Novel power distribution network large branch identification method and device based on whale swarm optimization algorithm

Abstract

The invention discloses a novel power distribution network large branch identification method and device based on a whale swarm optimization algorithm, which are used for solving the technical problem of low solving efficiency caused by the existing power distribution network large branch identification method. The method comprises the following steps: in response to an identification request, constructing a main branch distribution transformer target function and a branch distribution transformer load calculation target function; according to the trunk and branch distribution transformer target function and a preset trunk and branch distribution transformer identification constraint condition, constructing an identification model of the trunk distribution transformer and the branch distribution transformer; constructing a branch distribution transformer load calculation model according to the branch distribution transformer load calculation objective function and a preset branch distribution transformer load calculation constraint condition; respectively converting the identification model of the main distribution transformer and the branch distribution transformer and the load calculation model of the branch distribution transformer by adopting a relaxation technology to generate a first unconstrained optimization problem and a second unconstrained optimization problem; and solving the first unconstrained optimization problem and the second unconstrained optimization problem by adopting a whale swarm optimization algorithm, and outputting a power distribution network large branch identification result.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a novel method and device for identifying large branches of a distribution network based on a whale swarm optimization algorithm. Background Art

[0002] In recent years, the large-scale integration of distributed power sources, the rapid adoption of electric vehicle charging facilities, and the continued rise in diversified electricity demand have led to exponential growth in distribution network loads. Against this backdrop, the scale of large branches within the new distribution network architecture has continued to expand. These branch lines, carrying a high proportion of load, not only significantly increase network losses but also lead to a series of operational issues, such as increased voltage fluctuations and reduced power supply reliability.

[0003] In urban distribution networks with increasingly complex topologies, uneven load distribution among distribution transformers is a common problem. Even when the complete topology is known, large-branch diagnostic methods that rely on manual experience still have obvious limitations: planners need to check thousands of distribution transformer nodes one by one and analyze massive amounts of operating data. This working mode is not only time-consuming and labor-intensive, but the accuracy of the diagnostic results is highly dependent on personal experience, making it difficult to ensure consistency.

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

[0005] The present invention provides a novel method and device for identifying large branches in a distribution network based on a whale swarm optimization algorithm, which is used to solve the technical problem of low solution efficiency caused by existing methods for identifying large branches in distribution networks.

[0006] The first aspect of the present 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 trunk branch transmission objective function and the branch transmission load calculation objective function;

[0008] According to the trunk-branch distribution transformer objective function and the preset trunk-branch distribution transformer identification constraint conditions, an identification model of the trunk distribution transformer and the branch distribution transformer is constructed;

[0009] Constructing a branch transformer load calculation model according to the branch transformer load calculation objective function and preset branch transformer load calculation constraint conditions;

[0010] Using relaxation technology to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the branch distribution transformer load calculation model respectively, to generate a first unconstrained optimization problem and a second unconstrained optimization problem;

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

[0012] Optionally, the use of relaxation technology to convert the identification models of the trunk distribution transformer and the branch distribution transformer and the branch distribution transformer load calculation model to generate a first unconstrained optimization problem and a second unconstrained optimization problem includes:

[0013] The relaxation technology is used to convert the preset trunk distribution transformer identification constraint condition in the identification model of the trunk distribution transformer and the branch distribution transformer into a first penalty term;

[0014] Constructing a first unconstrained optimization problem according to the first penalty term and the trunk distribution transformer objective function in the identification model of the trunk distribution transformer and the branch distribution transformer;

[0015] The relaxation technology is used to convert the preset branch variable load calculation constraint condition in the branch variable load calculation model into a second penalty term;

[0016] A second unconstrained optimization problem is constructed according to the second penalty term and the branch variable load calculation objective function in the branch variable load calculation model.

