A method and system for treating three-phase imbalance of a power distribution network

Abstract

This application provides a method and system for managing three-phase imbalance in a distribution network, relating to the field of distribution network management technology. This application acquires the three-phase current parameters at the head end of major branches and calculates the comprehensive current imbalance. When the comprehensive current imbalance exceeds a preset threshold, it acquires the operating status parameters collected by the execution unit, and iteratively optimizes the commutation state combinations of each execution unit based on a genetic algorithm to obtain the optimal commutation scheme and control the execution unit to perform the commutation operation. Using this scheme, the intelligence level of three-phase imbalance management can be improved, the comprehensive current imbalance can be reduced, line and equipment losses can be reduced, and the stability, safety, and economy of distribution network operation can be improved. Simultaneously, by optimizing the commutation state combinations as a whole, the globality and rationality of commutation decisions can be improved, enhancing the adaptability to load fluctuation scenarios.

Description

Technical Field

[0001] This application relates to the field of power distribution network management technology, and in particular to a method and system for managing three-phase imbalance in power distribution networks. Background Technology

[0002] In low-voltage distribution networks, three-phase imbalance is a long-standing core power quality problem. With the surge in residential electricity load, increased distributed power supply, and the widespread use of single-phase loads such as air conditioners and electric vehicle charging stations, the characteristics of large load fluctuations and low simultaneous power consumption have become increasingly pronounced, leading to a more normalized and complex trend in three-phase imbalance. This problem can cause a series of serious hazards: on the one hand, it causes distribution transformers to operate asymmetrically, resulting in increased losses, excessive local temperature rise, significantly shortening equipment lifespan, and in extreme cases, even causing transformer burnout; on the other hand, it causes neutral point potential shift, increasing line voltage drop and power loss, not only wasting a large amount of electricity but also potentially leading to low voltage at end users, affecting the normal operation of electrical equipment, and causing safety accidents such as neutral line overcurrent burnout. Furthermore, three-phase imbalance increases motor reverse torque, leading to decreased equipment efficiency, increased energy consumption, and significantly increased operation and maintenance costs and equipment replacement frequency, bringing a dual negative impact on the safe and economical operation of the power grid and the user's electricity experience.

[0003] Currently, the mainstream governance technologies mainly fall into three categories: First, manual phase adjustment, which relies on maintenance personnel to conduct on-site surveys and adjust the load phase sequence on poles. This is not only inefficient and labor-intensive, but also poses safety hazards and is difficult to adapt to dynamic load changes, resulting in poor sustainability of the governance effect. Second, phase-to-phase reactive power compensation, which can only improve the transformer's own operating status and cannot fundamentally optimize the load distribution. Moreover, it has high equipment investment and maintenance costs, poses a resonance risk, and has limited applicability. Third, traditional intelligent commutation technology, some of which rely on manual intervention to formulate commutation strategies. Lacking scientific multi-objective optimization models, it is prone to problems such as excessive commutation times and insufficient balancing accuracy, making it difficult to balance the governance effect with the equipment's service life.

[0004] Therefore, in view of the shortcomings of traditional technologies in terms of dynamic adaptability, governance accuracy and economy, there is an urgent need for a three-phase imbalance governance method that can sense load changes in real time, intelligently optimize commutation strategies, and take into account both balancing effect and operating cost, so as to meet the actual needs of digital operation and maintenance and energy saving and loss reduction of distribution networks. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for managing three-phase imbalance in distribution networks, so as to overcome the shortcomings of traditional three-phase imbalance management technologies in terms of dynamic adaptability, management accuracy and economy, and the inability to meet the actual needs of digital operation and maintenance and energy saving and loss reduction in distribution networks.

[0006] Firstly, this application proposes a method for mitigating three-phase imbalance in a power distribution network. The power distribution network includes three-phase lines and at least one main branch connected to the three-phase lines. The main branch includes multiple distribution branches, each distribution branch having a corresponding execution unit. The distribution branches are electrically connected to the three-phase lines of the power distribution network through the execution unit. The method includes:

[0007] Obtain the three-phase current parameters at the beginning of the main branch;

[0008] The comprehensive current imbalance is calculated based on the three-phase current parameters, and when the comprehensive current imbalance exceeds a preset threshold, the operating status parameters collected by the execution unit are obtained. The operating status parameters include current data and phase data.

[0009] Based on the genetic algorithm and the operating state parameters, and using a preset objective function as the evaluation criterion, the candidate commutation state combinations of each execution unit are iteratively optimized to obtain the optimal commutation scheme and distribute it to each execution unit so that each execution unit can perform commutation operation according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management; wherein, the candidate commutation state combination is used to characterize the possible access phase combinations formed by each execution unit and the three-phase line.

[0010] In one embodiment, the step of iteratively optimizing the candidate commutation state combinations of each execution unit based on a genetic algorithm and the running state parameters, using a preset objective function as the evaluation criterion, to obtain the optimal commutation scheme includes:

[0011] Vector encoding is performed on the initial combination in the candidate commutation state combination to generate an initial policy population; wherein, the initial combination is randomly generated;

[0012] Based on a preset objective function, the fitness of each candidate individual in the initial strategy population is calculated;

[0013] Based on the fitness, selection, crossover, and mutation operations are performed sequentially to iteratively generate a new generation of strategy populations until a preset termination condition is met.

[0014] Candidate individuals that meet the preset termination conditions and whose fitness meets the preset screening conditions are determined as the optimal swapping scheme.

[0015] In one embodiment, the method of pre-constructing the objective function includes:

[0016] A first objective function is constructed to characterize the degree of three-phase current imbalance in order to evaluate the impact of the commutation state combination corresponding to the candidate individual on the three-phase load distribution of the distribution network.

[0017] A second objective function is constructed to characterize the degree of equipment wear, in order to evaluate the changes in the execution unit action caused by the combination of commutation states corresponding to the candidate individuals relative to the initial commutation state;

[0018] The first objective function and the second objective function are weighted and summed to obtain the objective function.

[0019] In one embodiment, constructing the first objective function to characterize the degree of three-phase current imbalance includes:

[0020] Based on the current data of the distribution branch where each execution unit is located and the commutation state combination corresponding to the candidate individuals, the three-phase load distribution result after commutation is determined;

[0021] The three-phase currents are determined based on the three-phase load distribution results, and the negative sequence and zero sequence components of the three-phase currents are calculated.

[0022] The negative sequence component and the zero sequence component are weighted and summed according to preset weights to obtain the comprehensive current imbalance.

[0023] The first objective function is constructed with minimizing the overall current imbalance as the optimization objective.

[0024] In one embodiment, constructing the second objective function for characterizing the degree of device wear includes:

[0025] Obtain the initial commutation state of each execution unit;

[0026] Based on the combination of commutation states corresponding to the candidate individuals, the candidate commutation states corresponding to each execution unit are determined;

[0027] The candidate commutation state corresponding to each execution unit is compared with the initial commutation state to determine whether each execution unit has performed a commutation action, and the number of commutations is counted.

[0028] The second objective function is constructed with the goal of minimizing the number of commutations.

[0029] In one embodiment, the step of sequentially performing selection, crossover, and mutation operations based on the fitness to iteratively generate a new generation of strategy population includes:

[0030] Based on the fitness of each candidate individual, select the parent individual from the current strategy population;

[0031] According to the preset crossover probability, a single-point crossover operation is performed on the parent individual to obtain the offspring individual;

[0032] Based on the adaptive mutation probability, a mutation operation is performed on the offspring individuals to obtain updated individuals; wherein, the adaptive mutation probability is adjusted based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population.

[0033] A new generation of strategy population is generated based on the updated individuals.

[0034] In one embodiment, adjusting the adaptive mutation probability based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population includes:

[0035] Obtain the initial mutation probability;

[0036] Calculate the average fitness of the current strategy population;

[0037] Obtain the fitness of candidate individuals to be mutated;

[0038] The initial mutation probability is adjusted based on the average fitness and the fitness of the candidate individuals to be mutated, to obtain the adaptive mutation probability, which is expressed as follows:

[0039]

[0040] in, The initial mutation probability; The fitness of the candidate individuals to be mutated; This represents the average fitness of the current strategy population.

[0041] Secondly, this application proposes a system for mitigating three-phase imbalance in a power distribution network. The power distribution network includes three-phase lines and at least one main branch connected to the three-phase lines. The main branch includes multiple distribution branches. The system includes:

[0042] An execution unit is installed on each of the power distribution branches to realize the electrical connection between the power distribution branch and the three-phase line, and to collect operating status parameters, including current data and phase data.

[0043] A control terminal, communicatively connected to the execution unit, is used to acquire the three-phase current parameters at the beginning of the main branch; calculate the comprehensive current imbalance based on the three-phase current parameters, and acquire the operating status parameters collected by the execution unit when the comprehensive current imbalance exceeds a preset threshold; based on a genetic algorithm and the operating status parameters, and using a preset objective function as the evaluation criterion, iteratively optimize the candidate commutation state combinations of each execution unit to obtain the optimal commutation scheme and distribute it to each execution unit; wherein, the candidate commutation state combinations are used to characterize the possible access phase combinations formed by each execution unit and the three-phase line;

[0044] The execution unit is also used to perform commutation operation according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management.

[0045] In one embodiment, when the distribution network has only one main branch, the control terminal is located on the low-voltage side of the distribution network.

[0046] In the case of multiple main branches in the power distribution network, each main branch is equipped with a control terminal;

[0047] The control terminal corresponding to each main branch is connected to the execution units associated with that main branch.

[0048] In one embodiment, the execution unit includes:

[0049] The data acquisition subunit is used to collect operating status parameters;

[0050] A phase-switching switch is used to switch the corresponding distribution branch to the target access phase based on the optimal phase-switching scheme, while maintaining continuous power supply to the load, thereby adjusting the three-phase load distribution.

[0051] The above-mentioned methods and systems for mitigating three-phase imbalance in power distribution networks have at least the following advantages:

[0052] This application obtains the three-phase current parameters at the beginning of the main branches and calculates the comprehensive current imbalance. When the comprehensive current imbalance exceeds a preset threshold, it acquires the operating status parameters collected by the execution units set in each distribution branch. Based on a genetic algorithm, it iteratively optimizes the commutation state combination of each execution unit to obtain the optimal commutation scheme and controls the execution units to perform the commutation operation. This allows for adaptive adjustment of the connected phases of each distribution branch according to the real-time operating status of the distribution network, achieving optimized reconfiguration of three-phase load distribution. Using this scheme, this application not only improves the intelligence level of three-phase imbalance management but also helps reduce the comprehensive current imbalance, decrease line and equipment losses, and improve the stability, safety, and economy of the distribution network operation. Furthermore, by optimizing the commutation state combination as a whole, it also improves the globality and rationality of commutation decisions and enhances adaptability to load fluctuation scenarios. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for mitigating three-phase imbalance in a power distribution network in one embodiment.

[0054] Figure 2 This is a wiring diagram of the distribution network in one embodiment;

[0055] Figure 3This is a flowchart illustrating the steps to obtain the optimal commutation scheme in one embodiment;

[0056] Figure 4 This is a flowchart illustrating the steps of pre-constructing the objective function in one embodiment;

[0057] Figure 5 This is a flowchart illustrating the iterative steps for generating a new generation of policy population in one embodiment.

[0058] Figure 6 This is a flowchart illustrating a method for managing three-phase imbalance in a power distribution network, as described in another embodiment.

[0059] Figure 7 This is a schematic diagram of the commutation switch in one embodiment;

[0060] Figures 8-11 for Figure 7 Schematic diagram of current flow direction. Detailed Implementation

[0061] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0062] Some exemplary embodiments of this application have been described for illustrative purposes. It should be understood that this application may be implemented in other ways not specifically shown in the accompanying drawings.

[0063] Please see Figure 1 In one exemplary embodiment, this application provides a method for mitigating three-phase imbalance in a power distribution network, specifically including the following steps:

[0064] Step 102: Obtain the three-phase current parameters at the beginning of the main branch.

[0065] Specifically, please refer to Figure 2 The power distribution network includes three-phase lines ( Figure 2 As shown in reference number 1), and at least one main branch connected to the three-phase line ( Figure 2As shown in reference number 2), each main branch includes multiple distribution branches. Each distribution branch is used to supply power to the corresponding load, and each distribution branch is equipped with an execution unit. The distribution branch establishes an electrical connection with the three-phase line of the distribution network through the execution unit, so that each distribution branch can be connected to any phase of the three-phase line. That is, the distribution branch can selectively connect to phase A, phase B or phase C, and together with the neutral line N, it forms a power supply path.

[0066] Furthermore, the starting point of a main branch refers to the connection point between the upstream three-phase line and multiple downstream distribution branches. By obtaining the three-phase current parameters at this location, the overall three-phase current distribution currently carried by the main branch can be directly reflected, thus providing a general basis for subsequent judgment on whether the three phases are unbalanced. For example, the aforementioned three-phase current parameters can be obtained through current sampling devices such as current transformers or Hall current sensors installed at the starting point of the main branch.

[0067] Step 104: Calculate the comprehensive current imbalance based on the three-phase current parameters, and if the comprehensive current imbalance exceeds the preset threshold, obtain the operating status parameters collected by the execution unit. The operating status parameters include current data and phase data.

[0068] Specifically, the comprehensive current imbalance is a parameter used to characterize the degree of balance in load current distribution among phases A, B, and C in a distribution network. By statistically analyzing the three-phase current parameters of the main branches, the differences in the three-phase currents can be determined. Generally speaking, a larger comprehensive current imbalance indicates a more unbalanced three-phase load distribution, while a smaller imbalance indicates a closer relationship to a balanced state.

[0069] Operating status parameters refer to data characterizing the current operating status, load characteristics, and phase connection status of each distribution branch. For example, operating status parameters include current data and phase data; the current data characterizes the load size of each distribution branch, and the phase data characterizes the phase currently connected to that distribution branch. Optionally, operating status parameters may also include address data.

[0070] Step 106: Based on the genetic algorithm and operating state parameters, and using the preset objective function as the evaluation criterion, the candidate commutation state combinations of each execution unit are iteratively optimized to obtain the optimal commutation scheme and distribute it to each execution unit so that each execution unit can perform commutation operation according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management.

[0071] Specifically, the candidate phase switching state combination refers to the combination state formed by the access phase corresponding to each execution unit, which is used to characterize the possible access phase combination formed by each execution unit and the three-phase line.

[0072] The objective function is used to quantitatively evaluate the merits of each commutation state combination under the current operating condition. Specifically, in this embodiment, the objective function is pre-constructed by comprehensively considering factors such as the degree of three-phase current imbalance, the cost of commutation operation, the degree of equipment loss, and the economic efficiency of operation.

[0073] If the overall current imbalance exceeds a preset threshold, the three-phase load distribution is considered unbalanced. In this case, the different access phase configurations that may be formed between each execution unit and the three-phase line are taken as candidate commutation state combinations. The merits of each candidate commutation state combination are evaluated in combination with the operating state parameters and the objective function. Based on this, this application further utilizes the population optimization mechanism of the genetic algorithm to iteratively update and optimize the candidate commutation state combinations, so that the commutation state combinations gradually evolve in a direction that is more conducive to reducing the overall current imbalance, reducing unnecessary commutation actions, and improving the overall operating economy, and finally obtains the optimal commutation scheme that meets the governance objectives.

[0074] The aforementioned method for managing three-phase imbalance in distribution networks involves acquiring the three-phase current parameters at the beginning of major branches and calculating the overall current imbalance. When the overall current imbalance exceeds a preset threshold, the method acquires the operating status parameters collected by the execution units located in each distribution branch. Based on a genetic algorithm, the method iteratively optimizes the commutation state combinations of each execution unit to obtain the optimal commutation scheme and controls the execution units to perform the commutation operation. This allows for adaptive adjustment of the connected phases of each distribution branch according to the real-time operating status of the distribution network, achieving optimized reconfiguration of three-phase load distribution. By adopting this scheme, this application not only improves the intelligence level of three-phase imbalance management but also helps reduce the overall current imbalance, decrease line and equipment losses, and improve the stability, safety, and economy of distribution network operation. Furthermore, by optimizing the commutation state combinations as a whole, it also improves the globality and rationality of commutation decisions and enhances adaptability to load fluctuation scenarios.

[0075] Please see Figure 3 Optionally, based on a genetic algorithm and running state parameters, and using a preset objective function as the evaluation criterion, the candidate commutation state combinations of each execution unit are iteratively optimized to obtain the optimal commutation scheme, including:

[0076] Step 302: Vector-encode the initial combination in the candidate phase-change state combination to generate an initial policy population; wherein the initial combination is randomly generated.

[0077] Step 304: Calculate the fitness of each candidate individual in the initial strategy population based on the objective function.

[0078] Step 306: Based on fitness, perform selection, crossover, and mutation operations sequentially to iteratively generate a new generation of strategy populations until a preset termination condition is met.

[0079] Step 308: The candidate individuals that meet the preset termination conditions and whose fitness meets the preset screening conditions are determined as the optimal swapping scheme.

[0080] Specifically, in the initial stage of iterative optimization, the candidate phase-switching state combinations include multiple initial combinations, each representing the access configuration formed when each execution unit accesses different phases. Considering the slow convergence and susceptibility to local optima inherent in traditional genetic algorithms, this application employs a random generation method to construct initial combinations, and multiple initial combinations form an initial population. Specifically, the number of chromosomes in the initial population can be preset, with each chromosome corresponding to one initial combination. Since the number of chromosomes directly affects the convergence and computational speed of the genetic algorithm, the number of chromosomes in this embodiment is set according to the actual application scenario to balance the optimization search range and computational efficiency. Obtaining multiple initial combinations through random generation helps improve the diversity of candidate schemes, enabling the genetic algorithm to search within the largest possible range and avoiding premature concentration on a small number of local schemes during the optimization process.

[0081] Furthermore, before randomly initializing the chromosome population, this application also represents the commutation state combinations of each execution unit as a chromosome structure that can be processed by the genetic algorithm, and encodes it into vector genes. Using this vector representation method adapted to the commutation state combinations can improve the effectiveness of genetic operations on candidate individuals.

[0082] Furthermore, fitness refers to an evaluation parameter calculated based on the objective function, used to characterize the degree to which each candidate individual adapts to the current optimization objective. Based on fitness, candidate individuals for subsequent evolution can be selected from the initial strategy population. Through selection operations, candidates with better fitness have a higher probability of entering the next iteration, thereby enhancing the retention of excellent phase-change state combinations in subsequent optimizations. Through crossover operations, better local phase-change configurations from different candidate individuals can be combined to generate new phase-change state combinations, thus improving the genetic algorithm's search ability for optimal solutions. Through mutation operations, new phase-change state combinations can be introduced based on the current search, increasing population diversity, preventing the genetic algorithm from prematurely converging to local optima, and helping to discover better phase-change schemes.

[0083] In each iteration, the selection, crossover, and mutation operations described above are performed sequentially on the current policy population to generate a new policy population. This allows the policy population to continuously evolve towards a better combination of commutation states, ultimately obtaining the optimal commutation scheme that is adapted to the current operating state.

[0084] The preset termination condition refers to the condition used to determine the end of the iterative optimization of the genetic algorithm, including any one of the following: the number of iterations reaches a preset upper limit, the population fitness tends to converge, or the fitness of the best individual reaches a preset target value.

[0085] The preset screening conditions are used to limit the selection criteria for the optimal commutation scheme, including the individual fitness being the best value in the current strategy population, or the individual fitness, the overall current imbalance, and the number of commutations simultaneously meeting the corresponding preset requirements.

[0086] By adopting the above scheme, an initial strategy population is formed by vector encoding of candidate commutation state combinations, and iterative optimization is performed based on the objective function and fitness. This application can quickly select the optimal commutation scheme that meets the preset screening conditions from multiple candidate commutation schemes, thereby realizing intelligent optimization and adjustment of the three-phase load distribution of the distribution network. This is conducive to improving the global search capability and optimization efficiency of commutation decision-making, enhancing the adaptability to complex load change scenarios, and improving the accuracy and stability of three-phase imbalance management.

[0087] Please see Figure 4 Optionally, the objective function can be pre-constructed in the following ways:

[0088] Step 402: Construct a first objective function to characterize the degree of three-phase current imbalance in order to evaluate the impact of the commutation state combination corresponding to the candidate individual on the three-phase load distribution of the distribution network.

[0089] Step 404: Construct a second objective function to characterize the degree of equipment wear, in order to evaluate the changes in the execution unit action caused by the combination of commutation states corresponding to the candidate individuals relative to the initial commutation state.

[0090] Step 406: Perform a weighted summation of the first objective function and the second objective function to obtain the objective function.

[0091] Specifically, based on the operating characteristics of the distribution network and equipment constraints, this application focuses on achieving precise management of three-phase imbalance while ensuring the lifespan of the commutation switch, with the optimization objectives being to minimize the degree of imbalance and the equipment loss.

[0092] Optionally, a first objective function is constructed to characterize the degree of three-phase current imbalance, including:

[0093] Based on the current data of the distribution branch where each execution unit is located and the commutation state combination corresponding to the candidate individuals, the three-phase load distribution result after commutation is determined; the three-phase phase current is determined based on the three-phase load distribution result, and the negative sequence component and zero sequence component of the three-phase phase current are calculated; the negative sequence component and zero sequence component are weighted and summed according to the preset weight to obtain the comprehensive current imbalance; the first objective function is constructed with minimizing the comprehensive current imbalance as the optimization objective.

[0094] Specifically, this application uses multiple distribution branches under the same main branch as the modeling object. For example, if a main branch connects to n distribution branches, and each distribution branch has an execution unit, then there are n execution units; the current of the distribution branch where the i-th execution unit is located is denoted as... Based on the current data of each distribution branch, a current column vector consisting of n distribution branches can be constructed. This is used to characterize the load current level of each distribution branch at the current moment, and its expression is:

[0095]

[0096] Furthermore, the commutation state of each execution unit is determined based on the phase data, and represented by a state vector corresponding to the three-phase line. For example, when an execution unit is switched to phase A, its commutation state is represented by the binary code 1; when it is not connected to phase A, it is represented by 0. Then, the commutation state of the i-th execution unit can be represented as a matrix, with the expression:

[0097]

[0098] Therefore, the commutation state combination corresponding to the candidate individual can be represented as a commutation state matrix composed of the state vectors of n execution units, and its expression is:

[0099]

[0100] in, This indicates the access phase corresponding to the i-th execution unit under this candidate individual.

[0101] Based on this, the branch current column vector is matched with the commutation state matrix to determine the post-commutation three-phase load distribution result for each candidate. Specifically, the phase current of phase A can be obtained by summing the currents of each distribution branch connected to phase A in the commutation state matrix; the phase current of phase B can be obtained by summing the currents of each distribution branch connected to phase B; and the phase current of phase C can be obtained by summing the currents of each distribution branch connected to phase C. Thus, the total load current borne by phases A, B, and C after commutation, i.e., the three-phase load distribution result, is obtained.

[0102] Furthermore, the negative-sequence and zero-sequence components of the three-phase currents are calculated. For example, based on the symmetrical component analysis method of the three-phase currents, the negative-sequence and zero-sequence current components can be obtained separately, and then weighted and summed according to preset weights to obtain the comprehensive current imbalance. Its expression is:

[0103]

[0104] in, , These are the preset weights for the negative-sequence component and the zero-sequence component, respectively. These preset weights are used to reflect the relative importance of the negative-sequence component and the zero-sequence component in the three-phase imbalance evaluation, and can be preset according to the actual operation requirements of the distribution network.

[0105] Finally, with minimizing the overall current imbalance as the optimization objective, the first objective function is constructed, and its expression is:

[0106]

[0107] In other words, for any candidate individual during the iteration process of the genetic algorithm, a set of comprehensive current imbalance can be calculated based on the commutation state combination corresponding to that candidate individual. The smaller the comprehensive current imbalance, the more conducive the commutation scheme corresponding to that candidate individual is to achieving balanced distribution of three-phase load. The genetic algorithm can use this to evaluate the merits of different candidate individuals and guide the candidate commutation state combination to continuously evolve in a direction that is more conducive to reducing three-phase imbalance.

[0108] Optionally, in this embodiment, the current actual access state of each execution unit can also be constructed as an initial commutation state matrix. Its expression is:

[0109]

[0110] The aforementioned initial commutation state matrix is ​​used for subsequent comparison with the commutation state matrix corresponding to the candidate individuals to further calculate the number of commutation actions, and participates in comprehensive optimization together with the first objective function.

[0111] Optionally, based on the changes in the execution unit's actions caused by the commutation state combination relative to the initial state, a second objective function is constructed to characterize the degree of equipment wear, including:

[0112] Obtain the initial commutation state of each execution unit; determine the candidate commutation state of each execution unit based on the combination of commutation states corresponding to the candidate individuals; compare the candidate commutation state of each execution unit with the initial commutation state to determine whether each execution unit has performed a commutation action, and count the number of commutations; construct the second objective function with the minimum number of commutations as the optimization objective.

[0113] Specifically, m is defined as the execution unit change factor, the value of which is determined based on the switching commutation state of each execution unit before and after commutation, and its expression is:

[0114]

[0115] in, This represents the actual phase transition state of the i-th execution unit before optimization begins. During the genetic algorithm iteration process, each candidate individual corresponds to a set of candidate phase transition state combinations. This represents the candidate commutation state corresponding to the i-th execution unit under this candidate individual. Based on this, the candidate commutation state corresponding to each execution unit is compared with the initial commutation state one by one to determine whether each execution unit needs to perform a commutation action. If... and If they are the same, it means that the execution unit does not need to take any action when using the current candidate commutation scheme, and the action flag is marked as 0; otherwise, it is marked as 1.

[0116] By summing the action flags of all execution units, the total number of actions performed by the n execution units during this commutation process is:

[0117]

[0118] Generally speaking, the higher the total number of commutations, the more frequently the execution unit operates, resulting in higher device switching losses, mechanical life consumption, and control execution costs. Conversely, the lower the total number of commutations, the lower the requirement for the number of execution unit operations while achieving three-phase load adjustment, which is more conducive to reducing equipment wear.

[0119] Finally, the second objective function is obtained by minimizing the number of commutations:

[0120]

[0121] The first and second objective functions described above respectively describe the improvement effect of current imbalance and the economic loss of the execution unit in the optimization model. Selecting appropriate weights between the two is necessary to optimize the benefits. Therefore, the expression of the objective function is:

[0122]

[0123] Where C0 is the weighting coefficient of the overall current imbalance, and C1 is the weighting coefficient of the number of commutation switch operations.

[0124] By adopting the above scheme, and simultaneously introducing a first objective function characterizing the improvement effect of current imbalance and a second objective function characterizing the economic losses of the execution unit, and then weighting and summing the two to determine the objective function, the balance effect requirements and equipment operation cost requirements in three-phase imbalance management can be incorporated into the same optimization framework for comprehensive evaluation. This ensures that the optimal commutation scheme balances both management effectiveness and economic operability. Furthermore, this objective function helps guide the genetic algorithm to select a scheme that simultaneously satisfies the requirements of current balance improvement and equipment loss control from candidate commutation state combinations, thereby improving the overall effect of three-phase imbalance management in the distribution network.

[0125] Please see Figure 5 Optionally, selection, crossover, and mutation operations are performed based on fitness to iteratively generate a new generation of policy populations, including:

[0126] Step 502: Select parent individuals from the current strategy population based on the fitness of each candidate individual.

[0127] Step 504: Perform a single-point crossover operation on the parent individuals according to the preset crossover probability to obtain the offspring individuals.

[0128] Step 506: Perform mutation operation on the offspring individuals according to the adaptive mutation probability to obtain updated individuals; wherein, the adaptive mutation probability is adjusted based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population.

[0129] Step 508: Generate a new generation of strategy population based on the updated individuals.

[0130] Specifically, the selection operation involves obtaining the fitness of each candidate individual based on the objective function. Then, by comparing the total fitness of all candidate individuals in the strategy population, we can obtain the probability that candidate individual i will be selected. Its expression is:

[0131]

[0132] Where M is the total number of candidate individuals in the current strategy population; k is the summation index; Let be the fitness of the k-th candidate individual in the strategy population.

[0133] According to the roulette wheel selection method, individuals with higher fitness have a greater chance of being selected, thus increasing their probability of becoming parents in the next generation and iterating again as parent individuals to evolve offspring with better traits. Since the objective function of this application aims to minimize current imbalance and commutation frequency, the fitness of each candidate individual is determined inversely based on its corresponding objective function value; that is, the smaller the objective function value, the greater the fitness. For example, the fitness of the i-th candidate individual can be expressed as... ,in, Let be the objective function value of the i-th candidate individual.

[0134] Furthermore, each parent individual is used to represent a set of candidate phase combinations between execution units and three-phase lines. During a single-point crossover operation, the same crossover point is selected in the coding sequences of two parent individuals, and the gene segments following that crossover point are exchanged to generate two offspring individuals. Thus, the local swapping state combinations corresponding to different execution units in the two parent individuals can be recombinated to form new candidate swapping schemes.

[0135] The preset crossover probability controls the frequency of crossover operations during the genetic algorithm's iteration process. If the crossover probability is set too high, it may excessively disrupt the superior gene combinations in parent individuals that have already achieved better fitness; if the crossover probability is set too low, it will reduce the generation efficiency of new individuals and affect the convergence speed of the genetic algorithm. Therefore, the crossover probability should be preset according to the actual optimization accuracy and computational efficiency requirements to ensure that, while retaining superior gene fragments in the parent individuals, recombination between different candidate phase transition state combinations can be achieved, promoting the generation of new candidate individuals and thus improving the genetic algorithm's ability to search for the optimal phase transition scheme.

[0136] Furthermore, this application addresses the problems of slow convergence and susceptibility to local optima in traditional genetic algorithms by optimizing the mutation operation. Specifically, the mutation operation is an improvement on the genetic algorithm that assists in obtaining new individuals, allowing a small number of individuals to mutate according to their probability. Genes to be mutated are randomly selected. The mutation probability controls the number of mutation operations. When the value is too large, the genetic algorithm tends to perform random searches and loses its evolutionary ability. Conversely, the population is more stable, but it is prone to getting stuck in local convergence and finds it difficult to escape. Therefore, this application improves the mutation probability by adjusting it with reference to the average fitness value of the parent population.

[0137] Optionally, the adaptive mutation probability can be adjusted based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population, including:

[0138] Obtain the initial mutation probability; calculate the average fitness of the current strategy population; obtain the fitness of the candidate individuals to be mutated; adjust the initial mutation probability based on the average fitness and the fitness of the candidate individuals to be mutated to obtain the adaptive mutation probability, the expression of which is:

[0139]

[0140] in, The initial mutation probability; The fitness of the candidate individuals to be mutated; This represents the average fitness of the current strategy population.

[0141] Specifically, after completing the selection and crossover operations, the initial mutation probability is obtained first. This initial mutation probability can be preset to a fixed value, which can be obtained through empirical settings, historical data statistics, or simulation debugging.

[0142] Furthermore, the average fitness of the current strategy population and the fitness of the candidate individuals to be mutated are calculated to adjust the initial mutation probability, thereby obtaining an adaptive mutation probability corresponding to the candidate individual. The adjustment strategy should satisfy the following relationship: the greater the difference between the candidate individual to be mutated and the average fitness of the current strategy population, the more significant the adjustment of the corresponding mutation probability; the closer the candidate individual to the average fitness of the current strategy population, the closer the corresponding mutation probability is to the initial mutation probability, thus enabling different candidate individuals to have differentiated mutation intensities during the mutation phase.

[0143] Furthermore, after obtaining the adaptive mutation probability, a mutation operation is performed on the gene coding sequence of the candidate individuals to be mutated to generate updated individuals. For example, the mutation operation can be applied to one or more gene positions in the candidate individuals to change the access phase of the corresponding execution unit, thereby generating new candidate phase-change state combinations. By adopting the above scheme, a new search direction can be introduced while preserving a better gene structure, avoiding premature convergence of the genetic algorithm to a local optimum.

[0144] Optionally, the above-mentioned methods for mitigating three-phase imbalance in power distribution networks also include:

[0145] The execution units acquire operating status parameters, including current data and phase data, according to the detection time interval. Based on the current data, the comprehensive current imbalance is calculated. When the comprehensive current imbalance exceeds a preset threshold multiple times, a genetic algorithm and the operating status parameters are used, with a preset objective function as the evaluation criterion, to iteratively optimize the candidate commutation state combinations for each execution unit, obtaining the optimal commutation scheme, which is then distributed to each execution unit. The aforementioned number of occurrences is calculated by an over-limit counter, and the detection time interval and the over-limit counter are set during the initialization phase.

[0146] The following combination Figure 6 The working principle of the three-phase imbalance mitigation method for power distribution networks proposed in this application is explained in detail below:

[0147] During the initialization phase, the detection time interval and the counter for the number of times the limit is exceeded are set.

[0148] For a main branch in the target area of ​​the distribution network, the three-phase current parameters at its starting end are read according to the detection time interval, and the comprehensive current imbalance is calculated. It is confirmed whether the comprehensive current imbalance exceeds a preset threshold. If it does, the value of the over-limit counter is incremented by 1, and the number of times the preset threshold is exceeded is counted. If it does not exceed the limit, the comprehensive current imbalance is calculated again after the next detection time interval. If it exceeds the limit, the operating status parameters collected by all execution units within the jurisdiction of this main branch are obtained, and the commutation state combination of the execution units is iteratively optimized based on a genetic algorithm. The operating status parameters include current data and phase data.

[0149] The commutation state combination of each execution unit is represented as a chromosome structure that can be processed by the genetic algorithm. It is then encoded into a vector gene and the chromosome population is randomly initialized to generate the initial strategy population.

[0150] In each iteration, the fitness of each candidate individual in the initial strategy population is calculated based on the preset objective function. Then, selection, crossover and mutation operations are performed sequentially based on the fitness to generate a new generation of strategy population until the preset termination condition is met. The candidate individuals that meet the preset termination condition and whose fitness meets the preset screening condition are determined as the optimal swapping scheme.

[0151] The optimal commutation scheme is distributed to all execution units within the jurisdiction of the main branch, so that each execution unit can perform commutation actions based on the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management.

[0152] The aforementioned method for managing three-phase imbalance in distribution networks involves acquiring the three-phase current parameters at the beginning of major branches and calculating the overall current imbalance. When the overall current imbalance exceeds a preset threshold, the method acquires the operating status parameters collected by the execution units located in each distribution branch. Based on a genetic algorithm, the method iteratively optimizes the commutation state combinations of each execution unit to obtain the optimal commutation scheme and controls the execution units to perform the commutation operation. This allows for adaptive adjustment of the connected phases of each distribution branch according to the real-time operating status of the distribution network, achieving optimized reconfiguration of three-phase load distribution. By adopting this scheme, this application not only improves the intelligence level of three-phase imbalance management but also helps reduce the overall current imbalance, decrease line and equipment losses, and improve the stability, safety, and economy of distribution network operation. Furthermore, by optimizing the commutation state combinations as a whole, it also improves the globality and rationality of commutation decisions and enhances adaptability to load fluctuation scenarios.

[0153] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0154] Based on the same inventive concept, this application also provides a system for managing three-phase imbalance in a power distribution network. This system is applicable to the aforementioned method for managing three-phase imbalance in a power distribution network. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations in one or more system embodiments provided below can be found in the limitations of the method described above, and will not be repeated here.

[0155] Please see Figure 2 In one embodiment, the three-phase imbalance management system for the power distribution network includes an execution unit and a control terminal.

[0156] The execution unit is set on each distribution branch to realize the three-phase line electrical connection between the distribution branch and the distribution network, and to collect operating status parameters, including current data and phase data.

[0157] The control terminal, which communicates with the execution unit, is used to acquire the three-phase current parameters at the beginning of the main branches; calculates the comprehensive current imbalance based on the three-phase current parameters, and acquires the operating status parameters collected by the execution unit when the comprehensive current imbalance exceeds a preset threshold; based on the genetic algorithm and the operating status parameters, and using a preset objective function as the evaluation criterion, iteratively optimizes the candidate commutation state combinations of each execution unit to obtain the optimal commutation scheme and distributes it to each execution unit; wherein, the candidate commutation state combinations are used to characterize the possible access phase combinations formed by each execution unit and the three-phase line.

[0158] The execution unit is also used to perform commutation operations according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management.

[0159] The control terminal is the core hub of the entire system, and its installation location needs to be determined based on the main branch conditions.

[0160] Optionally, if there is only one main branch in the distribution network, the control terminal is located on the low-voltage side of the distribution network; if there are multiple main branches in the distribution network, each main branch is equipped with a control terminal, and the control terminal corresponding to each main branch is communicatively connected to each execution unit associated with that main branch.

[0161] Specifically, each execution unit has communication capabilities, and the control terminal corresponding to each main road can establish a communication connection with the execution units within its jurisdiction. Thus, each execution unit can upload the collected operating status parameters to the corresponding control terminal and receive the optimal commutation scheme generated by the control terminal based on the operating status parameters.

[0162] The low-voltage side of the power distribution refers to the side where the power distribution transformer outputs low-voltage electrical energy after stepping down the voltage, and supplies power to the main branches through three-phase lines. Generally speaking, it can be the location of the low-voltage bus, the low-voltage outgoing line, or the low-voltage distribution cabinet.

[0163] In the case of a distribution network with multiple main branches, a control terminal is set up for each main branch. The control terminal can be set at the beginning or end of the corresponding main branch, that is, near the location where the main branch is connected to the upper-level low-voltage bus.

[0164] Optionally, the execution unit includes: a data acquisition subunit and a commutation switch.

[0165] The acquisition subunit is used to collect operating status parameters.

[0166] A phase-switching switch is used to switch the corresponding distribution branch from the currently connected phase to the target connected phase based on the optimal phase-switching scheme while maintaining continuous power supply to the load, thereby adjusting the three-phase load distribution.

[0167] Specifically, the acquisition subunit has communication capabilities, transmitting the acquired operating status parameters to the corresponding control terminal.

[0168] Please see Figure 7 For example, each commutation switch includes three sets of relays, each set of relays is connected in parallel with a pair of anti-parallel thyristors, forming a single-pole three-way switch. Specifically, thyristors VT1 and VT2 are connected in series and then in parallel with relay switch S1 to achieve electrical connection between the load and phase A; thyristors VT3 and VT4 are connected in series and then in parallel with relay switch S2 to achieve electrical connection between the load and phase B; and thyristors VT5 and VT6 are connected in series and then in parallel with relay switch S3 to achieve electrical connection between the load and phase C.

[0169] The following combination Figures 8-11 The commutation principle of the commutation switch is explained in detail.

[0170] After receiving a commutation command from the control terminal carrying the optimal commutation scheme, the commutator switch will execute the commutation operation to adjust the three-phase load distribution at its source, thereby achieving the goal of three-phase imbalance mitigation. During the commutation process, the commutation action should be as rapid as possible to minimize the impact on the load. Taking the switch from phase A to phase B as an example, the current path of the commutator switch uses... Figures 8-11 The black lines with arrows in the text represent...

[0171] Please see Figure 8 Before commutation, the load is connected to phase A through the commutation switch, and the load is powered by the relay branch. At this time, thyristors VT1 and VT2 are disconnected, and relay switch S1 is closed; thyristors VT3 and VT4 are disconnected, and relay switch S2 is open.

[0172] Please see Figure 9 When the control terminal receives the commutation command to switch to phase B, thyristors VT1 and VT2 turn on, and relay switch S1 opens. At this time, the voltage across S1 is equal to the voltage drop across the two thyristors, so relay switch S1 can be safely opened.

[0173] Please see Figure 10 When the load current flows fully through VT1 and VT2, VT1 and VT2 are turned off, causing VT3 and VT4 to enter the conducting state. The switching speed of this process is very fast and has little impact on the commutation time of the overall process.

[0174] Please see Figure 11 Finally, the relay switch S2 is closed to turn off VT3 and VT4, the phase commutation is successful, and the load is powered by phase B, thus completing the commutation operation and adjusting the three-phase load distribution from the source to achieve the purpose of three-phase imbalance control.

[0175] By adopting the above scheme and standardizing the phase switching execution process, uninterrupted and stable phase switching can be achieved, fundamentally improving the three-phase imbalance from the load side, reducing line losses and transformer burden, and improving the power supply quality and operational economy of the distribution network.

[0176] The aforementioned three-phase imbalance management system for distribution networks calculates the comprehensive current imbalance degree through the three-phase current parameters at the beginning of the main branches. When the comprehensive current imbalance degree exceeds a preset threshold, it acquires the operating status parameters collected by the execution units. Based on a genetic algorithm, it iteratively optimizes the commutation state combinations of each execution unit to obtain the optimal commutation scheme and controls the execution units to perform the commutation operation. This enables adaptive adjustment of the phases connected to each distribution branch according to the real-time operating status of the distribution network, achieving optimized reconfiguration of three-phase load distribution. Using this scheme, this application not only improves the intelligence level of three-phase imbalance management but also helps reduce the comprehensive current imbalance degree, decrease line and equipment losses, and improve the stability, safety, and economy of distribution network operation. Simultaneously, by optimizing the commutation state combinations as a whole, it also improves the globality and rationality of commutation decisions and enhances adaptability to load fluctuation scenarios. Furthermore, by standardizing the commutation execution process, it achieves uninterrupted and smooth commutation, fundamentally improving three-phase imbalance from the load side, reducing line losses and transformer burden, and improving the power supply quality and operational economy of the distribution network.

[0177] The modules in the aforementioned three-phase imbalance mitigation system for power distribution networks can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, allowing the processor to call and execute the corresponding operations of each module.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for treating three-phase imbalance of a power distribution network, characterized in that, The power distribution network includes three-phase lines and at least one main branch connected to the three-phase lines. The main branch includes multiple distribution branches, each distribution branch having a corresponding execution unit. The distribution branches are electrically connected to the three-phase lines of the power distribution network through the execution units. The method includes: Obtain the three-phase current parameters at the beginning of the main branch; The comprehensive current imbalance is calculated based on the three-phase current parameters, and when the comprehensive current imbalance exceeds a preset threshold, the operating status parameters collected by the execution unit are obtained. The operating status parameters include current data and phase data. Based on the genetic algorithm and the operating state parameters, and using a preset objective function as the evaluation criterion, the candidate commutation state combinations of each execution unit are iteratively optimized to obtain the optimal commutation scheme, which is then distributed to each execution unit so that each execution unit can perform commutation operations according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management. The candidate commutation state combinations are used to characterize the possible access phase combinations of each execution unit and the three-phase line.

2. The method of claim 1, wherein, The process, based on a genetic algorithm and the operating state parameters, uses a preset objective function as the evaluation criterion to iteratively optimize the candidate commutation state combinations of each execution unit to obtain the optimal commutation scheme, including: Vector encoding is performed on the initial combination in the candidate commutation state combination to generate an initial policy population; wherein, the initial combination is randomly generated; Based on a preset objective function, the fitness of each candidate individual in the initial strategy population is calculated; Based on the fitness, selection, crossover, and mutation operations are performed sequentially to iteratively generate a new generation of strategy populations until a preset termination condition is met. Candidate individuals that meet the preset termination conditions and whose fitness meets the preset screening conditions are determined as the optimal swapping scheme.

3. The method of claim 2, wherein, Methods for pre-constructing the objective function include: A first objective function is constructed to characterize the degree of three-phase current imbalance in order to evaluate the impact of the commutation state combination corresponding to the candidate individual on the three-phase load distribution of the distribution network. A second objective function is constructed to characterize the degree of equipment wear, in order to evaluate the changes in the execution unit action caused by the combination of commutation states corresponding to the candidate individuals relative to the initial commutation state; The first objective function and the second objective function are weighted and summed to obtain the objective function.

4. The method of claim 3, wherein, The construction of the first objective function to characterize the degree of three-phase current imbalance includes: Based on the current data of the distribution branch where each execution unit is located and the commutation state combination corresponding to the candidate individuals, the three-phase load distribution result after commutation is determined; The three-phase currents are determined based on the three-phase load distribution results, and the negative sequence and zero sequence components of the three-phase currents are calculated. The negative sequence component and the zero sequence component are weighted and summed according to preset weights to obtain the comprehensive current imbalance. The first objective function is constructed with minimizing the overall current imbalance as the optimization objective.

5. The method according to claim 3, characterized in that, The construction of the second objective function for characterizing the degree of equipment wear includes: Obtain the initial commutation state of each execution unit; Based on the combination of commutation states corresponding to the candidate individuals, the candidate commutation states corresponding to each execution unit are determined; The candidate commutation state corresponding to each execution unit is compared with the initial commutation state to determine whether each execution unit has performed a commutation action, and the number of commutations is counted. The second objective function is constructed with the goal of minimizing the number of commutations.

6. The method according to claim 2, characterized in that, The step of sequentially performing selection, crossover, and mutation operations based on the fitness to iteratively generate a new generation of strategy population includes: Based on the fitness of each candidate individual, select the parent individual from the current strategy population; According to the preset crossover probability, a single-point crossover operation is performed on the parent individual to obtain the offspring individual; Based on the adaptive mutation probability, a mutation operation is performed on the offspring individuals to obtain updated individuals; wherein, the adaptive mutation probability is adjusted based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population. A new generation of strategy population is generated based on the updated individuals.

7. The method according to claim 6, characterized in that, The method of adjusting the adaptive mutation probability based on the fitness of the candidate individuals to be mutated and the average fitness of the current strategy population includes: Obtain the initial mutation probability; Calculate the average fitness of the current strategy population; Obtain the fitness of candidate individuals to be mutated; The initial mutation probability is adjusted based on the average fitness and the fitness of the candidate individuals to be mutated, to obtain the adaptive mutation probability, which is expressed as follows: in, The initial mutation probability; The fitness of the candidate individuals to be mutated; This represents the average fitness of the current strategy population.

8. A system for mitigating three-phase imbalance in a power distribution network, characterized in that, The power distribution network includes a three-phase line and at least one main branch connected to the three-phase line, the main branch including multiple distribution branches, and the system includes: An execution unit is installed on each of the power distribution branches to realize the electrical connection between the power distribution branch and the three-phase line, and to collect operating status parameters, including current data and phase data. A control terminal, communicatively connected to the execution unit, is used to acquire the three-phase current parameters at the beginning of the main branch; calculate the comprehensive current imbalance based on the three-phase current parameters, and acquire the operating status parameters collected by the execution unit when the comprehensive current imbalance exceeds a preset threshold; based on a genetic algorithm and the operating status parameters, and using a preset objective function as the evaluation criterion, iteratively optimize the candidate commutation state combinations of each execution unit to obtain the optimal commutation scheme and distribute it to each execution unit; wherein, the candidate commutation state combinations are used to characterize the possible access phase combinations formed by each execution unit and the three-phase line; The execution unit is also used to perform commutation operation according to the optimal commutation scheme, adjust the three-phase load distribution, and complete the three-phase imbalance management.

9. The system according to claim 8, characterized in that: When the power distribution network has only one main branch, the control terminal is located on the low-voltage side of the power distribution network. In the case of multiple main branches in the power distribution network, each main branch is equipped with a control terminal; The control terminal corresponding to each main branch is connected to the execution units associated with that main branch.

10. The system according to claim 8, characterized in that, The execution unit includes: The data acquisition subunit is used to collect operating status parameters; A phase-switching switch is used to switch the corresponding distribution branch to the target access phase based on the optimal phase-switching scheme, while maintaining continuous power supply to the load, thereby adjusting the three-phase load distribution.

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