CN113837469B - Distribution network low-voltage regulator installation point selection optimization method, system and equipment - Google Patents
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Abstract
The invention discloses a method, a system and equipment for optimizing installation selection points of a distribution network low-voltage regulator, and relates to the technical field of power systems. A method for optimizing installation selection points of low-voltage regulators of a distribution network comprises the following steps: s101, taking the optimal mounting point of the voltage regulator as a target, generating an initial population based on a genetic algorithm, wherein the sum Z of the distances from the optimal mounting point to all low-voltage points is shortest; s102, calculating the fitness of individuals in an 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 intersecting and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the father and the filial generation according to the fitness to become a new father; s104, repeating the steps S102-S103 until the stopping criterion is met, and ending 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 mature, and accelerates the algorithm optimizing 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 selection points of low-voltage regulators of a distribution network.
Background
With the development of national economy, the power load is rapidly increased, and the requirements of users on the power supply quality are also higher. However, at present, more low-voltage transformer areas still have the quality problem of low power supply voltage, and influence the normal power consumption of low-voltage users, so that voltage regulators are required to be installed to improve the voltage of the low-voltage areas, and a proper installation position is selected to reduce line loss, reduce the number of the voltage regulators and further improve comprehensive benefits, so that the optimization selection of the installation positions of the voltage regulators 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 selection point of a low-voltage regulator of a distribution network. And the installation selection points of the voltage regulator are optimized to achieve the purposes of reducing loss and improving benefit.
The invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for optimizing installation selection points of a low-voltage regulator of a distribution network, including the following steps:
s101, generating an initial population based on a genetic algorithm by taking the optimal mounting point of the voltage regulator as a target, wherein the sum Z of the distances from the optimal mounting 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 intersecting and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the father and the filial generation according to the fitness to become a new father; s104, repeating the steps S102-S103 until the stopping criterion is met, and ending the algorithm.
Based on the first aspect, in some embodiments of the invention, the genetic algorithm expression is:wherein pc is the individual crossover probability, pm is the individual variation probability, f max Is the maximum fitness value f of the population avg Is 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 decay function, k 1 、k 2 、k 3 、k 4 Is a custom parameter.
Based on the first aspect, in some embodiments of the invention, the above-mentioned decay functionWherein T is time or iteration number, and alpha, beta and lambda are custom 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 genetic algorithm parameters including: population number, iteration number, mutation rate, and crossover rate.
Based on the first aspect, in some embodiments of the present invention, the fitness function is: fitness=1/z.
Based on the first aspect, in some embodiments of the present invention, in steps S102 and S103, the rules for selecting the parent include an elite policy and a roulette policy.
Based on the first aspect, in some embodiments of the present invention, a method for selecting a parent using the elite strategy described above includes: and selecting parent individuals with fitness larger than a threshold value from the parents to inherit to offspring.
Based on the first aspect, in some embodiments of the present invention, a method for selecting a parent using the elite strategy described above includes:
calculating the selection probability of the individual according to the selection probability formula;
calculating the cumulative probability of the individual based on the selection probability of the individual;
and determining the parent individual according to the accumulated probability.
Based on the first aspect, in some embodiments of the invention, the stopping criteria include: when the fitness of the individual reaches a given threshold, the fitness of the individual and the fitness of the group are not increased any more or the iteration number reaches a preset algebra.
In a second aspect, an embodiment of the present invention provides a system for optimizing installation selection points of a low-voltage regulator of a distribution network, including: and a population generation module: for generating an initial population based on a genetic algorithm; a first selection module: the fitness function is used for calculating the fitness of individuals in the initial population, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then intersecting and mutating the parents to obtain a fixed number of offspring; a second selection module: selecting a fixed number of individuals from said parent and said offspring to be new parent according to said fitness; and the circulation judging module is used for: and the algorithm is terminated by repeating the steps executed by the first selection module and the second selection module until the stopping criterion is reached.