[0017] Optionally, the trunk branch variable objective function is specifically:

[0018] ;

[0019] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

[0020] Optionally, the branch variable load calculation objective function is specifically:

[0021] ;

[0022] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

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

[0024] ;

[0025] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; P is a constant value; 、 is a binary variable used to relax constraints; It is a binary variable that represents whether the branch ij belongs to the main distribution transformer at the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the distribution transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; 、 is a binary slack variable; is the set of branches connected to distribution transformer i; is an integer; is the set of all interconnected distribution transformers; is the set of all substations; L is the set of all branches.

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

[0027] ;

[0028] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; is the set of branches connected to distribution transformer i; is an integer; L is the set of all branches; A binary variable representing whether distribution transformer j belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the branch ij belongs to the same branch as the distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If it does, it is equal to 1, otherwise it is 0; 、 is a binary slack variable.

[0029] A second aspect of the present invention provides a novel large branch identification device for a distribution network based on a whale swarm optimization algorithm, comprising:

[0030] An objective function building module is used to respond to identification requests and build a trunk branch variable load objective function and a branch variable load calculation objective function;

[0031] A first model building module is used to build an identification model of trunk distribution transformer and branch distribution transformer according to the trunk branch distribution transformer objective function and preset trunk branch distribution transformer identification constraint conditions;

[0032] A second model building module is used to build a branch variable load calculation model according to the branch variable load calculation objective function and preset branch variable load calculation constraint conditions;

[0033] a conversion module, configured to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer respectively by using a relaxation technique, to generate a first unconstrained optimization problem and a second unconstrained optimization problem;

[0034] A solution module is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem using a whale group optimization algorithm, and output a distribution network large branch identification result.

[0035] The third aspect of the present invention provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the new distribution network large branch identification method based on the whale group optimization algorithm as described in any one of the above items.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the new method for identifying large branches of a distribution network based on a whale group optimization algorithm as described in any one of the above.

[0037] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the new distribution network large branch identification method based on the whale group optimization algorithm as described in any one of the above items.

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

[0039] The above-mentioned solution of the present invention provides a novel method for identifying large branches in a distribution network based on the whale swarm optimization algorithm. When large branches in a distribution network need to be identified, the method first constructs a trunk-branch transformer objective function and a branch transformer load calculation objective function. Then, based on the trunk-branch transformer objective function and preset trunk-branch transformer identification constraints, an identification model for the trunk distribution transformer and the branch transformer is constructed. Based on the branch transformer load calculation objective function and preset branch transformer load calculation constraints, a branch transformer load calculation model is constructed. A relaxation technique is used to transform the trunk distribution transformer and branch transformer identification model and the branch transformer load calculation model, respectively, to generate 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, and the distribution network large branch identification results are output. Based on the above-mentioned solution, the present invention uses relaxation technology to transform the trunk distribution transformer and branch transformer identification model and the branch transformer load calculation model into an unconstrained optimization problem. Combined with the whale swarm optimization algorithm, this method can achieve rapid identification of large branch lines in the distribution network, thereby improving solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flowchart of the steps of a novel method for identifying large branches in a distribution network based on a whale group optimization algorithm provided in Example 1 of the present invention;

[0042] Figure 2 Schematic diagram of a trunk distribution transformer provided in the first embodiment of the present invention;

[0043] Figure 3 A schematic diagram of branch switching provided in the first embodiment of the present invention;

[0044] Figure 4 This is a flowchart of obtaining a trunk distribution transformer according to the first embodiment of the present invention;

[0045] Figure 5 This is a large branch judgment flow chart provided in Example 1 of the present invention;

[0046] Figure 6 A schematic flow chart of a novel method for identifying large branches in a distribution network based on a whale swarm optimization algorithm according to the first embodiment of the present invention;

[0047] Figure 7This is a structural block diagram of a new distribution network large branch identification device based on the whale group optimization algorithm provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0048] The embodiments of the present invention provide a novel method and device for identifying large branches in a distribution network based on a whale swarm optimization algorithm, which are used to solve 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] See also Figure 1 , Figure 1 A flowchart of the steps of a new method for identifying large branches in a distribution network based on a whale group optimization algorithm provided in Example 1 of the present invention.

