CN112003281A - Optimal configuration method, device and equipment for dynamic voltage restorer - Google Patents
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Abstract
The application discloses an optimal configuration method, device and equipment of a dynamic voltage restorer, wherein binary coding and real number coding are respectively carried out on the installation condition of the dynamic voltage restorer and the output compensation voltage per unit value to obtain a chromosome; generating an initialization population of a preset number of individuals; calculating the fitness value of each individual in the initialized population, and performing selection, crossing and mutation operations based on the fitness value of each individual to generate a new generation of population; judging whether the iteration times reach preset iteration times, if so, outputting an individual with the highest fitness value, if not, taking the new generation of population as an initialization population, and repeating the steps; and decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer. The dynamic voltage restorer solves the technical problems that when the traditional genetic algorithm is adopted to carry out optimization configuration solving, the existing dynamic voltage restorer is easy to fall into local optimization, so that the solving precision is not high and the optimizing result has certain volatility.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to an optimal configuration method, device and equipment of a dynamic voltage restorer.
Background
In the prior art, when solving an optimal configuration problem of a Dynamic Voltage Restorer (DVR) of a power distribution network, a traditional genetic algorithm is generally adopted for solving, the algorithm is easy to sink into local optimization, so that the solving precision is not high, an optimization result has certain fluctuation, and after repeated optimization, the installation position and the output compensation amplitude result of the DVR are slightly different, so that the installation capacity of the DVR is slightly different.
Disclosure of Invention
The application provides an optimal configuration method, device and equipment of a dynamic voltage restorer, which are used for solving the technical problems that when the traditional genetic algorithm is adopted for optimal configuration solution of the existing dynamic voltage restorer, the existing dynamic voltage restorer is easy to fall into local optimization, so that the solution precision is not high and the optimization result has certain fluctuation.
In view of the above, a first aspect of the present application provides an optimized configuration method for a dynamic voltage restorer, including:
carrying out binary coding on the installation condition of a dynamic voltage restorer, and carrying out real number coding on a per unit value of output compensation voltage of the dynamic voltage restorer to obtain a chromosome;
generating an initialization population of a preset number of individuals, wherein the individuals in the population are the chromosomes obtained by encoding;
calculating the fitness value of each individual in the initialized population based on a fitness function, wherein the fitness function is a target function with the lowest total investment cost of the dynamic voltage restorer;
based on the fitness value of each individual in the initialized population, carrying out selection, crossing and mutation operations on each individual to generate a new generation of population;
judging whether the iteration times reach preset iteration times, if so, outputting the individual with the highest fitness value, if not, taking the new generation of population as the initialized population, and returning to the step of calculating the fitness value of each individual in the initialized population based on a fitness function;
and decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
Optionally, the encoding format of the chromosome is:
wherein A is an installation coefficient gene, when A isiWhen the value is 0, the dynamic voltage restorer is not installed at the ith position, and when A isiWhen the value is 1, the dynamic voltage restorer is installed at the ith position, n is the number of candidate installation positions, and UcomIs an amplitude gene, Ucom_iAnd the voltage per unit value is output compensation voltage of the ith position dynamic voltage restorer.
Optionally, the decoding format of the chromosome is:
UDVR_i＝Ai×Ucom_i。
optionally, the selecting, crossing, and mutating each individual based on the fitness value of each individual in the initialized population to generate a new generation population includes:
based on the fitness value of each individual in the initialized population, carrying out selection and cross operation on each individual;
randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals;
and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in an interval [0,1 ].
Optionally, the method further comprises:
and after the mutation operation is finished, checking whether the new individual meets the constraint condition, if so, replacing the individual before the mutation operation with the new individual, and if not, removing the new individual.
Optionally, the method further comprises:
and after the selection operation, replacing the new individual with the lowest fitness value with the individual with the highest fitness value in the current population.
