective function is taken as compensation capacity minimum, and is constrained to node voltage operation
Penalty function, it may be assumed that
Wherein, QclFor the newly-increased reactive compensation capacity of first node；M is the number of nodes of newly-increased reactive power compensator；λ is one
The very big penalty factor of a numerical value；ViIt is the voltage of node i；VimaxIt is the upper voltage limit of node i； ViminIt is the voltage of i-node
Lower limit；T is by examination node number, does not include the midpoint of three-winding transformer equivalent circuit；
Step 4.1.2, equality constraint: the equality constraint of electric system is trend constraint, as follows:
In formula, Pi、QiFor the injection active power and reactive power of node i；J ∈ i refers to the section being directly connected with node i
Point j, self-admittance when including i=j；Gij、BijThe real and imaginary parts of transadmittance between node i and node j, Gii、BiiNode
The real and imaginary parts of the self-admittance of i；δij=δi-δj, δi、δjRespectively node i, j voltage phase angle；
Step 4.1.3, inequality constraints: control variable includes generator reactive power output, transformer voltage ratio, reactive compensation appearance
Amount, inequality constraints include:
Q in formulaGimax、QGiminFor generator reactive power output bound；Ttmax、TtminFor the bound of load tap changer；
QCkmax、QCkminFor the bound of existing reactive-load compensation equipment capacity, NG、Nc、NTRespectively generator node set, existing idle
It compensates equipment and installs node set, on-load regulator transformer set.
Preferably, in step 4.2, in immune genetic algorithm program,
Allele in antibody population indicates are as follows:
In formula: n=1,2 ..., N is antibody number；G is evolution the number of iterations；I=1,2 ..., the gene that M is antibody n is compiled
Number,Represent the gene on n-th of antibody of g iteration in i-th of gene position；
The diversity of antibody is calculated with average information entropy in population: assuming that population is made of N number of antibody, each antibody has
M gene, it is selective that each gene shares s character: k1,k2,…,ks, then the average information entropy of N number of antibody are as follows:
The comentropy of j-th of gene position of N number of antibody are as follows:
In formula:The probability in j-th of gene position is appeared in for character k, is defined as occurring symbol k's on gene position j
The quotient of sum and N；
For individual u and v, affinity are as follows:
The approximation ratio of feasible solution and optimal solution, the high individual institute of affinity are described using the affinity of antigen and antibody
The target function value of acquirement is preferable, antibodyIt is calculated with the affinity of antigen by following formula:
In formula: f is ideal adaptation angle value；N is the individual amount in population；
Antibody concentration is the antibody same or similar with certain antibody shared ratio in the entire population, certain antibody's
Concentration can be with is defined as:
In formulaWherein α, β are selected threshold value, which also illustrates that
With antibodyThere is the antibody number of larger affinity to account for the ratio of entire antibody sum；
The promotion and inhibition of antibody be according to the concentration level of affinity size and antibody between antibody and antigen come pair
Antibody in population is adjusted, and the promotion and inhibition of antibody are expressed by antibody desired value:
Preferably, in step 4.2, in immune genetic algorithm program, the distance between two antibody v, w are taken as Euclidean space
Distance under middle norm meaning, it may be assumed that
The concentration of antibodyWherein, the binding force between antibody and antibodyAntibody is fitted
Should value p by the antibody and antigen binding force FvWith the concentration c of the antibodyvTwo parts composition:
P=α Fv+(1-α)cv
α is a proportionality coefficient, 0 < α < 1, binding force F in formulavUsing the target function value of individual.
Compared with the existing technology, the invention has the following advantages that
Immune genetic algorithm proposed by the present invention, which has the regulation for containing multiple small power stations in power distribution network, to be generally applicable in
Property, first for small power station's distribution network voltage is rich in, acquisition can improve the adjusting pressure measure of quality of voltage, analyze different pressure regulation and arrange
Apply the influence to voltage；And practical power distribution network is built into simulation model, and simulate voltage problem existing for the power distribution network；Analysis
Influence of the adjusting pressure measure to built model, determines effective adjusting pressure measure；Voltage immune genetic algorithm program is finally used, is obtained
It is combined to optimal adjusting pressure measure, realizes and adjustment is controlled to the voltage optimization of power distribution network.This method is under different topology structure, all
The optimal solution of voltage can be found out and compared, the voltage in each power station of strict control avoids voltage out-of-limit or deficiency.The algorithm
It can guarantee diversification individual in algorithm population when solving optimal problem, and have good convergence, avoid local optimum
Property.
