CN117595316A - Three-phase unbalance optimization method and system - Google Patents
Three-phase unbalance optimization method and system Download PDFInfo
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- 238000000819 phase cycle Methods 0.000 claims abstract description 90
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 19
- 238000010187 selection method Methods 0.000 claims abstract description 4
- 239000003016 pheromone Substances 0.000 claims description 60
- 238000004891 communication Methods 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 18
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Classifications
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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
- H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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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
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
Abstract
The invention relates to the technical field of low-voltage distribution networks, in particular to a three-phase imbalance optimization method, which comprises the following steps: inputting initial parameters according to the selected existing three-phase circuit system; initializing ant colony algorithm parameters; randomly generating starting point load phase sequence nodes of all ants; determining the next load phase sequence node selected by the ant m by using a roulette selection method; when the ant m finishes the selection of all the load phase sequence nodes, recording the selection result of the load phase sequence nodes and the three-phase unbalance degree; selecting an optimal result after all ants finish the selection of the load phase sequence nodes; repeating iteration, and selecting an optimal load phase sequence node selection result according to the three-phase imbalance degree; forming a phase change switch setting strategy in the three-phase circuit system according to the load phase sequence node selection result; by optimizing the ant colony algorithm, the phase change selection of the whole three-phase circuit can be dynamically adjusted in real time under the condition of load change.
Description
Technical Field
The invention relates to the technical field of low-voltage distribution networks, in particular to a three-phase imbalance optimization method and system.
Background
The problem of three-phase unbalance of a low-voltage distribution transformer area is one of the common challenges in an electric power system, when a load is unbalanced, current unbalance can be caused, and the stability of the system and the service life of equipment are affected, so that the search for a three-phase unbalance optimization method has important practical significance.
In the prior art, the following methods are generally used:
1. based on power factor adjustment, the method realizes balance of three-phase current by adjusting the power factor of a load, and can balance the three-phase current by increasing or reducing output of reactive power, however, the method has the problems of limited adjustment range, high equipment requirement, high data timeliness and the like;
2. the method has the advantages that various constraint conditions can be considered to realize optimal control, however, the method has higher modeling requirements, and meanwhile, the built model cannot adapt to a distribution area system with high variability due to the fixity of the model;
3. the method based on the genetic algorithm optimizes current distribution by using the genetic algorithm, searches the optimal solution by using the evolution algorithm, and has the advantages of being capable of searching the optimal solution globally, but has the problems of high calculation complexity, a large amount of calculation caches in the calculation process, high parameter setting requirements before calculation and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a three-phase unbalanced optimization method and a system, which are used for solving the problems of limited adjustment range, high operation complexity, low method flexibility and the like in the existing three-phase unbalanced optimization method.
In a first aspect, an embodiment of the present application provides a three-phase imbalance optimization method, including the following steps:
c01, inputting initial parameters according to the selected existing three-phase circuit system, wherein the initial parameters comprise branch numbers, load phase sequence node numbers and topological relations between load phase sequence nodes and branches;
initializing ant colony algorithm parameters, wherein the ant colony algorithm parameters comprise a pheromone importance degree alpha, a heuristic function importance degree beta, a pheromone evaporation coefficient rho, a pheromone increment constant Q, a maximum iteration number Maxgen, an ant number Sizepop, an iteration number k and an ant number m;
starting the kth iteration, and randomly generating starting point load phase sequence node selection of all ants;
c04, determining the next load phase sequence node selected by the ant m by using a roulette selection method according to the probability model;
c05, judging whether the ants m finish the selection of all the load phase sequence nodes, if so, performing the next step, and if not, continuing to perform the step C04;
c06, recording a load phase sequence node selection result and three-phase unbalance degree of the ant m according to the three-phase unbalance degree model;
c07, judging whether the ant number m meets the ant number Sizepop, if so, carrying out the next step, otherwise, adding 1 to the ant number m, and executing the step C04;
c08, recording the optimal three-phase unbalance and the load phase sequence node selection result in the kth iteration;
c09, judging whether the iteration number k meets the maximum iteration number Maxgen, if so, executing the next step, otherwise, updating the pheromone according to the pheromone updating model, increasing the iteration number k by 1, and executing the step C03;
c10, selecting an optimal load phase sequence node selection result according to the three-phase imbalance degree;
and C11, forming a phase change switch setting strategy in the three-phase circuit system according to the load phase sequence node selection result.
Optionally, the initialization pheromone importance degree α is 1, the heuristic function importance degree β is 2, the pheromone evaporation coefficient ρ is 0.5, the pheromone increment constant Q is 100, the maximum iteration number Maxgen is 300, and the ant number Sizepop is 200.
