CN103136585A - Weighting Voronoi diagram substation planning method based on chaotic and genetic strategy - Google Patents

Abstract

The invention relates to the field of electric systems and discloses a weighting Voronoi diagram substation planning method based on a chaotic and genetic strategy. The method aims at solving the problems that a prior algorithm is low in rate of convergence, poor in capacity of local optimization and sensitive in initial value, premature convergence exists, the unreasonable phenomenon caused by division of power-supply districts according to the principle of proximity exists, the load rate of a planned substation can not be controlled, and the like, and optimizing site selection of the substation and division of the power-supply districts by means of certain algorithms. The method comprises the steps of setting parameters; chaotic initialization and generating initial population including N individuals; carrying out the site selection of the substation and load distribution on the N individuals; judging whether end criterion is satisfied; calculating the fitness variance sigma 2 of the population; chaotic search; and executing and saving an optimized genetic algorithm and then returning to the fourth step. The weighting Voronoi diagram substation planning method based on the chaotic and genetic strategy is mainly applicable to the electric systems.

Description

Weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy

Technical field

The present invention relates to field of power, relate in particular to the weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy.

Background technology

In the urban distribution network development plan, space layout, the grid structure of following area to be planned electrical network and the load level that can provide are provided for the position of High Voltage Distribution Substations, capacity and service area planning, in the situation that China's land resource more and more is becoming tight, scientific and reasonable transformer station's Planning of spatial arrangement and soil, corresponding site are reserved is the major issue that is related to the area to be planned Sustainable Development in Future, and tool is of great significance.

The substation planning problem is a multivariate, multiobject large-scale nonlinear optimization problem, and existing existing method is divided into 3 classes such as Mathematics Optimization Method, Heuristic Method and modern intelligent optimization method substantially.In recent years, Intelligent Optimization Technique had obtained application gradually in this problem, as tabu search method, simulated annealing, genetic algorithm, particle cluster algorithm etc.The deficiencies such as these methods not only exist that speed of convergence is slow to some extent, local optimal searching ability, computing time are long, but also have service area to divide only to adopt the problems such as unreasonable and transformer station's load factor planning of distributing and causing is uncontrollable nearby.

In recent years, computational geometry Voronoi figure is introduced into electric system and solves the substation planning problem, it is to solve in the inhomogeneous situation of load density aspect the service area classifying rationally effect fine, but single Voronoi drawing method has certain susceptibility to initial value, the weight of weighted Voronoi diagrams is chosen also and is had much room for improvement, and these class methods only can obtain the local extremum solution.And the substation site selection based on genetic algorithm takes full advantage of the robustness of genetic algorithm, by a series of genetic manipulations such as selection, intersection, variations, the colony that evolves gradually, the state that makes colony approach or comprise optimum solution, but have precocious phenomenon.

Summary of the invention

The present invention is intended to overcome the deficiencies in the prior art, overcome that previous algorithm the convergence speed is slow, the local optimal searching ability, initial value is responsive and precocious phenomenon and service area are divided the problems such as unreasonable and transformer station's load factor planning of distributing and causing is uncontrollable that adopt nearby, by certain algorithm, solve the optimization of substation site selection and division of the power supply area, for achieving the above object, the technical scheme that the present invention takes is, weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy, comprise the steps:

Step 1: setup parameter comprises setting:

Maxeranum: maximum iteration time; Popsize: population scale; N: number of individuals in initial population; The pCross crossover probability; The pMutation probability that makes a variation; PInversion inversion probability;

Step 2: the chaos initialization, produce and comprise N individual initial population, and using the coding of each individuality as substation location;

Step 3: to N the individual addressing of weighted Voronoi diagrams figure transformer station and the load distribution thereof of realizing regulating based on self-adaptation weight, and calculate each individual adaptive value f, from N initial population selectivity preferably Popsize solution as initial solution;

Step 4: judge whether to meet the termination criterion, if meet, algorithm stops, otherwise performs step 5;

Step 5: the adaptive value variances sigma of calculating population ²if, σ ²<ε, ε is adaptive value variance threshold values given in advance, precocity occurs, carries out Chaos Search; Otherwise, jump to step 7;

Step 6: Chaos Search;

Step 7: carry out and preserve optimum genetic algorithm, return to step 4 after end.

