Transformer area phase-change switch configuration method and system based on improved NSGA-II
By constructing a multi-objective optimization model through the improved NSGA-II algorithm, the problem of three-phase load imbalance in the distribution substation area was solved, achieving balanced power quality and extended equipment life for all users on the line, reducing equipment investment and carbon emissions, and improving system safety and economy.
Patent Information
- Application Number
- CN202511839260.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-02-27
AI Technical Summary
Existing technologies suffer from three-phase load imbalance in distribution substations, leading to voltage deviation, current overload, equipment failure, and high carbon emissions. Furthermore, existing optimization algorithms have slow convergence speeds and are prone to getting trapped in local optima, making it difficult to achieve balanced service for all users on the entire line and extend equipment lifespan.
An improved NSGA-II algorithm is used to construct a multi-objective optimization model. By collecting voltage and current data, objective functions are constructed to minimize the number of commutator configurations, the three-phase current imbalance coefficient, and the number of commutator operations. Adaptive crossover probability and mutation probability are introduced to optimize the commutator configuration.
It achieves balanced power quality for all users along the entire line, reduces equipment investment and operation and maintenance costs, improves system security and carbon emission reduction benefits, avoids problems caused by over-configuration and frequent switching, and optimizes the algorithm for better convergence and robustness.
Smart Images
Figure CN121584672A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of phase-change switch configuration technology, and in particular to a method and system for configuring phase-change switches in a transformer substation based on an improved NSGA-II. Background Technology
[0002] For the power grid, reducing power loss can indirectly reduce carbon emissions. As the largest and most user-facing part of the power grid, the distribution network's operating status directly affects energy efficiency and power quality. For a long time, distribution substations have generally suffered from three-phase load imbalance: this not only causes voltage deviations and current overloads, affecting power quality and even leading to equipment failures, but also increases line and transformer losses, reduces system efficiency, and indirectly increases carbon emissions.
[0003] Currently, three-phase load imbalance mitigation methods are mainly divided into two categories: reactive power compensation and commutator regulation. Among these, commutator regulation does not require reconstructing the original distribution network topology; it achieves three-phase balance simply through dynamic phase adjustment. It is characterized by its ease of operation and minimal disturbance to the power grid, and is therefore widely used in distribution substations. However, in practical application, this technology still faces three major unresolved issues: First, there is an imbalance in the user experience at the remote end. Near-end users of the phase-switching switch can usually obtain higher power quality and electricity satisfaction. However, as the power supply distance extends, the line voltage drop and losses gradually accumulate. End-end users are prone to problems such as low voltage and power fluctuations, which makes the electricity satisfaction of end-end users significantly lower than that of near-end users, making it difficult to achieve balanced service for all users on the line.
[0004] Secondly, finding the optimal number of switches to install is difficult. Too few switches will not meet the power quality needs of remote users; too many will significantly increase equipment investment and long-term operation and maintenance costs.
[0005] Third, the phase-switching switches operate too frequently. Excessive switching drastically accelerates the wear and tear on switching equipment, shortens its lifespan, and increases the risk of failure. Simultaneously, frequent instantaneous phase-switching processes cause continuous voltage dips and current surges to sensitive loads on the line, potentially leading to malfunctions and introducing new power quality problems.
[0006] Fourth, the optimization algorithms lack adaptability. Existing research mostly uses swarm intelligence optimization algorithms such as particle swarm optimization and genetic algorithms to solve the commutation switch configuration scheme. Although it can alleviate the three-phase imbalance problem to a certain extent, it generally suffers from slow convergence speed and is prone to getting trapped in local optima, making it difficult to adapt to the actual needs of engineering scenarios for "fast solution and reliable implementation".
[0007] In summary, under the premise of ensuring the safe operation of distribution substations and the power quality for users, constructing a configuration scheme that can simultaneously determine the "minimum number of phase-switching switches," "minimize the three-phase current imbalance coefficient," and "minimize the number of phase-switching switch operations" has become a pressing technical bottleneck in the current power distribution field. This scheme, with the core objectives of "minimizing the number of phase-switching switches," "minimizing the three-phase current imbalance coefficient," and "minimizing the number of phase-switching switch operations," achieves full coverage of the electricity needs of all users, maximizing system economy while ensuring the user's electricity experience, and ultimately helping the distribution network improve its overall operational efficiency and low-carbon level. Summary of the Invention
[0008] To address the problems in existing technologies, this invention provides a method and system for configuring transformer substation phase-commutation switches based on the improved NSGA-II. The specific technical solution is as follows: A method for configuring a transformer substation phase-commutation switch based on the improved NSGA-II includes the following steps: Step S1: Collect three-phase voltage and three-phase current data on the power supply line, and calculate the power P of each phase and the three-phase current imbalance coefficient. Step S2: Construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the distribution area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and an optimization model with constraints of voltage deviation, maximum allowable current carrying capacity of the line, and power balance. Step S3: Solve the optimization model using an improved multi-objective genetic algorithm; Step S4: Based on the final result of the algorithm, obtain the Pareto front solution that minimizes the number of commutator switches in the transformer substation, the three-phase current imbalance coefficient, and the number of commutator switch operations.