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

[0052] Step 101: In response to an identification request, a trunk branch transmission objective function and a branch transmission load calculation objective function are constructed.

[0053] It should be noted that in response to the identification request, that is, when it is necessary to identify the major branches of the distribution network, the trunk-branch distribution objective function and the branch distribution load calculation objective function are first constructed. Among them, the trunk-branch distribution objective function is specifically:

[0054] ;

[0055] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

[0056] The objective function for branch variable load calculation is:

[0057] ;

[0058] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

[0059] Step 102: Construct an identification model of trunk distribution transformer and branch distribution transformer according to the trunk branch distribution transformer objective function and preset trunk branch distribution transformer identification constraint conditions.

[0060] It should be noted that the variables in the identification model M1 of the trunk distribution transformer and branch distribution transformer are 、 ,in It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the branch ij belongs to the main distribution transformer at the terminal node of the interconnected distribution transformer. If it does, it is equal to 1, otherwise it is 0. The parameters involved in model M1 are and M, where is the load value carried by distribution transformer i, and M is a large integer. The sets involved in model M1 include N, L, S, K, 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 interconnected distribution transformers, L i is the set of branches connected to distribution transformer i, and L is the set of all branches.

[0061] Furthermore, the meaning of feeder trunk, such as Figure 2 As shown in Figure 1. The trunk between the feeder main transformer and the interconnecting distribution transformer k is the maximum load path connecting the two. Distribution transformers that do not belong to any trunk are considered branch transformers. Figure 2 In the example, the trunk distribution transformer between the main transformer and the interconnecting distribution transformer 12 includes distribution transformers 2 to 11. Similarly, when considering interconnecting distribution transformer 30, the trunk distribution transformer includes distribution transformers 2 to 6 and distribution transformers 27 to 29. Figure 3 The part surrounded by the green dotted line is shown in the figure.

[0062] Furthermore, for the constructed trunk branch distribution transformer objective function, the meaning of this objective function is to find the distribution transformer covered by the line with the maximum intermediate load when the feeder main transformer is the starting point and the connecting distribution transformer k is the end point, that is, to find a trunk.

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

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

[0065] ;

[0066] The trunk line constraint means that the number of branches connected to each distribution transformer in the trunk is less than 2, so that the trunk between the main transformer and the interconnecting distribution transformer k is the only 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 trunk, the distribution transformers i and j at its two points also belong to the trunk.

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

[0072] ;

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

[0074] Step 103: Construct a branch transformer load calculation model according to the branch transformer load calculation objective function and preset branch transformer load calculation constraint conditions.

[0075] It should be noted that the variables in the load calculation model M2 of the branch where each branch transformer is located (branch transformer load calculation model) are 、 ,in A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; It is a binary variable that represents 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. If it does, it is equal to 1, otherwise it is 0.

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

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

[0078] ;

[0079] Since the purpose of model M2 is to find all distribution transformers in the same branch as distribution transformer c, the trunk distribution transformer should be excluded. The set N obtained by model M1 is z All the main distribution transformers are included in N z Variables in All are set to 0.

[0080] The line connectivity constraints are as follows:

[0081] ;

[0082] ;

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

[0084] Step 104 : Using relaxation technology, respectively convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the branch distribution transformer load calculation model to generate a first unconstrained optimization problem and a 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, and do not contain continuous variables. Therefore, they can be converted into unconstrained problems through appropriate relaxation techniques.

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

[0087] Step S41: Using relaxation technology, convert the preset trunk distribution transformer identification constraint condition in the identification model of the trunk distribution transformer and the branch distribution transformer into a first penalty term;

[0088] Step S42: construct a first unconstrained optimization problem based on the first penalty term and the trunk-branch distribution transformer objective function in the identification model of the trunk distribution transformer and the branch distribution transformer;

[0089] The first penalty item includes the penalty item of trunk line constraint, the penalty item of line interconnection constraint, and the penalty item of main transformer and interconnection distribution transformer constraint.