The chaos initialization further is specially:

2.1 produce at random n-dimensional vector z ₀, its each component values is between-1～1

Z ₀=(z ₀₁, z ₀₂..., z _0n), n is transformer station's number, z ₀₁, z ₀₂..., z _0nfor the random z generated ₀each component of vector;

2.2 get chaos sequence: according to the cube mapping function obtain z ₁, z ₂, z ₃..., z _n, obtain thus chaos sequence

z＝(z ₁,z ₂,z ₃,…,z _N)

Z ₁, z ₂, z ₃..., z _neach component of the z phasor obtained by the cube mapping function for the vector of initial random generation;

2.3 according to

$x_{\mathrm{ij}}=\frac{a_j+b_{\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA00002827470800013.png"\; state="\{\{state\}\}">j</figure-callout>}}}{2}+\frac{(b_j-a_j)}{2}{z}_{\mathrm{ij}},i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N;j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,n$

By chaos sequence z _ij component carrier to the span (a of optimized variable _j, b _j) in, obtained thus N initial position of n the x of transformer station direction, a _j, b _jbe respectively lower limit and the higher limit of transformer station's actual power zone x direction, z _ijeach component z for the z vector ₁, z ₂, z ₃..., z _ncomponent; Obtain in the same way N initial position of n the y of transformer station direction;

2.4 N the initial position by x, y obtains N individual initial population according to individual cryptoprinciple respectively.

Determining with the self-adaptation of weight of initial weight regulated and is specially:

3.1 determining of initial weight: according to

Determine initial weight, wherein: l (i) is i substation's on-load; The rated capacity that e (i) is i transformer station; γ is that transformer station is in the high capacity rate met under main transformer " N-1 " principle; for transformer station's power factor;

3.2 the self-adaptation of each transformer station's weight is regulated.

Chaos Search is specially:

6.1 produce at random a n-dimensional vector cr ₀, by cr _i+1=4cr _i ³-3cr _iproduce cr=(cr ₁, cr ₂, cr ₃..., cr _i...), i=1,2 ..., Popsize, according to Δ x _ij=m+ (n-m) * cr _ijby cr _ieach component carrier in chaotic disturbance scope [m, n], obtain disturbance quantity Δ x=(Δ x ₁, Δ x ₂..., Δ x _i...), cr ₀for the random vector produced, cr ₁, cr ₂, cr ₃..., cr _i... each component for the cr vector;

6.2 chaotic disturbance amount component is loaded on to each individuality, forms each individual chaotic disturbance x direction position

x′＝(x′ ₁,x′ ₂,…,x′ _i,…)，i＝1,2,…,Popsize

Wherein

$\left\{\begin{array}{c}{x}_i^{\&prime;}=(x_{i1}^{\&prime;},x_{i2}^{\&prime;},\&CenterDot;\&CenterDot;\&CenterDot;,x_{\mathrm{in}}^{\&prime;})\\ x_{\mathrm{ij}}^{\&prime;}=x_{\mathrm{ij}}+{\mathrm{\&Delta;x}}_{\mathrm{ij}}\end{array}\right.$

X ' obtains for chaos sequence adds after disturbance quantity, x ' ₁, x ' ₂..., x ' _i... for its each component, x ' _i1, x ' _i2..., x ' _infor x ' _icomponent; Form each individual chaotic disturbance y direction position according to 6.1,6.2;

6.3 the chaotic disturbance position is carried out regulating based on self-adaptation to substation site selection and the load distribution thereof of the weighted Voronoi diagrams figure transformer station addressing algorithm of weight, calculates each individual adaptive value f ';

6.4 compare the adaptive value before and after each individual chaotic disturbance, if the feasible solution x ' that Chaos Search arrives _ithe feasible solution x that is better than corresponding individuality _i, use x ' _isubstitute x _i, perform step 7.