[0009] Preferably, in step S1, voltage transformers and current transformers are deployed on the power supply lines of the distribution substation to collect voltage and current data of each phase in real time. The collected voltage and current data are classified into voltage and current data of phases A, B, and C according to the phase, so as to calculate the three-phase current imbalance coefficient.
[0010] Preferably, the objective function in step S2 is expressed as: ; In the formula, The objective function is to minimize the number of phase-commutation switches configured in the transformer area; The objective function is to minimize the three-phase current imbalance coefficient; The objective function is to minimize the number of commutation switch operations; The number of phase-commutation switches to be configured for the transformer area; This represents the maximum value of the three-phase current. This is the minimum value of the three-phase current; This is the average value of the three-phase current; This represents the number of times the commutation switch has operated.
[0011] Preferably, the objective function The specific update steps are as follows: ; in, This is the influence coefficient of the commutation switch operation on the three-phase current imbalance coefficient.
[0012] Preferably, the constraints in step S2 include: (1) Voltage deviation constraint: ; In the formula, Rated voltage; This is the actual voltage; (2) Line current carrying capacity constraint: ; In the formula, This represents the actual operating current. This is the maximum allowable current carrying capacity of the line; (3) Power balance constraints: ; In the formula, This represents the power transfer amount during commutation of the commutator switch.
[0013] Preferably, in step S3, an improved NSGA-II algorithm is used to solve the model. Specific improvements to the NSGA-II algorithm include: (1) Adaptive crossover probability: ; In the formula, For adaptive crossover probability; d represents the adaptive crossover probability coefficient; d represents the current population diversity. For initial population diversity; (2) Adaptive mutation probability: ; In the formula, For adaptive mutation probability; For adaptive variation probability coefficients; This represents the number of iterations. This represents the maximum number of iterations.
[0014] A distribution area phase-change switch configuration system based on the improved NSGA-II, using the method described above, includes: The data acquisition module is used to collect three-phase voltage and three-phase current data on the power supply line, and to calculate the power P of each phase and the three-phase current imbalance coefficient. The objective construction module is used to construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the distribution area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and with voltage deviation, maximum allowable current carrying capacity of the line, and power balance as constraints. The model solving module is used to solve the optimization model using an improved multi-objective genetic algorithm; The model output module is used to obtain the Pareto front solution that minimizes the number of commutator switches in the transformer substation, the three-phase current imbalance coefficient, and the number of commutator switch operations based on the final result of the algorithm.
[0015] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the aforementioned method for configuring a transformer substation phase-change switch based on an improved NSGA-II.
[0016] A processor, characterized in that the processor is configured to run a program, wherein the program executes the aforementioned method for configuring a transformer substation phase-change switch based on an improved NSGA-II.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Improve user experience: By introducing multiple indicators such as voltage deviation and power balance, the power satisfaction of users can be fully reflected. This method fully considers the power quality attenuation caused by long-distance transmission when configuring the phase switching, thereby effectively improving the voltage quality and power experience of users at the far end of the line.
[0018] (2) Reduce equipment investment: Under the premise of meeting user needs, this invention aims to minimize the number of phase switching switches, minimize the three-phase current imbalance coefficient, and minimize the number of phase switching operations, thereby avoiding the investment and maintenance costs caused by over-configuration and improving the economy of the solution.
[0019] (3) Ensure operational safety: The commutation strategy considers both line current constraints and voltage constraints to avoid line overload and voltage over-limit phenomena, thus ensuring the safe and stable operation of the distribution network.
[0020] (4) Improve carbon emission reduction efficiency: By reducing system losses, this invention can indirectly reduce carbon emissions while meeting the "dual carbon" target, which is in line with the requirements of green and low-carbon development.
[0021] (5) The optimization algorithm has better convergence and robustness: By introducing an improved optimization algorithm, the problem of traditional swarm intelligence algorithms being prone to getting trapped in local optima or having slow convergence speed is avoided. It can obtain the optimal configuration result of the commutation switch more quickly and accurately, and has strong engineering practicality.