Detailed description of the invention
Fig. 1 is of the invention a kind of for the flow chart optimized and revised rich in small power station's distribution network voltage；
Fig. 2 is 6 node power system construction drawing of Ward-Hale；
Fig. 3 is the track data of 6 node system of Ward-Hale.
Specific embodiment
With reference to the accompanying drawing, a specific embodiment of the invention is described in detail, but the present invention is not limited to the implementations
Example.In order to make the public have thorough understanding to the present invention, is preferably applied in following present invention and concrete details is described in detail in example.
The present invention is a kind of method for optimizing adjustment for the voltage rich in small power station's power distribution network, as shown in Figure 1, tool
Body follows the steps below to implement:
Step 1 enumerates the adjusting pressure measure that can improve quality of voltage, analyzes influence of the different adjusting pressure measures to voltage.
Practical power distribution network is built simulation model, and simulates voltage problem existing for the power distribution network by step 2；
It in known geographical wiring diagram, needs that the figure is simplified and modified, removing influences not distribution network voltage
The big factor is simplified geographical wiring diagram, then draws further according to simplified geographical wiring diagram and simplifies isopleth map, last root
Matlab/Simulink simulation model is built according to isopleth map is simplified, and simulates voltage problem existing for the power distribution network.
The influence of the model built to step 2 of adjusting pressure measure cited by step 3, analytical procedure 1, determines effective pressure regulation
Measure；
The adjusting pressure measure that step 1 is previously mentioned is the factor influential on network voltage, might not can solve the distribution
The problems of pessimistic concurrency control, so by step 3, it should which screening can not play the tune of influence to this electricity distribution network model
Pressure measure.
Step 4, after step 3, using voltage optimization control algolithm, obtain optimal adjusting pressure measure combination, realize to matching
The voltage optimization of power grid controls adjustment, and specific algorithm flow chart is as shown in Figure 2.
Power system voltage is optimized and revised and is expressed as the nonlinear model containing constraint condition with governing problem by step 4.1.
Step 4.1.1, the establishment of objective function: voltage optimization adjustment with administer target be whole network voltage qualification rate most
It is minimum to increase candidate compensation buses investment newly for height.Objective function is taken as compensation capacity minimum, and is constrained to node voltage operation
Penalty function.That is:
QclFor the newly-increased reactive compensation capacity of node l；M is the number of nodes of newly-increased reactive power compensator；λ be a numerical value very
Big penalty factor；ViIt is the voltage (i.e. the amplitude of node i voltage vector) of node i；VimaxIt is the upper voltage limit of node i；Vimin
It is the lower voltage limit of i-node；T is by examination node number (not including the midpoint of three-winding transformer equivalent circuit).
Step 4.1.2, equality constraint: the equality constraint of electric system is trend constraint.It is as follows:
P in formulai、QiFor the injection active power and reactive power of node i；J ∈ i refers to the node being directly connected with node i
J, self-admittance when including i=j；Gij、BijThe real and imaginary parts of transadmittance between node i and node j, Gii、BiiNode i
Self-admittance real and imaginary parts；δij=δi-δj, δi、δjRespectively node i, j voltage phase angle.
Step 4.1.3, inequality constraints: control variable includes generator reactive power output, transformer voltage ratio, reactive compensation appearance
Amount, inequality constraints include:
Q in formulaGimax、QGiminFor generator reactive power output bound；Ttmax、TtminFor the bound of load tap changer；
QCkmax、QCkminFor the bound of existing reactive-load compensation equipment capacity.NG、Nc、NTRespectively generator node set, it is existing idle
It compensates equipment and installs node set, on-load regulator transformer set.
Step 4.2, using immune genetic algorithm, the allele in antibody population may be expressed as:
In formula: n=1,2 ..., N is antibody number；G is evolution the number of iterations；I=1,2 ..., the gene that M is antibody n is compiled
Number,Represent the gene on n-th of antibody of g iteration in i-th of gene position.
