CN113837469A - Distribution network low-voltage regulator installation point selection optimization method, system and equipment - Google Patents
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Abstract
The invention discloses a distribution network low-voltage regulator installation point selection optimization method, a distribution network low-voltage regulator installation point selection optimization system and distribution network low-voltage regulator installation point selection optimization equipment, and relates to the technical field of power systems. A distribution network low-voltage regulator installation point selection optimization method comprises the following steps: s101, generating an initial population based on a genetic algorithm by taking the obtained optimal installation point of the voltage regulator as a target, wherein the sum Z of the distances from the optimal installation point to all low-voltage points is shortest; s102, calculating the fitness of individuals in the initial population by using a fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the parent and the offspring according to the fitness to form a new parent; s104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm. The method provided by the invention adopts a new self-adaptive genetic algorithm, overcomes the defect that the original self-adaptive genetic algorithm is easy to get early, and accelerates the algorithm optimization speed.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method, a system and equipment for optimizing installation and point selection of a distribution network low-voltage regulator.
Background
With the development of national economy, the power load is rapidly increased, and the requirement of users on the power supply quality is higher and higher. However, at present, there are still many low-voltage transformer areas with quality problems of low supply voltage, which affect normal power consumption of low-voltage users, so that a voltage regulator needs to be installed to increase the voltage of the low-voltage transformer area, and selecting an appropriate installation position can reduce line loss, reduce the number of voltage regulators and further improve comprehensive benefits, so that the optimal selection of the installation position of the voltage regulator has important practical significance.
Disclosure of Invention
In order to overcome the problems or partially solve the problems, the invention provides a method, a system and equipment for optimizing the installation and point selection of a distribution network low-voltage regulator. The installation and selection points of the voltage regulator are optimized, so that the purposes of reducing loss and improving benefit are achieved.
The invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for optimizing an installation point selection of a distribution network low-voltage regulator, including the following steps:
s101, generating an initial population based on a genetic algorithm by taking the acquired optimal installation point of the voltage regulator as a target, wherein the sum Z of the distances from the optimal installation point to all low-voltage points is shortest; s102, calculating the fitness of the individuals in the population by using a fitness function, selecting a fixed number of individuals from the population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the parent and the offspring according to the fitness to form a new parent; s104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm.
Based on the first aspect, in some embodiments of the present invention, the above genetic algorithm expression is:
where pc is the individual cross-probability, pm is the individual variation probability, fmaxIs the maximum fitness value, f, of the populationavgIs the average fitness value of the population, F is the individual fitness value, F' is the derivative of the individual fitness value, F(t)To attenuateFunction, k1、k2、k3、k4Is a self-defined parameter.
In some embodiments of the invention based on the first aspect, the attenuation function is
Wherein T is time or iteration times, and alpha, beta and lambda are self-defined parameters.
Based on the first aspect, in some embodiments of the present invention, the generating the initial population based on the genetic algorithm includes determining parameters of the genetic algorithm including: population number, iteration number, mutation rate and cross rate.
Based on the first aspect, in some embodiments of the present invention, the fitness function is: and the fitness is 1/z.
Based on the first aspect, in some embodiments of the present invention, the rules for selecting the parent include elite strategy and roulette strategy in steps S102 and S103.
Based on the first aspect, in some embodiments of the present invention, the method for selecting a parent using the above elitism policy includes: and selecting parent individuals with the fitness larger than a threshold value from the parents to be inherited to the offspring.
Based on the first aspect, in some embodiments of the present invention, the method for selecting a parent using the above elitism policy includes:
calculating the individual selection probability according to a selection probability formula;
calculating the cumulative probability of the individuals based on the selection probability of the individuals;
and determining the parent individuals according to the accumulated probability.
Based on the first aspect, in some embodiments of the present invention, the stop criterion includes: when the fitness of the individual reaches a given threshold value, the fitness and the group fitness of the individual do not rise any more or the iteration times reach a preset algebra.
In a second aspect, an embodiment of the present invention provides a system for optimizing an installation point selection of a distribution network low-voltage regulator, including: a population generation module: for generating an initial population based on a genetic algorithm; a first selection module: the system comprises a population selection module, a population fitness function module and a population fitness function module, wherein the population fitness function module is used for calculating the fitness of individuals in the initial population by using the fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; a second selection module: selecting a fixed number of individuals from the parent and the child to become a new parent according to the adaptability; a circulation judgment module: and the step of repeating the steps executed by the first selection module and the second selection module until a stop criterion is reached and the algorithm is terminated.
