An island reconstruction method and system based on improved grey wolf algorithm

Abstract

The present invention relates to the technical field of power supply network reconstruction, and discloses an island reconstruction method and system based on an improved gray wolf algorithm, the method comprising: initializing the switch, node and branch numbering information in the island microgrid; setting the extraction time and obtaining the current isolated network structure and parameters, calculating the current comprehensive vulnerability function and making a judgment; randomly generating a variety of reconstruction schemes, and using depth-first search to analyze each reconstruction scheme, using forward-backward method to calculate the power flow of the reconstruction scheme, using the gray wolf algorithm to iteratively update the optimization variables of the reconstruction scheme, and outputting the best scheme. The present invention combines the circle-breaking method with the depth-first search to increase population diversity and enhance the global search capability in the reconstruction problem; proposes a method for shielding unsatisfactory schemes according to the threshold of individual target indicators in the multi-target decision-making process, selects the better method according to the comprehensive target function, shields the schemes outside the threshold of each indicator, and avoids falling into the problem of local optimality.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply network reconstruction, and in particular to an island reconstruction method and system based on an improved grey wolf algorithm. Background Art

[0002] Real-time reconstruction technology is an effective means to maintain the stable operation of the island. Without the support of the main grid, as a low-inertia system, the microgrid is very likely to become unstable due to fluctuations in equipment output in an island operation environment. When the system changes, by real-time adjustment of the operating status of the equipment connection switch, line section switch, and interconnection switch, and by changing the number of connected devices and the power supply topology of the island, the island can control the voltage and frequency of the system in dynamic changes and maintain the power balance of the network.

[0003] Reconfiguration is a process of changing the operation mode of the network by changing the operating status of each switch in the network, ensuring the safe and stable operation of the system under certain constraints, and making a certain indicator of the system reach the optimal level. The essence of island reconstruction is a multi-objective, multi-constrained nonlinear mixed integer programming problem. For the reconstruction of islands, enumeration methods, traditional mathematical optimization methods or artificial intelligence algorithms are currently used for reconstruction optimization, which are inefficient and time-consuming. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an island reconstruction method based on an improved grey wolf algorithm to solve the problems of low precision, poor stability and strong subjectivity of traditional island reconstruction schemes.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an island reconstruction method based on an improved grey wolf algorithm, comprising:

[0008] Initialize the switch, node and branch numbering information in the isolated island microgrid;

[0009] Set the extraction time and obtain the current isolated network structure and parameters, calculate the current comprehensive vulnerability function P and make a judgment;

[0010] Based on the comprehensive vulnerability function, a variety of reconstruction schemes are randomly generated, and each reconstruction scheme is analyzed by depth-first search to determine whether each scheme conforms to the radial structure. If it conforms, a node and branch traversal path of the depth-first search is generated. If it does not conform, a circle-breaking method is used to generate any radial structure.

[0011] The forward-backward method is used to calculate the power flow of the reconstruction scheme to determine whether the second optimization stage has been reached. If so, the comprehensive optimization objective function value of all schemes is calculated. If not, the target value of all reconstruction schemes is calculated.

[0012] The grey wolf algorithm is used to iteratively update the optimization variables of the reconstruction scheme and output the best scheme.

[0013] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the initialization of the switch, node and branch numbering information in the island microgrid includes:

[0014] Determine the location and connection relationship of switches, nodes and branches, and draw the corresponding topology diagram;

[0015] Mark the numbers of switches, nodes and branches on the topology diagram and record them;

[0016] The switch, node and branch numbers are regularly updated and maintained, and the topology map data is updated.