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 communicate with each other via the data bus; the memory stores program instructions executable by the processor that invoke the program instructions to perform the one or more programs or methods, such as performing: s101, taking an optimal mounting point of a voltage regulator as a target, generating an initial population based on a genetic algorithm, wherein the sum Z of distances from the optimal mounting 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 intersecting and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the father and the filial generation according to the fitness to become a new father; s104, repeating the steps S102-S103 until the stopping criterion is met, and ending the algorithm.
Compared with the prior art, the invention has at least the following advantages and beneficial effects:
according to the embodiment of the invention, a new adaptive genetic algorithm is adopted, so that the crossover and variation probability can be automatically adjusted according to the individual fitness and the variation of the evolution time in the evolution process, the defect that the original adaptive genetic algorithm is easy to mature is overcome, the diversity of the optimal solution is improved, the algorithm optimizing speed is increased, and the optimal solution is effectively protected from being damaged. The new algorithm is applied to the optimal selection of the installation sites of the line voltage regulators, and the aim of the minimum installation quantity 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 needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic flow chart of a method for optimizing installation points of a distribution network low-voltage regulator;
FIG. 2 is a schematic flow chart of a roulette strategy in a method for optimizing a low voltage regulator installation selection point of a distribution network;
FIG. 3 is a sequence diagram of chromosome accesses in an embodiment of a method for optimizing the installation and selection of distribution network low-voltage regulators;
FIG. 4 (a) is a schematic diagram of two chromosome gene exchanges (before) in a distribution network low voltage regulator installation site selection optimization method;
FIG. 4 (b) is a schematic diagram of two chromosome gene exchanges (post) in a distribution network low voltage regulator installation site selection optimization method;
FIG. 5 is a schematic diagram of repeated genes in two chromosomes in a distribution network low-voltage regulator installation site selection optimization method;
FIG. 6 is a schematic diagram of the switching repeat genes in two chromosomes in a distribution network low voltage regulator installation site selection optimization method;
FIG. 7 is a table of gene mapping in a method for optimizing the installation selection points of low-voltage regulators in a distribution network;
FIG. 8 is a schematic diagram of genetic variation in a method for optimizing the installation selection points of low-voltage regulators in a distribution network;
fig. 9 is a block diagram of a distribution network low voltage regulator installation point selection optimizing system;
fig. 10 is a block diagram of an electronic device.
Icon: 1-a processor; 2-memory; 3-a data bus; 100-population generation module; 200-a first selection module; 300-a second selection module; 400-a loop judgment module.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
Taking a 10KV voltage regulator as an example, the point selection requirements of the 10KV voltage regulator include:
(1) The application range is as follows.
Aiming at the conditions that the power supply radius of a distribution line is large, the section of a wire is small, the voltage of the high-voltage side of a 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 distribution transformer sub-joint is not obvious, a 10kV line voltage regulator can be adopted to boost the line voltage, so that the problem of low voltage of a rear-end line is solved. For heavy-load lines or lines with too long power supply distance, when the problem of low voltage of a transformer area cannot be completely solved by installing 1 voltage regulator, 2 voltage regulators can be installed, but at most, 3 voltage regulators are not suitable to be exceeded.
(2) And (5) determining the installation position.
1) And analyzing the distribution of the low-voltage transformer area and the low-voltage severity of the target line through inquiry or field actual measurement of the power supply service command system.
a) Searching the distribution of low-voltage transformer areas of the lines to be treated, and carding the tower numbers of the low-voltage transformer areas existing in the main line. The branch or branch line where the low voltage problem is most concentrated (i.e., the low voltage bay is most) is found.
b) The most serious low voltage area in the past 1 year is searched and analyzed, namely the area with the lowest distribution transformer outlet voltage, the most low voltage times and the longest low voltage accumulation time.
2) The voltage regulator should be installed at the exit side of branch T joint main line shaft tower of low voltage problem most serious district. If there is also a low voltage problem at the rear end of the main line of the worst bay T-junction, the voltage regulator should be installed before the T-junction.
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 drop of the actual input voltage after the installation do not cause the new problem of low voltage when the power supply point is matched with the installation point. The maximum current at the input side of the voltage regulator should not exceed the current carrying capacity of the line after installation.
