CN105069517B - Power distribution network multiple target fault recovery method based on hybrid algorithm - Google Patents
Power distribution network multiple target fault recovery method based on hybrid algorithm Download PDFInfo
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Abstract
A kind of novel mixed fault recovery game, includes the following steps：1) initial parameter is inputted；2) initial matrix is established；3) initial disaggregation is searched for；4) power verifies；5) networking amendment；6) type load partitioning algorithm convergence is examined；7) a type load division result is exported；8) algorithm transition；9) quanta particle swarm optimization parameter setting；10) initialization of quanta particle；11) object function calculates；12) parameter updates；13) positional value and optimal vector update；14) non-domination solution screens；15) elite collects screening；16) operation is eliminated；17) quanta particle swarm optimization test for convergence；18) result exports.The characteristics of present invention combination heuritic approach and intelligent optimization algorithm, proposes mixed fault restoration methods, solves the problems, such as that the distribution network failure containing DG restores.
Description
Technical field
The present invention relates to a kind of combination is heuristic and the novel mixed fault restoration methods of intelligent optimization algorithm, particular for
A kind of power distribution network multiple target fault recovery problem containing distributed generation resource considering load level.
Background technology
With the reinforcement of distribution network construction and reaching its maturity for micro-capacitance sensor technology, the fault recovery of power distribution network is increasingly becoming intelligence
The important link of energy power grid self-healing control.As distributed generation resource (distributed generation, DG) largely match by access
Power grid, traditional single source radiation shape power distribution network become multi-source system, and electric network composition is more complicated, if failure can not be directed to rapidly
Rational recovery policy is formulated, realizes each type load service restoration of power distribution network, distributed generation resource and power distribution network will bear huge damage
It loses, the safe and stable operation of entire power distribution network will be severely impacted.
Fault recovery containing distributed power distribution network is a kind of extensive, non-linear, multiple target combinatorial optimization problem,
Current main method for solving mainly enlightening formula searching method and intelligent optimization method.Heuristic search is by formulating phase
The heuristic search rule answered, obtains fail-over path, search speed is fast.But method set forth above belongs to elder generation
Search, the two-step optimization method adjusted afterwards, the rule of adjustment is usually artificial to be formulated, and due to the limitation of artificial experience, is inspired
The formulation of formula rule is often more difficult and not comprehensive, and final optimum results is easily made to be absorbed in local optimum, and algorithm lacks extensive suitable
The property used.And it when solving multi-objective optimization question with heuritic approach, generally requires to be translated into monocular by weight factor
Mark optimization problem is solved so that obtains the inefficiency of optimal solution.Intelligent optimization method mainly passes through particle cluster algorithm, something lost
Propagation algorithm, evolution algorithm etc. are solved applied to fault recovery multi-objective problem.Since intelligent algorithm is by the way of random optimizing,
Easily in optimization process generate largely violate power distribution network it is radial constraint, power-balance constraint infeasible solution, if without repairing
Just, the efficiency of algorithm will reduce, and easily be absorbed in locally optimal solution.Therefore, only by heuristic search or intelligent optimization
Method cannot preferably handle the multiple target fault recovery problem containing distributed generation resource.
Invention content
The present invention will overcome the disadvantages mentioned above of the prior art, propose that a kind of combination is heuristic novel with intelligent optimization algorithm
Mixed fault restoration methods.
Load, to society, the economic influence generated, is divided into a kind of, two classes and three type loads by electric system according to load.
Wherein, a type load refers to the load that power supply must be all kept under in office why hinder.Currently, for the distribution for considering load level
Net fault recovery problem cannot obtain preferable effect using heuritic approach and intelligent optimization algorithm：According to heuristic
Algorithm, which solves, considers that the distribution network failure of three classes load level restores problem, and heuristic rule is formulated sufficiently complex and regular
It is thorough to formulate more difficult consideration, rarely has document to be related at present；According to intelligent optimization algorithm, although corresponding heuristic without formulating
Rule, but need to be modified the infeasible solution in iterative process, real-time is not strong, is solving multiple constraint and multivariable
It can only obtain local solution when problem；And it is final it is difficult to ensure that all type loads restore electricity completely.Therefore, item of the present invention
The fault recovery PROBLEM DECOMPOSITION of the power distribution network containing DG is that a type load divides and system failure recovery reconstructs two sub-problems by mesh, is carried
Go out a kind of heuritic approach and mixed fault restoration methods that intelligent optimization algorithm is combined：Wherein, it is divided for a type load
Subproblem is solved using heuristic, to ensure that a type load can restore completely in final fail-over policy
Power supply；Subproblem is reconstructed for fault recovery, is solved using intelligent optimization algorithm, to ensure that weighting dead electricity load is minimum.