[0017] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the calculation of the current comprehensive vulnerability function value P and the judgment include:

[0018] Set the extraction time to T k , obtain the current isolated network structure and parameters; parameter information of switches, nodes and branches, key equipment, and electrical parameters such as isolated network voltage, current, power, etc.;

[0019] The comprehensive vulnerability function P is expressed as:

[0020]

[0021] Among them, D G represents the dispersion of network micro-sources, represents the betweenness of node i, represents the betweenness of branch l, μ1, μ2, μ3 represent weight coefficients, and dV i Represents the change in node voltage, dS G Indicates the real-time total capacity of all micro-sources when working, dP l Indicates the active power change of line transmission;

[0022] If the comprehensive vulnerability function P is greater than the set value Pset , then k = k + 1, and the extraction time is T k+1 The current isolated network structure topology diagram, recalculate the comprehensive vulnerability function P;

[0023] If the comprehensive vulnerability function P is less than the set value P set , then proceed to the next step.

[0024] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the depth-first search includes:

[0025] Set the initial parameters of the algorithm, set the population size to N, the maximum number of iterations to M, and the position of the gray wolf to Y t , initialize the population and randomly generate N different switch combinations, micro-source outputs, and flexible load reconstruction schemes;

[0026] Determine a starting node s∈V as the starting point of the search;

[0027] Define the node visit status visited i and search path path, marking the starting node s as visited and adding it to the search path, expressed as: visited s =1,path=[s];

[0028] For the current node u∈path, traverse the adjacent nodes v∈Adj(u) of the current node from bottom to top; if the adjacent node v has not been visited, mark the adjacent node v as visited and add it to the search path, represented as: visited v =1, path = path + [v];

[0029] If the current node u has unvisited adjacent nodes, one of the nodes is selected as the next traversal node and the next round of search begins;

[0030] If the current node u has no unvisited adjacent nodes, return to the previous node v and delete the current node from the search path, expressed as: path = path-[v];

[0031] Determine whether the search is completed. If completed, terminate and output all solutions; if not completed, continue traversing.

[0032] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the power flow of the reconstruction solution calculated by the forward-backward method includes:

[0033] Given the system power flow direction and node injected active power, reactive power, and branch admittance and admittance;

[0034] Starting from the power node, the voltage amplitude and phase angle of each node are calculated in turn according to the branch parameters and node load, and the calculation results are transmitted to the connected downstream nodes;

[0035] Setting the voltage amplitude and phase angle of the terminal nodes to known values;

[0036] Starting from the load node, the power flow of each branch is calculated according to the branch parameters and the voltage information of the upstream node, and the calculation results are transmitted to the connected upstream node.

[0037] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the comprehensive optimization objective function Q is:

[0038]

[0039] Among them, ε1, ε2, ε3, ε4 represent weight coefficients, ε1+ε2+ε3+ε4=1, H represents the penalty function, Q s Indicates the number of switching times, Q loss Represents the network loss index, Q v represents the node voltage deviation index, Q load Indicates the maximum power supply load indicator.

[0040] As a preferred solution of the island reconstruction method based on the improved grey wolf algorithm described in the present invention, the grey wolf algorithm includes:

[0041] According to the comprehensive optimization objective function Q, the gray wolf with the minimum function value Q is selected as the leader gray wolf, denoted by X best , and determine the location;

[0042] According to each gray wolf X i Distance and position from the leader gray wolf, update each gray wolf X i Position, expressed as:

[0043] X new =X best -β·D

[0044] Among them, X new represents the updated position, β is a random vector, and D represents the difference vector between the current position and the leader gray wolf position;

[0045] Calculate the updated Q(X) according to the new position of the gray wolf new ), if Q(X new ) is smaller, then the position of the leader gray wolf is set to X best ;

[0046] Repeat the above steps to determine whether the maximum number of iterations has been reached. If so, end and output the optimal solution X best and Q(X best ).

[0047] In a second aspect, the present invention provides an island reconstruction system based on an improved grey wolf algorithm, comprising:

[0048] Initialization module, used to initialize the switch, node and branch numbering information in the isolated island microgrid;

[0049] The calculation module is used to set the extraction time and obtain the current isolated network structure and parameters, calculate the current comprehensive vulnerability function P and make a judgment;

[0050] An analysis module, for randomly generating a plurality of reconstruction schemes based on the comprehensive vulnerability function, and analyzing each reconstruction scheme by using a depth-first search to determine whether each scheme conforms to the radial structure, and if so, generating a node and branch traversal path of the depth-first search, and if not, generating any radial structure by using a breaking circle method;

[0051] A judgment module is used to calculate the power flow of the reconstruction scheme by using the forward-backward method to judge whether the second optimization stage has been reached. If so, the comprehensive optimization objective function value of all schemes is calculated; if not, the target value of all reconstruction schemes is calculated;

[0052] The output module is used to iteratively update the optimization variables of the reconstruction plan using the gray wolf algorithm and output the best plan.

