CN113270865A - Voltage quality optimization treatment method based on chaotic inheritance - Google Patents

Voltage quality optimization treatment method based on chaotic inheritance Download PDF

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CN113270865A
CN113270865A CN202110567009.0A CN202110567009A CN113270865A CN 113270865 A CN113270865 A CN 113270865A CN 202110567009 A CN202110567009 A CN 202110567009A CN 113270865 A CN113270865 A CN 113270865A
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柴良明
赵静
王金芹
贺柱
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Ruili Power Supply Bureau of Yunnan Power Grid Co Ltd
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Nanjing Softcore Science & Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses a chaos inheritance-based voltage quality optimization management method, which comprises the steps of acquiring three-phase active power and reactive power at the head end of a feeder line and three-phase voltage of a bus through an SCADA (supervisory control and data acquisition) system, and constructing a voltage optimization model according to the three-phase active power and reactive power and the three-phase voltage of the bus; calculating individual fitness according to the voltage optimization model, and generating an initial population by using a chaotic genetic algorithm; selecting, crossing and mutating the initial population to obtain an evolved population; carrying out immigration operation on the evolved population by combining fitness and setting iteration conditions; if the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result to obtain the optimal solution of the voltage optimization model; the invention has the beneficial effects that: the invention improves the individual optimizing capability and the individual diversity by the chaotic genetic algorithm, and simultaneously improves the reactive loss reduction rate.

Description

Voltage quality optimization treatment method based on chaotic inheritance
Technical Field
The invention relates to the technical field of electric power, in particular to a voltage quality optimization treatment method based on chaotic inheritance.
Background
The problem of voltage quality is a civil problem related to the quality of life of thousands of households, and the elimination of low voltage is a basic requirement of a power supply company for fulfilling the purposes of social responsibility and maintenance service, and is an important mark for power supply enterprises to lean management. The voltage quality of a distribution network is directly related to the safe and economic operation of a power system and the service life of electrical equipment, voltage breakdown can be caused by too low voltage, large-area power failure is caused, the operation capacity of the equipment is reduced, the operation energy consumption of the equipment is increased, a user motor is burnt, and the power of an electric lamp is reduced. The low-voltage operation has great negative effects on power supply departments and power consumption customers, so the voltage quality of the distribution network and the safety of the distribution network are problems to be solved urgently by power supply enterprises.
The improvement of the power quality and the reduction of loss and energy conservation are always the targets pursued by power enterprises, power supply companies continuously develop special low-voltage treatment of power distribution networks in 2010, the distribution network structure, the equipment level and the distribution network comprehensive management level are obviously improved, the power supply quality of a user side is obviously improved, and a large promotion space is provided. The voltage quality is used as an index for measuring the power quality, which is not only the requirement of power customers for production and life, but also an important condition for ensuring the safety, reliability and economic operation of a power grid of a power supply enterprise.