The second aspect of the present application provides an optimized configuration device for a dynamic voltage restorer, comprising:
the encoding unit is used for carrying out binary encoding on the installation condition of the dynamic voltage restorer and carrying out real number encoding on the per-unit value of the output compensation voltage of the dynamic voltage restorer to obtain a chromosome;
the generating unit is used for generating an initialization population of a preset number of individuals, and the individuals in the population are the chromosomes obtained through coding;
the calculating unit is used for calculating the fitness value of each individual in the initialized population based on a fitness function, wherein the fitness function is a target function with the lowest total investment cost of the dynamic voltage restorer;
the processing unit is used for carrying out selection, crossing and mutation operations on each individual based on the fitness value of each individual in the initialized population to generate a new generation population;
the judging unit is used for judging whether the iteration times reach preset iteration times, if so, outputting the individual with the highest fitness value, and if not, taking the new generation of population as the initialization population and triggering the calculating unit;
and the decoding unit is used for decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
Optionally, the processing unit is specifically configured to:
based on the fitness value of each individual in the initialized population, carrying out selection and cross operation on each individual;
randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals;
and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in an interval [0,1 ].
Optionally, the processing unit is further configured to:
and after the mutation operation is finished, checking whether the new individual meets the constraint condition, if so, replacing the individual before the mutation operation with the new individual, and if not, removing the new individual.
A third aspect of the present application provides an optimized configuration device for a dynamic voltage restorer, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the optimal configuration method of the dynamic voltage restorer according to any one of the first aspect according to instructions in the program code.
According to the technical scheme, the method has the following advantages:
the application provides an optimal configuration method of a dynamic voltage restorer, which comprises the following steps: carrying out binary coding on the installation condition of the dynamic voltage restorer, and carrying out real number coding on the per-unit value of the output compensation voltage of the dynamic voltage restorer to obtain a chromosome; generating an initialization population of a preset number of individuals, wherein the individuals in the population are chromosomes obtained by coding; calculating the fitness value of each individual in the initialized population based on a fitness function, wherein the fitness function is a target function with the lowest total investment cost of the dynamic voltage restorer; based on the fitness value of each individual in the initialized population, carrying out selection, crossing and mutation operations on each individual to generate a new generation of population; judging whether the iteration times reach preset iteration times, if so, outputting an individual with the highest fitness value, if not, taking the new generation of population as an initialization population, and returning to the step of calculating the fitness value of each individual in the initialization population based on a fitness function; and decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
In the application, the installation condition of the dynamic voltage restorer is coded into a chromosome, a proper configuration position structure of the dynamic voltage restorer is searched through the optimization of the installation position, the optimal output compensation voltage of the dynamic voltage restorer is determined under the configuration structure, and the installation position and the output compensation voltage of the dynamic voltage restorer can be optimized in parallel; the output compensation voltage per unit value of the dynamic voltage restorer is coded through real number coding, continuous variables can be well described, a search space is enlarged, a global optimal solution is obtained, and the situation that the dynamic voltage restorer is trapped in local optimization is avoided, so that the solving precision is improved, the solving fluctuation is reduced, and the technical problems that the existing dynamic voltage restorer is easily trapped in local optimization when optimized configuration solving is carried out through a traditional genetic algorithm, the solving precision is low, and the optimizing result has certain fluctuation are solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of an optimal configuration method of a dynamic voltage restorer according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an optimal configuration apparatus of a dynamic voltage restorer according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an equivalent circuit of a dynamic voltage restorer according to an embodiment of the present disclosure.
Detailed Description
The application provides an optimal configuration method, device and equipment of a dynamic voltage restorer, which are used for solving the technical problems that when the traditional genetic algorithm is adopted for optimal configuration solution of the existing dynamic voltage restorer, the existing dynamic voltage restorer is easy to fall into local optimization, so that the solution precision is not high and the optimization result has certain fluctuation.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the prior art, when solving an optimal configuration problem of a Dynamic Voltage Restorer (DVR) of a power distribution network, a traditional genetic algorithm is generally adopted for solving, a traditional abnormal algorithm generally adopts binary coding, n candidate installation positions of the DVR are assumed, and an output compensation amplitude value of the DVR is expressed in a per-unit mode. If the binary system is adopted to represent the DVR output compensation amplitude value, the decimal needs to be coded and is accurate to the percentile, the chromosome length is 7n, the coding length is longer, continuous coding and decoding are also needed, the calculation efficiency is low, and the binary system coding cannot accurately represent continuous variables, so that the solving precision is reduced; if the coding precision is improved, the coding length needs to be increased, so that the search space is increased rapidly, and the global optimal solution is not obtained easily. When the real number codes are adopted to express the output amplitude of the DVR, most candidate devices in the actual configuration scheme are not provided with the DVR, the optimization of the installation position cannot be directly reflected, meanwhile, when the genetic algorithm of the real number codes is adopted to carry out variation operation to change individuals, the variation operation is realized by changing the real numerical value (not bit operation) of the genes in a certain range, when the algorithm is carried out to a certain stage, the probability of generating the individuals with higher fitness is very low after the genetic operation, the population failure is possibly caused, and the risk of the algorithm failure is increased.