[0090] It should be noted that the key to converting unconstrained optimization problems using relaxation techniques is to convert the constraints in the original problem into penalty terms in the objective function. The penalty terms for the trunk line constraints are as follows:

[0091] ;

[0092] Among them, P is a large constant value; 、 is a binary variable used to relax the constraint. Since for a certain distribution transformer i, the trunk line constraint ( ) The minimum value on the left is 0, and the gap with the constraint constant term is 2, so two binary variables are added for relaxation. The meaning is: If If the value of is not equal to 2, then will become a larger value and penalize the objective function; at the same time, due to 、 are greater than 0, so it is guaranteed The left-hand side term is less than 2.

[0093] Furthermore, the penalty term for line connectivity constraints is as follows:

[0094] ;

[0095] ;

[0096] in, 、 is a binary slack variable.

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

[0098] ;

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

[0100] ;

[0101] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; P is a constant value; 、 is a binary variable used to relax constraints; It is a binary variable that represents whether the branch ij belongs to the main distribution transformer at the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the distribution transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; 、 is a binary slack variable; is the set of branches connected to distribution transformer i; is an integer; is the set of all interconnected distribution transformers; is the set of all substations; L is the set of all branches.

[0102] Step S43: Using relaxation technology to convert the preset branch variable load calculation constraint condition in the branch variable load calculation model into a second penalty term;

[0103] Step S44: construct a second unconstrained optimization problem according to the second penalty term and the branch variable load calculation objective function in the branch variable load calculation model.

[0104] The second penalty item includes the penalty item of the trunk distribution transformer isolation constraint and the penalty item of the line connection constraint.

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

[0106] ;

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

[0108] ;

[0109] ;

[0110] in, 、 is a binary slack variable.

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

[0112] ;

[0113] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; is the set of branches connected to distribution transformer i; is an integer; L is the set of all branches; A binary variable representing whether distribution transformer j belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the branch ij belongs to the same branch as the distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If it does, it is equal to 1, otherwise it is 0; 、 is a binary slack variable.

[0114] Step 105: Use the whale swarm optimization algorithm to solve the first unconstrained optimization problem and the second unconstrained optimization problem, and output the distribution network large branch identification result.

[0115] The identification results of the large branches of the distribution network include the trunk 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 large 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 strategy of humpback whales. These whales employ a complex spiral bubble network strategy to encircle their prey, and WOA mimics this behavior to efficiently explore and find the optimal solution in the search space. In this algorithm, each candidate solution in the search space is considered a whale, and the optimal solution is analogous to the location of the target prey. The algorithm consists of three main phases to simulate the humpback whale's foraging behavior: the prey encirclement phase, the spiral position adjustment phase, and the random exploration phase.

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

[0118] ;

[0119] ;

[0120] Where t represents the current iteration number; The position corresponding to the best solution found so far in the current iteration (i.e. the best binary variable value); 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, and 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, the parameters The value of A and C decreases linearly from 2 to 0, while r is a random vector in the interval [0, 1]. By dynamically adjusting the values ​​of A and C, whales can navigate the search space in a variety of 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 the target prey. The algorithm simulates this behavior using the following formula:

[0125] ;

[0126] where b is a constant that defines the shape of the logarithmic spiral, and is in range By using this formula, Whale can conduct a detailed search near the optimal solution, thereby significantly improving the algorithm's local optimization ability.

[0127] Furthermore, in the random search phase, humpback whales search for prey randomly in the search space. When ≥1, the whale randomly selects a position in the search space to search, which helps the algorithm escape from the local optimal solution and enhance the global search capability. At this time, the whale's position update formula is:

[0128] ;

[0129] ;

[0130] in, Represents a randomly selected position 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 to obtain the trunk distribution transformer set and the branch distribution transformer set. The whale swarm optimization algorithm is used to solve the second unconstrained optimization problem to obtain the load value of each branch distribution transformer in the branch distribution transformer set. Then, combined with the existing method, the load value of each branch distribution transformer is used to determine whether the corresponding branch is a large branch.

