CN105305442B - Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm - Google Patents
Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm Download PDFInfo
- Publication number
- CN105305442B CN105305442B CN201510852370.2A CN201510852370A CN105305442B CN 105305442 B CN105305442 B CN 105305442B CN 201510852370 A CN201510852370 A CN 201510852370A CN 105305442 B CN105305442 B CN 105305442B
- Authority
- CN
- China
- Prior art keywords
- quantum
- node
- distribution network
- power distribution
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses the invention discloses a kind of Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm, Load flow calculation is carried out using the forward-backward sweep method of nodal hierarchy, the self-healing recovery reconstruct of power distribution network multiple target is carried out using quantum genetic algorithm, quantum genetic algorithm realizes the adjustment of chromosome using quantum bits of encoded chromosome and Quantum rotating gate, so that under less population scale, rapidly converging to globally optimal solution.Isolated island in reconstruct judges, by Load flow calculation, to reduce the dimension of infeasible solution.Using distribution network loss and switch motion number of times as reconstruct target, the complex optimum of multiple target is realized, with practical value.
Description
Technical field
The present invention relates to a kind of Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm, more particularly to
After a kind of intelligent distribution network breaks down, using the method for the carry out Multi-Objective Electric Power Network self-healing recovery reconstruct of quantum genetic algorithm,
Belong to technical field of electric power system control.
Background technology
Power distribution network is made up of facilities such as overhead transmission line, distribution transformer, switch etc., and important distribution is played in power network
The network of electric energy effect.Power distribution network typically uses closed loop design, open loop operation, and its operating structure is radially.Due to distribution wire
Line diameter ratio power transmission line it is small, cause the R/X of power distribution network larger so that in power transmission network commonly use power flow algorithm carry out distribution
Convergence is difficult to ensure that during the Load flow calculation of net.
Intelligent distribution network be mainly characterized by with complete self-healing function, it is necessary to as far as possible reduce electric network fault to user's
Influence, while ensureing the economical operation of power distribution network.After power distribution network breaks down, by adjusting block switch and interconnection switch
State, changes the network structure of power distribution network, reaches the purpose of power distribution network self-healing recovery and optimization operation.Power distribution network self-healing recovery weight
Structure problem is a multiple target, many dimensions, multiple constraint, multi-period nonlinear combinatorial optimization problem.Using fast and effectively side
The reconfiguration of electric networks that method carries out power distribution network after failure is the key issue of intelligent distribution network self-healing recovery control.
The method of current power distribution network self-healing recovery reconstruct is numerous, and four classes can be divided into substantially:Mathematical analysis method, optimal stream
Pattern algorithm, branch exchange method and intelligent algorithm.What mathematical analysis method was carried out is the search of Greedy, is taken very much,
And this method is handled power distribution network problem as pure mathematics problem in application, during not accounting for power distribution network reconfiguration
Practical problem need.The optimal flow pattern algorithm time increases with network size, and is solving optimal flow pattern between each ring
When can interact, the order of switch can influence result, and multiple distribution Load flow calculation just can determine that the state of a switch undetermined,
Computational efficiency is so as to substantially reduce.Branch exchange method reduce network loss during, do not ensure reconfiguration scheme be optimal or
Near-optimization, lacks the Global Optimality in mathematical meaning, easily converges on locally optimal solution.Parameter in intelligent algorithm is difficult
To determine, and hunting time is long.
Quantum genetic algorithm is a kind of method that quantum calculation is combined with genetic algorithm.Quantum calculation utilizes quantum superposition
Property, tangling property and coherence realize the parallel computation of quantum.Stepped on based on quantum bit and quantum state plus the quantum genetic of characteristic is calculated
Method (Quantum Genetic Algorithm, QGA).QGA represents chromosome with quantum bit coding, the effect of consumption cervical orifice of uterus and
Quantum door, which is more newly arrived, completes evolutionary search, it is achieved thereby that the Optimization Solution of target.Compared with traditional genetic algorithm, dynamic is adjusted
The quantum genetic algorithm of whole anglec of rotation mechanism can rapidly converge to globally optimal solution, have under less population scale
The characteristics of global optimizing ability is strong.Application of the quantum genetic algorithm in terms of Multiobjective Intelligent power distribution network self-healing recovery or a piece of
Blank.