Carrying out the optimum genetic algorithm of preservation is specially:

7.1 individual coding

Individual coding adopts the floating-point encoding mode, transformer station's number that individual code length equals 2 times, and each individual gene is substation location in the planning zone, i.e. the x direction coordinate of locus or y direction coordinate, site is floating number;

7.2 fitness function

Select transformer station and network investment and annual running cost to be used as fitness function and estimate each individual corresponding programme;

7.3.1 select operation

Select operation to adopt optimum conversation strategy to combine with definite formula sampling system of selection, determine formula sampling system of selection:

(1). calculate the expectation existence number N of each individuality in colony of future generation in colony _i, i=1,2 ..., M;

(2). use N _iintegral part

determine each corresponding individual existence number in colony of future generation;

(3). according to N _ifraction part individuality is carried out to descending sort, the order get before

individuality joins in colony of future generation;

7.4 crossover and mutation operation

Employing counts to intersect and carries out interlace operation, replaces the protogene value by a random number in genes of individuals value span and completes mutation operation;

7.5 inversion operation

Put upside down the sequence in the gene order between random two locus formulating in individual coded strings, thereby form a new better individuality;

7.6 transformer station's load area is divided

If d the collection S={s of transformer station in the planning zone ₁, s ₂..., s _d, arbitrary load x belongs to the s of transformer station _ibe defined as:

R(s _i,x)＝{d(x,s _i)≤d(x,s _j),j＝1,2,…,d,j≠i}

D in formula (x, s _i), d (x, s _j) mean respectively arbitrary load x and s on plane _iand s _jbetween Euclidean distance;

7.7 preserve optimum genetic algorithm.

Technical characterstic of the present invention and effect:

1. calculate weight according to substation capacity and load distribution thereof, on the basis of improving weight calculation, add load factor adaptive control flow process, but the substation planning method of the weighted Voronoi diagrams figure that formation weight self-adaptation is adjusted.

2. weighted Voronoi diagrams figure method and genetic algorithm are combined, realized preserving the weighted Voronoi diagrams figure transformer station addressing of optimum genetic algorithm.

3. introduce chaos thought, solve the premature convergence problem of genetic algorithm, make program results more be tending towards excellent, realize the weighted Voronoi diagrams figure transformer station site selecting method based on Chaos Genetic Algorithm.

By the weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy, be applied in different examples, result shows that no matter the method is from investment cost, transformer station's load distribution zone or transformer station's actual loading rate aspect all are better than simple genetic algorithm and simple weighted Voronoi diagrams figure transformer station site selecting method, and the two weighted Voronoi diagrams drawing method based on genetic algorithm combined, this fully shows the validity of the method to the inhomogeneous situation power supply area of load density classifying rationally, and no matter aspect substation location definite, or all reliable than single method aspect the division of transformer station's service area, rationally.

The accompanying drawing explanation

But the weighted Voronoi diagrams figure substation planning method that Fig. 1 weight self-adaptation is adjusted;

Expense: 11041.43 ten thousand yuan;

1～No. 9 transformer station's load factor: 0.7800.6500.6130.6070.5740.5610.5420.5310.822.

The weighted Voronoi diagrams figure transformer station site selecting method of Fig. 2 based on Chaos Genetic Algorithm;

Expense: 11017.79 ten thousand yuan;

1～No. 9 transformer station's load factor: 0.6960.6460.6210.6200.5590.5820.5800.5640.767.

But the weighted Voronoi diagrams figure substation planning method that Fig. 3 weight self-adaptation is adjusted;

Expense: 4736.98 ten thousand yuan

1～No. 7 transformer station's load factor: 0.7740.6900.6060.6020.5300.5100.451.