Project of the present invention is used to be examined based on heuritic approach and the solution of the mixed fault restoration methods of quanta particle swarm optimization
Consider the Restoration model of distribution network failure containing DG of load level, specific Optimizing Flow is as shown in Figure 1.It is of the present invention to be based on mixing
The power distribution network multiple target fault recovery method of hop algorithm, it is described that steps are as follows：
1) initial parameter is inputted
The generation position of input fault, the design parameter of power distribution network, including micro- source number NDG, power distribution network master switch number
Nb, load level parameter；
2) initial matrix is established
To realize that the division of a type load, the method for the present invention define four kinds of initial squares for heuristic search algorithm
The micro- source ownership matrix of battle array, the respectively micro- source scaling matrices of load-, the micro- source ordinal matrix of load-, load-and the micro- source networkings of Wei Yuan-
Matrix.Each matrix is defined as follows described：
2.1) the micro- source scaling matrices of load-
Micro- source-load proportion matrix AperIt is for recording in power distribution network a type load to each micro- source unicom branch road institute
There is load total amount to account for the percent information of micro- source capacity.In AperIn matrix, a type load corresponds to row vector, micro- source respective column to
Amount, then all load total amounts account for micro- source j capacity to the element representation load i of the i-th row of matrix jth row to micro- source j unicom branch on the road
Percent information, expression formula are：
Wherein, Li,jExpress the load aggregation on load i and the most short communication paths of micro- source j, PLD,zFor on all communication paths
The power of load z, PDG,jFor the active power of micro- source j.Aper(i,j)Numerical value is smaller, and it is micro- to indicate that load i is more possible to be divided to
Source j.
2.2) the micro- source ordinal matrix of load-
The micro- source ordinal matrix A of load-sorIt is for recording in power distribution network a type load to each micro- source unicom branch road
All load total amounts account for the sequencing information of micro- source capacity ratio.Wherein, the corresponding row vector of type load number, micro- source sequence pair
Column vector is answered, then the A corresponding to the element representation of the i-th row of matrix jth rowperElements A in matrixper(i,j)In the sequence of the i-th row,
Sequence is arranged from small to large by numerical values recited.Such as AperElements A in matrixper(2,2)Corresponding numerical value is 1.2, and place is advanced
After row sorts from small to large, it is located at first, so AsorCorresponding A in matrixsor(2,2)The numerical value of element is 1.
2.3) the micro- source of load-belongs to matrix
The micro- source of load-belongs to matrix AbelIndicate that according to certain rule, it is micro- to be divided to some for a type load in power distribution network
The information in source.Wherein, the corresponding row vector of type load number, micro- source, which is numbered, corresponds to column vector, and the i-th row, j column elements are in matrix
1, it represents load i and belongs to micro- source j, 0 expression load i is not belonging to micro- source j.
2.4) the micro- source networking matrix in micro- source-
When some micro- source cannot be satisfied the larger type load of one or more capacity, group is carried out between Wei Yuan and micro- source
Net meets the larger type load of capacity.Networking matrix AuniRow vector and column vector indicate micro- source number, if the i-th row,
Jth column element is 1, indicates that i-th of micro- source and j-th of micro- source carry out networking, forms an island network system.It is initial in algorithm
In the stage, each micro- each one network of self-forming in source, therefore, networking matrix are unit matrix.
3) initial disaggregation is searched for
To realize the division of a type load, the solution for formulating relevant rule set for heuristic search algorithm is needed.This
Inventive method proposes 2 kinds of heuristic rules for searching for initial disaggregation, and rule is described below：
Rule 1：In a final type load division result, a type load i can not possibly belong to AperNumber in the i-th row of matrix
Micro- source of the value more than or equal to 1.Because if AperJ-th of element of the i-th row is more than or equal to 1 in matrix, shows load i to micro- source
Load total amount on j communication paths is more than micro- source j capacity, is unsatisfactory for constraints (2), i.e. formula (6).
Rule 2：Compared to AsorIn the i-th row sequence serial number larger micro- source j, a type load i is more easy to belong to sequence serial number
Smaller micro- source j*.Because the serial number that sorts is small, show a type load i to micro- source j*Load total amount on communication path accounts for micro- source and holds
Amount ratio smaller, micro- source can accommodate more type loads.
According to above-mentioned heuristic rule, the search step of initial network disaggregation is described below：First, according to distribution network failure
The topological structure of network afterwards obtains the micro- source scaling matrices A of load-per, the micro- source ordinal matrix A of load-sor.Secondly, according to rule 1
With rule 2, the micro- source ownership matrix A of load-is obtainedbel.Finally, according to matrix Abel, you can show that intelligent distribution network failure is latter
The initial division result of type load.
4) power verifies
To realize that the power verification of each island network, project of the present invention propose following heuristic rule：
Rule 3：Island network where all micro- sources must satisfy power-balance verification, i.e., the capacity in micro- source must be big
Where equal to the micro- source on all type loads of network and a type load to micro- source communication path all loads power
Summation.Check formula is：
Wherein, PzIndicate the power of load z, set DjIndicate a type load all in the island network where micro- source j with
And all loads on all type loads to micro- source j communication paths；Ploss,jIndicate that the isolated island residing for micro- source j passes through trend meter
The network loss obtained after calculation.
Rule 4：If micro- source j is unsatisfactory for power-balance verification, A corresponding to a type load in micro- source j is rejectedperIn matrix
The maximum load of numerical value, and the load is divided to its AsorRow is corresponded in matrix to sort micro- source of latter cis-position.
According to above-mentioned heuristic rule, power verify the step of it is as described below：For an initial type load in step (3)
Splitting scheme carries out power verification to the network where each micro- source by rule 3, and all micro- sources verify a wheel and are denoted as once repeatedly
Generation.If in an iteration, all micro- sources are satisfied by rule 3, then enter step 6), if not satisfied, then being repaiied by rule 4
Just, matrix A is updatedbel, next round iteration is then carried out, until all micro- sources are satisfied by rule 3.If certain load is repeatedly by micro- source
It rejects, illustrates that single micro- source can not carry the load, then enter networking amendment step, i.e. step 5).