In order to solve the above problems, the present application provides an optimal configuration method for a dynamic voltage restorer.
It should be noted that, in the embodiment of the present application, before configuring the DVR optimally, a DVR configuration model needs to be configured, where the model includes an objective function and a constraint condition. The optimal configuration of the DVR in the power distribution network belongs to the multivariable nonlinear programming problem, and the optimization model can be expressed as:
wherein x is (x)1,x2,…,xN)TIs a group of N-dimensional column vectors of the maximum output compensation voltage of all DVRs in the power distribution network, f (x) is an objective function of optimal configuration of the DVRs, gi(x) To optimize the equality constraints of the configuration, hj(x) In order to optimize the configured inequality constraint conditions, k is the number of equality constraints, and l is the number of inequality constraints.
In order to realize effective optimization configuration of the DVR in the power distribution network, the total investment cost of the DVR is selected to be the lowest as a target function:
wherein N is the total number of DVRs in the power distribution network, SnIs the installation capacity of the nth DVR, C (S)n) The investment cost of the nth DVR is related to the capacity of the nth DVR.
The investment cost of a single DVR is related to the installation capacity and the unit capacity cost of the DVR, according to practical engineering application, the unit capacity cost of the DVR is not a constant value, and the unit capacity cost of the DVR with large capacity is reduced along with the increase of the capacity. Through customizing the electric power technology, the DVR matched with the capacity can be customized according to the optimized configuration structure, and a better treatment effect is obtained. Thus, the investment cost for a single DVR can be expressed as:
C(S)＝p(S)S；
in the formula, p(s) is a unit capacity cost function of a single DVR, and can be obtained by fitting statistical data, and the function expresses the trend that the unit capacity cost of the DVR is reduced along with the increment of the capacity. In addition, compared with the discrete optimization problem of configuring several DVRs, the configuration problem is converted into a continuous optimization problem, the problem of combined explosion in large-scale calculation can be effectively avoided, the solution is more convenient, and the method is suitable for engineering application analysis.
Relevant constraints are established on DVR installation capacity and load voltage requirements based on system operation and user requirements.
(1) DVR setup capacity constraints:
in configuring the optimal configuration of a DVR in a power distribution network, the DVR may be equivalent to a controlled voltage source, the equivalent circuit of which is shown in fig. 3. It is composed ofIn (1),
in order to generate a grid-side residual voltage after a voltage sag,
in order to output the compensation voltage for the DVR,
respectively, a sensitive load voltage and a sensitive load current.
According to fig. 3, it can be seen that after a voltage sag occurs, the sensitive load voltage is:
assuming that the load voltage amplitude per unit value is 1 before the voltage sag, the initial phase angle is 0 degrees, the load current initial phase angle is theta, and the power consumed by the load is:
after voltage sag occurs, the phase offset angle is alpha, and the power provided by the power grid side is as follows:
after the DVR compensation, the load side voltage amplitude and phase are restored to the state before the sag, and the DVR output power can be expressed as the difference between the power consumed by the load side and the power provided by the grid side:
the DVR adopts an in-phase compensation strategy, neglects phase jump, and the output power of the DVR is as follows:
as can be seen, the output power of the DVR is proportional to the supplemental voltage, the power consumed by the load.
From the above, the mounting capacity of a DVR is related to its maximum output compensation voltage, and neglecting DVR power loss, a single DVR mounting capacity constraint can be expressed as:
Sn≥UDVRnIDVRn；
in the formula of UDVRn、IDVRnThe maximum compensation voltage and the output current of the nth DVR are respectively.
According to kirchhoff's current law, the output current of DVR should meet the requirement of downstream load, which can be expressed as:
wherein T is the total load number in the system, SLt、UnomtPower and rated voltage respectively consumed for the t-th load, atnIs the correlation coefficient between the nth DVR and the t load when atnWhen the output voltage of the nth DVR is 1, the voltage of the t load can be compensated, when atnWhen the value is 0, the output voltage of the nth DVR cannot compensate the voltage of the tth load.