The content of the invention
In order to solve the above-mentioned technical problem, the invention provides a kind of Multiobjective Intelligent distribution based on quantum genetic algorithm
Net self-healing recovery method.
In order to achieve the above object, the technical solution adopted in the present invention is：
Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm, comprises the following steps,
Step one, initialize；
Determine the switch combination before failure and breakdown switch circuit；
The number m and evolutionary generation gs of Population Size n, quantum bit are determined, in power distribution network self-healing recovery, m is represented and opened
Number, i.e. branch travel permit number are closed, n represents reconfiguration scheme number, include the population Q={ q of n individual1,q1,…,qn, wherein qjFor
J-th of individual in population, j=1,2 ..., n；
I-th of quantum bit be | 0 ＞ probability amplitudeI-th of quantum bit be | 1 ＞ probability amplitudeTable
Show that in initial ranging institute is stateful to be overlapped with identical probability；
Step 2, defines evolutionary generation t=1；
Step 3, quantum caves in；
Caved in by population Q (t) quantum and generate p (t), i.e., Q (t) is once observed, to obtain the solution of one group of determination
Wherein, That is the observation of t j-th of chromosome of generation, has m quantum
Position, is the binary string that a length is m；；
Step 4, modification p (t) makes it meet fault bit for 0 and the requirement without isolated island；
Step 5, carries out Load flow calculation using nodal hierarchy forward-backward sweep method, calculates the network loss value in each group of this generation, and protect
It is filled with the network loss value of sufficient constraints；
Step 6, the fitness function integrated using distribution network loss and switch motion number of times as target, construction multiple target
All individuals of population are carried out adaptive value evaluation, and preserve reconfiguration scheme minimum FZ in this generation by FZ；
Step 7, according to FZ value, utilization cervical orifice of uterus Population Regeneration；
Renewal process is：
Wherein,It is t individual for j-th in population,It is t+1 individual for j-th in population, G (t) is
The quantum door in t generations,
Wherein, θ is the anglec of rotation of quantum door, θ=Δ θ × s (αi,βi), Δ θ is the coefficient relevant with algorithm the convergence speed,
s(αi,βi) be quantum rotation directivity function；
Step 8, t=t+1；
Step 9, judges whether t ＜ gs set up, if it is, step 3 is gone to, if it is not, then terminating.
Observation process is to produce the random number r between one 0~1, ifThen takeOtherwise take It is the probability that i-th of quantum bit observation is 0 in t j-th chromosomes of generation..
Use nodal hierarchy forward-backward sweep method carry out Load flow calculation process for,
The headend node voltage and endpoint node power of known power distribution network, using branch road as unit of account；Start setting all
Node voltage is all rated voltage, is calculated paragraph by paragraph backward from end-node to first node according to node power, calculates the electricity of each branch road
Crushing consumes the power with current value, then first node that back substitution is obtained on each branch road, and this is backward steps；Further according to
Voltage loss on first node voltage, first node power and each branch road, from first node to end-node it is piecewise before push away and obtain each section
Point magnitude of voltage, this pushes through journey before being；So calculate repeatedly, calculate an iteration number of times and add once, until the voltage of each node
Untill deviation is in allowed band.
Isolated island judges to realize by the node traverses in Load flow calculation；After Load flow calculation, magnitude of voltage is initial voltage
Total node number to be equal to root node number be the necessary and sufficient condition without isolated island in network structure after reconstruct.
Constraints is,
Load flow calculation is constrained：Whenever the power and voltage after structure change will meet Load flow calculation；
Capacity of trunk is constrained：sk≤smax；
Node voltage is constrained：Vmin≤Vk≤Vmax；
Network topology is constrained：Power distribution network or tree after reconstruct, are not allow for the generation of loop network；
Wherein, skAnd smaxRespectively branch road k flows overpowering calculated value and the maximum permissible value of capacity of trunk, VminWith
VmaxThe respectively lower limit and higher limit of node voltage.
Multiple target integrate fitness function FZ be,
FZ=K × s+f
Wherein, K is weight factor, and s is the switch motion number of times in reconfiguration scheme, and f is the network loss value in reconfiguration scheme.