[0053] In a third aspect, the present invention provides an electronic device, comprising:

[0054] Memory and processor;

[0055] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the island reconstruction method based on the improved grey wolf algorithm are implemented.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the island reconstruction method based on the improved grey wolf algorithm.

[0057] Compared with the prior art, the present invention has the following beneficial effects: the present invention combines the circle breaking method with the depth-first search to increase population diversity and enhance the global search capability in the reconstruction problem; a method for shielding unsatisfactory solutions according to the threshold of individual target indicators in the multi-target decision-making process is proposed, and a better method is selected according to the comprehensive objective function, and the solutions outside the threshold of each indicator are shielded to avoid falling into the problem of local optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0059] Figure 1 This is a schematic diagram of the overall process of an island reconstruction method based on an improved grey wolf algorithm according to an embodiment of the present invention;

[0060] Figure 2 The figure is a schematic diagram of a specific process of an island reconstruction method based on an improved grey wolf algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Reference Figure 1-Figure 2 , is an embodiment of the present invention, and provides an island reconstruction method based on an improved grey wolf algorithm, comprising:

[0068] S101, initializing the switch, node and branch numbering information in the isolated island microgrid;

[0069] S102, setting the extraction time and obtaining the current isolated network structure and parameters, calculating the current comprehensive vulnerability function P and making a judgment;

[0070] S103, based on the comprehensive vulnerability function, randomly generate multiple reconstruction schemes, and use depth-first search to analyze each reconstruction scheme to determine whether each scheme conforms to the radial structure. If so, generate a node and branch traversal path of the depth-first search. If not, use the circle-breaking method to generate any radial structure.

[0071] S104, using the forward-backward method to calculate the power flow of the reconstruction scheme, determine whether the second optimization stage is reached, if reached, calculate the comprehensive optimization objective function value of all schemes, if not reached, calculate the target value of all reconstruction schemes;

[0072] S105, using the grey wolf algorithm to iteratively update the optimization variables of the reconstruction scheme and output the best scheme.

[0073] It should be noted that this embodiment uses the switch state and the power of the adjustable device as the optimization variables, uses the circle breaking method and depth-first search to identify, analyze and process the network structure, uses the forward and back substitution method to calculate the network power distribution, uses a two-layer multi-objective decision-making method to process multiple reconstruction objective functions, and uses the gray wolf algorithm as a framework to obtain a reconstruction plan.

[0074] In a preferred implementation of step S101, initializing the switch, node, and branch numbering information in the isolated island microgrid includes:

[0075] Determine the location and connection relationship of switches, nodes and branches, and draw the corresponding topology diagram;

[0076] Mark the numbers of switches, nodes, and branches on the topology diagram and keep records;

[0077] Regularly update and maintain the switch, node and branch numbering, and update the topology map data.

[0078] Specifically, for switches, they can be numbered according to their type and location, such as "CB1", "CB2", etc.; for nodes, they can be numbered according to their physical location or function, such as "N1", "N2", etc.; for branches, they can be numbered according to the starting node and the ending node they are connected to, for example, "L12" represents a branch connecting node 1 and node 2.

[0079] Mark the numbers of switches, nodes and branches on the topology diagram and record them in the system management document or database. To facilitate identification and query, you can set different colors, fonts or shapes to identify symbols. According to different needs, you can also number other equipment and loads, such as generators, inverters, battery packs, etc. During system operation and maintenance, the numbers of switches, nodes and branches need to be updated and maintained to ensure the accuracy and completeness of system information.