(2) And (3) load voltage constraint:
in order to ensure that the electrical load normally operates, when voltage is temporarily generated, the voltage of the sensitive load needs to be maintained within an allowable range after being compensated by the DVR, and the voltage constraint of the node of the sensitive load is estimated as follows:
Utmin≤ULt≤Utmax；
in the formula of Utmin、UtmaxUpper and lower voltage limits, U, for the tth load, respectively, to maintain normal operationLtIs the node voltage of the t-th load.
DVR in this application adopts the cophase compensation strategy, when carrying out voltage compensation, can be equivalent to an ideal voltage source, neglects the line loss, when carrying out optimal configuration according to node residual voltage, the node voltage of sensitive load can be expressed as:
in the formula of UtAnd when the voltage sag occurs, the residual voltage on the bus side is supplied to the t-th load.
In summary, the optimal configuration model of the DVR is as follows:
for easy understanding, referring to fig. 1, an embodiment of a method for optimizing configuration of a dynamic voltage restorer provided in the present application includes:
In the optimal configuration method for the dynamic voltage restorer in the embodiment of the application, the installation positions of the DVRs are also encoded into chromosomes, each chromosome is composed of an installation coefficient gene and an amplitude gene, the installation coefficient genes are encoded in a binary mode to represent the installation situation of the corresponding position of the DVR, and the amplitude genes are encoded in a real number mode to represent the per-unit value of the output compensation voltage amplitude of the corresponding position of the DVR. Constraints are included in the DVR configuration model, and for processing of constraint conditions, common methods include a penalty function method, a feasible solution transformation method and a search space limitation method. Considering the problems that penalty factors are difficult to select by a penalty function method, the feasible solution transformation method expands a search space to cause the reduction of the solving efficiency and the like, the constraint conditions are processed by adopting a search space limiting method in the embodiment of the application. And (3) each time a chromosome is randomly generated, namely whether the chromosome meets the constraint condition is checked, only the chromosome meeting all the constraint conditions is valid, and otherwise, the chromosome needs to be regenerated until all the constraint conditions are met.
Further, the chromosome in the embodiment of the present application is composed of an installation coefficient gene and an amplitude gene, and the encoding format thereof may specifically be:
X＝[A|Ucom]＝[A1,A2,…Ai,…,An|Ucom_1,Ucom_2,…Ucom_i,…,Ucom_n]；
wherein A is an installation coefficient gene, when A isiWhen the value is 0, the dynamic voltage restorer is not installed at the ith position, and when A isiWhen 1, it means that the i-th position is equipped with a dynamic voltage restorer, UcomIs an amplitude gene, Ucom_iFor the output compensation voltage per unit value of the ith position dynamic voltage restorer, n is the number of candidate installation positions, the length of the chromosome is 2n, and the chromosome of the mixed code can refer to fig. 3.
In the embodiment of the application, a suitable DVR configuration position structure is found through the optimization of the installation position, and under the configuration structure, the optimal compensation voltage of the DVR is determined so as to optimize the installation position and the output compensation voltage of the DVR in parallel.
And 102, generating an initialization population of a preset number of individuals, wherein the individuals in the population are chromosomes obtained through coding.
After the encoding is completed, an initial population of sufficient numbers of individuals needs to be generated, and the size of the population directly affects the evolution process of the genetic algorithm and the accuracy of the obtained results. The population has fewer individuals, the solution can be accelerated, but the premature phenomenon can occur, and the obtained result is easy to fall into local optimum; the more individuals in the population can improve the accuracy of the solution result, but can slow down the convergence speed of the algorithm. In the embodiment of the application, the number of individuals in the population is between 50 and 200 according to the actual situation.
And 103, calculating the fitness value of each individual in the initialized population based on the fitness function.
In the embodiment of the application, the objective function with the lowest total investment cost of the DVR is used as the fitness function, and the fitness value of each individual is calculated through the fitness function in a linear sorting mode.
Further, when calculating the fitness value, the chromosome needs to be decoded, and the decoding format is as follows:
UDVR_i＝Ai×Ucom_i；
the actual compensation for the DVR at each candidate installation location is equal to the product of the corresponding installation factor gene and the magnitude gene, and only the magnitude gene for which the corresponding installation factor gene is 1 can improve the voltage.
And 104, performing selection, crossing and mutation operations on each individual based on the fitness value of each individual in the initialized population to generate a new generation population.
The selection operation in the embodiment of the application adopts a random sampling method, the cross operation adopts single-point crossing, the variation operation adopts single-point variation, each population is endowed with different cross rate and variation rate, the cross rate and the variation rate are set according to actual conditions, and the method is not specifically limited herein.