(3) Capacity selection
1) The 10kV voltage regulator capacity series comprises 1000kVA, 2000kVA, 3000kVA, 4000kVA and 5000kVA.
2) Calculating the sum of all distribution capacities after the installation site
3) Determining the capacity S of the voltage regulator:
if it isThen selecting 1000kVA of voltage regulator capacity;
if it isThen selecting the voltage regulator capacity of 2000kVA;
if it isThe regulator capacity is chosen to be 4000kVA but not less than the maximum load at the back end of the regulator.
Based on the above requirements, as shown in fig. 1, the embodiment of the invention provides a method for optimizing installation selection points of a distribution network low-voltage regulator, which comprises the following steps:
s101, generating an initial population based on a genetic algorithm by taking the optimal mounting point of the voltage regulator as a target, wherein the sum Z of the distances from the optimal mounting point to all the low voltage points is shortest;
the genetic algorithm expression is: wherein pc is the individual crossover probability, pm is the individual variation probability, f max Is the maximum fitness value f of the population avg Is 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 decay function, k 1 、k 2 、 k 3 、k 4 Is a custom parameter.
Genetic manipulation mainly comprises the operation processes of selection, crossover, mutation and the like. The selection operation is a process of simulating living things in nature to perform superior and inferior elimination, and the selection operator copies individuals with high fitness to the next generation with larger probability, and eliminates individuals with low fitness. The algorithm adopts a random tournament type selection operator, and meanwhile, the algorithm keeps elite individuals. Cross-over is the primary method by which genetic algorithms produce new individuals, and determines the global search capabilities of genetic algorithms. The self-adaptive crossover probability pc determined by the crossover operator carries out two-point crossover operation, namely two crossover points are randomly selected, and genes between the two points are exchanged with probability pm. Variant operations are a method of generating new genes that determine the local search capabilities of the algorithm. The mutation operator adopts the self-adaptive mutation probability pm to carry out two-point mutation operation.
The traditional self-adaptive genetic algorithm is easy to generate local optimum, but not global optimum solution, and in order to improve the capability of the algorithm for getting rid of the local optimum, the invention provides an improved self-adaptive genetic algorithm. The basic idea of the algorithm is to increase the cross variation probability of excellent individuals in the evolution process, and the cross variation probability is reduced along with the increase of time so as to develop a new search space and prevent the individuals from maturing early.
To raise mutation probability, the number of positions exchanged is doubled and mutation attenuation factor is introducedWherein T is time or iteration number, and alpha, beta and lambda are custom parameters. Thus, the probability of variation increases in early evolution and returns to the normal value in late evolution.
In this embodiment, it is assumed that there is one voltage regulator to select one of n points, and the path is selected so that the sum of paths from the voltage regulator to the respective low voltage points is required to be minimum.
Thus, an initial population is first generated based on a genetic algorithm and parameters of the genetic algorithm are determined, illustratively, in this embodiment, the genetic algorithm parameters include: population number, iteration number, mutation rate, and crossover rate. Specifically, the larger the population size, the more processing modes, the less likely it is to trap in local solutions, and the more likely it is to trap in premature convergence, but the larger the size, the more computationally intensive it will increase the algorithm efficiency. For example, in this embodiment, the population number is set to 100, the sequence of points is random, a chromosome is constructed by adopting a natural number coding mode, 10 points are represented by 0-19, and each chromosome represents the access sequence of points. As shown in fig. 3: the low voltage boosting device starts from 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 intersecting 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 better the overall hope is. The objective function of the problem is that the smaller the total distance is, the better, so in this embodiment, the fitness function of the design is: fitness=1/z. After the fitness of each individual is calculated, an elite strategy can be used for example, an individual with higher fitness is selected from an initial population as a parent, the selected parent is crossed and mutated to obtain offspring, the number of the selected parent can be preset to be fixed in number or proportion, or a fitness threshold is set to be selected, and the number of the generated offspring can be set to be fixed in number or multiplying power.