The beneficial effect that the present invention is reached：The present invention carries out Load flow calculation using the forward-backward sweep method of nodal hierarchy, adopts
The self-healing recovery reconstruct of power distribution network multiple target is carried out with quantum genetic algorithm, with practical value；Quantum genetic algorithm is employed
Quantum bit Encoded Chromosomes and Quantum rotating gate realize the adjustment of chromosome so that under less population scale, quick to receive
Hold back globally optimal solution；Isolated island in reconstruct judges, by Load flow calculation, to reduce the dimension of infeasible solution；With distribution network loss and
Switch motion number of times realizes the complex optimum of multiple target as reconstruct target.
Brief description of the drawings
Fig. 1 is flow chart of the invention.
Fig. 2 is the node radial networks figure of tri- feeder lines of IEEE 16
Fig. 3 is optimal self-healing recovery reconstructed network structure chart after No. 5 line faults
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following examples are only used for clearly illustrating the present invention
Technical scheme, and can not be limited the scope of the invention with this.
Quantum genetic algorithm is a kind of probability optimization algorithm based on quantum calculation principle, it using it is a kind of it is novel based on
The coded system of quantum bit, i.e., define a quantum bit position with a pair of plural numbers.Quantum bit is also known as quantum bit, the amount of being
Minimum information memory cell in sub- genetic algorithm a, quantum bit can not only represent 0 and 1 two states, and can be with table
Show any superposition state between both states, i.e., one quantum bit is likely to be at | 0>Or | the centre between 1 ＞, or both
State, i.e., | 0>With | 1>Different superposition states, the state representation of quantum bit is as follows：
Wherein,For the state of quantum bit, quantum bit is respectively by α and β | 0>With | 1>Probability amplitude, and meet normalization
Condition：
|αi|2+|βi|2=1 (i=1,2 ..., m)
Wherein, | αi|2The observation for representing quantum state is 0 probability, | βi|2Represent quantum state observation for 1 it is general
Rate, m is the number of quantum bit.
The probability amplitude of one quantum bit is designated as [α, β]T, then the probability amplitude of m quantum bit be represented by：
If Population Size is n, its chromosome is expressed as Q={ q with quantum bit1,q1,…,qn, wherein qjFor in population
J-th of individual, j=1,2 ..., n, Quantum logic gates carry out the renewal of population from quantum door G, i.e.,：
Wherein, θ is the anglec of rotation of quantum door, and value is：
θ=Δ θ × s (αi,βi)
Wherein, s (αi,βi) be quantum rotation directivity function, Δ θ be the coefficient relevant with algorithm the convergence speed.
Quantum door process be：
Wherein, t is evolutionary generation,It is t individual for j-th in population,It is t+1 for the jth in population
Individual, G (t) is the quantum door in t generations.
Quantum genetic algorithm represents probability amplitude the coding applied to chromosome, and concurrency is had more than traditional genetic algorithm
With diversity；And consumption cervical orifice of uterus is more newly arrived and completes evolutionary search, the conversion between any superposition state can be achieved, can preferably keep
Population diversity, search efficiency is higher.
The inquiry table of the renewal of design flow cervical orifice of uterus is as shown in Table 1：
The inquiry table of the quantum of table one door
The mentality of designing of table one is as follows：Quantum door will ensure that algorithm rapidly converges to the chromosome with more excellent fitness,
Such as：The i-th bit x of current chromosomei=0, optimum individual corresponding positions are b in colonyi=1, if fitness function f (xi)≤f(bi)
When, to converge to the chromosome of more excellent fitness, then need to increase the probability for taking 1, that is, reduce | αi|2Value, increment value | βi|2.This
Shi Ruguo (αi,βi) in first and third quadrant, then θ is to rotate counterclockwise；If (αi,βi) in second, four-quadrant, then θ to
Turn clockwise.The value of the anglec of rotation under the other conditions of table one can similarly be analyzed.