The diversity of antibody can be calculated with average information entropy (entropy) in population.Assuming that population is by N number of antibody group
At each antibody has M gene, and it is available that each gene shares s character: k1,k2,…,ks(decimal integer is compiled
Code takes s=10, that is, uses 0~9 character).Then the average information entropy of N number of antibody is
The comentropy of j-th of gene position of N number of antibody is
In formula:The probability in j-th of gene position is appeared in for character k, can be defined as according on gene position j
The quotient of the sum and N of number k.Hj(N) diversity of bigger each gene position of explanation is better, in immune genetic evolution initial stage species information
Compare abundant.With the increase of evolutionary generation and the continuous improvement of adaptive value, various degree of population can be gradually decreased, when in population
Individual when reaching consistent, various degree is zero.
For individual u and v, affinity is
The approximation ratio of feasible solution and optimal solution, the high individual institute of affinity are described using the affinity of antigen and antibody
The target function value of acquirement is preferable.AntibodyIt is calculated with the affinity of antigen by following formula:
In formula: f is ideal adaptation angle value；N is the individual amount in population.
Antibody concentration is the antibody same or similar with certain antibody shared ratio in the entire population, certain antibody's
Concentration can be with is defined as:
In formulaWherein α, β are selected threshold value.The formula also illustrates that
With antibodyThere is the antibody number of larger affinity to account for the ratio of entire antibody sum.
The promotion of antibody and inhibit be exactly according to the concentration level of affinity size and antibody between antibody and antigen come
Antibody in population is adjusted, the more big then select probability of the concentration of antibody is smaller, it is ensured that the diversity of population；Antibody
The more big then select probability of affinity it is bigger, defect individual can be retained.The promotion of antibody and inhibit by antibody desired value come
Expression:
As it can be seen that the antibody desired value the high, the probability selected is bigger, so that low anti-of and concentration high with antigen affinity
Body is promoted；Otherwise, antibody is inhibited.The effect for calculating desired value is that excessive generate of control is suitable for the identical anti-of antigen
Body.
Step 4.3, program are realized: it includes Load Flow Program part and excellent that power system voltage, which is optimized and revised with the program of improvement,
Change adjustment programme part.Load Flow Program uses the Newton method program under polar coordinates not consider load with voltage in Load Flow Program
The static characteristic of variation.Therefore, in addition to balance nodes, keep the injection active power of other nodes constant.Equally, except balance
Outside node and candidate compensation buses, the reactive power of other each nodes does not change.In addition, using dynamic in optimization program
Memory, to improve calculating speed.Immune genetic algorithm program part, by generator voltage VGi, capacitor switching gear
DCi, load tap changer is adjustable gear BiThree classes control variable is encoded to antibody, wherein VGiFor real coding, DCi、BiFor integer
Coding, transformer voltage ratio KiIt is mapped as adjustable gear Bi, reactive compensation capacity QCiIt is mapped as capacitor switching gear DCi.This
Sample avoids truncated error caused by truncation fractional part, reduces the unnecessary assortment of genes, accelerates convergence rate.To control
Variable processed carries out chromosome coding, and initial population is randomly generated, and carries out Load flow calculation, calculates the fitness function value of each individual
And concentration, with ranking method come selected section parents' individual, and the new individual in part is supplemented, accelerates Evolution of Population speed, keep
Diversity of individuals.If being not met by voltage requirement, newly-increased compensation scheme of nodes is studied and defined, is increased newly in certain nodes idle
Equipment is compensated, a control variable is encoded again, Load flow calculation is carried out, until meeting the requirements.In a program, two antibody v, w
The distance between be taken as distance in Euclidean space under norm meaning.That is:
The concentration of antibodyWherein, the binding force between antibody and antibodyAntibody is fitted
Should value p by the antibody and antigen binding force FvWith the concentration c of the antibodyvTwo parts composition:
P=α Fv+(1-α)cv
α is a proportionality coefficient, 0 < α < 1 in formula.Binding force FvUsing the target function value of individual.Obviously, antibody is suitable
The meaning that should be worth is that the binding force of antibody and antigen is smaller, and corresponding adaptive value is smaller；The concentration probability of antibody is smaller, phase
The adaptive value answered is smaller.Adaptive value is smaller, closer to optimal solution.Can both it retain in this way and small (the i.e. target letter of the binding force of antigen
Number is small) antibody, and can mutually promote and inhibit based on the concentration between antibody, it is ensured that individual diversification is conducive to mention in this way
Convergence near high optimal solution.