Further, the mutation method in the embodiment of the present application is different from the conventional genetic algorithm, and specifically includes: based on the fitness value of each individual in the initialized population, after the selection and crossing operation is performed on each individual, randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals; and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in the interval [0,1 ].
Further, after the mutation operation is completed, whether a new individual meets the constraint condition is checked, if so, the new individual is adopted to replace the individual before the mutation operation, and if not, the new individual is removed, and the mutation operation is considered to be invalid.
Further, in order to avoid loss of excellent individuals in the optimization process, in the embodiment of the present application, after the selection operation, the individuals with the highest fitness value in the current population do not perform the crossover and mutation operations any more, but directly replace the new individuals with the lowest fitness value after the crossover and mutation operations. Through selection, crossover and mutation operations, new individuals are obtained, and a new generation of population is generated.
And 105, judging whether the iteration times reach the preset iteration times, if so, outputting the individual with the highest fitness value, otherwise, taking the new generation of population as an initialization population, and returning to the step 103.
When the iteration times reach the preset iteration times, the algorithm is considered to be converged, and the individual with the highest fitness value is output to obtain the optimal individual; and when the iteration times do not reach the preset iteration times, taking the new generation of population as an initialization population, and returning to the step 103 until the algorithm converges.
And 106, decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
And decoding the individual with the highest fitness value to obtain the installation position of the dynamic voltage restorer and the output compensation voltage of the dynamic voltage restorer, thereby obtaining the optimal configuration scheme of the dynamic voltage restorer.
In the embodiment of the application, the installation condition of the dynamic voltage restorer is coded into a chromosome, a proper configuration position structure of the dynamic voltage restorer is searched through the optimization of the installation position, the optimal output compensation voltage of the dynamic voltage restorer is determined under the configuration structure, and the installation position and the output compensation voltage of the dynamic voltage restorer can be optimized in parallel; the output compensation voltage per unit value of the dynamic voltage restorer is coded through real number coding, continuous variables can be well described, a search space is enlarged, a global optimal solution is obtained, and the situation that the dynamic voltage restorer is trapped in local optimization is avoided, so that the solving precision is improved, the solving fluctuation is reduced, and the technical problems that the existing dynamic voltage restorer is easily trapped in local optimization when optimized configuration solving is carried out through a traditional genetic algorithm, the solving precision is low, and the optimizing result has certain fluctuation are solved.
The above provides an embodiment of a method for optimizing configuration of a dynamic voltage restorer, and the following provides an embodiment of an apparatus for optimizing configuration of a dynamic voltage restorer.
For easy understanding, referring to fig. 2, the present application provides an embodiment of an apparatus for optimizing configuration of a dynamic voltage restorer, including:
and the encoding unit 201 is configured to perform binary encoding on the installation condition of the dynamic voltage restorer, and perform real number encoding on the per unit value of the output compensation voltage of the dynamic voltage restorer to obtain a chromosome.
A generating unit 202, configured to generate an initialization population of a preset number of individuals, where the individuals in the population are chromosomes obtained through encoding.
A calculating unit 203, configured to calculate a fitness value of each individual in the initialization population based on the fitness function.
And the processing unit 204 is configured to perform selection, crossover and mutation operations on each individual based on the fitness value of each individual in the initialized population, so as to generate a new generation population.
The determining unit 205 is configured to determine whether the iteration number reaches a preset iteration number, if so, output an individual with a highest fitness value, and if not, take the new generation of population as an initialization population and trigger the calculating unit 203.
And the decoding unit 206 is configured to decode the individual with the highest fitness value to obtain an optimal configuration scheme of the dynamic voltage restorer.
As a further improvement, the processing unit 204 is specifically configured to:
based on the fitness value of each individual in the initialized population, carrying out selection and cross operation on each individual;
randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals;
and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in the interval [0,1 ].
As a further improvement, the processing unit 204 is further configured to:
and after the mutation operation is finished, checking whether the new individual meets the constraint condition, if so, replacing the individual before the mutation operation with the new individual, and if not, removing the new individual.