As shown in figure 1, the Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm, including following step
Suddenly：
Step one, initialize；
Determine the switch combination before failure and breakdown switch circuit；
The number m and evolutionary generation gs of Population Size n, quantum bit are determined, in power distribution network self-healing recovery, m is represented and opened
Number, i.e. branch travel permit number are closed, n represents reconfiguration scheme number, include the population Q={ q of n individual1,q1,…,qn, wherein qjFor
J-th of individual in population, j=1,2 ..., n；
I-th of quantum bit be | 0>Probability amplitudeI-th of quantum bit be | 1>Probability amplitudeTable
Show that in initial ranging institute is stateful to be overlapped with identical probability.
Step 2, defines evolutionary generation t=1.
Step 3, quantum caves in.
Caved in by population Q (t) quantum and generate p (t), i.e., Q (t) is once observed, to obtain the solution of one group of determination
Wherein,The observation of i.e. j-th chromosome, is that a length is entered for the two of m
System string,That is the observation of t j-th of chromosome of generation, has m quantum bit, is the binary string that a length is m.
Observation process is to produce the random number r between one 0~1, ifThen takeOtherwise take It is the probability that i-th of quantum bit observation is 0 in t j-th chromosomes of generation.
Step 4, modification p (t) makes it meet fault bit for 0 and the requirement without isolated island.
Isolated island is meant that there are one or several nodes is not connected to power supply node or is connected to containing power supply node
Branch road on, i.e. dead electricity node.
Step 5, carries out Load flow calculation using nodal hierarchy forward-backward sweep method, calculates the network loss value in each group of this generation, and protect
It is filled with the network loss value of sufficient constraints.
Use nodal hierarchy forward-backward sweep method carry out Load flow calculation process for,
The headend node voltage and endpoint node power of known power distribution network, using branch road as unit of account；Start setting all
Node voltage is all rated voltage, is calculated paragraph by paragraph backward from end-node to first node according to node power, calculates the electricity of each branch road
Crushing consumes the power with current value, then first node that back substitution is obtained on each branch road, and this is backward steps；Further according to
Voltage loss on first node voltage, first node power and each branch road, from first node to end-node it is piecewise before push away and obtain each section
Point magnitude of voltage, this pushes through journey before being；So calculate repeatedly, calculate an iteration number of times and add once, until the voltage of each node
Untill deviation is in allowed band.
Power distribution network is typically that N bars feeder line is designed with N number of interconnection switch, makes total system open loop operation, so net after reconstruct
It is the necessary condition for ensureing network open loop to have in network and can only have N number of switch to open.The master switch number opened in network is ensured
On the premise of equal to interconnection switch number, there is isolated island then to have closed loop, no isolated island then must be without closed loop, therefore works as the master switch number opened
It is exactly no closed network during equal to interconnection switch number and without island network.Therefore isolated island judgement can be by Load flow calculation
Node traverses are realized；After Load flow calculation, it is net after reconstructing that magnitude of voltage is equal to root node number for total node number of initial voltage
Necessary and sufficient condition without isolated island in network structure.
Constraints is as follows：
Load flow calculation is constrained：Whenever the power and voltage after structure change will meet Load flow calculation；
Capacity of trunk is constrained：sk≤smax；
Node voltage is constrained：Vmin≤Vk≤Vmax；
Network topology is constrained：Power distribution network or tree after reconstruct, are not allow for the generation of loop network；
Wherein, skAnd smaxRespectively branch road k flows overpowering calculated value and the maximum permissible value of capacity of trunk, VminWith
VmaxThe respectively lower limit and higher limit of node voltage.
Step 6, the fitness function integrated using distribution network loss and switch motion number of times as target, construction multiple target
All individuals of population are carried out adaptive value evaluation, and preserve reconfiguration scheme minimum FZ in this generation by FZ.
Multiple target integrate fitness function FZ be,
FZ=K × s+f
Wherein, K is weight factor, and s is the switch motion number of times in reconfiguration scheme, and f is the network loss value in reconfiguration scheme.
The calculation formula of network loss is,
Wherein, RkFor branch road k resistance, IkFor branch road k electric current.
The calculation formula of switch motion number of times is,
Wherein, t 'iAnd tiOn off state after respectively being reconstructed before and after power distribution network reconfiguration, ti=0 represents to switch off, ti
=1 represents switch closure.