At present, the measurement of a distribution network mainly aims at a 10kV line, the difficulty of voltage acquisition at a user side is high, so that an intelligent control device for the voltage quality of a regional power grid which takes the user voltage as a guide is not provided in China at the present stage, the existing reactive voltage and power quality control device is only distributed in a single point, a data uploading acquisition platform is not provided, an integrated control platform of equipment is not provided, the operation state, fault information, reactive voltage and power quality control effect and the like of the equipment cannot be known, the existing research on reactive voltage control of the power grid in China still stays in an independent, reactive optimization control and voltage regulation stage of changing a transformer tap, the technologies of the systems are respectively mastered by different units, integration is lacked, the information integration degree is not high, a data interface is not standardized enough, operators cannot master the real-time working condition of power distribution operation, and the operation is complicated, the work efficiency is low, and the power distribution dispatching operation management level is still lower.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a chaos-inheritance-based voltage quality optimization management method, which can solve the problems of complicated operation and poor voltage quality optimization effect of the traditional power grid reactive voltage management.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of acquiring three-phase active power and reactive power at the head end of a feeder line and three-phase voltage of a bus through an SCADA system, and constructing a voltage optimization model according to the three-phase active power and reactive power and the three-phase voltage of the bus; calculating individual fitness according to the voltage optimization model, and generating an initial population by using a chaotic genetic algorithm; selecting, crossing and mutating the initial population to obtain an evolved population; carrying out immigration operation on the evolved population by combining the fitness and setting an iteration condition; if the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result and obtaining the optimal solution of the voltage optimization model.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the voltage optimization model includes a voltage optimization objective function:
Figure BDA0003081125340000021
wherein, PlossFor the active loss of the whole network, n is the number of feeder nodes, alpha is the voltage out-of-range penalty coefficient, UiIs the voltage of the i-th node, UisetIs a set voltage of the i-th node, UmaxIs the upper voltage limit of the i-th node, UminIs the voltage lower limit of the ith node, m is the number of feeders, Delta Q is the power variation of the feeders, QmaxIs the upper limit of reactive power at the head end of the feeder line, k is the number of load nodes, QminAnd the lower limit of the reactive power of the head end of the feeder line is set.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: further included, is a constraint equation:
Figure BDA0003081125340000022
wherein, Δ PiFor the active power variation of the whole network, QiFor reactive power at the feeder node, GiIs the conductance of node i, ZiIs the impedance of node i and θ is the phase difference between adjacent nodes.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the active loss of the whole network comprises that,
Figure BDA0003081125340000031
the preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the calculating of the fitness includes calculating a fitness value,
f＝1/F
wherein f is the fitness.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: generating the initial population comprises generating a random vector through floating point number coding, and then performing chaotic iteration j times on each element of the random vector to further obtain the initial population.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the chaotic iteration comprises chaotic iteration through An chaotic mapping random number generator, and the function expression of the An chaotic mapping random number generator is as follows:
Figure BDA0003081125340000032
wherein x istAnd xt+1Is a time sequence and t is time.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the selecting, crossing and mutating operations comprise the selecting operation: selecting individuals by adopting a tournament competition selection operator; the cross operation comprises the following steps: performing the crossover operation according to a crossover probability of 75%; the mutation operation comprises the following steps: performing the mutation operation according to a mutation probability of 2%.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: and the immigration operation comprises the step of eliminating the individual with the lowest fitness in the evolved population or the individual with the fitness repeating more than 5.
The preferred scheme of the chaos heredity-based voltage quality optimization treatment method is as follows: the iteration condition includes stopping the iteration when the number of iterations reaches 200.
The invention has the beneficial effects that: the invention improves the individual optimizing ability and the individual diversity by the chaotic genetic algorithm, and simultaneously improves the active loss reduction rate.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a voltage quality optimization governing method based on chaotic inheritance according to a first embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a voltage quality optimization governance method based on chaotic inheritance, including:
s1: and acquiring three-phase active power and reactive power at the head end of the feeder line and three-phase voltage of the bus through the SCADA system.
The existing SCADA system is fully integrated and utilized, a voltage monitoring network which is sound to cover all voltage levels of a power grid is established, online real-time monitoring of the voltage quality of the power grid is achieved, and specifically three-phase active power and reactive power of the head end of a feeder line and three-phase voltage (line voltage and phase voltage) of a bus are obtained through the SCADA system.
It should be noted that, the scada (supervisory Control And Data acquisition) system is a Data acquisition And monitoring Control system, which is a DCS And power automation monitoring system based on a computer.
S2: and constructing a voltage optimization model according to the three-phase active power, the reactive power and the three-phase voltage of the bus.
Under the condition of ensuring the reactive power balance of the power system, establishing a voltage optimization model by taking the reduction of the network loss of the system and the improvement of the power quality as targets; in this embodiment, a voltage optimization model is established with a goal of minimizing system active power loss based on a classical optimization model of power distribution system operation economy, and the model includes a voltage optimization objective function and a constraint equation.