[0022] In summary, this invention can achieve a minimum configuration of the number of commutation switches while meeting the requirements of power quality and operational safety, taking into account user experience, system stability and economy, and has significant engineering application value and promotion significance. Attached Figure Description
[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.
[0024] Figure 1 This is an overall flowchart of the method of the present invention; Figure 2 Graph of the objective function; Figure 3 To improve the NSGA-II algorithm flowchart; Figure 4 This is a schematic diagram of the Pareto front solution. Detailed Implementation
[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0026] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0029] Example 1: like Figure 1 As shown, this embodiment provides a method for configuring a transformer substation phase-commutation switch based on the improved NSGA-II, including the following steps: Step S1: Collect three-phase voltage and three-phase current data on the power supply line, and calculate the power P of each phase and the three-phase current imbalance coefficient.
[0030] Specifically, voltage transformers and current transformers are deployed on the power supply lines of the distribution substation to collect voltage and current data of each phase in real time. The collected voltage and current data are classified into voltage and current data of phases A, B, and C according to the phase, in order to calculate the three-phase current imbalance coefficient.
[0031] The power P of each phase is calculated based on the collected data, and key indicators such as voltage deviation and current imbalance of each user node are statistically analyzed. The model is constructed based on these indicators to optimize the objective function.
[0032] In this embodiment, the sampling frequency is: 102 nodes are selected, and data is collected once every 15 minutes, covering 96 time points throughout the day; Parameters collected: voltage and current of each phase; Data storage: Historical operational data is stored using a time-series database; Algorithm parameters: 100 iterations, 100 population size, 35 maximum number of commutation switches, and 5 minimum number of commutation switches.
[0033] Step S2: Construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the transformer area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and an optimization model with constraints of voltage deviation, maximum allowable current carrying capacity of the line, and power balance.
[0034] Voltage deviation constraint: The voltage deviation at each node is within the allowable range; Line current carrying capacity constraint: The current in each line segment shall not exceed its maximum allowable current carrying capacity; Power balance constraint: The total power input and output of the system are kept in balance.
[0035] The objective function is expressed as: ; In the formula, The objective function is to minimize the number of phase-commutation switches configured in the transformer area; The objective function is to minimize the three-phase current imbalance coefficient; The objective function is to minimize the number of commutation switch operations; The number of phase-commutation switches to be configured for the transformer area; This represents the maximum value of the three-phase current. This is the minimum value of the three-phase current; This is the average value of the three-phase current; This represents the number of times the commutation switch has operated.
[0036] The constraints include: (1) Voltage deviation constraint: ; In the formula, Rated voltage; This is the actual voltage; (2) Line current carrying capacity constraint: ; In the formula, This represents the actual operating current. This is the maximum allowable current carrying capacity of the line; (3) Power balance constraints: ; In the formula, This represents the power transfer amount during commutation of the commutator switch.
[0037] Among them, such as Figure 2 As shown, the input parameters, objective function, and constraints are used to update the three-phase current imbalance coefficient until the commutation switch stops operating. The objective function is then defined. The specific update steps are as follows: ; in, This is the influence coefficient of the commutation switch operation on the three-phase current imbalance coefficient.
[0038] in, The specific calculation formula is as follows: ; In the formula, This is the three-phase current imbalance coefficient after the commutation switch is activated. Let be the three-phase current imbalance coefficient before the commutation switch operates. The updated objective function is used for solving and iteration to finally output the optimal solution.
[0039] Step S3, as follows Figure 3 As shown, an improved multi-objective genetic algorithm is used to solve the optimization model; the specific improvements to the NSGA-II algorithm include: (1) Adaptive crossover probability: ; In the formula, For adaptive crossover probability; d represents the adaptive crossover probability coefficient; d represents the current population diversity. For initial population diversity; (2) Adaptive mutation probability: ; In the formula, For adaptive mutation probability; For adaptive variation probability coefficients; This represents the number of iterations. This represents the maximum number of iterations.
[0040] The objective function, initialization parameters, and population initialization are constructed. The adaptive crossover probability and adaptive mutation probability mentioned above are introduced into the improved NSGA-II algorithm. The adaptive crossover probability controls the probability of two parent individuals exchanging genes, and the adaptive mutation probability controls the probability of random changes in individual genes. The optimal solution is output iteratively.
[0041] With the optimization objectives of "minimizing the number of commutator switches", "minimizing the three-phase current imbalance coefficient" and "minimizing the number of commutator switch operations", and with voltage deviation constraints, line current carrying capacity constraints and power balance constraints as model constraints, the constructed model is optimized and calculated, and finally a 3D Pareto front plot is output.