Exemplary, the implementation of the crossover algorithm 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 = 2 and cut2 = 4, the light grey portions of chrom1 and chrom2 are swapped. After the exchange is completed, as shown in FIG. 5
At this time, both chrom1 and chrom2 contain repeated genes, gene 0 of chrom1 (point 1) repeated, and gene 1 of chrom2 (point 3) repeated (i.e., dark grey portion in fig. 6).
The dark grey part is adjusted, one idea is to use a mapping table. The adjustment process is shown in fig. 7. The light grey part (crossing segment) is a mapping table (shown in fig. 8), namely, the genes involved in crossing are checked, and if the genes are in the mapping table, the genes are interchanged according to the mapping table. For example, in chrom1, point 1 and point 5 are non-intersecting genes, point 1 needs to be replaced with point 2, point 2 needs to be replaced with point 4, and point 4 needs to be replaced with point 3 in the mapping table. The final point 1 is replaced with point 3.
Exemplary, the implementation of the mutation algorithm is: the 2-opt method, i.e., selection of chromosomes, randomly selects 2 positions, and the genes at these two positions are exchanged as shown in FIG. 9.
S103, selecting a fixed number of individuals from the father and the filial generation according to the fitness to become a new father;
in the step S102, if the fitness of the offspring obtained by the crossover and mutation still cannot meet the requirement, the steps of selecting the parent-generating the offspring-selecting the parent may be repeated until an individual meeting the requirement is obtained. The selection mode can also adopt elite strategy or roulette strategy.
Taking roulette selection as an example, implementing a roulette strategy first requires calculating an adaptation value for generating a new individual, assigning probabilities for the individuals based on the adaptation value, and selecting the individuals as parents of the next generation population based on the probabilities. The basic idea is as follows: the probability that each individual is selected is proportional to its fitness level, the better the fitness value, the greater the probability that each individual is selected. The selection steps comprise:
s201, calculating the selection probability of an individual according to a selection probability formula; the method comprises the steps of carrying out a first treatment on the surface of the
The probability of selection is the probability that an individual is inherited to the next generation, and obviously the greater the fitness, the more the individual can adapt to the environment and the higher the probability of inheriting to the next generation. The selection probability calculation formula is:where i represents individuals and N represents population size.
S202, calculating the cumulative probability of the individual based on the selection probability of the individual;
calculating the cumulative probability of each individual, wherein the cumulative probability calculation formula is as follows:
s203, determining a parent individual according to the accumulated probability.
At [0,1]Generating a uniformly distributed random number rand i If p (culmulative) i-1 ≤rand i ≤p(culmulative) i The individual i is selected to the next generation.
S104, repeating the steps S102-S103 until the stopping criterion is met, and ending the algorithm.
Illustratively, in this embodiment, the stopping criteria include: 1) When the fitness of the optimal individual reaches a given threshold; 2) The fitness and population fitness of the optimal individual no longer rise; 3) The iteration number reaches a preset algebra.
For example, in this embodiment, the 3 rd stopping criterion is selected, and if the fitness of the individual generated after the iteration number reaches the preset algebra still fails to meet the requirement, 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 system for optimizing installation selection points of a low-voltage regulator of a distribution network is provided, including:
population generation module 100: for generating an initial population based on a genetic algorithm; the first selection module 200: the fitness function is used for calculating the fitness of individuals in the initial population, selecting a fixed number of individuals from the initial population as parents according to the fitness, and then intersecting and mutating the parents to obtain a fixed number of offspring; the second selection module 300: selecting a fixed number of individuals from said parent and said offspring as new parent according to said fitness; the loop judgment module 400: the steps performed by the first selection module 200 and the second selection module 300 are repeated until the stopping 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 of the embodiments, and the embodiment 1 is specifically shown. And will not be described in detail herein.