Specifically, in order to consider the safety and stability of the power grid operation, a voltage optimization objective function is constructed by combining a penalty function:
Figure BDA0003081125340000051
wherein, PlossFor the active loss of the whole network, n is the number of feeder nodes, alpha is the voltage out-of-range penalty coefficient, UiIs the voltage of the i-th node, UisetIs a set voltage of the i-th node, UmaxIs the upper voltage limit of the i-th node, UminIs the voltage lower limit of the ith node, m is the number of feeders, Delta Q is the power variation of the feeders, QmaxIs the upper limit of reactive power at the head end of the feeder line, k is the number of load nodes, QminAnd the lower limit of the reactive power of the head end of the feeder line is set.
Active loss P of whole networklossThe calculation formula of (a) is as follows:
Figure BDA0003081125340000061
wherein Q isiFor reactive power at the feeder node, GiIs the conductance of node i, ZiIs the impedance of node i and θ is the phase difference between adjacent nodes.
It should be noted that the penalty function is to add a barrier function to the original objective function to obtain an augmented objective function when solving the optimization problem (wireless constraint optimization and nonlinear constraint optimization).
The reactive power optimization of the power system is limited by a plurality of constraint conditions, wherein the node power should satisfy a power balance equation, and specifically, the constraint equation is as follows:
Figure BDA0003081125340000062
wherein, Δ PiFor the active power variation of the whole network, QiFor reactive power at the feeder node, GiIs the conductance of node i, ZiIs the impedance of node i, θ is the phase difference.
S3: and calculating individual fitness according to the voltage optimization model, and generating an initial population by using a chaotic genetic algorithm. The fitness function of the chaotic genetic algorithm is converted from a voltage optimization objective function, and is shown as the following formula:
f＝1/F
wherein f fitness.
Preferably, the optimization target and the individual characteristics of the population are directly connected through the treatment, and the individual evaluation is facilitated.
It should be noted that the chaotic genetic algorithm is based on chaotic optimized genetic operation, and ensures that the filial generation individuals are uniformly distributed in a defined space, thereby avoiding precocity and realizing global optimal search with higher probability; the chaos optimization algorithm has the advantages that the occurrence of the situation of local optimum in the searching process can be effectively prevented, meanwhile, the calculation efficiency is high, and the defects of the genetic algorithm can be compensated; chaos (Chaos) refers to seemingly random, irregular motion occurring in a deterministic system, and Chaos refers to a system whose behavior described by the deterministic theory behaves indefinitely, unrepeatably, unpredictably. Chaos is an inherent characteristic of a nonlinear power system and is ubiquitous in the nonlinear system; the chaos is used as an additional factor to be added into the genetic algorithm, after the population of the genetic algorithm is evolved once, the chaos factor is introduced into the current population, and individuals with poor fitness in the population are replaced by chaotic immigration, so that the randomness and the ergodicity of gene individuals in the population are increased, the effective evolution of the population is promoted, and the algorithm is prevented from falling into local optimization.
Further, a random vector is generated by encoding a floating point number, specifically, the encoding steps are as follows:
firstly, setting the number of closed branches as H and a set formed by H closed branches as D;
selecting a closed branch from the set D and disconnecting the closed branch;
numbering the broken branches, and taking the broken branch numbers as gene numbers, wherein the length of the chromosome is H;
fourthly, the genes of each chromosome are subjected to integer coding of random probability to form random vectors, and the coding operation is completed.
And repeating the step (i) and the step (iv) ten times to generate random vectors with the same number as the population scale, and performing chaotic iteration j times on each element of the random vectors to further obtain an initial population.
Because the code string generated by chaotic mapping is not a direct solution of the optimization problem, the code string also needs to be mapped to a solution space according to a certain rule, in order to enable the particles to be early-maturing, the chaotic state is attracted into An optimization variable, the initial value mapped to a chaotic variable interval is subjected to chaotic iteration through An chaotic mapping random number generator to generate a chaotic variable sequence, the chaotic variable sequence is inversely mapped to the original particle swarm optimization space, and a corresponding adaptive value is calculated.