In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor, at least one memory, and a data bus; wherein, the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the one or more programs or methods, such as: s101, aiming at obtaining an optimal installation point of a voltage regulator, generating an initial population based on a genetic algorithm, wherein the sum Z of the distances from the optimal installation point to all low-voltage points is shortest; s102, calculating the fitness of the individuals in the population by using a fitness function, selecting a fixed number of individuals from the population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the parent and the offspring according to the fitness to form a new parent; s104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm.
Compared with the prior art, the invention at least has the following advantages and beneficial effects:
the embodiment of the invention adopts the new self-adaptive genetic algorithm to automatically adjust the cross and variation probability according to the individual fitness and the change of the evolution time in the evolution process, overcomes the defect that the original self-adaptive genetic algorithm is easy to get early, improves the diversity of the optimal solution, accelerates the optimization speed of the algorithm, and effectively protects the optimal solution from being damaged. The new algorithm is applied to the optimization selection of the installation site of the line voltage regulator, and the aim of minimizing the installation number of the line voltage regulators is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
fig. 1 is a schematic flow chart of a method for optimizing installation and point selection of a distribution network low-voltage regulator;
FIG. 2 is a schematic flow diagram of a roulette strategy in a distribution network low-voltage regulator installation point selection optimization method;
FIG. 3 is a chromosome access sequence diagram in an embodiment of a method for optimizing installation sites of distribution network low voltage regulators;
fig. 4(a) is a schematic diagram of gene exchange (before) of two chromosomes in a distribution network low-voltage regulator installation point selection optimization method;
fig. 4(b) is a schematic diagram of gene exchange (after) of two chromosomes in a distribution network low-voltage regulator installation point selection optimization method;
FIG. 5 is a schematic diagram of repetitive genes in two chromosomes in a distribution network low-voltage regulator installation point selection optimization method;
FIG. 6 is a schematic diagram of the exchange repeat genes in two chromosomes in a distribution network low-voltage regulator installation site selection optimization method;
FIG. 7 is a gene mapping table in a distribution network low voltage regulator installation site selection optimization method;
FIG. 8 is a schematic diagram of genetic variation in a method for optimizing installation sites of distribution network low-voltage regulators;
fig. 9 is a block diagram of a distribution network low-voltage regulator installation point selection optimization system;
fig. 10 is a block diagram of an electronic device.
Icon: 1-a processor; 2-a memory; 3-a data bus; 100-a population generation module; 200-a first selection module; 300-a second selection module; 400-loop judgment module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not used as limitations of the present invention.
Example 1
For example, taking a 10KV voltage regulator as an example, the point selection requirement of the 10KV voltage regulator includes:
(1) and (4) application range.
The power distribution transformer has the advantages that the power supply radius of the distribution line is large, the cross section of a conducting wire is small, the voltage of a high-voltage side of the distribution transformer is lower than 9.3kV, 3 or more transformer area outlet low voltages appear in 1 year, and when the effect of adjusting a tap of the distribution transformer is not obvious, a 10kV line voltage regulator can be adopted to increase the voltage of the line, so that the problem of low voltage of a rear-end line in a flaky mode is solved. For a heavy-load line or a line with too long power supply distance, 2 voltage regulators can be installed when the problem of low voltage in a transformer area cannot be completely solved by installing 1 voltage regulator, but the number of the voltage regulators is not more than 3 at most.
(2) And (4) determining the installation position.
1) And analyzing the distribution of the low-voltage transformer area and the severity of the low voltage of the target line by inquiring or field actual measurement of the power supply service command system.
a) And searching the distribution of the low-voltage platform area of the line to be managed, and combing the pole tower number of the main line with the low-voltage platform area. Find the branch or branch line where the low voltage problem is most concentrated (i.e., where the low voltage plateau is the most).
b) And searching and analyzing the most serious low-voltage distribution area in the past 1 year, namely the distribution transformer area with the lowest outlet voltage, the highest low-voltage times and the longest low-voltage accumulation time.
2) The voltage regulator is arranged on the outlet side of a branch line T connected with a main line tower where the most serious low-voltage problem is located. If the problem of low voltage also exists at the rear end of the main line of the worst station area T connection, the voltage regulator is arranged in front of the T connection point.
3) The voltage regulator is installed and selected, and the actual input voltage is not lower than 9.3kV after installation. And the voltage of the installation point and the actual input voltage after installation drop, should not cause the distribution between power supply point and installation point to appear new "low voltage" problem. The maximum current on the input side of the voltage regulator should not exceed the current capacity of the line after installation.