The weighted Voronoi diagrams figure transformer station site selecting method of Fig. 4 based on Chaos Genetic Algorithm;

Expense: 4736.02 ten thousand yuan;

1～No. 7 transformer station's load factor: 0.6710.6330.6330.6230.600.5990.536.

Fig. 5 preserves optimum genetic algorithm process flow diagram.

Embodiment

The present invention:

1. calculate weight according to substation capacity and load distribution thereof, on the basis of improving weight calculation, add load factor adaptive control flow process, but the substation planning method of the weighted Voronoi diagrams figure that formation weight self-adaptation is adjusted.

2. weighted Voronoi diagrams figure method and genetic algorithm are combined, realized preserving the weighted Voronoi diagrams figure transformer station addressing of optimum genetic algorithm.

3. introduce chaos thought, solve genetic algorithm ground premature convergence problem, make program results more be tending towards excellent, realize the weighted Voronoi diagrams figure transformer station site selecting method based on Chaos Genetic Algorithm.

Further illustrate the present invention below in conjunction with the drawings and specific embodiments.

1. the high capacity rate that transformer station meets under " N-1 " principle is as shown in table 1.

High capacity rate under table 1 transformer station " N-1 " principle

Transformer station is containing the main transformer number	Load factor
		Two main transformers	0.65
Three main transformers	0.87

When the actual loading rate of transformer station, during higher than the high capacity rate under " N-1 " principle, program results does not meet the demands.

2. the transformer station that example 1 is 83 * 50MVA, 13 * 40MVA.But Fig. 1 and Fig. 2 mean respectively to utilize the substation planning method of the weighted Voronoi diagrams figure adjusted based on the weight self-adaptation and the program results of the weighted Voronoi diagrams figure transformer station site selecting method based on Chaos Genetic Algorithm.

Step 1: setup parameter

Maxeranum: maximum iteration time; Popsize: population scale number; N: number of individuals in initial population; The pCross crossover probability; The pMutation probability that makes a variation; PInversion inversion probability.

Step 2: the chaos initialization produces and comprises N individual initial population.

2.1 produce at random n-dimensional vector z ₀, its each component values is between-1～1

Z ₀=(z ₀₁, z ₀₂..., z _0n), n is transformer station's number, z ₀₁, z ₀₂..., z _0nfor the random z generated ₀each component of vector;

2.2 get chaos sequence: according to the cube mapping function

obtain z ₁, z ₂, z ₃..., z _n, obtain thus chaos sequence

z＝(z ₁,z ₂,z ₃,…,z _N)

Z ₁, z ₂, z ₃..., z _neach component of the z phasor obtained by the cube mapping function for the vector of initial random generation;

2.3 according to

$x_{\mathrm{ij}}=\frac{a_j+{\mathrm{<figure-callout\; id="2"\; label="b\; j"\; filenames="HDA00002827470800013.png"\; state="\{\{state\}\}">b</figure-callout>}}_j}{2}+\frac{(b_j-a_j)}{2}{z}_{\mathrm{ij}},i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N;j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,n$

By chaos sequence z _ij component carrier to the span (a of optimized variable _j, b _j) in, obtained thus N initial position of n the x of transformer station direction, a _j, b _jbe respectively lower limit and the higher limit of transformer station's actual power zone x direction, z _ijeach component z for the z vector ₁, z ₂, z ₃..., z _ncomponent; Obtain in the same way N initial position of n the y of transformer station direction;

2.4 N the initial position by x, y obtains N individual initial population according to individual cryptoprinciple respectively.

Step 3: to N the individual addressing of weighted Voronoi diagrams figure transformer station and the load distribution thereof of realizing regulating based on self-adaptation weight, and calculate each individual adaptive value f, from N initial population selectivity preferably Popsize solution as initial solution.The key of the method is the self-adaptation adjusting of determining of initial weight and weight.