5) networking amendment
If certain load is repeatedly rejected by micro- source, illustrates that single micro- source can not carry the load, networking operation need to be carried out.For reality
Networking amendment between existing each micro- source, the method for the present invention propose following heuristic rule：
Rule 5：If a type load i is divided in any one micro- source according to rule 2, the net where micro- source can be caused
Network is unsatisfactory for formula (2), then by A corresponding to the loadsorSequence forms networking, phase near two preceding micro- sources in the i-th row of matrix
The type load answered is divided in the networking；If after inspection, it is still unsatisfactory for formula (2), then first three micro- source should form
Networking, and so on.
According to above-mentioned heuristic rule, networking is modified, and steps are as follows：Record each load quilt in each iterative process
The number of rejecting carries out networking according to rule 5 and repaiies if the number that certain load is removed in the secondary iteration is more than micro- source sum
Just, while and matrix A is updatedper、AbelAnd Auni, and be back to power verification step and carry out next round iteration, successively to each group
Net is verified.
6) type load partitioning algorithm convergence is examined
Judge whether that all micro- sources are satisfied by formula (2), if satisfied, (7) are then entered step, if not satisfied, returning to step
Suddenly (4) continue to carry out power verification to the network where each micro- source, carry out next round iteration.
7) a type load division result is exported
Heuritic approach terminates, and exports a type load division result, the switch serial number set being closed.
8) algorithm transition
According to the switch serial number set of closure, to not determining that the switch of on off state is set as quantum discrete particle cluster algorithm
Optimized variable.
9) quanta particle swarm optimization parameter setting
Dimension, iterations and the corresponding parameter value of quantum discrete particle cluster algorithm are set.
10) initialization of quanta particle
Initialize the positional value x of each particlek(quantity of state switched), quantum bit position, rotation angle, local optimum to
The elite collection for measuring xp and non-domination solution picks out elite and concentrates the non-domination solution of crowding distance minimum as global optimum's vector
xg。
11) object function calculates
According to the positional value of each particle, the operating status of power distribution network is obtained in conjunction with a type load division result, and according to
Object function (3) and (4), calculate the adaptive value of each particle.
12) parameter updates
According to the more new formula of quantum particle swarm, quantum rotation angle guiding value, quantum rotation angle and quantum grain are updated successively
The bit of son.
13) positional value and optimal vector update
According to the more new formula of quantum particle swarm, more new position value xkAnd local optimal vector.
14) non-domination solution screens
According to the target function value of each particle, Pareto optimal solutions are filtered out, and are deposited into elite collection.
15) elite collects screening
Finally obtained non-domination solution is put into elite collection by the operation that non-domination solution is carried out to current population.It selects
The maximum non-domination solution of distance is as globally optimal solution between elite concentrates individual.
16) operation is eliminated
Using microhabitat filtering technique is improved, the non-domination solution that elite concentrates is carried out to eliminate operation, elite concentrates population
Diversity.
17) quanta particle swarm optimization test for convergence.
Whether check algorithm restrains.If so, entering step 18)；If it is not, then returning to step 11).
18) result exports
According to the final result that heuritic approach and quanta particle swarm optimization are obtained, the fault recovery plan of output distribution net
Slightly.
It is an advantage of the invention that：The method of the present invention can be reduced directly using generated a large amount of during intelligent optimization algorithm
Infeasible solution, and principle is simple, is suitable for considering that the distribution network failure containing DG of load level restores so that final fault recovery
A type load can ensure fully powered-on in scheme.
Description of the drawings
Fig. 1 is the algorithm flow of the present invention
Fig. 2 is the network structure of the example of the present invention
Fig. 3 is all previous iteration result of three matroids of the present invention, wherein Fig. 3 a are AperMatrix, Fig. 3 b are AbelMatrix,
Fig. 3 c are AuniMatrix
Fig. 4 is the final network structure that the type load of the present invention divides
Fig. 5 is the Pareto optimality face that the two class algorithms of the present invention are obtained
Specific implementation mode
Below in conjunction with the accompanying drawings, the technical solution further illustrated the present invention.
1. technical scheme of the present invention.
Method is put in distribution network failure containing DG recovery of the present invention based on hybrid algorithm, and it is described that steps are as follows：
1) initial parameter is inputted
The generation position of input fault, the design parameter of power distribution network, including micro- source number NDG, power distribution network master switch number
Nb, load level parameter；
2) initial matrix is established
To realize that the division of a type load, the method for the present invention define four kinds of initial squares for heuristic search algorithm
The micro- source ownership matrix of battle array, the respectively micro- source scaling matrices of load-, the micro- source ordinal matrix of load-, load-and the micro- source networkings of Wei Yuan-
Matrix.Each matrix is defined as follows described：
2.1) the micro- source scaling matrices of load-
Micro- source-load proportion matrix AperIt is for recording in power distribution network a type load to each micro- source unicom branch road institute
There is load total amount to account for the percent information of micro- source capacity.In AperIn matrix, a type load corresponds to row vector, micro- source respective column to
Amount, then all load total amounts account for micro- source j capacity to the element representation load i of the i-th row of matrix jth row to micro- source j unicom branch on the road
Percent information, expression formula are：
Wherein, Li,jExpress the load aggregation on load i and the most short communication paths of micro- source j, PLD,zFor on all communication paths
The power of load z, PLD,jFor the active power of micro- source j.Aper(i,j)Numerical value is smaller, and it is micro- to indicate that load i is more possible to be divided to
Source j.