(3) Capacity selection
1) The capacity series of the 10kV voltage regulator comprises 1000kVA, 2000kVA, 3000kVA, 4000kVA and 5000 kVA.
2) Calculating the sum of all distribution capacities after installation
3) Determining the capacity S of the voltage regulator:
if it is
Selecting the capacity of a voltage regulator to be 1000 kVA;
if it is
Selecting the capacity of a voltage regulator to be 2000 kVA;
if it is
The regulator capacity is chosen to be 4000kVA but not less than the regulator back end maximum load.
Based on the above requirements, as shown in fig. 1, an embodiment of the present invention provides a method for optimizing an installation point selection of a distribution network low-voltage regulator, including the following steps:
s101, aiming at obtaining an optimal installation point of a voltage regulator, generating an initial population based on a genetic algorithm, wherein the sum Z of the distances from the optimal installation point to all the low-voltage points is shortest;
the genetic algorithm expression is as follows:
where pc is the individual cross probability, pm is the individual variation probability, fmaxIs the maximum fitness value, f, of the populationavgIs the average fitness value of the population, F is the individual fitness value, F' is the derivative of the individual fitness value, F(t)As a function of attenuation, k1、k2、 k3、k4Is a self-defined parameter.
The genetic manipulation mainly includes operations such as selection, crossing and mutation. The selection operation is a process of simulating organisms in the nature to win or lose, and the selection operator copies the individuals with high fitness to the next generation with high probability and eliminates the individuals with low fitness. The algorithm adopts a random tournament method type selection operator, and simultaneously, the algorithm reserves elite individuals. The cross operation is the main method for generating new individuals by genetic algorithm, and determines the global searching capability of the genetic algorithm. And (3) carrying out two-point crossing operation on the self-adaptive crossing probability pc determined by the crossing operator, namely randomly selecting two crossing points and exchanging genes between the two points according to the probability pm. Mutation operations are methods for generating new genes, which determine the local search capabilities of the algorithm. And the mutation operator performs two-point mutation operation by adopting the self-adaptive mutation probability pm.
The traditional adaptive genetic algorithm is easy to generate local optimal solution rather than global optimal solution, and in order to improve the capability of the algorithm to get rid of the local optimal solution, the invention provides an improved adaptive genetic algorithm. The basic idea of the algorithm is to increase the cross mutation probability of good individuals in the evolution process, and the cross mutation probability is reduced along with the increase of time, so as to develop a new search space and prevent the individuals from getting premature.
In order to improve the mutation probability, the number of the exchanged positions is doubled during the calculation of the mutation probability, and a mutation attenuation factor is introduced
Wherein T is time or iteration times, and alpha, beta and lambda are self-defined parameters. This allows for an increased probability of mutation early in evolution, while evolution is in progressThe late mutation probability returns to the normal value.
For example, in this embodiment, assuming that there is one voltage regulator to select one of n points, the path selection target is to require the sum of the paths of the voltage regulators to the respective low voltage points to be minimum.
Therefore, first, an initial population is generated based on a genetic algorithm, and parameters of the genetic algorithm are determined, and in an exemplary embodiment, the parameters of the genetic algorithm include: population number, iteration number, mutation rate and cross rate. Specifically, the larger the population size is, the more the processing modes are, the lower the possibility of trapping in a local solution is, and thus the premature convergence is easily caused, but the larger the size is, the more the calculation amount is increased, and the algorithm efficiency is affected. Illustratively, in this embodiment, the population number is set to 100, the sequence of the points is random, chromosomes are constructed by adopting a natural number coding mode, 10 points are represented by 0-19, and each chromosome represents the access sequence of each point. As shown in fig. 3, as: the low voltage boost device starts at point 2, passes through point 8, and finally returns to point 2.
S102, calculating the fitness of individuals in the initial population by using a fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of children
After the initial population is generated, the fitness of each individual in the population needs to be calculated, the fitness function is non-negative, and in any case, the larger the total expectation is, the better the fitness function is. The objective function of the problem is that the smaller the total distance is, the better, so in this embodiment, the fitness function is designed as follows: and the fitness is 1/z. After the fitness of each individual is calculated, an elite strategy can be exemplarily adopted, individuals with high fitness are selected from an initial group as parents, the selected parents are crossed and mutated to obtain offspring, the number of the selected parents can be preset with a fixed number or proportion, or a fitness threshold value is set for selection, and the number of the generated offspring can be set with a fixed number or multiplying power.