Step 7, according to FZ value, utilization cervical orifice of uterus Population Regeneration；
Renewal process is：
Wherein,It is t individual for j-th in population,It is t+1 individual for j-th in population, G (t) is
The quantum door in t generations,
Wherein, θ is the anglec of rotation of quantum door, θ=Δ θ × s (αi,βi), Δ θ is the coefficient relevant with algorithm the convergence speed,
s(αi,βi) be quantum rotation directivity function；
Step 8, t=t+1；
Step 9, judges whether t ＜ gs set up, if it is, step 3 is gone to, if it is not, then terminating.
In order to further illustrate the above method, by taking the node radial networks figure of tri- feeder lines of IEEE 16 as shown in Figure 2 as an example,
Wherein 10,7, No. 14 switches be interconnection switch, in open mode, No. 5 line failures are chosen in examination, and now 4,6 nodes lose
Electricity, operation program, which is found, disclosure satisfy that the fault recovery scheme of trend constraint condition has 23 groups, and wherein multiple target integrates optimal
Be the 21st group, optimal self-healing recovery reconstructed network structure chart is as shown in figure 3, result is exported, preceding four groups of optimizing results such as table
Shown in two.
The self-healing recovery reconstruction result of table two
| Scheme | 1 | 2 | 3 | 4 |
| Switch 1 | 1 | 1 | 1 | 1 |
| Switch 2 | 1 | 1 | 1 | 0 |
| Switch 3 | 1 | 1 | 1 | 1 |
| Switch 4 | 1 | 1 | 1 | 1 |
| Switch 5 | 0 | 0 | 0 | 0 |
| Switch 6 | 1 | 1 | 1 | 1 |
| Switch 7 | 1 | 1 | 1 | 1 |
| Switch 8 | 1 | 1 | 0 | 0 |
| Switch 9 | 1 | 1 | 1 | 1 |
| Switch 10 | 0 | 1 | 0 | 1 |
| Switch 11 | 1 | 1 | 1 | 1 |
| Switch 12 | 1 | 1 | 1 | 1 |
| Switch 13 | 1 | 1 | 1 | 1 |
| Switch 14 | 1 | 0 | 1 | 14 |
| Switch 15 | 0 | 0 | 1 | 1 |
| Switch 16 | 1 | 1 | 1 | 1 |
| Network loss value | 0.0939 | 0.0942 | 0.0947 | 0.0950 |
| Switching manipulation number | 4 | 4 | 4 | 4 |
| Multiple objective function value | 0.1339 | 0.1342 | 0.1347 | 0.1350 |
On the basis of the power distribution network reconfiguration scheme found out by the above method is using minimum network loss as main target of optimization, also
The minimum objective optimization function of switch motion number of times is added, the Optimization goal of algorithm is considered in power distribution network practical operation, opened
Action frequency small factor as far as possible is closed, more with practicality.
Above method quantum genetic algorithm employs quantum bit Encoded Chromosomes and Quantum rotating gate realizes chromosome
Adjustment so that under less population scale, rapidly converges to globally optimal solution, when General Evolution algebraically is 3, you can obtain complete
Office's optimal solution；Isolated island in above method reconstruct judges, by Load flow calculation, to reduce the dimension of infeasible solution；The above method with
Grid net loss and switch motion number of times realize the complex optimum of multiple target as reconstruct target.
In summary, the present invention carries out Load flow calculation using the forward-backward sweep method of nodal hierarchy, using quantum genetic algorithm
The self-healing recovery reconstruct of power distribution network multiple target is carried out, with practical value.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, some improvement and deformation can also be made, these improve and deformed
Also it should be regarded as protection scope of the present invention.
