CN104867062A - Low-loss power distribution network optimization and reconfiguration method based on genetic algorithm - Google Patents
Low-loss power distribution network optimization and reconfiguration method based on genetic algorithm Download PDFInfo
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
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Abstract
The invention provides a low-loss power distribution network optimization and reconfiguration method based on a genetic algorithm. According to the method, firstly, original progenitor chromosome populations are generated and encoded; whether lonely islands exist in the original progenitor chromosome populations or not is judged; chromosomes with the lonely islands are removed, and progenitor chromosome populations are obtained; the adaptation degrees of each chromosome in the progenitor chromosome populations are calculated, and sequencing is carried out according to the values of the adaptation degrees; whether the adaptation degrees meet design requirements or not is judged; if the adaptation degrees do not meet the design requirements, duplication, intersection and variation of the chromosome populations are carried out; the obtained chromosome populations are subjected to Elitism processing, i.e., before next iteration, the chromosomes with the best adaptation degree in the iterated progenitor chromosome populations are put into the next iteration; the iteration is repeated until iteration stopping conditions are met; and the optimum value is output. The low-loss power distribution network optimization and reconfiguration method based on the genetic algorithm provided by the invention has the advantages that Elitism is introduced; convergent results are more accurate; the problem of inconsistent convergent results of the genetic algorithm is solved; and the optimization efficiency is high.
Description
Technical field
The present invention relates to a kind of reconstructing method of low-loss distribution network, belong to distribution network planning technical field.
Background technology
For Distribution Networks Reconfiguration is also known as distribution network configuration, or distribution network feeder line configuration, the reconstruct of distribution network feeder line etc.For Distribution Networks Reconfiguration be exactly ensure distribution radially, under the prerequisite that meets feeder line thermal capacitance, voltage-drop requirement and transformer capacity etc., change the assembled state of block switch, interconnection switch, namely select the supply path of user, make the distribution method of operation that a certain index of distribution (as: Line Loss of Distribution Network System, load balancing or supply voltage quality etc.) is best.In the optimal reconfiguration of distribution network, the active loss reducing distribution network is easy to realize, and therefore the many active losses from reducing distribution network of the existing research to distribution network optimization are set about.
Genetic algorithm is a kind of searching algorithm used for reference organic sphere natural selection and natural evolution mechanism and grow up, use for reference theory of biological evolution, the problem that will solve is modeled to the process of a biological evolution, by copying, intersecting, the operation such as sudden change produces follow-on solution, and progressively eliminate the low solution of fitness function value, increase the solution that fitness function value is high.Such evolution N is for the rear individuality very high with regard to fitness function value of probably evolving out.Genetic algorithm computing time is few and precision is high, convergence good, meet the requirement of distribution network optimization method, but genetic algorithm is a kind of intelligent optimization algorithm searching for optimum solution, approximate optimal solution can only be obtained, so convergence result is each time all different, there is certain difference, the improvement of genetic algorithm is become to the target of research.
Summary of the invention
The object of the invention is to solve the deficiencies in the prior art, and provide that a kind of step is simple, convergence result consistance is preferably based on the optimal reconfiguration method of the low-loss distribution network of genetic algorithm.
Realizing the technical scheme that the object of the invention adopts is that a kind of optimal reconfiguration method of the low-loss distribution network based on genetic algorithm, comprises the steps:
(1) primitive progenitor chromosome population is generated, chromosome represents the connected mode of distribution network, gene represents the state of each branch road of distribution network, the connected mode of string of binary characters to distribution network of regular length is used to encode, be encoded to 0 when wherein each branch road of distribution network is in closure state, when being in off-state, be encoded to 1;
(2) whether the chromosome adopting Newton-Raphson approach to calculate in primitive progenitor chromosome population exists isolated island, and remove the chromosome that there is isolated island and obtain chromosome population for generations, described isolated island is the connected mode forming looped network in distribution network;
(3) each chromosomal fitness in chromosome population is for generations calculated, and each chromosome in chromosome population is for generations sorted by fitness is descending, described fitness is the active loss of distribution network connected mode, fitness is larger, and to represent active loss less, and the computing function of active loss is as follows:
Wherein P
lfor the active loss of distribution network, P is the active power of distribution network, and Q is the reactive power of distribution network, and R is the line resistance of distribution network, and U is the voltage of distribution network;
(4) carry out convergence to judge, the value of the active loss of chromosome population meets design requirement for generations, exports this chromosome population for generations, if do not meet design requirement, copied by the chromosome population for generations after sequence, copying probability is 0 ~ 1, obtains original parent chromosome population;
(5) insert crossover operator and mutation operator in original parent chromosome population, obtain parent chromosome population;
(6) carry out elitism process, retain active loss in parent chromosome population less 10% ~ 50% part and 2 ~ 10 minimum for active loss in chromosome population for generations chromosomes are dropped into wherein, obtain original child chromosome population;
(7) original child chromosome population is returned step 2 as primitive progenitor chromosome population and carry out iteration, the then termination of iterations when iterations arrives setting iterations, the chromosome population obtained when exporting iteration ends;
(8) chromosome population obtained during the iteration ends that the chromosome population for generations step (4) exported or step (7) export is decoded, and obtains the distribution network connected mode that active loss is minimum.