3.1 determining of initial weight: according to

Determine initial weight, wherein: l (i) is i substation's on-load; The rated capacity that e (i) is i transformer station; γ is that transformer station is in the high capacity rate met under main transformer " N-1 " principle;

for transformer station's power factor;

3.2 the self-adaptation of each transformer station's weight is regulated.For avoiding requiring the load factor that causes too high or too low because initial weight can't meet transformer station actual loading rate, the self-adaptation that principle is carried out weight is as shown in Table 2 regulated.

Table 2 weight self-adaptation is regulated principle

In table: a and b are respectively higher limit and the lower limit of transformer station's actual loading rate; Δ is the weight adjustment amount, the self-adaptation adjustment;

it is the weight after the t time iteration of i transformer station.When the variation of actual loading rate does not meet table 2 condition, weight remains unchanged.Weight increases, and the power supply area of corresponding transformer station diminishes, otherwise it is large that corresponding power supply area becomes.

Step 4: judge whether to meet the termination criterion, if meet, algorithm stops, otherwise performs step 5;

Step 5: the adaptive value variances sigma of calculating population ²if, σ ²precocity, appear in<ε (ε is adaptive value variance threshold values given in advance), carries out Chaos Search; Otherwise, jump to step 7.

Step 6: Chaos Search.

6.1 produce at random a n-dimensional vector cr ₀, by cr _i+1=4cr _i ³-3cr _iproduce cr=(cr ₁, cr ₂, cr ₃..., cr _i...), i=1,2 ..., Popsize, according to Δ x _ij=m+ (n-m) * cr _ijby cr _ieach component carrier in chaotic disturbance scope [m, n], obtain disturbance quantity Δ x=(Δ x ₁, Δ x ₂..., Δ x _i...), cr ₀for the random vector produced, cr ₁, cr ₂, cr ₃..., cr _i... each component for the cr vector;

6.2 chaotic disturbance amount component is loaded on to each individuality, forms each individual chaotic disturbance x direction position

x′＝(x′ ₁,x′ ₂,…,x′ _i,…)(i＝1,2,…,Popsize)，

Wherein

$\left\{\begin{array}{c}{x}_i^{\&prime;}=(x_{i1}^{\&prime;},{\mathrm{<figure-callout\; id="2"\; label="x\; i"\; filenames="HDA00002827470800013.png"\; state="\{\{state\}\}">x</figure-callout>}}_i^2,\&CenterDot;\&CenterDot;\&CenterDot;,x_{\mathrm{in}}^{\&prime;})\\ x_{\mathrm{ij}}^{\&prime;}=x_{\mathrm{ij}}+{\mathrm{\&Delta;x}}_{\mathrm{ij}}\end{array}\right.$

X ' obtains for chaos sequence adds after disturbance quantity, x ' ₁, x ' ₂..., x ' _i... for its each component, x ' _i1, x ' _i2..., x ' _infor x ' _icomponent; Form each individual chaotic disturbance y direction position according to 6.1,6.2;

6.3 the chaotic disturbance position is carried out regulating based on self-adaptation to substation site selection and the load distribution thereof of the weighted Voronoi diagrams figure transformer station addressing algorithm of weight, calculates each individual adaptive value f ';

6.4 compare the adaptive value before and after each individual chaotic disturbance, if the feasible solution x ' that Chaos Search arrives _ithe feasible solution x that is better than corresponding individuality _i, use x ' _isubstitute x _i, perform step 7.

Step 7: carry out and preserve optimum genetic algorithm, return to step 4 after end.