2.2) the micro- source ordinal matrix of load-
The micro- source ordinal matrix A of load-sorIt is for recording in power distribution network a type load to each micro- source unicom branch road
All load total amounts account for the sequencing information of micro- source capacity ratio.Wherein, the corresponding row vector of type load number, micro- source sequence pair
Column vector is answered, then the A corresponding to the element representation of the i-th row of matrix jth rowperElements A in matrixper(i,j)In the sequence of the i-th row,
Sequence is arranged from small to large by numerical values recited.Such as AperElements A in matrixper(2,2)Corresponding numerical value is 1.2, and place is advanced
After row sorts from small to large, it is located at first, so AsorCorresponding A in matrixsor(2,2)The numerical value of element is 1.
2.3) the micro- source of load-belongs to matrix
The micro- source of load-belongs to matrix AbelIndicate that according to certain rule, it is micro- to be divided to some for a type load in power distribution network
The information in source.Wherein, the corresponding row vector of type load number, micro- source, which is numbered, corresponds to column vector, and the i-th row, j column elements are in matrix
1, it represents load i and belongs to micro- source j, 0 expression load i is not belonging to micro- source j.
2.4) the micro- source networking matrix in micro- source-
When some micro- source cannot be satisfied the larger type load of one or more capacity, group is carried out between Wei Yuan and micro- source
Net meets the larger type load of capacity.Networking matrix AuniRow vector and column vector indicate micro- source number, if the i-th row,
Jth column element is 1, indicates that i-th of micro- source and j-th of micro- source carry out networking, forms an island network system.It is initial in algorithm
In the stage, each micro- each one network of self-forming in source, therefore, networking matrix are unit matrix.
3) initial disaggregation is searched for
To realize the division of a type load, the solution for formulating relevant rule set for heuristic search algorithm is needed.This
Inventive method proposes 2 kinds of heuristic rules for searching for initial disaggregation, and rule is described below：
Rule 1：In a final type load division result, a type load i can not possibly belong to AperNumber in the i-th row of matrix
Micro- source of the value more than or equal to 1.Because if AperJ-th of element of the i-th row is more than or equal to 1 in matrix, shows load i to micro- source
Load total amount on j communication paths is more than micro- source j capacity, is unsatisfactory for constraints (2), i.e. formula (6).
Rule 2：Compared to AsorIn the i-th row sequence serial number larger micro- source j, a type load i is more easy to belong to sequence serial number
Smaller micro- source j*.Because the serial number that sorts is small, show a type load i to micro- source j*Load total amount on communication path accounts for micro- source and holds
Amount ratio smaller, micro- source can accommodate more type loads.
According to above-mentioned heuristic rule, the search step of initial network disaggregation is described below：First, according to distribution network failure
The topological structure of network afterwards obtains the micro- source scaling matrices A of load-per, the micro- source ordinal matrix A of load-sor.Secondly, according to rule 1
With rule 2, the micro- source ownership matrix A of load-is obtainedbel.Finally, according to matrix Abel, you can show that intelligent distribution network failure is latter
The initial division result of type load.
4) power verifies
To realize that the power verification of each island network, project of the present invention propose following heuristic rule：
Rule 3：Island network where all micro- sources must satisfy power-balance verification, i.e., the capacity in micro- source must be big
Where equal to the micro- source on all type loads of network and a type load to micro- source communication path all loads power
Summation.Check formula is：
Wherein, PzIndicate the power of load z, set DjIndicate a type load all in the island network where micro- source j with
And all loads on all type loads to micro- source j communication paths；Ploss,jIndicate that the isolated island residing for micro- source j passes through trend meter
The network loss obtained after calculation.
Rule 4：If micro- source j is unsatisfactory for power-balance verification, A corresponding to a type load in micro- source j is rejectedperIn matrix
The maximum load of numerical value, and the load is divided to its AsorRow is corresponded in matrix to sort micro- source of latter cis-position.
According to above-mentioned heuristic rule, power verify the step of it is as described below：For an initial type load in step (3)
Splitting scheme carries out power verification to the network where each micro- source by rule 3, and all micro- sources verify a wheel and are denoted as once repeatedly
Generation.If in an iteration, all micro- sources are satisfied by rule 3, then enter step 6), if not satisfied, then being repaiied by rule 4
Just, matrix A is updatedbel, next round iteration is then carried out, until all micro- sources are satisfied by rule 3.If certain load is repeatedly by micro- source
It rejects, illustrates that single micro- source can not carry the load, then enter networking amendment step.
5) networking amendment
If certain load is repeatedly rejected by micro- source, illustrates that single micro- source can not carry the load, networking operation need to be carried out.For reality
Networking amendment between existing each micro- source, the method for the present invention propose following heuristic rule：
Rule 5：If a type load i is divided in any one micro- source according to rule 2, the net where micro- source can be caused
Network is unsatisfactory for formula (2), then by A corresponding to the loadsorSequence forms networking, phase near two preceding micro- sources in the i-th row of matrix
The type load answered is divided in the networking；If after inspection, it is still unsatisfactory for formula (2), then first three micro- source should form
Networking, and so on.