The function expression of An chaotic mapping random number generator is as follows:
Figure BDA0003081125340000071
wherein x istAnd xt+1Is a time sequence and t is time.
S4: and carrying out selection, crossing and mutation operations on the initial population to obtain the evolved population.
(1) Selecting operation: and selecting individuals by adopting a tournament competition selection operator.
The method is characterized in that a plurality of individuals are randomly selected from a group to be compared each time, and the individual with the largest adaptive value wins, so that the probability of the selected individual is prevented from being directly proportional to the adaptive value, and the selected individual can be ensured to have a higher adaptive value.
(2) And (3) cross operation: and performing a crossover operation on the population generated by the selection according to the crossover probability of 75%.
The crossover operator is a very core operator in the genetic algorithm, which affects the search efficiency and performance of the algorithm, and the present embodiment performs the following arithmetic crossover operation according to the crossover probability of 75%:
Figure BDA0003081125340000072
wherein d isa' and db' is parent daAnd dbAnd c is a random number of (0, 1) and is used for controlling the variation degree.
(3) Mutation operation: mutation operation is performed according to 2% mutation probability.
Performing mutation operation on the population generated after crossing according to the 2% mutation rate, and performing mutation according to the following formula:
Figure BDA0003081125340000081
wherein d isab' is parent dabThe rand of the filial generation generated after the variation is random number 0 or 1, wherein the rand represents that the individual variable changes towards the decreasing direction when the rand is 0, and the rand represents that the individual variable changes towards the increasing direction when the rand is 1; e is a random number in the interval (0, 1) for controlling the degree of variation.
S5: and carrying out immigration operation on the evolved population by combining fitness and setting iteration conditions.
Immigration operation: and eliminating the individual with the lowest fitness or the individual with the fitness repeating more than 5 in the evolved population.
Further, an iteration condition is set, and the iteration is stopped when the iteration number reaches 200.
S6: if the iteration condition is not met, returning to the step S3; otherwise, outputting the result and obtaining the optimal solution of the voltage optimization model.
According to the invention, an initial population is generated through a chaotic genetic algorithm, immigration operation is performed regularly on the premise of not changing the basic operation principle of the genetic algorithm so as to ensure the excellence and diversity of the population, and chaotic search is performed on the final excellent individual again so as to obtain a global optimal solution.
Example 2
In order to verify and explain the technical effects adopted in the method, the embodiment selects a genetic algorithm and adopts the method to perform comparison test, and compares test results by means of scientific demonstration to verify the real effect of the method.
The traditional genetic algorithm is low in searching speed and easy to fall into a local optimal solution, so that the reactive power optimization effect is not ideal.
In order to verify that the method has higher search efficiency and better voltage optimization capability compared with the genetic algorithm, the basic ant colony algorithm and the method are adopted to respectively plan and compare the lines of the power grid system in the embodiment.
In this embodiment, a three-phase alternating current power grid simulation system model is established in an MATLAB/Simulink software environment, and a genetic algorithm and the method are adopted to perform optimization comparison on voltages of a power grid system respectively.
The system comprises 10 branches, three-phase voltages are symmetrical, the fundamental voltage of the system is 6kV, the maximum active power of a load is 1.8MW, the actual power factor is 0.72, 3 transformers, 2 power supply nodes, 7 load nodes, a node 4 serves as a balance node of the system, the rest nodes are PQ nodes, the voltage limit value of the PQ node is 0.95-1.05pu, the voltage limit value of the PV node is 0.9-1 pu, 2 adjustable transformers are arranged, the transformation ratio is 0.8-1.0, 3 compensation points are arranged, the capacity limit value of an adjustable capacitor is 0-0.5 pu., the size of an evolution population is 100, the maximum iteration number is 200, and the optimization result is shown in Table 1.