[0042] Step S4: Based on the final result of the algorithm, obtain the Pareto front solution that minimizes the number of commutator switches in the distribution area, the three-phase current imbalance coefficient, and the number of commutator switch operations. For example... Figure 4 As shown.
[0043] The 3D Pareto front plot clearly reveals the significant multi-objective conflict among the three indicators: number of units, three-phase current imbalance coefficient, and number of commutations. Reducing the number of units usually leads to an increase in the current imbalance coefficient or the number of commutations; reducing the current imbalance coefficient requires a corresponding increase in the number of commutations; and reducing the number of commutations results in a simultaneous increase in both the number of units and the imbalance coefficient. This invention uses the Pareto front scatter points obtained through an optimization algorithm to form a gradually converging optimization front surface, which can intuitively present the trade-off relationship among the three indicators. It can quickly determine the optimal compromise solution that takes into account multiple indicators, significantly improving the efficiency of screening multi-objective solutions.
[0044] The scatter plot of the three-phase current imbalance coefficient versus the number of units clearly shows the distribution characteristics of the current imbalance coefficient under different equipment number scenarios, which can quickly identify the matching range of the number of units and the current imbalance coefficient, effectively enhancing the adaptability of this solution to power systems of different scales.
[0045] The scatter plot of the three-phase current imbalance coefficient versus the number of commutations visually demonstrates the decreasing trend of the current imbalance coefficient as the number of commutations increases, quantifying the effect of commutation operation on improving the three-phase current imbalance problem, and providing direct and reliable data for precise control of commutation strategies.
[0046] The scatter plot of the number of units versus the number of commutations clearly shows the distribution pattern of the number of commutations corresponding to different numbers of units. This allows for precise optimization of the number of commutations for power systems with different numbers of units, effectively improving the adaptability and control accuracy of this solution in multi-scale application scenarios.
[0047] This invention proposes a method for configuring phase-switching switches in distribution substations based on an improved NSGA-II algorithm. Specifically, it is applied to addressing three-phase load imbalance and improving power quality in distribution substations, and is particularly suitable for optimizing phase-switching switch configurations in multi-user, multi-branch line scenarios. By constructing a multi-objective optimization model, with the objectives of "minimizing the number of phase-switching switches," "minimizing the three-phase current imbalance coefficient," and "minimizing the number of phase-switching switch operations," and comprehensively considering multiple constraints such as voltage deviation, line current carrying capacity, and power balance, the method achieves precise optimization of the number and location of phase-switching switches.
[0048] This method introduces an adaptive crossover and mutation probability mechanism into the traditional NSGA-II, significantly improving the algorithm's convergence speed and global optimization capability, and effectively avoiding getting trapped in local optima. Simulation results show that, under the condition of reasonably adding commutation switches, this method can improve the three-phase current imbalance coefficient from 20.81 to 10.40, significantly improving configuration economy and carbon emission reduction benefits while ensuring system safety and user experience.
[0049] This invention provides a scientific, efficient, and reliable solution for managing three-phase load imbalance in distribution transformer areas, and has good engineering application and promotion value.
[0050] The technical solution proposed in this invention achieves efficient and accurate matching between commutation strategies and power system parameters through multi-dimensional visualization analysis. It has core technical advantages such as high scheme selection efficiency, strong system adaptability, and precise strategy control. It can effectively improve the three-phase current imbalance problem in power systems and can be widely applied to power system scenarios of various scales, with significant practical value and promotion significance.
[0051] Example 2: Based on the same inventive concept as Embodiment 1, this embodiment provides a distribution area commutation switch configuration system based on the improved NSGA-II, and the method described includes: The data acquisition module is used to collect three-phase voltage and three-phase current data on the power supply line, and to calculate the power P of each phase and the three-phase current imbalance coefficient. The objective construction module is used to construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the distribution area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and with voltage deviation, maximum allowable current carrying capacity of the line, and power balance as constraints. The model solving module is used to solve the optimization model using an improved multi-objective genetic algorithm; The model output module is used to obtain the Pareto front solution that minimizes the number of commutator switches in the transformer substation, the three-phase current imbalance coefficient, and the number of commutator switch operations based on the final result of the algorithm.
[0052] Example 3: Based on the same inventive concept as Embodiment 1, this embodiment provides a computer-readable storage medium, which includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the aforementioned method for configuring a transformer substation phase-changing switch based on the improved NSGA-II.