Further, the probability model is:
wherein i is the current load phase sequence node of ant m; j. s is an ant m target load phase sequence node; mu (mu) ij For visibility between points i and j, μ is As a preferred embodiment, the visibility between the i point and the s point is the inverse of the current value of the target load phase sequence node; τ ij For the intensity of pheromone between point i and point j, τ is The intensity of the pheromone between the i point and the s point; allowed k Determining allowed for the load phase sequence node i where ant m is currently located and the topological relation of load nodes in initial parameters for a load phase sequence node set which is not yet selected k Aggregating content; alpha and beta are the importance of pheromones and the importance of heuristic functions respectively.
Further, the three-phase imbalance model is:
wherein I is A 、I B 、I C And respectively recording current values of phase sequence load connection nodes for current data of the phase A, the phase B and the phase C in the three-phase circuit system, classifying according to the phase sequence of the phase sequence load nodes, and summing the current data under the same phase sequence to obtain the current data of the phase A, the phase B and the phase C.
Optionally, the three-phase circuit system current data acquisition method includes the following steps:
s01, constructing a load phase sequence node matrix;
s02, constructing a current data matrix;
s03, multiplying the load phase sequence node matrix by the current data matrix to calculate, and outputting a current data result of the three-phase circuit system.
Further, the pheromone update model is as follows:
wherein Sizepop is the number of ants, ρ is the evaporation coefficient of pheromone,is the pheromone of ant m remaining between paths i and j in the kth iteration, τ ij The original pheromone intensity between the i point and the j point is obtained.
Optionally, theMeeting the requirements of ant Zhou Moxing:
wherein Q is an information increment constant, tour is a load phase sequence node path set selected by ants m, epsilon m And selecting the three-phase unbalance of the result for the load phase sequence node of the ant m.
On the other hand, the embodiment of the application provides a three-phase unbalanced optimization system, which comprises a main control switch and a phase change switch, wherein the main control switch is arranged on each branch outgoing line, one end of the main control switch is connected with a three-phase line at the front end of a branch, the other end of the main control switch is connected with a three-phase line at the rear end of the branch, the phase change switch is arranged at the front end of a user, one end of the phase change switch is connected with the three-phase line of the branch, and the other end of the phase change switch is connected with the user; the main control switch and the phase change switch are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected under the branch line form an independent working system; the master switch is used for executing a phase change switch setting strategy formed by the method according to any one of the previous claims.
In another aspect, an embodiment of the present application provides a terminal device, including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is configured to execute a program stored on the memory, and implement a method according to any one of the preceding claims.
Through optimizing an ant colony algorithm, constructing a pheromone updating model by using three-phase unbalance, solving a topological model of a three-phase circuit system to obtain an optimal load phase sequence node selection combination, solving the topological model of the three-phase circuit system to obtain the optimal load phase sequence node selection combination, realizing that the phase change selection of the whole three-phase circuit can be dynamically adjusted in real time under the condition of load change, and simultaneously, compared with other optimizing methods, the optimizing method provided by the invention has better optimizing effect.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow diagram of a three-phase imbalance optimization method;
FIG. 2 is a schematic flow chart of a method for acquiring current data of a three-phase circuit;
FIG. 3 is a topology of a three-phase circuit system according to an embodiment of the present invention;
FIG. 4 is a graph showing current contrast for three-phase imbalance optimization in an embodiment of the present invention;
FIG. 5 is a waveform comparison chart of three-phase imbalance optimization in an embodiment of the present invention; FIG. 5 (a) is a waveform diagram of three-phase current before commutation; FIG. 5 (b) is a waveform diagram of three-phase current before commutation;
FIG. 6 is a graph showing the comparison of three-phase imbalance after optimization and iteration time of each strategy in the embodiment of the invention;
FIG. 7 is a broken line data graph of three-phase unbalance optimization on-line adaptation and iteration number in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a three-phase imbalance optimization system according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Referring to fig. 1, the three-phase imbalance optimization method provided by the embodiment of the invention includes the following steps:
c01, inputting initial parameters according to the selected existing three-phase circuit system;
specifically, the initial parameters include a branch number, a load phase sequence node number, a topological relation between the load phase sequence node and the branch, and the like.
C02, initializing ant colony algorithm parameters;
specifically, initializing a pheromone importance degree alpha, a heuristic function importance degree beta, a pheromone evaporation coefficient rho, a pheromone increment constant Q, a maximum iteration number Maxgen and an ant number Sizepop, and initializing an iteration number k and an ant number m.