Claims (5)
1. the Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm, it is characterised in that：Comprise the following steps,
Step one, initialize；
Determine the switch combination before failure and breakdown switch circuit；
The number m and evolutionary generation gs of Population Size n, quantum bit are determined, in power distribution network self-healing recovery, m representation switch
Number, i.e. branch travel permit number, n represent reconfiguration scheme number, include the population Q={ q of n individual1,q1,…,qn, wherein qjFor population
In j-th individual, j=1,2 ..., n；
I-th of quantum bit be | 0>Probability amplitudeI-th of quantum bit be | 1>Probability amplitudeRepresent
Institute is stateful during initial ranging is overlapped with identical probability；
Step 2, defines evolutionary generation t=1；
Step 3, quantum caves in；
Caved in by population Q (t) quantum and generate p (t), i.e., Q (t) is once observed, to obtain the solution of one group of determination
Wherein, That is the observation of t j-th of chromosome of generation, has m quantum bit, is
The binary string that one length is m；
Step 4, modification p (t) makes it meet fault bit for 0 and the requirement without isolated island；
Step 5, Load flow calculation is carried out using nodal hierarchy forward-backward sweep method, calculates the network loss value in each group of this generation, and preserve full
The network loss value of sufficient constraints；
Step 6, using distribution network loss and switch motion number of times as target, the fitness function FZ that construction multiple target is integrated is right
All individuals of population carry out adaptive value evaluation, and preserve reconfiguration scheme minimum FZ in this generation；
Step 7, according to FZ value, utilization cervical orifice of uterus Population Regeneration；
Renewal process is：
Wherein,It is t individual for j-th in population,It is t+1 individual for j-th in population, G (t) is t generations
Quantum door,
Wherein, θ is the anglec of rotation of quantum door, θ=Δ θ × s (αi,βi), Δ θ is the coefficient relevant with algorithm the convergence speed, s
(αi,βi) be quantum rotation directivity function；
Step 8, t=t+1；
Step 9, judges whether t ＜ gs set up, if it is, step 3 is gone to, if it is not, then terminating；
Multiple target integrate fitness function FZ be,
FZ=K × s+f
Wherein, K is weight factor, and s is the switch motion number of times in reconfiguration scheme, and f is the network loss value in reconfiguration scheme.
2. the Multiobjective Intelligent power distribution network self-healing recovery method according to claim 1 based on quantum genetic algorithm, it is special
Levy and be：Observation process is to produce the random number r between one 0~1, ifThen takeOtherwise take It is the probability that i-th of quantum bit observation is 0 in t j-th chromosomes of generation.
3. the Multiobjective Intelligent power distribution network self-healing recovery method according to claim 1 based on quantum genetic algorithm, it is special
Levy and be：Use nodal hierarchy forward-backward sweep method carry out Load flow calculation process for,
The headend node voltage and endpoint node power of known power distribution network, using branch road as unit of account；Start to set all nodes
Voltage is all rated voltage, is calculated paragraph by paragraph backward from end-node to first node according to node power, and the voltage for calculating each branch road is damaged
The power of current value, then first node that back substitution is obtained in consumption and each branch road, this is backward steps；Further according to head sections
Voltage loss on point voltage, first node power and each branch road, from first node to end-node it is piecewise before push away and obtain each node electricity
Pressure value, this pushes through journey before being；So calculate repeatedly, calculate an iteration number of times and add once, until the voltage deviation of each node
Untill in allowed band.
4. the Multiobjective Intelligent power distribution network self-healing recovery method according to claim 3 based on quantum genetic algorithm, it is special
Levy and be：Isolated island judges to realize by the node traverses in Load flow calculation；After Load flow calculation, magnitude of voltage is initial voltage
It is the interior necessary and sufficient condition without isolated island of network structure after reconstruct that total node number, which is equal to root node number,.