[0080] In a preferred implementation of step S102, calculating the current comprehensive vulnerability function value P and making a judgment includes:

[0081] Set the extraction time to T k , obtain the current isolated network structure and parameters; parameter information of switches, nodes and branches, key equipment, and electrical parameters such as isolated network voltage, current, power, etc.;

[0082] The comprehensive vulnerability function P is expressed as:

[0083]

[0084] Among them, D G represents the dispersion of network micro-sources, represents the betweenness of node i, represents the betweenness of branch l, μ1, μ2, μ3 represent weight coefficients, and dV i Represents the change in node voltage, dS G Indicates the real-time total capacity of all micro-sources when working, dP l Indicates the active power change of line transmission;

[0085] If the comprehensive vulnerability function P is greater than the set value P set , then k = k + 1, and the extraction time is T k+1 Current isolated network structure topology diagram, recalculate the comprehensive vulnerability function P;

[0086] If the comprehensive vulnerability function P is less than the set value P set , then proceed to the next step.

[0087] Specifically, when the network structure remains unchanged, the fewer the micro sources working normally, the greater the comprehensive vulnerability value of the system; when the micro sources working normally are certain, the greater the dispersion of the network micro sources, the more reliable the system structure, and the greater the comprehensive vulnerability value of the system; the lower the system's operating stability, the greater its comprehensive vulnerability value.

[0088] In a preferred implementation of step S103, the depth-first search includes:

[0089] Set the initial parameters of the algorithm, set the population size to N, the maximum number of iterations to M, and the position of the gray wolf to Y t , initialize the population and randomly generate N different switch combinations, micro-source outputs, and flexible load reconstruction schemes;

[0090] Determine a starting node s∈V as the starting point of the search;

[0091] Define the node visit status visited i And the search path path, mark the starting node s as visited and add it to the search path, represented as: visited s =1,path=[s];

[0092] For the current node u∈path, traverse the adjacent nodes v∈Adj(u) of the current node from bottom to top; if the adjacent node v has not been visited, mark the adjacent node v as visited and add it to the search path, represented as: visited v =1, path = path + [v];

[0093] If the current node u has unvisited adjacent nodes, one of the nodes is selected as the next traversal node and the next round of search begins;

[0094] If the current node u has no unvisited adjacent nodes, return to the previous node v and delete the current node from the search path, expressed as: path = path-[v];

[0095] Determine whether the search is completed. If completed, terminate and output all solutions; if not completed, continue traversing.

[0096] Specifically, the population is initialized to determine the combination of switch states, the power output range of the micro-source, and the corresponding range of the flexible load; a random number generation algorithm is used to generate N different switch combinations, and the initial solutions of the micro-source output and the flexible load are determined as the initial individuals of the population.

[0097] In an optional implementation of step S103, the depth-first search process based on the circle-breaking method includes:

[0098] Select a structure containing q ring networks as the initial structure;

[0099] According to q loops, all branches are divided into q branches;

[0100] Randomly delete one branch from each branch, and the remaining branches form a radial shape.

[0101] The node traversal path obtained through depth-first search.

[0102] In a preferred implementation of step S104, using the forward-backward method to calculate the power flow of the reconstruction solution includes:

[0103] Given the system power flow direction and node injected active power, reactive power, and branch admittance and admittance;

[0104] Starting from the power node, the voltage amplitude and phase angle of each node are calculated in turn according to the branch parameters and node load, and the calculation results are transmitted to the connected downstream nodes;

[0105] Setting the voltage magnitude and phase angle of the terminal node to known values ​​(e.g., the voltage magnitude and phase angle of the generator node are known);

[0106] Starting from the load node, the power flow of each branch is calculated according to the branch parameters and the voltage information of the upstream node, and the calculation results are transmitted to the connected upstream node.

[0107] In an optional implementation of step S104, the target value of the reconstruction scheme is the switch switching number index Q s , Network loss index Q loss , node voltage offset index Q v , Maximum power supply load index Q load .