The embodiment of the present application further provides an optimal configuration device for a dynamic voltage restorer, where the device includes a processor and a memory:
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is used for executing the optimal configuration method of the dynamic voltage restorer according to the instructions in the program codes.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An optimal configuration method for a dynamic voltage restorer, comprising the following steps:
carrying out binary coding on the installation condition of a dynamic voltage restorer, and carrying out real number coding on a per unit value of output compensation voltage of the dynamic voltage restorer to obtain a chromosome;
generating an initialization population of a preset number of individuals, wherein the individuals in the population are the chromosomes obtained by encoding;
calculating the fitness value of each individual in the initialized population based on a fitness function, wherein the fitness function is a target function with the lowest total investment cost of the dynamic voltage restorer;
based on the fitness value of each individual in the initialized population, carrying out selection, crossing and mutation operations on each individual to generate a new generation of population;
judging whether the iteration times reach preset iteration times, if so, outputting the individual with the highest fitness value, if not, taking the new generation of population as the initialized population, and returning to the step of calculating the fitness value of each individual in the initialized population based on a fitness function;
and decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
2. The optimal configuration method for dynamic voltage restorer according to claim 1, wherein the encoding format of the chromosome is as follows:
wherein A is an installation coefficient gene, when A isiWhen the value is 0, the dynamic voltage restorer is not installed at the ith position, and when A isiWhen the value is 1, the dynamic voltage restorer is installed at the ith position, n is the number of candidate installation positions, and UcomIs an amplitude gene, Ucom_iAnd the voltage per unit value is output compensation voltage of the ith position dynamic voltage restorer.
3. The optimal configuration method for dynamic voltage restorer according to claim 2, wherein the decoding format of the chromosome is as follows:
UDVR_i＝Ai×Ucom_i。
4. the method according to claim 2, wherein the selecting, crossing and mutating each individual based on the fitness value of each individual in the initialization population to generate a new generation population comprises:
based on the fitness value of each individual in the initialized population, carrying out selection and cross operation on each individual;
randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals;
and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in an interval [0,1 ].
5. The method for optimized configuration of a dynamic voltage restorer of claim 4, further comprising:
and after the mutation operation is finished, checking whether the new individual meets the constraint condition, if so, replacing the individual before the mutation operation with the new individual, and if not, removing the new individual.
6. The method for optimized configuration of a dynamic voltage restorer of claim 4, further comprising:
and after the selection operation, replacing the new individual with the lowest fitness value with the individual with the highest fitness value in the current population.
7. An optimized configuration device for a dynamic voltage restorer, comprising:
the encoding unit is used for carrying out binary encoding on the installation condition of the dynamic voltage restorer and carrying out real number encoding on the per-unit value of the output compensation voltage of the dynamic voltage restorer to obtain a chromosome;
the generating unit is used for generating an initialization population of a preset number of individuals, and the individuals in the population are the chromosomes obtained through coding;
the calculating unit is used for calculating the fitness value of each individual in the initialized population based on a fitness function, wherein the fitness function is a target function with the lowest total investment cost of the dynamic voltage restorer;
the processing unit is used for carrying out selection, crossing and mutation operations on each individual based on the fitness value of each individual in the initialized population to generate a new generation population;
the judging unit is used for judging whether the iteration times reach preset iteration times, if so, outputting the individual with the highest fitness value, and if not, taking the new generation of population as the initialization population and triggering the calculating unit;
and the decoding unit is used for decoding the individual with the highest fitness value to obtain the optimal configuration scheme of the dynamic voltage restorer.
8. The optimal configuration apparatus of a dynamic voltage restorer according to claim 7, wherein the processing unit is specifically configured to:
based on the fitness value of each individual in the initialized population, carrying out selection and cross operation on each individual;
randomly selecting variant individuals from the crossed individuals according to the variation rate of the current population, and randomly selecting mutant genes of the variant individuals;
and when the mutant gene is positioned in the installation coefficient gene segment, carrying out inversion operation on the mutant gene, and when the mutant gene is positioned in the amplitude gene segment, replacing the mutant gene with a random number generated randomly to generate a new individual, wherein the random number is generated randomly in an interval [0,1 ].
9. The optimal configuration apparatus of dynamic voltage restorer according to claim 8, wherein the processing unit is further configured to:
and after the mutation operation is finished, checking whether the new individual meets the constraint condition, if so, replacing the individual before the mutation operation with the new individual, and if not, removing the new individual.
10. An optimized configuration device for a dynamic voltage restorer, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the method for optimized configuration of a dynamic voltage restorer of any of claims 1-6 in accordance with instructions in the program code.
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