Specifically, the importance degree alpha of the pheromone is used for adjusting the importance degree of the ants on the concentration of the pheromone in the path selection process, and the larger alpha value enables the ants to depend on the global optimal pheromone more, and conversely enables the ants to depend on the local pheromone more; the importance degree beta of the heuristic function is used for adjusting importance degree of the ant on heuristic information (such as distance, energy consumption and the like) in the path selection process, the ant pays more attention to the heuristic information due to a larger beta value, and the ant pays more attention to the pheromone due to a smaller beta value; the pheromone evaporation coefficient rho is used for adjusting the evaporation speed of the pheromone in each iteration, a larger rho value enables the pheromone to evaporate faster, and a smaller value enables the pheromone to evaporate slower; the pheromone increment constant Q is used for adjusting the amount of the pheromone released by the ants in the path selection process, the larger Q value enables the amount of the pheromone released by the ants to be larger, and the smaller Q value enables the amount of the pheromone released by the ants to be smaller; the maximum iteration number Maxgen designates the maximum iteration number in the algorithm; the ant number Sizepop specifies the number of ants in each iteration.
As a preferred embodiment, the initialization pheromone importance degree α is 1, the heuristic function importance degree β is 2, the pheromone evaporation coefficient ρ is 0.5, the pheromone increment constant Q is 100, the maximum iteration number Maxgen is 300, and the ant number Sizepop is 200.
And C03, starting the kth iteration, and randomly generating the starting point load phase sequence node selection of all ants.
C04, determining the next load phase sequence node selected by the ant m by using a roulette selection method according to the probability model;
specifically, the probability model is:
wherein i is the current load phase sequence node of ant m; j. s is an ant m target load phase sequence node; mu ij is the visibility between the i point and the j point, mu is As a preferred embodiment, the visibility between the i point and the s point is the inverse of the current value of the target load phase sequence node; τ ij For the intensity of pheromone between point i and point j, τ is The intensity of the pheromone between the i point and the s point; allowed k Determining allowed for the load phase sequence node i where ant m is currently located and the topological relation of load nodes in initial parameters for a load phase sequence node set which is not yet selected k Aggregating content; alpha and beta are the importance of pheromones and the importance of heuristic functions respectively.
C05, judging whether the ants m finish the selection of all the load phase sequence nodes, if so, performing the next step, and if not, continuing to perform the step C04;
specifically, the step of judging whether the ant m finishes the selection of all the load phase sequence nodes comprises the following steps: and determining whether the ant m has selectable load phase sequence nodes according to the current load phase sequence node i of the ant m and the topological relation of the load nodes in the initial parameters.
C06, recording a load phase sequence node selection result and three-phase unbalance degree of the ant m according to the three-phase unbalance degree model;
specifically, the three-phase imbalance model is:
wherein I is A 、I B 、I C And respectively recording current values of phase sequence load connection nodes for current data of A phase, B phase and C phase in the three-phase circuit, classifying according to phase sequences of the phase sequence load nodes, and summing the current data under the same phase sequence to obtain the current data of the three phase sequences of the A phase, the B phase and the C phase.
Referring to fig. 2, in the present embodiment, the current data acquisition method in a three-phase circuit includes the steps of:
s01, constructing a load phase sequence node matrix;
specifically, according to the phase sequence to which each load phase sequence node belongs, defining a data set of each load phase sequence node, in this embodiment, phase A is defined as [1, 0], phase B is defined as [1, 0], and phase C is defined as [1, 0]; longitudinally combining the data sets of all the load phase sequence nodes to form a load phase sequence node matrix with the number of lines as the number of the load phase sequence nodes and the number of columns as three columns;
s02, constructing a current data matrix;
specifically, according to the current value of each phase sequence load connection node, constructing a current data matrix with row number as one row and column number as the number of load phase sequence nodes;
s03, multiplying the load phase sequence node matrix by the current data matrix for calculation, and outputting a three-phase circuit current data result;
specifically, multiplying the load phase sequence node matrix by the current data matrix to obtain a result matrix with one row and three columns, wherein three elements in the result matrix respectively correspond to current values of A phase, B phase and C phase in the three-phase circuit.
By the method, classifying time is saved, and current data of three phase sequences in the three-phase circuit can be rapidly acquired.
And C07, judging whether the ant number m meets the ant number Sizepop, if so, carrying out the next step, otherwise, adding 1 to the ant number m, and executing the step C04.
And C08, recording the optimal three-phase unbalance and the load phase sequence node selection result in the kth iteration.