[0132] For comparison purposes, existing technologies can be used as a reference. With the development of new power systems, the load level within distribution networks is increasing, and the scale of large branches is growing, severely impacting the economic efficiency and reliability of distribution network operations. Due to the complex topology of distribution networks and the uneven load distribution among distribution transformers, even with a known distribution network topology, manually identifying large branches requires significant human and material resources, hindering the efficient implementation of planning operations. Current approaches transform this problem into a binary classification problem, determining whether each distribution transformer belongs to a feeder trunk or branch, and statistically analyzing the load values ​​of each branch to determine whether it is a large branch. Commonly used techniques include analytical and heuristic methods. Analytical methods require solving large integer optimization problems, resulting in low solution efficiency. Heuristic methods cannot guarantee that the results conform to physical constraints and are therefore difficult to ensure rationality. The novel whale swarm optimization method offers a novel approach to integer optimization, achieving both efficient and rational results. Therefore, it has considerable potential for identifying large branches in distribution networks.

[0133] Further, see Figure 4-Figure 5The current processing method uses model M1 to obtain all trunk distribution transformers and branch distribution transformers, and uses model M2 to determine whether the branch to which each branch distribution transformer belongs is a large branch. However, this method has low solving efficiency in large-scale distribution networks. Therefore, the present invention uses relaxation technology to convert it into an unconstrained optimization problem and uses quantum optimization algorithm for efficient solution. For details, please refer to Figure 6 First, we construct an integer optimization model with pure binary variables. This model can directly classify distribution transformers into trunk distribution transformers and branch distribution transformers based on the feeder topology, the interconnection relationship between each feeder, and the load value carried by the distribution transformer. The load value carried by each connected branch is then obtained, which is used as a basis to determine whether the branch is a large branch. Directly solving the above model will face the problem of low computational efficiency. Therefore, we use equivalent relaxation to convert the above model into an unconstrained optimization problem. Then, using this unconstrained optimization problem as the fitness function, we apply the whale swarm optimization algorithm to achieve efficient and accurate solution of the model. Compared with the existing technology, the present invention constructs a pure integer optimization model for identifying the distribution network feeder trunk and large-load branches, which realizes the accurate diagnosis of the large branch problem of the distribution network; at the same time, the relaxation technology is used to convert the above model into an equivalent quadratic unconstrained binary optimization problem, which provides a basis for accelerating the application of the optimization algorithm; in addition, the whale group optimization algorithm is applied to realize the rapid solution of the above integer optimization model, which can help planning business personnel to efficiently diagnose the large branch problem of the distribution network, that is, the whale group optimization algorithm is used to realize the intelligent identification of large branch lines in the new distribution network grid, reducing the manpower and material costs in the process of constructing the planning problem library.

[0134] In summary, in order to solve the problem of low solution efficiency in large-scale distribution networks, the present invention uses relaxation technology to convert the integer optimization model into an unconstrained optimization problem, and introduces the whale group optimization algorithm for efficient solution. The whale group optimization algorithm simulates the hunting behavior of humpback whales and is divided into three stages: prey capture, spiral position adjustment, and random search. It dynamically adjusts parameters to efficiently explore the solution space and find the optimal solution. Compared with traditional methods, the present invention significantly improves the solution efficiency while ensuring the solution accuracy, reduces the manpower and material costs in the process of building the planning problem library, and provides a new solution to the problem of identifying large branches in the distribution network.