According to above-mentioned heuristic rule, networking is modified, and steps are as follows：Record each load quilt in each iterative process
The number of rejecting carries out networking according to rule 5 and repaiies if the number that certain load is removed in the secondary iteration is more than micro- source sum
Just, while and matrix A is updatedper、AbelAnd Auni, and be back to power verification step and carry out next round iteration, successively to each group
Net is verified.
6) type load partitioning algorithm convergence is examined
Judge whether that all micro- sources are satisfied by formula (2), if satisfied, (7) are then entered step, if not satisfied, returning to step
Suddenly (4) continue to carry out power verification to the network where each micro- source, carry out next round iteration.
7) a type load division result is exported
Heuritic approach terminates, and exports a type load division result, the switch serial number set being closed.
8) algorithm transition
According to the switch serial number set of closure, to not determining that the switch of on off state is set as quantum discrete particle cluster algorithm
Optimized variable.
9) quanta particle swarm optimization parameter setting
Dimension, iterations and the corresponding parameter value of quantum discrete particle cluster algorithm are set.
10) initialization of quanta particle
Initialize the positional value x of each particlek(quantity of state switched), quantum bit position, rotation angle, local optimum to
The elite collection for measuring xp and non-domination solution picks out elite and concentrates the non-domination solution of crowding distance minimum as global optimum's vector
xg。
11) object function calculates
According to the positional value of each particle, the operating status of power distribution network is obtained in conjunction with a type load division result, and according to
Object function (3) and (4), calculate the adaptive value of each particle.
12) parameter updates
According to the more new formula of quantum particle swarm, quantum rotation angle guiding value, quantum rotation angle and quantum grain are updated successively
The bit of son.
13) positional value and optimal vector update
According to the more new formula of quantum particle swarm, more new position value xkAnd local optimal vector.
14) non-domination solution screens
According to the target function value of each particle, Pareto optimal solutions are filtered out, and are deposited into elite collection.
15) elite collects screening
Finally obtained non-domination solution is put into elite collection by the operation that non-domination solution is carried out to current population.It selects
The maximum non-domination solution of distance is as globally optimal solution between elite concentrates individual.
16) operation is eliminated
Using microhabitat filtering technique is improved, the non-domination solution that elite concentrates is carried out to eliminate operation, elite concentrates population
Diversity.
17) quanta particle swarm optimization test for convergence.
Whether check algorithm restrains.If so, entering step 18)；If it is not, then returning to step 11).
18) result exports
According to the final result that heuritic approach and quanta particle swarm optimization are obtained, the fault recovery plan of output distribution net
Slightly.
2. the specific implementation mode of project of the present invention
The fault recovery PROBLEM DECOMPOSITION of the power distribution network containing DG is type load division and system failure recovery by project of the present invention
Two sub-problems are reconstructed, propose the mixed fault restoration methods that a kind of heuritic approach and intelligent optimization algorithm are combined：Wherein,
Subproblem is divided for a type load, is solved using heuristic, it is a kind of in final fail-over policy to ensure
Load can restore electricity completely；Subproblem is reconstructed for fault recovery, is solved using intelligent optimization algorithm, to ensure to add
It is minimum to weigh dead electricity load.Specific Optimizing Flow is as shown in Figure 1.Solution procedure is as described below.
1) initial parameter is inputted
The generation position of input fault, the design parameter of power distribution network, including micro- source number NDG, power distribution network master switch number
Nb, load level parameter；
2) initial matrix is established
To realize that the division of a type load, the method for the present invention define four kinds of initial squares for heuristic search algorithm
The micro- source ownership matrix of battle array, the respectively micro- source scaling matrices of load-, the micro- source ordinal matrix of load-, load-and the micro- source networkings of Wei Yuan-
Matrix.Self-defined initial matrix is established according to each.
3) initial disaggregation is searched for
First, according to the topological structure of network after distribution network failure, the micro- source scaling matrices A of load-is obtainedper, load-it is micro-
Source ordinal matrix Asor.Secondly, according to rule 1 and rule 2, the micro- source ownership matrix A of load-is obtainedbel.Finally, according to matrix
Abel, you can obtain the initial division result of the latter type load of intelligent distribution network failure.
4) power verifies
For the splitting scheme of an initial type load in step (3), work(is carried out by rule 3 to the network where each micro- source
Rate verifies, and all micro- sources verify a wheel and are denoted as an iteration.If in an iteration, all micro- sources be satisfied by rule 3, then into
Enter step 6), if not satisfied, being then modified by rule 4, updates matrix Abel, next round iteration is then carried out, until all micro-
Source is satisfied by rule 3.If certain load is repeatedly rejected by micro- source, illustrates that single micro- source can not carry the load, then repaiied into networking
Positive step.
5) networking amendment
The number that each load is removed in each iterative process is recorded, if what certain load was removed in the secondary iteration
Number is more than micro- source sum, then carries out networking amendment according to rule 5, while and updating matrix Aper、AbelAnd Auni, and be back to
Power verification step carries out next round iteration, is verified successively to each networking.
6) type load partitioning algorithm convergence is examined
Judge whether that all micro- sources are satisfied by formula (2), if satisfied, (7) are then entered step, if not satisfied, returning to step
Suddenly (4) continue to carry out power verification to the network where each micro- source, carry out next round iteration.