In step (4), the chromosome population for generations after sequence is divided into three parts, the probability that copies of chromosome population three part is followed successively by 3/4,1/2 and 1/4 from small to large by active loss for generations.
In step (5), the particular content of insert crossover operator is select two chromosomes arbitrarily, and two genes also exchange by two genes that Select gene value is identical arbitrarily respectively in two chromosome, and described crossover probability is 0.4 ~ 0.9.
Described crossover probability is 0.6.
The particular content inserting mutation operator in step (5) is that the genic value of a chromosomal gene is sported 1 by 0, and the genic value of another gene sports 0 by 1 simultaneously, and described mutation probability is 0.01 ~ 0.1.
Described mutation probability is 0.15.
Retain in step (6) active loss in parent chromosome population less 20% part and 3 minimum for active loss in chromosome population for generations chromosomes are dropped into wherein, obtain original child chromosome population.
The designing requirement of active loss is that the active loss difference between the maximum chromosome of active loss is minimum in chromosome population for generations chromosome and active loss is not more than 0.0001KW.
As shown from the above technical solution, the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm provided by the invention, before chromosome replication, judge whether there is isolated island in chromosome, namely judge whether there is looped network in the different connected modes of distribution network, because distribution network is open loop operation, therefore before chromosome replication, delete the chromosome that there is isolated island can greatly reduce chromosomal treatment capacity, improve optimal speed; Elitism is introduced in genetic algorithm, namely before next iteration, chromosome best for fitness in the chromosome population for generations of current iteration is dropped in next iteration, three chromosomes that in chromosome population for generations, fitness is best are comprised in the new chromosome population formed, namely the chromosomal ratio that after breeding, survival probability is high increases, after overall breeding, the chromosome quantity of existence also increases greatly, makes convergence result more accurate.
Compared with prior art, advantage of the present invention is:
(1) the optimal reconfiguration method of distribution network provided by the invention specify that design cycle, simplifies design procedure, is easy to operation, before each iteration, chromosome population is screened, remove the chromosome that there is isolated island, therefore greatly reduce chromosomal treatment capacity, improve optimal speed;
(2) introduce elitism in this optimal reconfiguration method, make best fit approximation solution closer to standard results, convergence result more accurate and effective, overcomes the problem that genetic algorithm converges result is inconsistent;
(3) optimal reconfiguration method provided by the invention introduces elitism after crossover and mutation, avoid introducing too early the chiasma and variation that cause fitness excellent, to guarantee in current iteration that three chromosomes that in chromosome population for generations, fitness is best directly enter next iteration without any process, make convergence result more accurate;
(4) optimal reconfiguration method provided by the invention can meet the design of different size requirement by changing parameter (copying probability, crossover probability, mutation probability and active loss difference), be applicable to the distribution network under different situations, and the distribution network optimal reconfiguration of minimum network loss can be realized;
(5) 0 is encoded to when branch road is in closure state by optimal reconfiguration method provided by the invention when encoding, 1 is encoded to when being in off-state, because branch road many places in distribution network are in closure state, and " 0 " is easy to computing during Computing, therefore the method is applicable to computer programming computing, and in chromosome coding, 0 in the majorityly reduces program complexity and shortcut calculation.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm provided by the invention.
Fig. 2 is the distribution structure schematic diagram of standard IEEE 14 node power distribution network.