An exemplary, cross-algorithm implementation is: two chromosomes, chrom1 and chrom2 (as shown in fig. 4) are randomly selected, two cut points cut1 and cut2 are determined, and if cut1 is equal to 2 and cut2 is equal to 4, the light gray parts of chrom1 and chrom2 are exchanged. After the exchange is completed, as shown in FIG. 5
At this time, both chrom1 and chrom2 contained repeated genes, gene 0 (point 1) of chrom1 repeated, and gene 1 (point 3) of chrom2 repeated (i.e., dark gray portion in fig. 6).
The dark grey part is adjusted, and one idea is to use a mapping table. The adjustment process is shown in fig. 7. The light gray part (cross segment) is the mapping table (as shown in fig. 8), i.e. the genes involved in the crossing are checked, and if the genes are in the mapping table, the genes are exchanged according to the mapping table. For example, in chrom1, point 1 and point 5 are non-intersecting genes, point 1 needs to be replaced by point 2, point 2 needs to be replaced by point 4, and point 4 needs to be replaced by point 3 in the mapping table. The final point 1 is replaced with point 3.
Exemplary, the variant algorithm implementation is: the genes at these two positions were swapped using the 2-opt method, i.e., chromosome selection, 2 positions were randomly selected, as shown in FIG. 9.
S103, selecting a fixed number of individuals from the parent and the offspring according to the fitness to form a new parent;
in step S102, if the fitness of the offspring obtained through intersection and mutation still fails to meet the requirement, the steps of parent selection, generation of the offspring, and parent selection may be repeated until an individual meeting the requirement is obtained. The choice may also be made using the elite strategy or the roulette strategy.
Taking roulette selection as an example, implementing a roulette strategy first requires calculating an adaptive value for generating new individuals, assigning the probability of the individuals according to the size of the adaptive value, and selecting the individuals as parents of the next generation population according to a probability mode. The basic idea is as follows: the probability of each individual being selected is proportional to the fitness of the individual, and the better the fitness value of the individual is, the greater the probability of being selected. The selection steps comprise:
s201, calculating individual selection probability according to a selection probability formula; (ii) a
The selection probability is the probability that an individual is inherited to the next generation, and obviously the greater the fitness, the more environmentally adaptable the individual will beThe probability of inheritance to the next generation is higher. The choice probability calculation formula is:
where i represents an individual and N represents the population size.
S202, calculating the cumulative probability of the individuals based on the selection probability of the individuals;
calculating the cumulative probability of each individual, wherein the cumulative probability is calculated by the formula:
and S203, determining the parent individuals according to the accumulated probability.
In [0,1 ]]Generating a uniformly distributed random number randiIf p (culmulative)i-1≤randi≤p(culmulative)iThen individual i is selected to the next generation.
S104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm.
Illustratively, in this embodiment, the stopping criteria include: 1) when the fitness of the optimal individual reaches a given threshold value; 2) the fitness and the group fitness of the optimal individual are not increased any more; 3) the iteration times reach a preset algebra.
For example, in this embodiment, the 3 rd stopping criterion is selected, and if the fitness of the generated individual still cannot meet the requirement after the iteration number reaches the preset algebra, the algorithm is terminated, and the user is required to reset the parameters of the genetic algorithm.
Example 2
Referring to fig. 2, in some embodiments of the present invention, a distribution network low voltage regulator installation point selection optimization system is provided, including:
the population generation module 100: for generating an initial population based on a genetic algorithm; the first selection module 200: the system comprises a population selection module, a population fitness module and a population fitness module, wherein the population fitness module is used for calculating the fitness of individuals in the initial population by using a fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; the second selection module 300: selecting a fixed number of individuals from the parent and the offspring to become a new parent according to the fitness; the loop judgment module 400: for repeating the steps executed by the first selection module 200 and the second selection module 300 until a stop criterion is reached and the algorithm is terminated.
The system provided by the embodiment of the invention can be used for executing the method of any one of the embodiments, specifically embodiment 1. And will not be described in detail herein.