7) a type load division result is exported
Heuritic approach terminates, and exports a type load division result, the switch serial number set being closed.
8) algorithm transition
According to the switch serial number set of closure, to not determining that the switch of on off state is set as quantum discrete particle cluster algorithm
Optimized variable.
9) quanta particle swarm optimization parameter setting
Dimension, iterations and the corresponding parameter value of quantum discrete particle cluster algorithm are set.
10) initialization of quanta particle
Initialize the positional value x of each particlek(quantity of state switched), quantum bit position, rotation angle, local optimum to
The elite collection for measuring xp and non-domination solution picks out elite and concentrates the non-domination solution of crowding distance minimum as global optimum's vector
xg。
11) object function calculates
According to the positional value of each particle, the operating status of power distribution network is obtained in conjunction with a type load division result, and according to
Object function calculates the adaptive value of each particle.
12) parameter updates
According to the more new formula of quantum particle swarm, quantum rotation angle guiding value, quantum rotation angle and quantum grain are updated successively
The bit of son.
13) positional value and optimal vector update
According to the more new formula of quantum particle swarm, more new position value xkAnd local optimal vector.
14) non-domination solution screens
According to the target function value of each particle, Pareto optimal solutions are filtered out, and are deposited into elite collection.
15) elite collects screening
Finally obtained non-domination solution is put into elite collection by the operation that non-domination solution is carried out to current population.It selects
The maximum non-domination solution of distance is as globally optimal solution between elite concentrates individual.
16) operation is eliminated
Using microhabitat filtering technique is improved, the non-domination solution that elite concentrates is carried out to eliminate operation, elite concentrates population
Diversity.
17) quanta particle swarm optimization test for convergence.
Whether check algorithm restrains.If so, entering step 18)；If it is not, then returning to step 11).
18) result exports
According to the final result that heuritic approach and quanta particle swarm optimization are obtained, the fault recovery plan of output distribution net
Slightly.
3. analysis of cases
To verify this item purpose reasonability and practicability, verified by following example.Project of the present invention is using improvement
IEEE33 node systems be example, the topological structure of distribution system is as shown in Figure 2.The distribution system is opened comprising 5 contacts
It closes, load total amount is 3715kW, and load level parameter setting is as shown in table 1；
Table 1
Table 2
The 5 DG parameters introduced are as shown in table 2, and node type is set as PQ nodes.Assuming that break down at circuit (1),
Interpretation of result is as described below.
3.1) a type load partitioning algorithm interpretation of result
1) division that a type load is carried out using heuritic approach, obtains the division result of a type load, and algorithm changes at 2 times
It is restrained after generation.Fig. 3 illustrates the micro- source scaling matrices A of the load-in all previous iterative processper, the micro- source of load belong to matrix AbelWith
Micro- micro- source networking matrix A in source-uniAs a result.As shown in Figure 3, three matrixes for being located at first row indicate the heuritic approach starting stage
Initial solution, secondary series and third column matrix indicate the result after first time iteration and second of iteration respectively.Table 3, which illustrates, to be opened
A type load division result in all previous iterative process of hairdo algorithm；Serial number in table is divided to some when representing all previous iteration
Node (load) serial number in micro- source.Fig. 4 is illustrated divides final result by the type load that heuritic approach obtains.
Table 3
2) three matroid initial solutions are as shown in the first column matrix in Fig. 3.Observe AperThe initial results of matrix, node 30
The numerical value that each element in row is corresponded to node 32 is all higher than 1, is not inconsistent normally 1.It therefore, will be to node in lower whorl iteration
30 and the load of node 32 repartitioned, for the new micro- source of its matching, or corresponding micro- source is subjected to networking amendment.
3) the first time iteration result of three matroids is as shown in the second column matrix in Fig. 3.Second in observation chart 3 (c)
AuniMatrix, DG2 and DG5 form networking, identical with the 5th column element numerical value according to the 2nd row of regular (2) matrix.Observation chart 3
(a) second A inperMatrix, each element numerical value that node 30 corresponds to row are all higher than 1, are unsatisfactory for rule 1, algorithm continues to change
Generation.
4) in second of iteration result such as Fig. 3 of three matroids shown in third column matrix.By AuniSecond of iteration of matrix
As a result it is found that DG1, DG2 and DG5 form networking；Observe AperSecond of iteration result of matrix, per row element numerical value in matrix
Minimum value be respectively less than 1, meet rule 1.Formula (2) is examined according to power-balance, power-balance is satisfied by about in each networking
Beam, algorithm iteration terminate.A final type load division result is as shown in figure 4, serial number 3,4,9,15-20,22,30- in figure
32, the switch serial number for the closed state that 34 and 36 expressions are obtained by heuritic approach.It is calculated as it can be seen that being divided by a type load
Method can obtain the initial division result for meeting type load power supply constraint so that the system failure recovery of second step quickly
Restructing algorithm contributes to rapid solving without considering the constraints；Simultaneously, it is determined that the state of partial switch variable reduces
The dimension of variable to be solved, reduces the possibility that a large amount of infeasible solutions are generated in intelligent optimization algorithm.