It for the voltage method that optimizes adjustment rich in small power station's power distribution network is with improved that the present invention, which is a kind of,
For 6 node of Ward-Hale, 6 node power system structure diagram of Ward-Hale is as shown in figure 3, its node and circuitry number
According to as shown in Table 1 and Table 2, the 6th node 6 is balance nodes, and there are two generator G for installing altogether1And G2；2 on-load voltage regulation transformations
Device T1, T2.Third node 3 and fourth node 4 are installed with switched capacitors group C1, C2。
Table 1 is the transformer branch data of 6 node system of Ward-Hale
Table 2 is that (per unit value) is compared in load bus voltage magnitude optimization front and back.
Then voltage optimization adjustment is carried out to Ward-Hale6 node system and administered.In this example, there are 6 electric power
System network nodes, 7 electric system branches.Wherein, power generation node has 2, capacitor compensation second node 2, has load can
Tapped transformer 2 are adjusted, the set end voltage of generator 1 maintains the 100%~110% of rated value, the set end voltage of generator 2
The 110%~115% of rated value is maintained, the range of operation of load bus is the 90%~107% of rated value.In program
In, trend iteration error precision is taken as 10-6, and voltage magnitude is real coding, and the precision of per unit value is taken as 0.01, adjustable tap
Transformer gear step-length is ai=0.025, and the step-length of each gear of reactive power compensator is taken as 1MVar, the nothing of fourth node 4
The upper limit that function compensates power is 5MVar, and the upper limit of the reactive compensation power of the 6th node 6 is 5.5MVar.Power reference value is
100MVA.Penalty factor λ in objective function is taken as 10000.Table 3 is that load node voltage amplitude optimizes front and back comparison sheet, table 4
For the variation table of each control variable in voltage optimization adjustment front and back, the target function value situation of change of two kinds of algorithm optimization adjustment front and backs
As shown in table 5.
Table 3 is the variation of each control variable in voltage optimization adjustment front and back
Table 4 is the target function value situation of change of two kinds of algorithm optimization adjustment front and backs
Table 5 is the target function value situation of change of two kinds of algorithm optimization adjustment front and backs
More than, only presently preferred embodiments of the present invention is not limited only to practical range of the invention, all according to the invention patent
The equivalence changes and modification that the content of range is done all should be technology scope of the invention.
Claims (7)
1. a kind of distribution network voltage rich in small power station optimizes and revises method, which comprises the following steps:
Step 1 is directed to rich in small power station's distribution network voltage, and acquisition can improve the adjusting pressure measure of quality of voltage, analyze the not people having the same aspiration and interest
Influence of the pressure measure to voltage；
Practical power distribution network is built simulation model, and simulates voltage problem existing for the power distribution network by step 2；
The influence of adjusting pressure measure model built to step 2 in step 3, analytical procedure 1, determines effective adjusting pressure measure；
Step 4, after step 3, using voltage immune genetic algorithm program, obtain optimal adjusting pressure measure combination, realize to matching
The voltage optimization of power grid controls adjustment.
2. a kind of distribution network voltage rich in small power station according to claim 1 optimizes and revises method, which is characterized in that step
In rapid 2: in known geographical wiring diagram, needing that the figure is simplified and modified, removing influences less distribution network voltage
The factor, be simplified geographical wiring diagram, then further according to simplified geographical wiring diagram draw simplify isopleth map, last basis
Simplify isopleth map and builds Matlab/Simulink simulation model.
3. a kind of distribution network voltage rich in small power station according to claim 1 optimizes and revises method, which is characterized in that step
In rapid 3: the adjusting pressure measure that step 1 is previously mentioned is the factor influential on network voltage, might not can solve the power distribution network
The problems of model, so screening can not play the adjusting pressure measure of influence to electricity distribution network model by step 3.
4. a kind of distribution network voltage rich in small power station according to claim 1 optimizes and revises method, which is characterized in that step
Rapid 4 specifically include:
Power system voltage is optimized and revised and is expressed as the nonlinear model containing constraint condition with governing problem by step 4.1；
It includes Load Flow Program part and immune genetic algorithm program that step 4.2, power system voltage, which are optimized and revised with administering method,
Part, Load Flow Program use the Newton method program under polar coordinates；
Immune genetic algorithm program part, by generator voltage VGi, capacitor switching gear DCi, load tap changer adjusting shift
Position BiThree classes control variable is encoded to antibody, wherein VGiFor real coding, DCi、BiFor integer coding, transformer voltage ratio KiIt is reflected
It penetrates as adjustable gear Bi, reactive compensation capacity QCiIt is mapped as capacitor switching gear DCi；Chromosome volume is carried out to control variable
Code, is randomly generated initial population, carries out Load flow calculation, calculates the fitness function value and concentration of each individual, with ranking method come
Selected section parents' individual, and supplement the new individual in part；If being not met by voltage requirement, newly-increased expansion joint is formulated
Point increases reactive-load compensation equipment newly in certain nodes, encodes again to a control variable, Load flow calculation is carried out, until meeting the requirements.