Example 3
Referring to fig. 6, an embodiment of the invention provides an electronic device, including: at least one processor 1, at least one memory 2 and a data bus 3; wherein, the processor 1 and the memory 2 complete the communication with each other through the data bus 3; the memory 2 stores program instructions executable by the processor 1, and the processor 1 calls the program instructions to execute the method of embodiment 1, for example, to execute: s101, aiming at obtaining an optimal installation point of a voltage regulator, generating an initial population based on a genetic algorithm, wherein the sum Z of the distances from the optimal installation point to all low-voltage points is shortest; s102, calculating the fitness of individuals in the initial population by using a fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the parent and the offspring according to the fitness to form a new parent; s104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A distribution network low-voltage regulator installation point selection optimization method is characterized by comprising the following steps:
s101, generating an initial population based on a genetic algorithm by taking the acquired optimal installation point of the voltage regulator as a target, wherein the sum Z of the distances from the optimal installation point to all low-voltage points is shortest;
s102, calculating the fitness of individuals in the population by using a fitness function, selecting a fixed number of individuals from the population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring;
s103, selecting a fixed number of individuals from the parent and the offspring to become a new parent according to the fitness;
s104, repeating the steps S102-S103 until the stop criterion is met, and terminating the algorithm.
2. The method of optimizing installation and point selection for a distribution network low voltage regulator according to claim 1, wherein the genetic algorithm expression is:
where pc is the individual cross probability, pm is the individual variation probability, fmaxIs the maximum fitness value, f, of the populationavgIs the average fitness value of the population, F is the individual fitness value, F' is the derivative of the individual fitness value, F(t)As a function of attenuation, k1、k2、k3、k4Is a self-defined parameter.
3. The method of optimizing installation site selection for a distribution network low voltage regulator of claim 1, wherein the attenuation function is based on a linear function
Wherein T is time or iteration times, and alpha, beta and lambda are self-defined parameters.
4. The method of optimizing installation and point selection for a distribution network low voltage regulator according to claim 1, wherein the fitness function is: and the fitness is 1/z.
5. The method of optimizing the installation and selection points of distribution network low voltage regulators according to claim 1, wherein in steps S102 and S103, the rules for selecting the parent include an elite strategy and a roulette strategy.
6. The method of optimizing installation and selection points of a distribution network low voltage regulator according to claim 5, wherein the method of selecting a parent using the elite strategy comprises: and selecting parent individuals with the fitness larger than a threshold value from the parents to be inherited to the offspring.
7. The method of optimizing installation and selection points of a distribution network low voltage regulator according to claim 5, wherein the method of selecting a parent using the elite strategy comprises:
calculating the individual selection probability according to a selection probability formula;
calculating an accumulated probability of an individual based on the selection probability of the individual;
and determining parent individuals according to the accumulated probability.
8. The method of optimizing installation site selection for a distribution network low voltage regulator of claim 1, wherein the stopping criteria comprises: when the fitness of the individual reaches a given threshold value, the fitness and the group fitness of the individual do not rise any more or the iteration times reach a preset algebra.
9. The utility model provides a join in marriage net low voltage regulator installation selection point optimizing system which characterized in that includes:
a population generation module: for generating an initial population based on a genetic algorithm;
a first selection module: the system comprises a population selection module, a population fitness function module and a population fitness function module, wherein the population fitness function module is used for calculating the fitness of individuals in the initial population by utilizing the fitness function, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then crossing and mutating the parents to obtain a fixed number of offspring;
a second selection module: the system is used for selecting a fixed number of individuals from the parent and the offspring to become a new parent according to the fitness;
a circulation judgment module: for repeating the steps executed by said first selection module and said second selection module until a stop criterion is reached, the algorithm is terminated.
10. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, the processor calling the program instructions to perform the method of any of claims 1 to 8.
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| CN103745258A (en) * | 2013-09-12 | 2014-04-23 | 北京工业大学 | Minimal spanning tree-based clustering genetic algorithm complex web community mining method |
| CN110763953A (en) * | 2019-10-30 | 2020-02-07 | 国网四川省电力公司电力科学研究院 | Troubleshooting line patrol path planning method under distribution automation condition |
| CN111460374A (en) * | 2020-04-10 | 2020-07-28 | 南方电网科学研究院有限责任公司 | Power distribution network D-PMU optimal configuration method considering node differences |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745258A (en) * | 2013-09-12 | 2014-04-23 | 北京工业大学 | Minimal spanning tree-based clustering genetic algorithm complex web community mining method |
| CN110763953A (en) * | 2019-10-30 | 2020-02-07 | 国网四川省电力公司电力科学研究院 | Troubleshooting line patrol path planning method under distribution automation condition |
| CN111460374A (en) * | 2020-04-10 | 2020-07-28 | 南方电网科学研究院有限责任公司 | Power distribution network D-PMU optimal configuration method considering node differences |
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