Table 1: and comparing the voltage optimization results with a table.
Figure BDA0003081125340000091
The table shows that the method is superior to the genetic algorithm in the aspects of reducing the network loss and improving the voltage, the active loss reduction rate of the method is 14.36% higher than that of the genetic algorithm, and the experimental result shows that the method is effective and feasible.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (10)

1. A chaos heredity-based voltage quality optimization treatment method is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring three-phase active power and reactive power of the head end of a feeder line and three-phase voltage of a bus through an SCADA system, and constructing a voltage optimization model according to the three-phase active power and reactive power and the three-phase voltage of the bus;
calculating individual fitness according to the voltage optimization model, and generating an initial population by using a chaotic genetic algorithm;
selecting, crossing and mutating the initial population to obtain an evolved population;
carrying out immigration operation on the evolved population by combining the fitness and setting an iteration condition;
if the iteration condition is not met, recalculating the individual fitness; otherwise, outputting the result and obtaining the optimal solution of the voltage optimization model.
2. The voltage quality optimization treatment method based on gene repair chaotic inheritance according to claim 2, characterized in that: the voltage-optimizing model may include,
voltage optimization objective function:
Figure FDA0003081125330000011
wherein, PlossFor the active loss of the whole network, n is the number of feeder nodes, alpha is the voltage out-of-range penalty coefficient, UiIs the voltage of the i-th node, UisetIs a set voltage of the i-th node, UmaxIs the upper voltage limit of the i-th node, UminIs the voltage lower limit of the ith node, m is the number of feeders, Delta Q is the power variation of the feeders, QmaxIs the upper limit of reactive power at the head end of the feeder line, k is the number of load nodes, QminAnd the lower limit of the reactive power of the head end of the feeder line is set.
3. The chaos genetic-based voltage quality optimization governance method according to claim 3, comprising: also comprises the following steps of (1) preparing,
constraint equation:
Figure FDA0003081125330000012
wherein, Δ PiFor the active power variation of the whole network, QiFor reactive power at the feeder node, GiIs the conductance of node i, ZiIs the impedance of node i and θ is the phase difference between adjacent nodes.
4. The chaos-genetic-based voltage quality optimization governance method according to claim 2 or 3, wherein: the active loss of the whole network comprises that,
Figure FDA0003081125330000013
5. the chaos genetic-based voltage quality optimization governance method according to claim 4, wherein: the calculating of the fitness includes calculating a fitness value,
f＝1/F
wherein f is the fitness.
6. The chaos-genetic-based voltage quality optimization governance method according to claim 1 or 5, wherein: generating the initial population includes generating the initial population by,
generating a random vector through floating point number coding, and then performing chaos iteration on each element of the random vector j times to further obtain the initial population.
7. The chaos genetic-based voltage quality optimization governance method of claim 6, comprising: the chaotic iteration comprises that the chaotic iteration comprises,
performing chaotic iteration through An chaotic mapping random number generator, wherein the function expression of the An chaotic mapping random number generator is as follows:
Figure FDA0003081125330000021
wherein x istAnd xt+1Is a time sequence and t is time.
8. The chaos genetic-based voltage quality optimization governance method according to any one of claims 1, 2 and 7, comprising: the selection, crossover and mutation operations include,
the selecting operation: selecting individuals by adopting a tournament competition selection operator;
the cross operation comprises the following steps: performing the crossover operation according to a crossover probability of 75%;
the mutation operation comprises the following steps: performing the mutation operation according to a mutation probability of 2%.
9. The chaos genetic-based voltage quality optimization governance method of claim 8, comprising: the immigration operation comprises the following steps of,
and eliminating the individual with the lowest fitness or the individual with the repeated fitness of more than 5 in the evolved population.
10. The chaos genetic-based voltage quality optimization governance method of claim 1, comprising: the iteration condition includes that the iteration condition includes,
the iteration is stopped when the number of iterations reaches 200.
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