[0135] In an embodiment of the present invention, a novel method for identifying large branches in a distribution network based on a whale swarm optimization algorithm is provided. When large branches in a distribution network need to be identified, first, a trunk-branch transformer objective function and a branch transformer load calculation objective function are constructed. Then, based on the trunk-branch transformer objective function and preset trunk-branch transformer identification constraints, an identification model for the trunk distribution transformer and the branch transformer is constructed. Based on the branch transformer load calculation objective function and preset branch transformer load calculation constraints, a branch transformer load calculation model is constructed. A relaxation technique is used to transform the trunk distribution transformer and the branch transformer identification model and the branch transformer load calculation model, respectively, to generate a first unconstrained optimization problem and a second unconstrained optimization problem. Finally, the whale swarm optimization algorithm is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem, and the distribution network large branch identification result is output. Based on the above scheme, the present invention uses relaxation technology to transform the trunk distribution transformer and the branch transformer identification model and the branch transformer load calculation model into an unconstrained optimization problem. Combined with the whale swarm optimization algorithm, the problem can be solved to achieve rapid identification of large branch lines in the distribution network, thereby improving solution efficiency.

[0136] See also Figure 7 , Figure 7 This is a structural block diagram of a new distribution network large branch identification device based on the whale group optimization algorithm provided in Example 2 of the present invention.

[0137] The present invention provides a novel large branch identification device for distribution network 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 trunk branch variable load objective function and the branch variable load calculation objective function;

[0139] The first model building module 702 is used to build an identification model of trunk distribution transformer and branch distribution transformer according to the trunk branch distribution transformer objective function and the preset trunk branch distribution transformer identification constraint conditions;

[0140] The second model building module 703 is used to build a branch transformer load calculation model according to the branch transformer load calculation objective function and the preset branch transformer load calculation constraint conditions;

[0141] A conversion module 704 is configured to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the branch distribution transformer load calculation model respectively using a relaxation technique to generate a first unconstrained optimization problem and a second unconstrained optimization problem;

[0142] The solution module 705 is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem using the whale swarm optimization algorithm, and output the distribution network large branch identification result.

[0143] Furthermore, the conversion module 704 is specifically configured to:

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

[0145] According to the first penalty term and the trunk distribution transformer and branch distribution transformer objective function in the identification model of the trunk distribution transformer and the branch distribution transformer, a first unconstrained optimization problem is constructed;

[0146] The relaxation technology is used to convert the preset branch variable load calculation constraint conditions in the branch variable load calculation model into the second penalty term;

[0147] According to the second penalty term and the branch variable load calculation objective function in the branch variable load calculation model, a second unconstrained optimization problem is constructed.

[0148] Furthermore, the trunk branch dominates the variable objective function, specifically:

[0149] ;

[0150] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

[0151] Furthermore, the objective function of the branch variable load calculation is as follows:

[0152] ;

[0153] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

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

[0155] ;

[0156] in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; P is a constant value; 、 is a binary variable used to relax constraints; It is a binary variable that represents whether the branch ij belongs to the main distribution transformer at the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the distribution transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; 、 is a binary slack variable; is the set of branches connected to distribution transformer i; is an integer; is the set of all interconnected distribution transformers; is the set of all substations; L is the set of all branches.

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

[0158] ;

[0159] in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; is the set of branches connected to distribution transformer i; is an integer; L is the set of all branches; A binary variable representing whether distribution transformer j belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the branch ij belongs to the same branch as the distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If it does, it is equal to 1, otherwise it is 0; 、 is a binary slack variable.

[0160] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the new distribution network large branch identification method based on the whale group optimization algorithm as in any of the above embodiments.

[0162] An embodiment of the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the new distribution network large branch identification method based on the whale group optimization algorithm as in any of the above embodiments are implemented.

[0163] An embodiment of the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a new distribution network large branch identification method based on a whale group optimization algorithm as in any of the above embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separate, and 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 these units may 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 distribution networks based on a whale swarm optimization algorithm, characterized in that: include: In response to the identification request, construct the trunk branch transmission objective function and the branch transmission load calculation objective function; According to the trunk-branch distribution transformer objective function and the preset trunk-branch distribution transformer identification constraint conditions, an identification model of the trunk distribution transformer and the branch distribution transformer is constructed; Constructing a branch transformer load calculation model according to the branch transformer load calculation objective function and preset branch transformer load calculation constraint conditions; Using relaxation technology to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the branch distribution transformer load calculation model respectively, to generate a first unconstrained optimization problem and a 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 distribution network large branch identification result is output.