[0108] In a preferred implementation of step S104, the comprehensive optimization objective function Q is:

[0109]

[0110] Among them, ε1, ε2, ε3, ε4 represent weight coefficients, ε1+ε2+ε3+ε4=1, H represents the penalty function, Qs Indicates the number of switching times, Q loss Represents the network loss index, Q v represents the node voltage deviation index, Q load It represents the maximum indicator of power supply load. The more stable the system operation state is, the smaller H is. By setting the weight of each indicator, the smaller the optimization objective function value is, the better the reconstruction scheme is.

[0111] In a preferred implementation of step S105, the grey wolf algorithm includes:

[0112] According to the comprehensive optimization objective function Q, the gray wolf with the minimum function value Q is selected as the leader gray wolf, denoted by X best , and determine the location;

[0113] According to each gray wolf X i Distance and position from the leader gray wolf, update each gray wolf X i Position, expressed as:

[0114] X new =X best -β·D

[0115] Among them, X new represents the updated position, β is a random vector, and D represents the difference vector between the current position and the leader gray wolf position;

[0116] Calculate the updated Q(X) according to the new position of the gray wolf new ), if Q(X new ) is smaller, then the position of the leader gray wolf is set to X best ;

[0117] Repeat the above steps to determine whether the maximum number of iterations has been reached. If so, end and output the optimal solution X best and Q(X best ).

[0118] In an optional implementation manner of step S105, outputting the best solution includes:

[0119] The Grey Wolf Algorithm is used to iteratively update the optimization variables of the reconstruction scheme to determine whether the maximum number of iterations has been reached;

[0120] If yes, output the best solution;

[0121] If not, repeat the analysis of each reconstruction scheme through depth-first search and continue iterating;

[0122] The island is reconstructed according to the best solution and the process ends.

[0123] It should be noted that the present invention combines the circle breaking method with the depth-first search to increase population diversity and enhance the global search capability in the reconstruction problem; a method for shielding unsatisfactory solutions according to the threshold of individual target indicators in the multi-target decision-making process is proposed, and the better method is selected according to the comprehensive objective function, and the solutions outside the threshold of each indicator are shielded to avoid falling into the problem of local optimality.

[0124] The island reconstruction model based on the improved gray wolf algorithm combines the global search capability of the gray wolf algorithm and the improvement of the optimization algorithm parameters, and has the following characteristics: High solution accuracy: The improved gray wolf algorithm searches for the optimal solution by continuously updating the gray wolf position, and has a strong global search capability. In the island reconstruction, the optimal reconstruction scheme can be better found, thereby improving the reconstruction accuracy. Short running time: The improved gray wolf algorithm can find a better solution in a shorter time by optimizing the algorithm parameters and adjusting the number of iterations. Compared with traditional methods and other artificial intelligence algorithms, the algorithm has a faster operation speed and can meet the needs of real-time reconstruction. Solving multi-objective and multi-constraint problems: Island reconstruction is a multi-objective, multi-constrained nonlinear mixed integer programming problem. The improved gray wolf algorithm can flexibly handle multi-objective problems and ensure the safety and stability of the system during the reconstruction process through appropriate constraints.

[0125] In practical applications, the island reconstruction model based on the improved grey wolf algorithm can effectively optimize the island reconstruction scheme and improve the reliability and stability of the system. By dynamically adjusting the operating status of the equipment connection switch, line section switch, and tie switch, it can respond to system changes in a timely manner, maintain the power balance of the island, and enable the system to remain stable in the island operation environment. This is of great significance to the reliability and power supply quality of the microgrid system.

[0126] In summary, real-time reconstruction technology is an effective means to maintain the stable operation of the island, and the island reconstruction model based on the improved grey wolf algorithm can improve the reconstruction efficiency and accuracy, and provide support for the stable operation of the microgrid system.

[0127] The above is a schematic scheme of an island reconstruction method based on an improved gray wolf algorithm in this embodiment. It should be noted that the technical scheme of the island reconstruction system based on the improved gray wolf algorithm and the technical scheme of the island reconstruction method based on the improved gray wolf algorithm belong to the same concept. For the details not described in detail in the technical scheme of the island reconstruction system based on the improved gray wolf algorithm in this embodiment, please refer to the description of the technical scheme of the island reconstruction method based on the improved gray wolf algorithm.