Example 3
Referring to fig. 6, an embodiment of the present 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 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 perform the method described in the embodiment 1, for example, to perform: s101, taking an optimal mounting point of a voltage regulator as a target, generating an initial population based on a genetic algorithm, wherein the sum Z of distances from the optimal mounting 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 intersecting and mutating the parents to obtain a fixed number of offspring; s103, selecting a fixed number of individuals from the father and the offspring to be new father according to the fitness; s104, repeating the steps S102-S103 until the stopping criterion is met, and ending the algorithm.
The foregoing description of the embodiments provides further details of the present invention with regard to its objects, technical solutions and advantages, and it should be understood that the foregoing description is only illustrative of the embodiments of the present invention and is not intended to limit the scope of the present invention, but any modifications, equivalents, improvements or etc. within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. The method for optimizing the installation selection points of the distribution network low-voltage regulator is characterized by comprising the following steps of:
s101, taking an optimal mounting point of a voltage regulator as a target, and generating an initial population based on a genetic algorithm, wherein the sum Z of distances from the optimal mounting 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 intersecting and mutating the parents to obtain a fixed number of offspring;
s103, selecting a fixed number of individuals from the father and the filial generation according to the fitness to become a new father;
s104, repeating the steps S102-S103 until the stopping criterion is met, and terminating the algorithm;
wherein the stopping criteria include: when the fitness of the individual reaches a given threshold value, the fitness of the individual and the fitness of the group are not increased any more or the iteration number reaches a preset algebra;
wherein, with the preferred mounting point that obtains the voltage regulator as the target, specifically include:
inquiring or actually measuring on site through a power supply service command system, and analyzing the distribution of a low-voltage transformer area and the severity of low voltage of a target line; the method comprises the following steps: searching the distribution of low-voltage areas of the lines to be treated, carding the tower numbers of the low-voltage areas of the main lines, and searching the branch lines or branch lines with the most low-voltage problems, namely the branch lines or branch lines with the most low-voltage areas, wherein the tower numbers of the low-voltage areas exist in the main lines; searching and analyzing the area with the most serious low voltage in the past 1 year, namely the area with the lowest distribution transformer outlet voltage, the most low voltage times and the longest low voltage accumulation time;
the voltage regulator is arranged at the outlet side of a branch line T-junction main line tower where the most serious low-voltage problem station area is located, and if the low-voltage problem exists at the rear end of the main line T-junction of the most serious station area, the voltage regulator is arranged in front of the T-junction;
the voltage regulator is installed and selected, and the actual input voltage is not lower than 9.3kV after the voltage regulator is installed; the voltage of the installation point and the drop of the actual input voltage after the installation do not cause a new low-voltage problem of the distribution transformer from the power supply point to the installation point, and the maximum current of the input side of the voltage regulator after the installation does not exceed the current-carrying capacity of the circuit;
wherein, the genetic algorithm expression is:
wherein pc is the individual crossover probability, pm is the individual variation probability, f max Is the maximum fitness value f of the population avg Is 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 decay function, k 1 、k 2 、k 3 、k 4 Is a custom parameter.
2. The distribution network low voltage regulator installation choice optimization method of claim 1, wherein the attenuation functionWherein T is time or iteration number, and alpha, beta and lambda are custom parameters.
3. The distribution network low voltage regulator installation point selection optimization method according to claim 1, wherein the fitness function is: fitness=1/z.
4. The method for optimizing installation choice of low-voltage regulators in a distribution network according to claim 1, wherein in steps S102 and S103, the rules for selecting the parent include elite policy and roulette policy.
5. The method for optimizing installation selection points of distribution network low-voltage regulators according to claim 4, wherein the method for selecting a parent by adopting the elite strategy comprises the following steps: and selecting parent individuals with fitness larger than a threshold value from the parents to inherit to offspring.
6. The method for optimizing installation selection points of distribution network low-voltage regulators according to claim 4, wherein the method for selecting a parent by adopting the elite strategy comprises the following steps:
calculating the selection probability of the individual according to the selection probability formula;
calculating an accumulated probability of an individual based on the selection probabilities of the individuals;
and determining the parent individual according to the accumulated probability.
7. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
wherein the processor and the memory complete communication with each other through the data bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-6.
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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 |
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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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