7.1 individual coding

Individual coding adopts the floating-point encoding mode, transformer station's number that individual code length equals 2 times, and each individual gene is substation location in the planning zone, i.e. the x direction coordinate of locus or y direction coordinate, site is floating number;

7.2 fitness function

Select transformer station and network investment and annual running cost to be used as fitness function and estimate each individual corresponding programme;

7.3.1 select operation

Select operation to adopt optimum conversation strategy to combine with definite formula sampling system of selection, determine formula sampling system of selection:

(1). calculate the expectation existence number N of each individuality in colony of future generation in colony _i, i=1,2 ..., M;

(2). use N _iintegral part

determine each corresponding individual existence number in colony of future generation;

(3). according to N _ifraction part individuality is carried out to descending sort, the order get before

individuality joins in colony of future generation;

7.4 crossover and mutation operation

Employing counts to intersect and carries out interlace operation, replaces the protogene value by a random number in genes of individuals value span and completes mutation operation;

7.5 inversion operation

Put upside down the sequence in the gene order between random two locus formulating in individual coded strings, thereby form a new better individuality;

7.6 transformer station's load area is divided

If d the collection S={s of transformer station in the planning zone ₁, s ₂..., s _d, arbitrary load x belongs to the s of transformer station _ibe defined as:

R(s _i,x)＝{d(x,s _i)≤d(x,s _j),j＝1,2,…,d,j≠i}

D in formula (x, s _i), d (x, s _j) mean respectively arbitrary load x and s on plane _iand s _jbetween Euclidean distance;

7.7 preserve optimum genetic algorithm.

Claims (5)

1. the weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy, is characterized in that, comprises the steps:

Step 1: setup parameter comprises setting:

Maxeranum: maximum iteration time; Popsize: population scale; N: number of individuals in initial population; The pCross crossover probability; The pMutation probability that makes a variation; PInversion inversion probability;

Step 2: the chaos initialization, produce and comprise N individual initial population, and using the coding of each individuality as substation location;

Step 3: to N the individual addressing of weighted Voronoi diagrams figure transformer station and the load distribution thereof of realizing regulating based on self-adaptation weight, and calculate each individual adaptive value f, from N initial population selectivity preferably Popsize solution as initial solution;

Step 4: judge whether to meet the termination criterion, if meet, algorithm stops, otherwise performs step 5;

Step 5: the adaptive value variances sigma of calculating population ²if, σ ²<ε, ε is adaptive value variance threshold values given in advance, precocity occurs, carries out Chaos Search; Otherwise, jump to step 7;

Step 6: Chaos Search;

Step 7: carry out and preserve optimum genetic algorithm, return to step 4 after end.

2. a kind of weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy as claimed in claim 1, is characterized in that, the chaos initialization further is specially:

2.1 produce at random n-dimensional vector z ₀, its each component values is between-1～1

Z ₀=(z ₀₁, z ₀₂..., z _0n) n is transformer station's number, z ₀₁, z ₀₂..., z _0nfor the random z generated ₀each component of vector;

2.2 get chaos sequence: according to the cube mapping function obtain z ₁, z ₂, z ₃..., z _n, obtain thus chaos sequence

z＝(z ₁,z ₂,z ₃,…,z _N)

Z ₁, z ₂, z ₃..., z _neach component of the z phasor obtained by the cube mapping function for the vector of initial random generation;

2.3 according to

$x_{\mathrm{ij}}=\frac{a_j+b_j}{2}+\frac{(b_j-a_j)}{2}{z}_{\mathrm{ij}},i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N;j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,n$

By chaos sequence z _ij component carrier to the span (a of optimized variable _j, b _j) in, obtained thus N initial position of n the x of transformer station direction, a _j, b _jbe respectively lower limit and the higher limit of transformer station's actual power zone x direction, z _ijeach component z for the z vector ₁, z ₂, z ₃..., z _ncomponent; Obtain in the same way N initial position of n the y of transformer station direction;

2.4 N the initial position by x, y obtains N individual initial population according to individual cryptoprinciple respectively.

3. a kind of weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy as claimed in claim 1, is characterized in that, determining with the self-adaptation of weight of initial weight regulated and be specially:

3.1 determining of initial weight: according to

Determine initial weight, wherein: l (i) is i substation's on-load; The rated capacity that e (i) is i transformer station; γ is that transformer station is in the high capacity rate met under main transformer " N-1 " principle; for transformer station's power factor;

3.2 the self-adaptation of each transformer station's weight is regulated.