[0128] The island reconstruction system based on the improved grey wolf algorithm in this embodiment includes:

[0129] Initialization module, used to initialize the switch, node and branch numbering information in the isolated island microgrid;

[0130] The calculation module is used to set the extraction time and obtain the current isolated network structure and parameters, calculate the current comprehensive vulnerability function and make judgments;

[0131] The analysis module is used to randomly generate multiple reconstruction schemes based on the comprehensive vulnerability function, and analyze each reconstruction scheme using depth-first search to determine whether each scheme conforms to the radial structure. If so, a depth-first search node and branch traversal path is generated. If not, a circle-breaking method is used to generate any radial structure.

[0132] A judgment module is used to calculate the power flow of the reconstruction scheme by using the forward-backward method to judge whether the second optimization stage has been reached. If so, the comprehensive optimization objective function value of all schemes is calculated; if not, the target value of all reconstruction schemes is calculated;

[0133] The output module is used to iteratively update the optimization variables of the reconstruction plan using the gray wolf algorithm and output the best plan.

[0134] This embodiment further provides an electronic device, which is applicable to the case of island reconstruction based on the improved grey wolf algorithm, including:

[0135] A memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the island reconstruction method based on the improved grey wolf algorithm as proposed in the above embodiment.

[0136] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for reconstructing an island based on an improved grey wolf algorithm as proposed in the above embodiment is implemented.

[0137] The storage medium proposed in this embodiment and the island reconstruction method based on the improved grey wolf algorithm proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0138] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An island reconstruction method based on an improved grey wolf algorithm, characterized in that: include: Initialize the switch, node and branch numbering information in the isolated island microgrid; Set the extraction time and obtain the current isolated network structure and parameters, calculate the current comprehensive vulnerability function and make a judgment; Based on the comprehensive vulnerability function, a variety of reconstruction schemes are randomly generated, and each reconstruction scheme is analyzed by depth-first search to determine whether each scheme conforms to the radial structure. If it conforms, a node and branch traversal path of the depth-first search is generated. If it does not conform, a circle-breaking method is used to generate any radial structure. The forward-backward method is used to calculate the power flow of the reconstruction scheme to determine whether the second optimization stage has been reached. If so, the comprehensive optimization objective function value of all schemes is calculated. If not, the target value of all reconstruction schemes is calculated. The grey wolf algorithm is used to iteratively update the optimization variables of the reconstruction scheme and output the best scheme.

2. The island reconstruction method based on the improved grey wolf algorithm according to claim 1, characterized in that: The information of the numbering of switches, nodes and branches in the initialization of the isolated island microgrid includes: Determine the location and connection relationship of switches, nodes and branches, and draw the corresponding topology diagram; Mark the numbers of switches, nodes and branches on the topology diagram and record them; The switch, node and branch numbers are regularly updated and maintained, and the topology map data is updated.

3. The island reconstruction method based on the improved grey wolf algorithm according to claim 1 or 2, characterized in that: Calculating the current comprehensive vulnerability function value P and making judgments include: Set the extraction time to Tk, and obtain the current isolated network structure and parameters; parameter information of switches, nodes and branches, key equipment, and electrical parameters such as isolated network voltage, current, and power; The comprehensive vulnerability function P is expressed as: Among them, D G represents the dispersion of network micro-sources, represents the betweenness of node i, represents the betweenness of branch l, μ1, μ2, μ3 represent weight coefficients, and dV i Represents the change in node voltage, dS G Indicates the real-time total capacity of all micro-sources when working, dP l Indicates the active power change of line transmission; If the comprehensive vulnerability function P is greater than the set value P set , then k = k + 1, and the extraction time is T k+1 The current isolated network structure topology diagram, recalculate the comprehensive vulnerability function P; If the comprehensive vulnerability function P is less than the set value P set , then proceed to the next step.