And C09, judging whether the iteration number k meets the maximum iteration number Maxgen, if so, executing the next step, otherwise, updating the pheromone according to the pheromone updating model, increasing the iteration number k by 1, and executing the step C03.
Specifically, the pheromone updating model is as follows:
wherein Sizepop is the number of ants, ρ is the evaporation coefficient of pheromone,for the sum of the pheromones remaining between paths i and j for all ants for the kth iteration, τ ij The original pheromone intensity between the i point and the j point is obtained.
As a preferred embodiment, theMeeting the requirements of ant Zhou Moxing:
wherein Q is an information increment constant, tour is a load phase sequence node path set selected by ants m, epsilon m And selecting the three-phase unbalance of the result for the load phase sequence node of the ant m.
C10, selecting an optimal load phase sequence node selection result according to the three-phase imbalance degree;
specifically, according to each suboptimal three-phase imbalance in k iterations, an optimal load phase sequence node selection result in k iterations is selected.
And C11, forming a phase change switch setting strategy in the three-phase circuit system according to the load phase sequence node selection result.
The phase change switch setting strategy in the three-phase circuit system is formed through the steps, a topology model is built aiming at the three-phase circuit system, an ant colony algorithm is optimized, three-phase unbalance is used as a pheromone updating model parameter, the three-phase circuit system topology model is solved to obtain an optimal load phase sequence node selection combination, dynamic adjustment can be carried out on phase change selection of the whole three-phase circuit in real time under the condition of load change, and compared with other optimization methods, the optimization method disclosed by the invention optimizes the three-phase circuit system to have lower three-phase unbalance, and meanwhile, dynamic compatibility under different circuit environments can be realized by adjusting other parameters of the ant colony algorithm.
The invention also provides an application embodiment of the three-phase imbalance optimization method, and for the embodiment, the initial parameters of the three-phase circuit system are as follows:
referring to fig. 3, a topology diagram of the three-phase circuit system according to the present embodiment is shown;
wherein N1-N4 are branches in the three-phase circuit system, and P1-P10 are switches in the three-phase circuit system.
Referring to fig. 4 and 5, a comparison chart of current and waveform before and after the three-phase unbalance optimization method provided by the invention is that the current and waveform of three phases after optimization are more balanced than those before optimization.
Referring to fig. 6, in the embodiment of the present invention, the iteration time of each strategy and the comparison graph of the three-phase imbalance after optimization are specifically provided for the three-phase imbalance optimization method and the optimization method using the genetic algorithm, the iteration time of the optimization method using the particle swarm algorithm, and the comparison graph of the three-phase imbalance after optimization, and compared with the optimization method using the genetic algorithm and the optimization method using the particle swarm algorithm, the three-phase imbalance optimization method provided by the present invention can achieve a better three-phase imbalance optimization effect by using the improved ant swarm algorithm.
Referring to fig. 7, a broken line data diagram of online self-adaption degree and iteration times of the three-phase unbalance optimization method provided by the invention is provided.
Referring to fig. 8, another aspect of the present application further provides a three-phase imbalance optimization system, including a main control switch and a phase change switch, where the main control switch is installed on each branch outgoing line, one end of the main control switch is connected to a three-phase line at the front end of the branch, the other end of the main control switch is connected to a three-phase line at the rear end of the branch, the phase change switch is installed at the front end of a user, one end of the phase change switch is connected to the three-phase line of the branch, and the other end of the phase change switch is connected to the user; the main control switch and the phase change switches are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected below the branch line form an independent working system; the main control switch is used for executing a phase change switch setting strategy formed by the three-phase unbalance optimization method.
Referring to fig. 9, another aspect of the present application further provides a terminal device, where the terminal device is in communication connection with the three-phase unbalanced optimization system, and is configured to accept data information of the three-phase unbalanced optimization system and send a control instruction to the three-phase unbalanced optimization system, where the control instruction is a policy for setting a phase change switch, and the terminal device includes: the processor 1, the communication interface 2, the memory 3 and the communication bus 4, wherein the processor 1, the communication interface 2 and the memory 3 complete communication with each other through the communication bus 4.
A memory 3 for storing a computer program;
the processor 1 is configured to execute a program stored in the memory 3, and implement the steps of the method embodiment described above.