5. a kind of distribution network voltage rich in small power station according to claim 4 optimizes and revises method, which is characterized in that step
In rapid 4.1, the establishment step of the nonlinear model containing constraint condition includes:
Step 4.1.1, the establishment of objective function: voltage optimization adjustment is whole network voltage qualification rate highest with the target administered, newly
It is minimum to increase candidate compensation buses investment；Objective function is taken as compensation capacity minimum, and is constrained to node voltage operation and penalizes letter
Number, it may be assumed that
Wherein, QclFor the newly-increased reactive compensation capacity of first node；M is the number of nodes of newly-increased reactive power compensator；λ is a number
It is worth very big penalty factor；ViIt is the voltage of node i；VimaxIt is the upper voltage limit of node i；ViminIt is the lower voltage limit of i-node；T
By examination node number, not include the midpoint of three-winding transformer equivalent circuit；
Step 4.1.2, equality constraint: the equality constraint of electric system is trend constraint, as follows:
In formula, Pi、QiFor the injection active power and reactive power of node i；J ∈ i refers to the node j being directly connected with node i,
Self-admittance when including i=j；Gij、BijThe real and imaginary parts of transadmittance between node i and node j, Gii、BiiNode i
The real and imaginary parts of self-admittance；δij=δi-δj, δi、δjRespectively node i, j voltage phase angle；
Step 4.1.3, inequality constraints: control variable includes generator reactive power output, transformer voltage ratio, reactive compensation capacity,
Inequality constraints includes:
Q in formulaGimax、QGiminFor generator reactive power output bound；Ttmax、TtminFor the bound of load tap changer；QCkmax、
QCkminFor the bound of existing reactive-load compensation equipment capacity, NG、Nc、NTRespectively generator node set, existing reactive compensation are set
Standby installing node set, on-load regulator transformer set.
6. a kind of distribution network voltage rich in small power station according to claim 4 optimizes and revises method, which is characterized in that step
In rapid 4.2, in immune genetic algorithm program,
Allele in antibody population indicates are as follows:
In formula: n=1,2 ..., N is antibody number；G is evolution the number of iterations；The gene that i=1,2 ..., M are antibody n is numbered,Represent the gene on n-th of antibody of g iteration in i-th of gene position；
The diversity of antibody is calculated with average information entropy in population: assuming that population is made of N number of antibody, each antibody has M
Gene, it is selective that each gene shares s character: k1,k2,…,ks, then the average information entropy of N number of antibody are as follows:
The comentropy of j-th of gene position of N number of antibody are as follows:
In formula:The probability in j-th of gene position is appeared in for character k, is defined as the sum for occurring symbol k on gene position j
With the quotient of N；
For individual u and v, affinity are as follows:
The approximation ratio of feasible solution and optimal solution described using the affinity of antigen and antibody, acquired by the high individual of affinity
Target function value it is preferable, antibodyIt is calculated with the affinity of antigen by following formula:
In formula: f is ideal adaptation angle value；N is the individual amount in population；
Antibody concentration is the antibody same or similar with certain antibody shared ratio in the entire population, certain antibodyConcentration can
With is defined as:
In formulaWherein α, β are selected threshold value, which also illustrates that and resist
BodyThere is the antibody number of larger affinity to account for the ratio of entire antibody sum；
The promotion and inhibition of antibody are according to the concentration level of affinity size and antibody between antibody and antigen come to population
In antibody be adjusted, the promotion of antibody and inhibit to express by antibody desired value:
7. a kind of distribution network voltage rich in small power station according to claim 4 optimizes and revises method, which is characterized in that step
In rapid 4.2, in immune genetic algorithm program, the distance between two antibody v, w be taken as in Euclidean space under norm meaning away from
From, it may be assumed that
The concentration of antibodyWherein, the binding force between antibody and antibodyThe adaptive value of antibody
P by the antibody and antigen binding force FvWith the concentration c of the antibodyvTwo parts composition:
P=α Fv+(1-α)cv
α is a proportionality coefficient, 0 < α < 1, binding force F in formulavUsing the target function value of individual.
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