4. The island reconstruction method based on the improved grey wolf algorithm according to claim 1, characterized in that: The depth-first search includes: Set the initial parameters of the algorithm, set the population size to N, the maximum number of iterations to M, and the position of the gray wolf to Y t , initialize the population and randomly generate N different switch combinations, micro-source outputs, and flexible load reconstruction schemes; Determine a starting node s∈V as the starting point of the search; Define the node visit status visited i and search path path, marking the starting node s as visited and adding it to the search path, expressed as: visited s =1,path=[s]; For the current node u∈path, traverse the adjacent nodes v∈Adj(u) of the current node from bottom to top; if the adjacent node v has not been visited, mark the adjacent node v as visited and add it to the search path, represented as: visited v =1, path = path + [v]; If the current node u has unvisited adjacent nodes, one of the nodes is selected as the next traversal node and the next round of search begins; If the current node u has no unvisited adjacent nodes, return to the previous node v and delete the current node from the search path, expressed as: path = path-[v]; Determine whether the search is completed. If completed, terminate and output all solutions; if not completed, continue traversing.

5. The island reconstruction method based on the improved grey wolf algorithm as claimed in claim 4, characterized in that: The trends for calculating reconstruction solutions using forward-backward substitution include: Given the system power flow direction and node injected active power, reactive power, and branch admittance and admittance; Starting from the power node, the voltage amplitude and phase angle of each node are calculated in turn according to the branch parameters and node load, and the calculation results are transmitted to the connected downstream nodes; Setting the voltage amplitude and phase angle of the terminal nodes to known values; Starting from the load node, the power flow of each branch is calculated according to the branch parameters and the voltage information of the upstream node, and the calculation results are transmitted to the connected upstream node.

6. The island reconstruction method based on the improved grey wolf algorithm as claimed in claim 5, characterized in that: The comprehensive optimization objective function Q is: Among them, ε1, ε2, ε3, ε4 represent weight coefficients, ε1+ε2+ε3+ε4=1, H represents the penalty function, Q s Indicates the number of switching times, Q loss Represents the network loss index, Q v represents the node voltage deviation index, Q load Indicates the maximum power supply load indicator.

7. The island reconstruction method based on the improved grey wolf algorithm according to any one of claims 4 to 6, characterized in that: The grey wolf algorithm includes: According to the comprehensive optimization objective function Q, the gray wolf with the minimum function value Q is selected as the leader gray wolf, denoted by X best , and determine the location; According to each gray wolf X i Distance and position from the leader gray wolf, update each gray wolf X i Position, expressed as: X new =X best -β·D Among them, X new represents the updated position, β is a random vector, and D represents the difference vector between the current position and the position of the leader gray wolf; Calculate the updated Q(X) according to the new position of the gray wolf new ), if Q(X new ) is smaller, then the position of the leader gray wolf is set to X best ; Repeat the above steps to determine whether the maximum number of iterations has been reached. If so, end and output the optimal solution X best and Q(X best ).

8. An island reconstruction system based on an improved grey wolf algorithm, characterized in that: include, Initialization module, used to initialize the switch, node and branch numbering information in the isolated island microgrid; The calculation module is used to set the extraction time and obtain the current isolated network structure and parameters, calculate the current comprehensive vulnerability function and make judgments; An analysis module, for randomly generating a plurality of reconstruction schemes based on the comprehensive vulnerability function, and analyzing each reconstruction scheme by using a depth-first search to determine whether each scheme conforms to the radial structure, and if so, generating a node and branch traversal path of the depth-first search, and if not, generating any radial structure by using a breaking circle method; A judgment module is used to calculate the power flow of the reconstruction scheme by using the forward-backward method to judge whether the second optimization stage has been reached. If so, the comprehensive optimization objective function value of all schemes is calculated; if not, the target value of all reconstruction schemes is calculated; The output module is used to iteratively update the optimization variables of the reconstruction plan using the gray wolf algorithm and output the best plan.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the island reconstruction method based on the improved grey wolf algorithm described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the island reconstruction method based on the improved grey wolf algorithm as described in any one of claims 1 to 7.

Images