4. a kind of weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy as claimed in claim 1, is characterized in that, Chaos Search is specially:

6.1 produce at random a n-dimensional vector cr ₀, by cr _i+1=4cr _i ³-3cr _iproduce cr=(cr ₁, cr ₂, cr ₃..., cr _i...), i=1,2 ..., Popsize, according to Δ x _ij=m+ (n-m) * cr _ijby cr _ieach component carrier in chaotic disturbance scope [m, n], obtain disturbance quantity Δ x=(Δ x ₁, Δ x ₂..., Δ x _i...), cr ₀for the random vector produced, cr ₁, cr ₂, cr ₃..., cr _i... each component for the cr vector;

6.2 chaotic disturbance amount component is loaded on to each individuality, forms each individual chaotic disturbance x direction position

x′＝(x′ ₁,x′ ₂,…,x′ _i,…)，i＝1,2,…,Popsize

Wherein

$\left\{\begin{array}{c}{x}_i^{\&prime;}=(x_{i1}^{\&prime;},x_{i2}^{\&prime;},\&CenterDot;\&CenterDot;\&CenterDot;,x_{\mathrm{in}}^{\&prime;})\\ x_{\mathrm{ij}}^{\&prime;}=x_{\mathrm{ij}}+{\mathrm{\&Delta;x}}_{\mathrm{ij}}\end{array}\right.$

X ' obtains for chaos sequence adds after disturbance quantity, x ' ₁, x ' ₂..., x ' _i... for its each component, x ' _i1, x ' _i2..., x ' _infor x ' _icomponent; Form each individual chaotic disturbance y direction position according to 6.1,6.2;

6.3 the chaotic disturbance position is carried out regulating based on self-adaptation to substation site selection and the load distribution thereof of the weighted Voronoi diagrams figure transformer station addressing algorithm of weight, calculates each individual adaptive value f ';

6.4 compare the adaptive value before and after each individual chaotic disturbance, if the feasible solution x ' that Chaos Search arrives _ithe feasible solution x that is better than corresponding individuality _i, use x ' _isubstitute x _i, perform step 7.

5. a kind of weighted Voronoi diagrams figure substation planning method based on the Chaos-Genetic strategy as claimed in claim 1, is characterized in that, carries out the optimum genetic algorithm of preservation and be specially:

7.1 individual coding

Individual coding adopts the floating-point encoding mode, transformer station's number that individual code length equals 2 times, and each individual gene is substation location in the planning zone, i.e. the x direction coordinate of locus or y direction coordinate, site is floating number;

7.2 fitness function

Select transformer station and network investment and annual running cost to be used as fitness function and estimate each individual corresponding programme;

7.3.1 select operation

Select operation to adopt optimum conversation strategy to combine with definite formula sampling system of selection, determine formula sampling system of selection:

(1). calculate the expectation existence number N of each individuality in colony of future generation in colony _i, i=1,2 ..., M;

(2). use N _iintegral part

determine each corresponding individual existence number in colony of future generation;

(3). according to N _ifraction part individuality is carried out to descending sort, the order get before

individuality joins in colony of future generation;

7.4 crossover and mutation operation

Employing counts to intersect and carries out interlace operation, replaces the protogene value by a random number in genes of individuals value span and completes mutation operation;

7.5 inversion operation

Put upside down the sequence in the gene order between random two locus formulating in individual coded strings, thereby form a new better individuality;

7.6 transformer station's load area is divided

If d the collection S={s of transformer station in the planning zone ₁, s ₂..., s _d, arbitrary load x belongs to the s of transformer station _ibe defined as:

R(s _i,x)＝{d(x,s _i)≤d(x,s _j),j＝1,2,…,d,j≠i}

D in formula (x, s _i), d (x, s _j) mean respectively arbitrary load x and s on plane _iand s _jbetween Euclidean distance;

7.7 preserve optimum genetic algorithm.

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