3.2) multiple-objection optimization result
1) it is directed to that a type load divides as a result, quantum particle swarm multi-objective optimization algorithm is only needed for remainder
The switch serial number and regular (1)-(5) for not determining state carry out multiple-objection optimization calculating, greatly reduce and solve difficulty and can not
The quantity of row solution.The switch serial number 2 of solution, 5-8,10-14,21,23-29,33,35 and 37 amount to 21 switches.Therefore,
The dimension that each particle of multi-objective particle swarm algorithm is arranged is 21, population 500, iterations sum 2000 times.
2) the Pareto optimal solutions and fault recovery scheme that the fault recovery method based on hybrid algorithm acquires such as 4 institute of table
Show.In table, scheme 1 and 2 switching manipulation number of scheme are relatively fewer, and it is less that scheme 3 and scheme 4 then weight load loss.Therefore
When determining final scheme, different schemes can be selected according to corresponding demand.It is obtained based on traditional quanta particle swarm optimization
Pareto optimal solutions and fault recovery scheme it is as shown in table 5.As can be seen that compared to hybrid algorithm acquire as a result, in phase
In the case of with switch motion number, in the scheme that traditional quanta particle swarm optimization obtains weight load loss it is larger, scheme compared with
Difference.
Table 4
Table 5
3) the optimal faces Bi-objective Pareto acquired based on hybrid algorithm and traditional quanta particle swarm optimization are as shown in Figure 5.
As seen from the figure, context of methods is due to using a type load division methods early period, the optimal faces Pareto finally obtained relative to
Traditional quantum particle swarm obtains preferable, and the number of optimal solution is also more compared with traditional quantum particle swarm, in actual application process
In, more more efficiently strategies can be provided to dispatcher.
Claims (1)
1. the power distribution network multiple target fault recovery method based on hybrid algorithm, it is described that steps are as follows：
1) initial parameter is inputted；
The generation position of input fault, the design parameter of power distribution network, including micro- source number NDG, power distribution network master switch number Nb, it is negative
Lotus class parameter；
2) initial matrix is established；
To realize that the division of a type load, the method for the present invention define four kinds of initial matrixs for heuristic search algorithm, point
It Wei not the micro- source scaling matrices of load-, the micro- source ordinal matrix of load-, the micro- source ownership matrix of load-and the micro- source networking matrixes of Wei Yuan-；
Each matrix is defined as follows described：
2.1) the micro- source scaling matrices of load-；
Micro- source-load proportion matrix AperIt is all negative to each micro- source unicom branch road for recording a type load in power distribution network
Lotus total amount accounts for the percent information of micro- source capacity；In AperIn matrix, a type load corresponds to row vector, and micro- source corresponds to column vector, then
The element representation load i of matrix the i-th row jth row accounts for the ratio letter of micro- source j capacity to all load total amounts in micro- source j unicom branch road
Breath, expression formula are：
Wherein, Li,jExpress the load aggregation on load i and the most short communication paths of micro- source j, PLD,zFor load z on all communication paths
Power, PDG,jFor the active power of micro- source j；Aper(i,j)Numerical value is smaller, indicates that load i is more possible to be divided to micro- source j；
2.2) the micro- source ordinal matrix of load-；
The micro- source ordinal matrix A of load-sorIt is for recording a type load owning to each micro- source unicom branch road in power distribution network
Load total amount accounts for the sequencing information of micro- source capacity ratio；Wherein, the corresponding row vector of type load number, sort respective column in micro- source
Vector, the then A corresponding to element representation that the i-th row of matrix jth arrangesperElements A in matrixper(i,j)In the sequence of the i-th row, sequence
It is arranged from small to large by numerical values recited；Such as AperElements A in matrixper(2,2)Corresponding numerical value be 1.2, where row carry out from
It is small to after big sequence, be located at first, so AsorCorresponding A in matrixsor(2,2)The numerical value of element is 1；
2.3) the micro- source of load-belongs to matrix；
The micro- source of load-belongs to matrix AbelIndicate that according to certain rule, a type load is divided to some micro- source in power distribution network
Information；Wherein, the corresponding row vector of type load number, micro- source, which is numbered, corresponds to column vector, and the i-th row, j column elements are 1 in matrix, generation
Table load i belongs to micro- source j, and 0 expression load i is not belonging to micro- source j；
2.4) the micro- source networking matrix in micro- source-；
Networking is carried out when some micro- source cannot be satisfied the larger type load of one or more capacity, between Wei Yuan and micro- source to come
Meet the larger type load of capacity；Networking matrix AuniRow vector and column vector indicate micro- source number, if the i-th row, jth
Column element is 1, indicates that i-th of micro- source and j-th of micro- source carry out networking, forms an island network system；In the initial rank of algorithm
Section, each micro- each one network of self-forming in source, therefore, networking matrix are unit matrix；
3) initial disaggregation is searched for；
To realize the division of a type load, the solution for formulating relevant rule set for heuristic search algorithm is needed；Propose 2
For searching for initial disaggregation, rule is described below kind heuristic rule：
Rule 1：In a final type load division result, a type load i can not possibly belong to AperNumerical value is big in the i-th row of matrix
In micro- source equal to 1；Because if AperJ-th of element of the i-th row is more than or equal to 1 in matrix, shows load i to micro- source j unicom road
Load total amount on diameter is more than micro- source j capacity, is unsatisfactory for regular (2)；
Rule 2：Compared to AsorIn the i-th row sequence serial number larger micro- source j, a type load i is more easy to belong to sequence serial number smaller