5. the Multiobjective Intelligent power distribution network self-healing recovery method according to claim 3 based on quantum genetic algorithm, it is special
Levy and be：Constraints is,
Load flow calculation is constrained：Whenever the power and voltage after structure change will meet Load flow calculation；
Capacity of trunk is constrained：sk≤smax；
Node voltage is constrained：Vmin≤Vk≤Vmax；
Network topology is constrained：Power distribution network or tree after reconstruct, are not allow for the generation of loop network；
Wherein, skAnd smaxRespectively branch road k flows overpowering calculated value and the maximum permissible value of capacity of trunk, VminAnd VmaxRespectively
For the lower limit and higher limit of node voltage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510852370.2A CN105305442B (en) | 2015-11-30 | 2015-11-30 | Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510852370.2A CN105305442B (en) | 2015-11-30 | 2015-11-30 | Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105305442A CN105305442A (en) | 2016-02-03 |
| CN105305442B true CN105305442B (en) | 2017-08-15 |
Family
ID=55202373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510852370.2A Active CN105305442B (en) | 2015-11-30 | 2015-11-30 | Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105305442B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109873409A (en) * | 2019-04-09 | 2019-06-11 | 中国计量大学 | A kind of restorative reconstructing method of distribution network failure |
| CN111775140A (en) * | 2020-08-24 | 2020-10-16 | 合肥工业大学 | Optimization method for multi-joint mechanical arm dynamics parameter identification excitation track |
| CN112152238A (en) * | 2020-09-24 | 2020-12-29 | 南方电网海南数字电网研究院有限公司 | Genetic algorithm-based power distribution station three-phase load unbalance adjusting method |
| CN112488367A (en) * | 2020-11-18 | 2021-03-12 | 国网山西省电力公司晋城供电公司 | User phase sequence loss reduction method and system based on quantum inheritance |
| CN113297531B (en) * | 2021-04-21 | 2022-09-02 | 广西大学 | Method for solving direct current power flow equation under perfect phase estimation |
| CN114039331B (en) * | 2021-10-27 | 2023-07-21 | 成都交大许继电气有限责任公司 | Traction network intelligent reconstruction self-healing method and system |
| CN115292652B (en) * | 2022-10-08 | 2023-02-03 | 北京航天驭星科技有限公司 | Method and device for determining initial orbit of space debris by improving quantum genetic algorithm |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050125104A1 (en) * | 2003-12-05 | 2005-06-09 | Wilson Thomas L. | Electrical power distribution control systems and processes |
| CN101242103B (en) * | 2008-03-13 | 2011-01-05 | 上海交通大学 | Intelligent optimization method for power system stabilizer parameter |
| CN103036234B (en) * | 2013-01-10 | 2014-09-03 | 南京软核科技有限公司 | Power distribution network anti-error optimization method |
-
2015
- 2015-11-30 CN CN201510852370.2A patent/CN105305442B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN105305442A (en) | 2016-02-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105305442B (en) | Multiobjective Intelligent power distribution network self-healing recovery method based on quantum genetic algorithm | |
| Li et al. | Distributed adaptive robust voltage/var control with network partition in active distribution networks | |
| CN104332995B (en) | Improved particle swarm optimization based power distribution reconstruction optimization method | |
| CN102945296B (en) | The uncertain rebuild modeling method of power distribution network under a kind of demand response visual angle | |
| CN107979092A (en) | It is a kind of to consider distributed generation resource and the power distribution network dynamic reconfiguration method of Sofe Switch access | |
| CN108242807B (en) | A kind of reconstruction method of power distribution network containing photovoltaic considering multidimensional security constraint | |
| CN104362623A (en) | Multi-target network reestablishing method for active power distribution network | |
| CN108306303A (en) | A kind of consideration load growth and new energy are contributed random voltage stability assessment method | |
| CN105098762A (en) | Island dividing method for power distribution network having distributed power sources | |
| CN106602557A (en) | Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles | |
| CN106709607A (en) | Intelligent operation and maintenance monitoring system of distributed photovoltaic power station | |
| CN112491037A (en) | Multi-target multi-stage dynamic reconstruction method and system for urban power distribution network | |
| CN108182498A (en) | The restorative reconstructing method of distribution network failure | |
| CN106786543A (en) | A kind of distribution network optimization drop for considering net capability constraint damages reconstructing method | |
| CN108155649A (en) | A kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG | |
| CN105512472A (en) | Large-scale wind power base power influx system topology composition layered optimization design and optimization design method thereof | |
| Gao et al. | Multi-objective dynamic reconfiguration for urban distribution network considering multi-level switching modes | |
| CN106803130A (en) | Distributed power source accesses the planing method of power distribution network | |
| CN104866921A (en) | Power distribution system network reconstruction method based on safety domain | |
| CN110854891A (en) | Power distribution network pre-disaster resource allocation method and system | |
| CN108270216B (en) | Multi-target-considered complex power distribution network fault recovery system and method | |
| CN113673065B (en) | Loss reduction method for automatic reconstruction of power distribution network | |
| CN113344283B (en) | Energy internet new energy consumption capability assessment method based on edge intelligence | |
| CN105391053A (en) | Urban grid load balancing operation strategy based on high-voltage distribution network capacity topological reconstruction | |
| CN107808256A (en) | A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |