A novel method and device for identifying large branches in distribution networks based on whale swarm optimization algorithm

Abstract

This invention discloses a novel method and apparatus for identifying large branches in a distribution network based on the whale swarm optimization algorithm, addressing the technical problem of low solution efficiency in existing large branch identification methods. The method includes responding to an identification request and constructing objective functions for main distribution transformers and branch transformer load calculations; constructing identification models for main distribution transformers and branch transformers based on the main distribution transformer and branch transformer identification objective functions and preset main distribution transformer and branch transformer identification constraints; constructing branch transformer load calculation models based on the branch transformer load calculation objective functions and preset branch transformer load calculation constraints; using relaxation techniques to transform the identification models for main distribution transformers and branch transformers, and the branch transformer load calculation models, respectively, generating a first unconstrained optimization problem and a second unconstrained optimization problem; and using the whale swarm optimization algorithm to solve the first and second unconstrained optimization problems, outputting the large branch identification results of the distribution network.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a novel method and device for identifying large branches in a distribution network based on a whale swarm optimization algorithm. Background Technology

[0002] In recent years, the large-scale grid connection of distributed power sources, the rapid popularization of electric vehicle charging facilities, and the continuous increase in diversified electricity demand have led to an exponential growth in the load level of distribution networks. Against this backdrop, the scale of large branches in the new distribution network structure is constantly expanding. These branch lines, which carry a high proportion of load, not only significantly increase network losses but also lead to a series of operational problems such as increased voltage fluctuations and decreased power supply reliability.

[0003] In urban power distribution networks with increasingly complex topologies, uneven load distribution among transformers presents significant limitations, even when the complete topology is known. Large-branch diagnostic methods that rely on human experience still have obvious limitations: planners need to check thousands of transformer nodes one by one and analyze massive amounts of operational data. This working mode is not only time-consuming and labor-intensive, but the accuracy of the diagnostic results also depends heavily on personal experience, making it difficult to guarantee consistency.

[0004] Most existing new methods for identifying large branches in distribution networks transform the problem into a binary classification problem and use analytical methods to construct an optimization model containing integer variables to solve it accurately. However, when dealing with large-scale distribution networks, the computational complexity increases exponentially, resulting in low solution efficiency. Summary of the Invention

[0005] This invention provides a novel method and apparatus for identifying large branches in a distribution network based on the whale swarm optimization algorithm, which solves the technical problem of low solution efficiency caused by existing methods for identifying large branches in a distribution network.

[0006] The first aspect of this invention provides a novel method for identifying large branches in a distribution network based on a whale swarm optimization algorithm, comprising:

[0007] In response to the identification request, construct the objective function for the main branch distribution variable and the objective function for calculating the load of the branch distribution variable;

[0008] Based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer, an identification model for the main distribution transformer and the distribution transformer is constructed.

[0009] Based on the objective function for calculating the distributed variable load and the preset constraints for calculating the distributed variable load, a calculation model for the distributed variable load is constructed.

[0010] The relaxation technique is used to transform the identification model of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, respectively, to generate the first unconstrained optimization problem and the second unconstrained optimization problem;

[0011] The whale swarm optimization algorithm is used to solve the first and second unconstrained optimization problems, and the results of large branch identification of the distribution network are output.

[0012] Optionally, the step of using relaxation techniques to transform the identification model of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, to generate a first unconstrained optimization problem and a second unconstrained optimization problem includes:

[0013] The relaxation technique is used to convert the preset main and branch distribution transformer identification constraints in the identification model of the main distribution transformer and the branch distribution transformer into a first penalty term;

[0014] Based on the first penalty term and the objective function of the main and branch distribution variables in the identification model of the main distribution variable and the branch distribution variable, a first unconstrained optimization problem is constructed.

[0015] The relaxation technique is used to convert the preset sub-control variable load calculation constraints in the sub-control variable load calculation model into a second penalty term;

[0016] Based on the second penalty term and the objective function for calculating the distributed variable load in the distributed variable load calculation model, a second unconstrained optimization problem is constructed.

[0017] Optionally, the principal branch's dominant variable objective function is specifically:

[0018] ;

[0019] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0020] Optionally, the objective function for calculating the distributed variable load is specifically:

[0021] ;

[0022] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0023] Optionally, the first unconstrained optimization problem is specifically:

[0024] ;

[0025] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N be the set of all transformers; and P be a constant value. , These are binary variables used to relax constraints; To characterize whether branch ij belongs to the main distribution transformer when it is the terminal node of the main distribution transformer, the binary variable is equal to 1 if it belongs to the main distribution transformer, and 0 otherwise. To characterize whether transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. , Binary slack variables; The set of branches connected to distribution transformer i; It is an integer; For the set of all interconnected transformers; Let L be the set of all substations; L is the set of all branches.

[0026] Optionally, the second unconstrained optimization problem is specifically:

[0027] ;

[0028] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers. The set of branches connected to distribution transformer i; L is an integer; L is the set of all branches; To characterize whether transformer j and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch and 0 otherwise. To characterize whether branch ij belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located, the binary variable is equal to 1 if they belong to the same branch and 0 otherwise. , These are binary slack variables.

[0029] The second aspect of this invention provides a novel large branch identification device for power distribution networks based on a whale swarm optimization algorithm, comprising:

[0030] The objective function construction module is used to respond to identification requests and construct the objective function for the main branch distribution variable and the objective function for calculating the load of the branch distribution variable;

[0031] The first model construction module is used to construct an identification model of the main distribution transformer and the distribution transformer based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer.

[0032] The second model construction module is used to construct a sub-control variable load calculation model based on the sub-control variable load calculation objective function and the preset sub-control variable load calculation constraints.

[0033] The conversion module is used to convert the identification models of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, respectively, using relaxation techniques to generate a first unconstrained optimization problem and a second unconstrained optimization problem.

[0034] The solution module is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem using the whale swarm optimization algorithm, and output the identification results of the large branches of the distribution network.

[0035] A computer device provided in a third aspect of the present invention includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any of the preceding claims.

[0036] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any of the preceding claims.

[0037] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the novel large branch identification method for distribution networks based on the whale swarm optimization algorithm as described in any of the preceding claims.

[0038] As can be seen from the above technical solutions, the present invention has the following advantages:

[0039] The present invention provides a novel method for identifying large branches in a distribution network based on the whale swarm optimization algorithm. When identifying large branches in a distribution network, firstly, objective functions for main distribution transformers and branch distribution transformers and their load calculations are constructed. Next, based on the objective functions and preset constraints for identifying main distribution transformers and branch distribution transformers, identification models for main distribution transformers and branch distribution transformers are constructed. Based on the objective functions and preset constraints for calculating the load of branch distribution transformers, a load calculation model for branch distribution transformers is constructed. Relaxation techniques are used to transform the identification models and load calculation models for main distribution transformers and branch distribution transformers, respectively, generating a first unconstrained optimization problem and a second unconstrained optimization problem. Finally, the whale swarm optimization algorithm is used to solve the first and second unconstrained optimization problems, outputting the identification results of large branches in the distribution network. Based on the above scheme, the present invention uses relaxation techniques to transform the identification models and load calculation models for main distribution transformers and branch distribution transformers into unconstrained optimization problems. Combined with the whale swarm optimization algorithm, this enables rapid identification of large branch lines in the distribution network structure, thereby improving solution efficiency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The flowchart illustrates the steps of a novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm provided in Embodiment 1 of the present invention.

[0042] Figure 2 This is a schematic diagram of the main distribution transformer provided in Embodiment 1 of the present invention;

[0043] Figure 3 This is a schematic diagram of the distribution variation provided in Embodiment 1 of the present invention;

[0044] Figure 4 This is a flowchart of the main distribution transformer acquisition process provided in Embodiment 1 of the present invention;

[0045] Figure 5 This is a flowchart of the large branch judgment provided in Embodiment 1 of the present invention;

[0046] Figure 6 This is a flowchart illustrating a novel method for identifying large branches in a power distribution network based on a whale swarm optimization algorithm, provided in Embodiment 1 of the present invention.

[0047] Figure 7This is a structural block diagram of a novel power distribution network large branch identification device based on the whale swarm optimization algorithm provided in Embodiment 2 of the present invention. Detailed Implementation

[0048] This invention provides a novel method and apparatus for identifying large branches in a distribution network based on the whale swarm optimization algorithm, which solves the technical problem of low solution efficiency caused by existing methods for identifying large branches in a distribution network.

[0049] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm, as provided in Embodiment 1 of the present invention.

[0051] This invention provides a novel method for identifying large branches in a distribution network based on a whale swarm optimization algorithm, comprising:

[0052] Step 101: Respond to the identification request and construct the objective function for the main branch distribution variable and the objective function for calculating the load of the branch distribution variable.

[0053] It should be noted that, in response to an identification request, i.e., when large branch network identification is required, the objective function for the main branch distribution transformer and the objective function for calculating the load of the branch distribution transformer are first constructed. Specifically, the objective function for the main branch distribution transformer is as follows:

[0054] ;

[0055] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0056] The objective function for calculating the distributed variable load is as follows:

[0057] ;

[0058] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0059] Step 102: Based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer, construct the identification model of the main distribution transformer and the distribution transformer.

[0060] It should be noted that the variables in the identification model M1 for main distribution transformers and branch distribution transformers are... , ,in To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. To characterize whether branch ij belongs to the main distribution transformer at the end node of the connecting distribution transformer, the binary variable is set to 1 if it belongs and 0 otherwise. The parameters involved in model M1 are: And M, of which Let M be the load value carried by distribution transformer i, where M is a relatively large integer. The set involved in model M1 includes N, L, S, K, and L. i Where N is the set of all distribution transformers, is the set of all branches, S is the set of all substations, K is the set of all interconnecting distribution transformers, and L... i Let L be the set of branches connected to transformer i, and let L be the set of all branches.

[0061] Furthermore, regarding the meaning of the feeder backbone, such as Figure 2 As shown. The main trunk line between the feeder transformer and the connecting transformer k represents the maximum load path connecting the two. Transformers that do not belong to any main trunk line are considered branch transformers. Figure 2 In this configuration, the main distribution transformers between the main transformer and the connecting distribution transformer 12 include distribution transformers 2 to 11. Similarly, when considering the connecting distribution transformer 30, the main distribution transformers include distribution transformers 2 to 6 and distribution transformers 27 to 29. Branches are as follows... Figure 3 The part shown is enclosed in a green dashed line.

[0062] Furthermore, the objective function for constructing the main trunk distribution transformer is to find the distribution transformers covered by the line with the largest intermediate load when the feeder main transformer is the starting point and the connecting distribution transformer k is the ending point, that is, to find a main trunk.

[0063] Furthermore, the preset main trunk distribution transformer identification constraints include main trunk line constraints, line connection constraints, main transformer and interconnection distribution transformer constraints.

[0064] For trunk line constraints, when obtaining the trunk line from the feeder transformer to the connecting transformer k, the following constraints need to be used to limit the number of branches in the transformer to ensure that branch transformers are not included in the trunk line:

[0065] ;

[0066] The meaning of the main line constraint is: the number of branches connected to each distribution transformer in the main line is less than 2, so that the main line between the main transformer and the connecting distribution transformer k is a unique line.

[0067] The line connectivity constraints for model M1 are as follows:

[0068] ;

[0069] ;

[0070] The meaning of the line connectivity constraint is: when line ij belongs to the main trunk, both distribution transformer i and distribution transformer j at its two points belong to the main trunk.

[0071] The constraints for the main transformer and the interconnecting transformer are as follows:

[0072] ;

[0073] Among them, the constraints of the main transformer and the interconnection transformer indicate that both the main transformer and the interconnection transformer belong to the backbone.

[0074] Step 103: Construct a calculation model for the distributed variable load based on the objective function for calculating the distributed variable load and the preset constraints for calculating the distributed variable load.

[0075] It should be noted that the variables in the load calculation model (sub-branch load calculation model) M2 for each branch where the substation is located are... , ,in To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. To characterize whether branch ij belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located, a binary variable is used. It is equal to 1 if they belong to the same branch, and 0 otherwise.

[0076] Furthermore, the objective function for calculating the load of the distribution transformer is similar to the objective function of model M1 (the objective function of the main distribution transformer), except that its purpose is to find all distribution transformers that are on the same branch as distribution transformer c.

[0077] Furthermore, the preset load calculation constraints for distribution transformers include main distribution transformer isolation constraints and line connectivity constraints; the main distribution transformer isolation constraints are as follows:

[0078] ;

[0079] Since the purpose of model M2 is to find all distribution transformers on the same branch as distribution transformer c, the main distribution transformer should be excluded. The set N obtained from model M1... z It includes all the main distribution transformers, therefore let it belong to N. z variables in Take 0 for all.

[0080] The line connectivity constraints are as follows:

[0081] ;

[0082] ;

[0083] The meaning of the line connectivity constraint is: when line ij belongs to the branch where c is located, the distribution transformers i and j at its two points both belong to that branch.

[0084] Step 104: Use relaxation techniques to transform the identification model of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, respectively, to generate the first unconstrained optimization problem and the second unconstrained optimization problem.

[0085] It should be noted that both Model M1 and Model M2 are pure integer optimization problems, and the variables are all binary integer variables, without any continuous variables. Therefore, they can be transformed into unconstrained problems through appropriate relaxation techniques.

[0086] Specifically, step 104 may include the following sub-steps S41-S44:

[0087] Step S41: Use relaxation techniques to convert the preset main and branch distribution transformer identification constraints in the identification model of main distribution transformers and branch distribution transformers into the first penalty term;

[0088] Step S42: Based on the first penalty term and the objective function of the main distribution variable and the distribution variable in the identification model of the main distribution variable and the distribution variable, construct the first unconstrained optimization problem;

[0089] The first penalty item includes penalties for trunk line constraints, penalties for line connectivity constraints, and penalties for main transformer and interconnecting transformer constraints.

[0090] It should be noted that the key to transforming an unconstrained optimization problem using relaxation techniques is converting the constraints in the original problem into penalty terms in the objective function. The penalty term for the backbone constraint is shown below:

[0091] ;

[0092] Where P is a relatively large constant value; , This is a binary variable used to relax constraints. Because for a given transformer i, the main line constraints ( The minimum value on the left side of the equation is 0, which is 2 away from the constraint constant term. Therefore, two binary variables are added for relaxation. The meaning is: if If the value is not equal to 2, then This will become a large value and penalize the objective function; at the same time, because , All are greater than 0, therefore it can be guaranteed The left-hand side item is less than 2.

[0093] Furthermore, the penalty terms for line connectivity constraints are as follows:

[0094] ;

[0095] ;

[0096] in, , These are binary slack variables.

[0097] Furthermore, the penalty terms for the constraints on the main transformer and the interconnecting transformer are as follows:

[0098] ;

[0099] In summary, the first unconstrained optimization problem of model M1 transformation is as follows:

[0100] ;

[0101] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N be the set of all transformers; and P be a constant value. , These are binary variables used to relax constraints; To characterize whether branch ij belongs to the main distribution transformer when it is the terminal node of the main distribution transformer, the binary variable is equal to 1 if it belongs to the main distribution transformer, and 0 otherwise. To characterize whether transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. , Binary slack variables; The set of branches connected to distribution transformer i; It is an integer; For the set of all interconnected transformers; Let L be the set of all substations; L is the set of all branches.

[0102] Step S43: Use relaxation techniques to convert the preset sub-controllable variable load calculation constraints in the sub-controllable variable load calculation model into a second penalty term;

[0103] Step S44: Based on the second penalty term and the objective function of the distributed variable load calculation model, construct the second unconstrained optimization problem.

[0104] The second penalty item includes penalties for main transformer isolation constraints and penalties for line connectivity constraints.

[0105] It should be noted that the transformation process of model M2 is similar to that of model M1. The penalty term for the isolation constraint of the main distribution transformer is as follows:

[0106] ;

[0107] Furthermore, the penalty term for line connectivity constraints is as follows:

[0108] ;

[0109] ;

[0110] in, , These are binary slack variables.

[0111] In summary, the second unconstrained optimization problem transformed from model M2 is as follows:

[0112] ;

[0113] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers. The set of branches connected to distribution transformer i; L is an integer; L is the set of all branches; To characterize whether transformer j belongs to the same branch as transformer c when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. To characterize whether branch ij belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located, the binary variable is equal to 1 if they belong to the same branch and 0 otherwise. , These are binary slack variables.

[0114] Step 105: Use the whale swarm optimization algorithm to solve the first and second unconstrained optimization problems, and output the identification results of the large branches of the distribution network.

[0115] The results of the distribution network major branch identification include the main distribution transformer set, the branch distribution transformer set, the load value corresponding to each branch distribution transformer in the branch distribution transformer set, and the major branch set.

[0116] It's important to note that the Whale Optimization Algorithm (WOA) is a cutting-edge, advanced heuristic optimization technique modeled after the unique hunting strategies of humpback whales. These whales employ complex spiral bubble web strategies to surround prey, and WOA mimics this behavior to efficiently explore the search space and find the optimal solution. In this algorithm, each candidate solution in the search space is treated as a whale, while the optimal solution is analogous to the location of the target prey. The algorithm comprises three main phases to simulate the foraging behavior of humpback whales: the prey-surrounding phase, the spiral position adjustment phase, and the random exploration phase.

[0117] Furthermore, during the prey-hunting phase, the humpback whale identifies and surrounds its prey. This is achieved in the algorithm using the following formula:

[0118] ;

[0119] ;

[0120] Where t represents the current iteration number; This corresponds to the position of the best solution found so far in the current iteration (i.e., the best binary variable value); This represents the position of the whale considered in this iteration (i.e., the value of the binary variable in this iteration); vectors A and C are coefficient parameters, while D measures the distance between the current whale and the optimal solution. The expressions for calculating A and C are as follows:

[0121] ;

[0122] ;

[0123] Among them, parameters The values ​​of A and C decrease linearly from 2 to 0, while r is a random vector within the interval [0,1]. By dynamically adjusting the values ​​of A and C, whales can navigate the search space in multiple ways, which enhances their ability to efficiently find the optimal solution.

[0124] Furthermore, during the spiral position update phase, the humpback whale swims in a spiral trajectory around its target prey. The algorithm simulates this behavior using the following formula:

[0125] ;

[0126] Where b is a constant defining the shape of the logarithmic spiral, and Within the range The random number within the range. By using this formula, the whale can perform a detailed search around the optimal solution, thereby significantly improving the algorithm's local optimization capability.

[0127] Furthermore, during the random search phase, humpback whales randomly search for prey within their search space. When the value is ≥1, the whale randomly selects a location in the search space to search. This helps the algorithm escape local optima and enhances its global search capability. The whale's position update formula at this time is:

[0128] ;

[0129] ;

[0130] in, This represents a position randomly selected in the search space at the current iteration t.

[0131] In summary, the whale swarm optimization algorithm is used to solve the first unconstrained optimization problem, obtaining the main distribution transformer set and the branch distribution transformer set. The whale swarm optimization algorithm is then used to solve the second unconstrained optimization problem, obtaining the load value of each branch distribution transformer in the branch distribution transformer set. Finally, combined with existing methods, the load value of each branch distribution transformer is used to determine whether the corresponding branch is a large branch.

[0132] For comparison of technical effectiveness, existing technologies can be referenced. With the development of new power systems, the load level within the distribution network is increasing daily, and the scale of large branches in the network structure is growing rapidly, seriously affecting the economy and reliability of distribution network operation. Due to the complex topology of the distribution network and the uneven load distribution among transformers, even if the distribution network topology is known, relying on manual large branch diagnosis methods still consumes a lot of manpower and resources, which is not conducive to the efficient implementation of planning operations. The existing approach is to transform it into a binary classification problem, determining whether each transformer belongs to the main trunk or a branch of the feeder, and statistically analyzing the load value of each branch to determine whether it is a large branch. Commonly used techniques include analytical methods and heuristic methods. Among them, analytical methods require solving large-scale integer optimization problems, resulting in low solution efficiency; heuristic methods cannot guarantee that the results conform to physical constraints, making it difficult to guarantee rationality. The novel whale swarm optimization method has become a new approach to solving integer optimization problems, which can simultaneously guarantee solution efficiency and result rationality, and therefore has certain applicability potential in the problem of large branch identification in distribution networks.

[0133] Further, see Figures 4-5Current methods use model M1 to obtain all main distribution transformers and branch distribution transformers, and model M2 to determine whether the branch to which each branch distribution transformer belongs is a large branch. However, this method is inefficient in large-scale distribution networks. Therefore, this invention uses relaxation techniques to transform it into an unconstrained optimization problem and uses a quantum optimization algorithm for efficient solution. For details, please refer to [link to relevant documentation]. Figure 6 First, an integer optimization model with pure binary variables is constructed. This model can directly classify distribution transformers into two categories—main distribution transformers and branch distribution transformers—based on the feeder topology, the interconnection relationships between each feeder, and the load value carried by the distribution transformers. It also obtains the load value carried by each connecting branch, using this as a basis to determine whether a branch belongs to a large branch. Directly solving this model would result in low computational efficiency. Therefore, an equivalent relaxation technique is used to transform the model into an unconstrained optimization problem. Then, using this unconstrained optimization problem as the fitness function, the whale swarm optimization algorithm is applied to achieve efficient and accurate solution of the model. Compared with existing technologies, this invention constructs a pure integer optimization model for identifying the main feeder lines and high-load branches of a distribution network, achieving accurate diagnosis of large branch problems in the distribution network. Simultaneously, relaxation techniques are used to transform the model into an equivalent quadratic unconstrained binary optimization problem, providing a foundation for accelerating the application of optimization algorithms. Furthermore, the whale swarm optimization algorithm is applied to quickly solve the integer optimization model, helping planning personnel efficiently diagnose large branch problems in the distribution network. Specifically, the whale swarm optimization algorithm enables intelligent identification of large branch lines in new distribution network structures, reducing the human and material costs in the process of constructing the planning problem database.

[0134] In summary, to address the problem of low solution efficiency in large-scale distribution networks, this invention employs relaxation techniques to transform the integer optimization model into an unconstrained optimization problem and introduces the whale pod optimization algorithm for efficient solution. The whale pod optimization algorithm simulates the hunting behavior of humpback whales, consisting of three stages: prey encirclement, spiral position adjustment, and random search. It dynamically adjusts parameters to efficiently explore the solution space and find the optimal solution. Compared to traditional methods, this invention significantly improves solution efficiency while maintaining accuracy, reduces the human and material costs in constructing the planning problem library, and provides a new solution for the problem of identifying large branches in distribution networks.

[0135] In this embodiment of the invention, a novel method for identifying large branches in a distribution network based on the whale swarm optimization algorithm is provided. When identifying large branches in a distribution network, firstly, objective functions for main distribution transformers and sub-distribution transformers and their load calculations are constructed. Next, based on the objective functions and preset constraints for identifying main distribution transformers and sub-distribution transformers, identification models for main distribution transformers and sub-distribution transformers are constructed. Based on the load calculation objectives and preset constraints for sub-distribution transformers, a load calculation model for sub-distribution transformers is constructed. Relaxation techniques are used to transform the identification models and load calculation models for main distribution transformers and sub-distribution transformers, respectively, generating a first unconstrained optimization problem and a second unconstrained optimization problem. Finally, the whale swarm optimization algorithm is used to solve the first and second unconstrained optimization problems, outputting the identification results of large branches in the distribution network. Based on the above scheme, this invention uses relaxation techniques to transform the identification models and load calculation models for main distribution transformers and sub-distribution transformers into unconstrained optimization problems. Combined with the whale swarm optimization algorithm, this enables rapid identification of large branch lines in the distribution network structure, thereby improving solution efficiency.

[0136] Please see Figure 7 , Figure 7 This is a structural block diagram of a novel power distribution network large branch identification device based on the whale swarm optimization algorithm provided in Embodiment 2 of the present invention.

[0137] This invention provides a novel large branch identification device for power distribution networks based on a whale swarm optimization algorithm, comprising:

[0138] The objective function construction module 701 is used to respond to the identification request and construct the objective function of the main branch distribution transformer and the objective function for calculating the load of the branch distribution transformer.

[0139] The first model construction module 702 is used to construct an identification model of the main distribution transformer and the distribution transformer based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer.

[0140] The second model construction module 703 is used to construct the sub-control variable load calculation model based on the sub-control variable load calculation objective function and the preset sub-control variable load calculation constraints.

[0141] The conversion module 704 is used to convert the identification model of the main distribution transformer and the branch distribution transformer and the load calculation model of the branch distribution transformer respectively using relaxation techniques to generate the first unconstrained optimization problem and the second unconstrained optimization problem.

[0142] The solver module 705 is used to solve the first and second unconstrained optimization problems using the whale swarm optimization algorithm, and output the identification results of the large branches of the distribution network.

[0143] Furthermore, the conversion module 704 is specifically used for:

[0144] The relaxation technique is used to convert the preset main and branch distribution transformer identification constraints in the identification model of main distribution transformers and branch distribution transformers into the first penalty term;

[0145] Based on the first penalty term and the objective function of the main and branch distribution variables in the identification model of the main distribution variable and the branch distribution variable, the first unconstrained optimization problem is constructed.

[0146] The relaxation technique is used to convert the preset sub-control variable load calculation constraints in the sub-control variable load calculation model into a second penalty term;

[0147] Based on the second penalty term and the objective function for calculating the distributed variable load in the distributed variable load calculation model, a second unconstrained optimization problem is constructed.

[0148] Furthermore, the principal component governs the variable objective function, specifically:

[0149] ;

[0150] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0151] Furthermore, the objective function for calculating the distributed variable load is as follows:

[0152] ;

[0153] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

[0154] Furthermore, the first unconstrained optimization problem is specifically:

[0155] ;

[0156] in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N be the set of all transformers; and P be a constant value. , These are binary variables used to relax constraints; To characterize whether branch ij belongs to the main distribution transformer when it is the terminal node of the main distribution transformer, the binary variable is equal to 1 if it belongs to the main distribution transformer, and 0 otherwise. To characterize whether transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. , Binary slack variables; The set of branches connected to distribution transformer i; It is an integer; For the set of all interconnected transformers; Let L be the set of all substations; L is the set of all branches.

[0157] Furthermore, the second unconstrained optimization problem is as follows:

[0158] ;

[0159] in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers. The set of branches connected to distribution transformer i; L is an integer; L is the set of all branches; To characterize whether transformer j and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch and 0 otherwise. To characterize whether branch ij belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located, the binary variable is equal to 1 if they belong to the same branch and 0 otherwise. , These are binary slack variables.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0161] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any of the above embodiments.

[0162] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any of the above embodiments.

[0163] This invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any of the above embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A novel method for identifying large branches in a distribution network based on a whale swarm optimization algorithm, characterized in that, include: In response to the identification request, construct the objective function for the main branch distribution variable and the objective function for calculating the load of the branch distribution variable; Based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer, an identification model for the main distribution transformer and the distribution transformer is constructed. Based on the objective function for calculating the distributed variable load and the preset constraints for calculating the distributed variable load, a calculation model for the distributed variable load is constructed. The relaxation technique is used to transform the identification model of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, respectively, to generate the first unconstrained optimization problem and the second unconstrained optimization problem; The whale swarm optimization algorithm is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem, and the results of large branch identification of the distribution network are output. The relaxation technique is used to transform the identification model of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, to generate a first unconstrained optimization problem and a second unconstrained optimization problem, including: The relaxation technique is used to convert the preset main and branch distribution transformer identification constraints in the identification model of the main distribution transformer and the branch distribution transformer into a first penalty term; Based on the first penalty term and the objective function of the main and branch distribution variables in the identification model of the main distribution variable and the branch distribution variable, a first unconstrained optimization problem is constructed. The relaxation technique is used to convert the preset sub-control variable load calculation constraints in the sub-control variable load calculation model into a second penalty term; Based on the second penalty term and the objective function for calculating the distributed variable load in the distributed variable load calculation model, a second unconstrained optimization problem is constructed.

2. The novel large branch identification method for power distribution networks based on whale swarm optimization algorithm according to claim 1, characterized in that, The main branch's dominant variable objective function is specifically as follows: ； in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

3. The novel large branch identification method for power distribution networks based on whale swarm optimization algorithm according to claim 1, characterized in that, The objective function for calculating the distributed variable load is specifically as follows: ； in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers.

4. The novel large branch identification method for power distribution networks based on whale swarm optimization algorithm according to claim 1, characterized in that, The first unconstrained optimization problem is as follows: ； in, To characterize whether transformer i belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. Let N be the load value carried by transformer i; N be the set of all transformers; and P be a constant value. , These are binary variables used to relax constraints; To characterize whether branch ij belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer, the binary variable is equal to 1 if it belongs to the main distribution transformer, and 0 otherwise. To characterize whether transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk line, the binary variable is equal to 1 if it belongs to the main distribution transformer trunk line, and 0 otherwise. , Binary slack variables; The set of branches connected to distribution transformer i; It is an integer; For the set of all interconnected transformers; Let L be the set of all substations; L is the set of all branches.

5. The novel large branch identification method for power distribution networks based on whale swarm optimization algorithm according to claim 1, characterized in that, The second unconstrained optimization problem is as follows: ； in, To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. Let N be the load value carried by transformer i; N is the set of all transformers. The set of branches connected to distribution transformer i; L is an integer; L is the set of all branches; To characterize whether transformer j belongs to the same branch as transformer c when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. To characterize whether branch ij belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. , Binary slack variables; To characterize whether transformer i and transformer c belong to the same branch when calculating the load of the branch where the c-th transformer is located, a binary variable is used; it is equal to 1 if they belong to the same branch, and 0 otherwise. This is the set obtained from the identification model of the main distribution variable and the branch distribution variable.

6. A novel distribution network large branch identification device based on whale swarm optimization algorithm, applied to the novel distribution network large branch identification method based on whale swarm optimization algorithm described in claim 1, characterized in that, include: The objective function construction module is used to respond to identification requests and construct the objective function for the main branch distribution variable and the objective function for calculating the load of the branch distribution variable. The first model construction module is used to construct an identification model of the main distribution transformer and the distribution transformer based on the objective function of the main distribution transformer and the preset identification constraints of the main distribution transformer. The second model construction module is used to construct a sub-control variable load calculation model based on the sub-control variable load calculation objective function and the preset sub-control variable load calculation constraints. The conversion module is used to convert the identification models of the main distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer, respectively, using relaxation techniques to generate a first unconstrained optimization problem and a second unconstrained optimization problem. The solution module is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem using the whale swarm optimization algorithm, and output the identification results of large branches of the distribution network.

7. A computer device, characterized in that, The device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the novel large branch identification method for power distribution networks based on the whale swarm optimization algorithm as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the novel large branch identification method for distribution networks based on the whale swarm optimization algorithm as described in any one of claims 1-5.

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