2. The novel large branch identification method of distribution network based on whale group optimization algorithm according to claim 1 is characterized in that: The relaxation technique is used to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the branch distribution transformer load calculation model to generate a first unconstrained optimization problem and a second unconstrained optimization problem, including: The relaxation technology is used to convert the preset trunk distribution transformer identification constraint condition in the identification model of the trunk distribution transformer and the branch distribution transformer into a first penalty term; Constructing a first unconstrained optimization problem according to the first penalty term and the trunk distribution transformer objective function in the identification model of the trunk distribution transformer and the branch distribution transformer; The relaxation technology is used to convert the preset branch variable load calculation constraint condition in the branch variable load calculation model into a second penalty term; A second unconstrained optimization problem is constructed according to the second penalty term and the branch variable load calculation objective function in the branch variable load calculation model.

3. The novel method for identifying large branches of distribution network based on whale group optimization algorithm according to claim 1 is characterized in that: The trunk branch variable objective function is specifically: ； in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

4. The novel large branch identification method of distribution network based on whale group optimization algorithm according to claim 1 is characterized in that: The objective function of the branch variable load calculation is specifically: ； in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers.

5. The novel method for identifying large branches of distribution network based on whale group optimization algorithm according to claim 1 is characterized in that: The first unconstrained optimization problem is specifically: ； in, It is a binary variable that represents whether the distribution transformer i belongs to the main distribution transformer when it is the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; P is a constant value; 、 is a binary variable used to relax constraints; It is a binary variable that represents whether the branch ij belongs to the main distribution transformer at the terminal node of the interconnected distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the distribution transformer j belongs to the main distribution transformer when it is the terminal node of the connecting distribution transformer trunk. If it belongs to the main distribution transformer, it is equal to 1, otherwise it is 0; 、 is a binary slack variable; is the set of branches connected to distribution transformer i; is an integer; is the set of all interconnected distribution transformers; is the set of all substations; L is the set of all branches.

6. The novel method for identifying large branches of distribution network based on whale group optimization algorithm according to claim 1 is characterized in that: The second unconstrained optimization problem is specifically: ； in, A binary variable representing whether distribution transformer i belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; is the load value carried by distribution transformer i; N is the set of all distribution transformers; is the set of branches connected to distribution transformer i; is an integer; L is the set of all branches; A binary variable representing whether distribution transformer j belongs to the same branch as distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If yes, it is equal to 1, otherwise it is 0; It is a binary variable that represents whether the branch ij belongs to the same branch as the distribution transformer c when calculating the load of the branch where the c-th distribution transformer is located. If it does, it is equal to 1, otherwise it is 0; 、 is a binary slack variable.

7. A novel large branch identification device for distribution network based on whale group optimization algorithm, characterized in that: include: An objective function building module is used to respond to identification requests and build a trunk branch variable load objective function and a branch variable load calculation objective function; A first model building module is used to build an identification model of trunk distribution transformer and branch distribution transformer according to the trunk branch distribution transformer objective function and preset trunk branch distribution transformer identification constraint conditions; A second model building module is used to build a branch variable load calculation model according to the branch variable load calculation objective function and preset branch variable load calculation constraint conditions; a conversion module, configured to convert the identification models of the trunk distribution transformer and the branch distribution transformer, and the load calculation model of the branch distribution transformer respectively by using a relaxation technique, to generate a first unconstrained optimization problem and a second unconstrained optimization problem; A solution module is used to solve the first unconstrained optimization problem and the second unconstrained optimization problem using a whale group optimization algorithm, and output a distribution network large branch identification result.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the new distribution network large branch identification method based on the whale group optimization algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the novel large branch identification method of the distribution network based on the whale group optimization algorithm as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the new distribution network large branch identification method based on the whale group optimization algorithm as described in any one of claims 1 to 6.

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