[0053] Example 4: Based on the same inventive concept as Embodiment 1, this embodiment provides a processor for running a program, wherein the program executes the aforementioned method for configuring a transformer substation phase-changing switch based on the improved NSGA-II.
[0054] Those skilled in the art will recognize that the modules of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.
[0055] In the embodiments provided by this invention, it should be understood that the division of modules is only a logical functional division. In actual implementation, there may be other division methods, such as multiple modules can be combined into one module, one module can be split into multiple modules, or some features can be ignored.
[0056] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0057] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.
Claims
1. A method for configuring phase-commutation switches in a distribution area based on an improved NSGA-II, characterized in that, Includes the following steps: Step S1: Collect three-phase voltage and three-phase current data on the power supply line, and calculate the power P of each phase and the three-phase current imbalance coefficient. Step S2: Construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the distribution area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and an optimization model with constraints of voltage deviation, maximum allowable current carrying capacity of the line, and power balance. Step S3: Solve the optimization model using an improved multi-objective genetic algorithm; Step S4: Based on the final result of the algorithm, obtain the Pareto front solution that minimizes the number of commutator switches in the transformer substation, the three-phase current imbalance coefficient, and the number of commutator switch operations.
2. The method for configuring a transformer substation phase-commutation switch based on the improved NSGA-II according to claim 1, characterized in that, In step S1, voltage transformers and current transformers are deployed on the power supply lines of the distribution substation to collect voltage and current data of each phase in real time. The collected voltage and current data are classified into voltage and current data of phases A, B, and C according to the phase, in order to calculate the three-phase current imbalance coefficient.
3. The method for configuring a transformer substation phase-commutation switch based on the improved NSGA-II according to claim 1, characterized in that, The objective function in step S2 is expressed as: ； In the formula, The objective function is to minimize the number of phase-commutation switches configured in the transformer area; The objective function is to minimize the three-phase current imbalance coefficient; The objective function is to minimize the number of commutation switch operations; The number of phase-commutation switches to be configured for the transformer area; This represents the maximum value of the three-phase current. This is the minimum value of the three-phase current; This is the average value of the three-phase current; This represents the number of times the commutation switch has operated.
4. The method for configuring a transformer substation phase-commutation switch based on the improved NSGA-II according to claim 3, characterized in that, objective function The specific update steps are as follows: ； in, This is the influence coefficient of the commutation switch operation on the three-phase current imbalance coefficient.
5. A method for configuring a transformer substation commutator based on an improved NSGA-II according to claim 1, characterized in that, The constraints in step S2 include: (1) Voltage deviation constraint: ； In the formula, Rated voltage; This is the actual voltage; (2) Line current carrying capacity constraint: ； In the formula, This represents the actual operating current. This is the maximum allowable current carrying capacity of the line; (3) Power balance constraints: ； In the formula, This represents the power transfer amount during commutation of the commutator switch.
6. The method for configuring a transformer substation commutator based on the improved NSGA-II according to claim 1, characterized in that, Step S3 uses the improved NSGA-II algorithm to solve the model. The specific improvements to the NSGA-II algorithm include: (1) Adaptive crossover probability: ； In the formula, For adaptive crossover probability; d represents the adaptive crossover probability coefficient; d represents the current population diversity. For initial population diversity; (2) Adaptive mutation probability: ； In the formula, For adaptive mutation probability; For adaptive variation probability coefficients; This represents the number of iterations. This represents the maximum number of iterations.
7. A distribution area commutation switch configuration system based on the improved NSGA-II, characterized in that, The method described by any one of claims 1 to 6 includes: The data acquisition module is used to collect three-phase voltage and three-phase current data on the power supply line, and to calculate the power P of each phase and the three-phase current imbalance coefficient. The objective construction module is used to construct a multi-objective function with the objectives of minimizing the number of phase-switching switches in the distribution area, minimizing the three-phase current imbalance coefficient, and minimizing the number of phase-switching switch operations, and with voltage deviation, maximum allowable current carrying capacity of the line, and power balance as constraints. The model solving module is used to solve the optimization model using an improved multi-objective genetic algorithm; The model output module is used to obtain the Pareto front solution that minimizes the number of commutator switches in the transformer substation, the three-phase current imbalance coefficient, and the number of commutator switch operations based on the final result of the algorithm.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform a method for configuring a transformer substation phase-change switch based on any one of claims 1 to 6.
9. A processor, characterized in that, The processor is used to run a program, wherein the program executes a method for configuring a transformer substation phase-change switch based on an improved NSGA-II as described in any one of claims 1 to 6.