The bus mentioned by the above-mentioned terminal device may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the terminal equipment and other equipment.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also Digital signal processors (Digital SignalProcessing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The embodiment of the application also provides a storage medium, which comprises a stored program, wherein the program executes the method steps of the method embodiment.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (9)
1. A method for optimizing a three-phase imbalance, comprising the steps of:
c01, inputting initial parameters according to the selected existing three-phase circuit system, wherein the initial parameters comprise branch numbers, load phase sequence node numbers and topological relations between load phase sequence nodes and branches;
initializing ant colony algorithm parameters, wherein the ant colony algorithm parameters comprise a pheromone importance degree alpha, a heuristic function importance degree beta, a pheromone evaporation coefficient rho, a pheromone increment constant Q, a maximum iteration number Maxgen, an ant number Sizepop, an iteration number k and an ant number m;
starting the kth iteration, and randomly generating starting point load phase sequence node selection of all ants;
c04, determining the next load phase sequence node selected by the ant m by using a roulette selection method according to the probability model;
c05, judging whether the ants m finish the selection of all the load phase sequence nodes, if so, performing the next step, and if not, continuing to perform the step C04;
c06, recording a load phase sequence node selection result and three-phase unbalance degree of the ant m according to the three-phase unbalance degree model;
c07, judging whether the ant number m meets the ant number Sizepop, if so, carrying out the next step, otherwise, adding 1 to the ant number m, and executing the step C04;
c08, recording the optimal three-phase unbalance and the load phase sequence node selection result in the kth iteration;
c09, judging whether the iteration number k meets the maximum iteration number Maxgen, if so, executing the next step, otherwise, updating the pheromone according to the pheromone updating model, increasing the iteration number k by 1, and executing the step C03;
c10, selecting an optimal load phase sequence node selection result according to the three-phase imbalance degree;
and C11, forming a phase change switch setting strategy in the three-phase circuit system according to the load phase sequence node selection result.
2. The method of claim 1, wherein the initialization pheromone importance α is 1, the heuristic importance β is 2, the pheromone evaporation coefficient ρ is 0.5, the pheromone increment constant Q is 100, the maximum iteration number Maxgen is 300, and the ant number Sizepop is 200.
3. The method of claim 1, wherein the probability model is:
wherein i is the current load phase sequence node of ant m; j. s is ant m targetLoad phase sequence nodes; mu (mu) ij For visibility between points i and j, μ is Visibility between the i point and the s point; τ ij For the intensity of pheromone between point i and point j, τ is The intensity of the pheromone between the i point and the s point; allowed k Determining allowed for the load phase sequence node i where ant m is currently located and the topological relation of load nodes in initial parameters for a load phase sequence node set which is not yet selected k Aggregating content; alpha and beta are the importance of pheromones and the importance of heuristic functions respectively.
4. The method of claim 1, wherein the three-phase imbalance model is:
wherein I is A 、I B 、I C And respectively recording current values of phase sequence load connection nodes for current data of the phase A, the phase B and the phase C in the three-phase circuit system, classifying according to the phase sequence of the phase sequence load nodes, and summing the current data under the same phase sequence to obtain the current data of the phase A, the phase B and the phase C.
5. The method of optimizing three-phase imbalance according to claim 4, wherein the method of acquiring three-phase circuit system current data comprises the steps of:
s01, constructing a load phase sequence node matrix;
s02, constructing a current data matrix;
s03, multiplying the load phase sequence node matrix by the current data matrix to calculate, and outputting a current data result of the three-phase circuit system.
6. The method of claim 1, wherein the pheromone update model is:
wherein Sizepop is the number of ants, ρ is the evaporation coefficient of pheromone,is the pheromone of ant m remaining between paths i and j in the kth iteration, τi j The original pheromone intensity between the i point and the j point is obtained.
7. A method of optimizing three-phase imbalance according to claim 6, wherein said method comprisesMeeting the requirements of ant Zhou Moxing:
wherein Q is an information increment constant, tour is a load phase sequence node path set selected by ants m, epsilon m And selecting the three-phase unbalance of the result for the load phase sequence node of the ant m.
8. The three-phase unbalanced optimization system is characterized by comprising a main control switch and a phase change switch, wherein the main control switch is arranged on each branch line outlet, one end of the main control switch is connected with a three-phase line at the front end of a branch line, the other end of the main control switch is connected with a three-phase line at the rear end of the branch line, the phase change switch is arranged at the front end of a user, one end of the phase change switch is connected with the three-phase line of the branch line, and the other end of the phase change switch is connected with the user; the main control switch and the phase change switch are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected under the branch line form an independent working system; the master switch is configured to implement the phase change switch setting strategy formed by the method of any one of claims 1-7.
9. A terminal device, comprising: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor being configured to execute a program stored on the memory to implement the method of any one of claims 1-7.
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