Micro- source j*；Because the serial number that sorts is small, show a type load i to micro- source j*Load total amount on communication path accounts for micro- source capacity ratio
Example smaller, which can accommodate more type loads；
According to above-mentioned heuristic rule, the search step of initial network disaggregation is described below：First, according to net after distribution network failure
The topological structure of network obtains the micro- source scaling matrices A of load-per, the micro- source ordinal matrix A of load-sor；Secondly, according to rule 1 and rule
Then 2, obtain the micro- source ownership matrix A of load-bel；Finally, according to matrix Abel, you can show that intelligent distribution network failure latter class is negative
The initial division result of lotus；
4) power verifies；
To realize the power verification of each island network, following heuristic rule is proposed：
Rule 3：Island network where all micro- sources must satisfy power-balance verification, i.e. the capacity in micro- source has to be larger than
In the power summation of all loads on all type loads and a type load to micro- source communication path of micro- source place network；
Check formula is：
Wherein, PzIndicate the power of load z, set DjIndicate a type load and institute all in the island network where micro- source j
There are all loads on a type load to micro- source j communication paths；Ploss,jAfter indicating the isolated island residing for micro- source j by Load flow calculation
Obtained network loss；
Rule 4：If micro- source j is unsatisfactory for power-balance verification, A corresponding to a type load in micro- source j is rejectedperNumerical value in matrix
Maximum load, and the load is divided to its AsorRow is corresponded in matrix to sort micro- source of latter cis-position；
According to above-mentioned heuristic rule, power verify the step of it is as described below：For the division of an initial type load in step (3)
Scheme carries out power verification to the network where each micro- source by rule 3, and all micro- sources verify a wheel and are denoted as an iteration；
If in an iteration, all micro- sources are satisfied by rule 3, then enter step 6), if not satisfied, being then modified by rule 4, more
New matrix Abel, next round iteration is then carried out, until all micro- sources are satisfied by rule 3；If certain load is repeatedly rejected by micro- source,
Illustrate that single micro- source can not carry the load, then enters networking amendment step, i.e. step 5)；
5) networking amendment；
If certain load is repeatedly rejected by micro- source, illustrates that single micro- source can not carry the load, networking operation need to be carried out；It is each to realize
Networking amendment between a micro- source, proposes following heuristic rule：
Rule 5：If 2 a type load i is divided in any one micro- source according to rule, network where micro- source can be caused not
Meet formula (2), then by A corresponding to the loadsorSequence forms networking near two preceding micro- sources in the i-th row of matrix, accordingly
One type load is divided in the networking；If after inspection, being still unsatisfactory for formula (2), then first three micro- source should form networking,
And so on；
According to above-mentioned heuristic rule, networking is modified, and steps are as follows：Each load is recorded to be removed in each iterative process
Number carry out networking amendment according to rule 5 if the number that is removed in the secondary iteration of certain load is more than micro- source sum,
Simultaneously and update matrix Aper、AbelAnd Auni, and be back to power verification step and carry out next round iteration, successively to each networking
It is verified；
6) type load partitioning algorithm convergence is examined；
Judge whether that all micro- sources are satisfied by formula (2), if satisfied, (7) are then entered step, if not satisfied, returning to step
(4), continue to carry out power verification to the network where each micro- source, carry out next round iteration；
7) a type load division result is exported；
Heuritic approach terminates, and exports a type load division result, the switch serial number set being closed；
8) algorithm transition；
According to the switch serial number set of closure, to not determining that the switch of on off state is set as the excellent of quantum discrete particle cluster algorithm
Change variable；
9) quanta particle swarm optimization parameter setting；
Dimension, iterations and the corresponding parameter value of quantum discrete particle cluster algorithm are set；
10) initialization of quanta particle；
Initialize the positional value x of each particlek, quantum bit position, rotation angle, local optimum vector x p and non-domination solution elite
Collection, positional value xkThe quantity of state switched, pick out elite concentrate crowding distance minimum non-domination solution as global optimum to
Measure xg；
11) object function calculates；
According to the positional value of each particle, the operating status of power distribution network is obtained in conjunction with a type load division result, and according to target
Function calculates the adaptive value of each particle；
12) parameter updates；
According to the more new formula of quantum particle swarm, quantum rotation angle guiding value, quantum rotation angle and quanta particle are updated successively
Bit；
13) positional value and optimal vector update；
According to the more new formula of quantum particle swarm, more new position value xkAnd local optimal vector；
14) non-domination solution screens；
According to the target function value of each particle, Pareto optimal solutions are filtered out, and are deposited into elite collection；
15) elite collects screening；
Finally obtained non-domination solution is put into elite collection by the operation that non-domination solution is carried out to current population；Select elite
The maximum non-domination solution of distance is as globally optimal solution between concentrating individual；
16) operation is eliminated；
Using microhabitat filtering technique is improved, the non-domination solution that elite concentrates is carried out to eliminate operation, elite concentrates the more of population
Sample；
17) quanta particle swarm optimization test for convergence；
Whether check algorithm restrains；If so, entering step 18)；If it is not, then returning to step 11)；
18) result exports；
According to the final result that heuritic approach and quanta particle swarm optimization are obtained, the fail-over policy of output distribution net.
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