Embodiment
Illustrate in detail the present invention below in conjunction with drawings and Examples, content of the present invention is not limited to following examples.
For standard IEEE 14 node power distribution network, as shown in Figure 2, this distribution network is the typical radial electrical distribution network structure (each digitized representation different branch) of 14 nodes, 13 branch roads, the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm provided by the invention, its Optimizing Flow as shown in Figure 1, comprises the steps:
(1) stochastic generation primitive progenitor chromosome population, the population quantity of primitive progenitor chromosome population is 20000, chromosome represents the connected mode of distribution network, gene represents the state of each branch road of distribution network, the connected mode of string of binary characters to distribution network of regular length is used to encode, be encoded to 0 when wherein each branch road of distribution network is in closure state, when being in off-state, be encoded to 1;
(2) whether the chromosome adopting Newton-Raphson approach to calculate in primitive progenitor chromosome population exists isolated island, and remove the chromosome that there is isolated island and obtain chromosome population for generations, described isolated island is the connected mode forming looped network in distribution network;
(3) each chromosomal fitness in chromosome population is for generations calculated, and each chromosome in chromosome population is for generations sorted by fitness is descending, described fitness is the active loss of distribution network connected mode, fitness is larger, and to represent active loss less, and the computing function of active loss is as follows:
Wherein P
lfor the active loss of distribution network, P is the active power of distribution network, and Q is the reactive power of distribution network, and R is the line resistance of distribution network, and U is the voltage of distribution network;
(4) judge whether the active loss difference between chromosome that in chromosome population for generations, active loss is minimum and the maximum chromosome of active loss is not more than 0.0001KW, if difference is not more than 0.0001KW, export this chromosome population for generations, if difference is greater than 0.0001KW, the chromosome population for generations after sequence is copied, to chromosome population trisection for generations, the probability that copies of chromosome population three part is followed successively by 3/4,1/2 and 1/4 from small to large by active loss for generations, obtains original parent chromosome population;
(5) in original parent chromosome population, select two chromosomes arbitrarily, two genes also exchange by two genes that Select gene value is identical arbitrarily respectively in two chromosome, and described crossover probability is 0.6; In original parent chromosome population, the genic value of a chromosomal gene is sported 1 by 0, the genic value of another gene sports 0 by 1 simultaneously, and described mutation probability is 0.15, obtains parent chromosome population;
(6) carry out elitism process, retain active loss in parent chromosome population less 20% part and 3 minimum for active loss in chromosome population for generations chromosomes are dropped into wherein, obtain original child chromosome population;
(7) original child chromosome population is returned step 2 as primitive progenitor chromosome population and carry out iteration, the then termination of iterations when iterations arrives setting iterations, the chromosome population obtained when exporting iteration ends;
(8) chromosome population obtained during the iteration ends that the chromosome population for generations step (4) exported or step (7) export is decoded, and obtains the distribution network connected mode that active loss is minimum.
Claims (8)
1., based on an optimal reconfiguration method for the low-loss distribution network of genetic algorithm, it is characterized in that, comprise the steps:
(1) primitive progenitor chromosome population is generated, chromosome represents the connected mode of distribution network, gene represents the state of each branch road of distribution network, the connected mode of string of binary characters to distribution network of regular length is used to encode, be encoded to 0 when wherein each branch road of distribution network is in closure state, when being in off-state, be encoded to 1;
(2) whether the chromosome adopting Newton-Raphson approach to calculate in primitive progenitor chromosome population exists isolated island, and remove the chromosome that there is isolated island and obtain chromosome population for generations, described isolated island is the connected mode forming looped network in distribution network;
(3) each chromosomal fitness in chromosome population is for generations calculated, and each chromosome in chromosome population is for generations sorted by fitness is descending, described fitness is the active loss of distribution network connected mode, fitness is larger, and to represent active loss less, and the computing function of active loss is as follows:
Wherein P
lfor the active loss of distribution network, P is the active power of distribution network, and Q is the reactive power of distribution network, and R is the line resistance of distribution network, and U is the voltage of distribution network;
(4) carry out convergence to judge, the value of the active loss of chromosome population meets design requirement for generations, exports this chromosome population for generations, if do not meet design requirement, copied by the chromosome population for generations after sequence, copying probability is 0 ~ 1, obtains original parent chromosome population;
(5) insert crossover operator and mutation operator in original parent chromosome population, obtain parent chromosome population;
(6) carry out elitism process, retain active loss in parent chromosome population less 10% ~ 50% part and 2 ~ 10 minimum for active loss in chromosome population for generations chromosomes are dropped into wherein, obtain original child chromosome population;
(7) original child chromosome population is returned step 2 as primitive progenitor chromosome population and carry out iteration, the then termination of iterations when iterations arrives setting iterations, the chromosome population obtained when exporting iteration ends;
(8) chromosome population obtained during the iteration ends that the chromosome population for generations step (4) exported or step (7) export is decoded, and obtains the distribution network connected mode that active loss is minimum.
2. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 1, it is characterized in that: in step (4), the chromosome population for generations after sequence is divided into three parts, the probability that copies of chromosome population three part is followed successively by 3/4,1/2 and 1/4 from small to large by active loss for generations.
3. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 1, it is characterized in that: in step (5), the particular content of insert crossover operator is for selecting two chromosomes arbitrarily, two genes also exchange by two genes that Select gene value is identical arbitrarily respectively in two chromosome, and described crossover probability is 0.4 ~ 0.9.
4. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 3, is characterized in that: described crossover probability is 0.6.
5. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 1, it is characterized in that: the particular content inserting mutation operator in step (5) is that the genic value of a chromosomal gene is sported 1 by 0, the genic value of another gene sports 0 by 1 simultaneously, and described mutation probability is 0.01 ~ 0.1.
6. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 5, is characterized in that: described mutation probability is 0.15.
7. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 1, it is characterized in that: retain in step (6) active loss in parent chromosome population less 20% part and 3 minimum for active loss in chromosome population for generations chromosomes are dropped into wherein, obtain original child chromosome population.
8. the optimal reconfiguration method of the low-loss distribution network based on genetic algorithm according to claim 1, is characterized in that: the designing requirement of active loss is that the active loss difference between the maximum chromosome of active loss is minimum in chromosome population for generations chromosome and active loss is not more than 0.0001KW.
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| CN106257477A (en) * | 2016-07-29 | 2016-12-28 | 南京工程学院 | A kind of intermediate frequency amorphous alloy transformer optimization method based on multi-objective genetic algorithm |
| CN106257477B (en) * | 2016-07-29 | 2019-01-22 | 南京工程学院 | A kind of intermediate frequency amorphous alloy transformer optimization method based on multi-objective genetic algorithm |
| CN108334950A (en) * | 2018-04-17 | 2018-07-27 | 国网冀北电力有限公司唐山供电公司 | A kind of Distribution Network Reconfiguration using partheno genetic algorithm |
| CN109409583A (en) * | 2018-10-08 | 2019-03-01 | 吉林大学 | Low voltage power distribution network decreasing loss reconstructing method |
| CN109885401A (en) * | 2019-01-27 | 2019-06-14 | 中国人民解放军国防科技大学 | Structured grid load balancing method based on LPT local optimization |
| CN109885401B (en) * | 2019-01-27 | 2020-11-24 | 中国人民解放军国防科技大学 | Structured grid load balancing method based on LPT local optimization |
| CN110032902A (en) * | 2019-03-12 | 2019-07-19 | 佛山市顺德区中山大学研究院 | A kind of reader collision-proof method and its device based on partheno genetic algorithm |
| CN110032902B (en) * | 2019-03-12 | 2022-04-15 | 佛山市顺德区中山大学研究院 | Reader anti-collision method and device based on single parent genetic algorithm |
| CN110932270A (en) * | 2019-12-12 | 2020-03-27 | 南方电网科学研究院有限责任公司 | Power distribution network fault recovery method and device comprising flexible switch |
| CN111463778A (en) * | 2020-04-20 | 2020-07-28 | 南昌大学 | Active power distribution network optimization reconstruction method based on improved suburb optimization algorithm |
| CN112541626A (en) * | 2020-12-08 | 2021-03-23 | 国网江苏省电力有限公司经济技术研究院 | Multi-target power distribution network fault reconstruction method based on improved genetic algorithm |
| CN112541626B (en) * | 2020-12-08 | 2022-08-02 | 国网江苏省电力有限公司经济技术研究院 | Multi-target power distribution network fault reconstruction method based on improved genetic algorithm |
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