A low-voltage topology intelligent generation and analysis method, system, device and storage medium

Abstract

The present invention discloses a low-voltage topology intelligent generation and analysis method, system, device, and storage medium, relating to the field of power system technology. The method comprises constructing a complete electromagnetic topology graph, identifying and merging non-critical nodes through a depth-first search algorithm in graph theory, and obtaining an identified topology structure; obtaining electromagnetic field performance indicators through finite element analysis, and optimizing the electromagnetic field performance indicators to obtain optimization results; obtaining optimized device connection relationships and operating parameters; and obtaining an effectiveness evaluation through electromagnetic field performance verification, and performing real-time monitoring and analysis of the effectiveness evaluation to obtain a final power supply optimization solution. The present invention has the beneficial effects of effectively solving the problems existing in the prior art and can also achieve efficient operation and optimized scheduling of distributed energy systems in complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, device and storage medium for intelligently generating and analyzing low-voltage topology. Background Art

[0002] In distributed energy generation systems in remote mountainous areas, traditional power supply assurance methods struggle to effectively respond to sudden failures and optimize power supply efficiency due to the complex geographical environment, dispersed equipment distribution, and difficulty in data collection. Furthermore, the complex equipment layout and connectivity of distributed energy systems limit the stability and efficiency of system operation. Furthermore, energy supply is highly intermittent and volatile, making it difficult to ensure stable energy supply, especially in remote areas or off-grid applications. Furthermore, the system's insufficient optimization, scheduling, and fault diagnosis capabilities limit its application in complex environments. For example, a microgrid system in a mountainous area may include small wind turbines, solar panels, energy storage devices, and a small number of diesel generators. These devices are connected through a complex distribution network, and real-time data from some devices is difficult to obtain. In such scenarios, a method is needed to efficiently optimize the electromagnetic topology and ensure power supply reliability. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that in an environment with complex geographical environment, dispersed equipment distribution and difficult data collection, traditional power supply guarantee methods are difficult to effectively deal with sudden failures and optimize power supply efficiency.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a low-voltage topology intelligent generation and analysis method, which includes: using intelligent sensors and Internet of Things technology to collect operating data and topological information of distributed energy equipment, constructing a complete electromagnetic topology map, identifying and merging non-critical nodes through a depth-first search algorithm in graph theory to obtain an identified topological structure, wherein the critical nodes include important load nodes, nodes with complete data collection, and distributed energy access nodes, wherein the operating data includes power generation equipment data, load demand data, energy storage system data, and environmental data, and the topological information includes equipment connection relationships and geographic location information; based on the identified topological structure, electromagnetic field performance indicators are obtained through finite element analysis processing, and the electromagnetic field performance indicators are optimized to obtain optimization results; initializing a particle swarm, where each particle represents a device connection relationship and operating parameter configuration, evaluating the fitness of each particle through non-dominated sorting and crowding distance calculation, and updating the individual and global optimal solutions to obtain optimized device connection relationships and operating parameters, wherein the speed and position of the particles are dynamically adjusted during the calculation process, and the operating data of the distributed energy equipment is used as a constraint condition; Based on the optimized equipment connection relationship and operating parameters, after electromagnetic field performance verification, an effectiveness evaluation is obtained, and the effectiveness evaluation is monitored, evaluated and analyzed in real time to obtain the final power supply optimization plan.

[0006] As a preferred solution of the low-voltage topology intelligent generation and analysis method described in the present invention, wherein: the construction of a complete electromagnetic topology diagram includes defining the attributes of each node according to the type and location of the device, wherein the attributes of the node include the node type and location coordinates; defining the connection mode of the edge according to the connection relationship between the devices, wherein each edge Representation node and nodes The electrical connections between them; use the adjacency matrix to construct a topological graph, where Representation node and nodes If there is a connection between .

[0007] As a preferred solution of the low-voltage topology intelligent generation and analysis method described in the present invention, the method includes: identifying and merging non-critical nodes through the depth-first search algorithm in graph theory to obtain the identified topological structure, which includes starting from a starting node, searching along the path until it is impossible to continue, and backtracking. During the depth-first search algorithm, the access order and access status of each node are recorded, and the visited nodes are marked. The node access order and preliminary identification results of the key nodes are obtained through depth-first search traversal; according to the node access order and preliminary identification results, the classification results of the key nodes and non-critical nodes are obtained through node importance evaluation processing, wherein during the search process, the key nodes are identified and the non-critical nodes are merged; according to the classification results of the key nodes and non-critical nodes, the identified topological structure is obtained through node merging processing.

[0008] As a preferred solution of the low voltage topology intelligent generation and analysis method of the present invention, wherein: the identification of key nodes includes identifying key nodes by calculating the local influence and global influence of the nodes, wherein the local influence The calculation formula is expressed as,

[0009] ,

[0010] in, Representation node The degree, that is, the number of connected edges, Indicates the total number of nodes in the graph; global impact The calculation formula is expressed as,

[0011] ,

[0012] in, Representation node The degree, Representation node and nodes The shortest path length between Is a constant used to adjust the weight of the global influence; the total influence The calculation formula is expressed as,

[0013] ,

[0014] By calculating the LGC value of each node, the key nodes can be identified.

[0015] Another object of the present invention is to provide a low-voltage topology intelligent generation and analysis system, which can provide power supply optimization solutions based on scenario analysis.

[0016] In order to solve the above technical problems, the present invention provides the following technical solutions: a system for low-voltage topology intelligent generation and research and analysis method, including: building an identification module, an optimization module, an evaluation calculation module and a processing module; the building identification module is used to use intelligent sensors and Internet of Things technology to collect operating data and topological information of distributed energy equipment, build a complete electromagnetic topology map, and identify and merge non-critical nodes through the depth-first search algorithm in graph theory to obtain the identified topological structure, wherein the critical nodes include important load nodes, nodes with complete data collection and distributed energy access nodes, wherein the operating data includes power generation equipment data, load demand data, energy storage system data According to the data and environmental data, the topology information includes the device connection relationship and geographic location information; the optimization module is used to obtain the electromagnetic field performance indicators based on the identified topological structure through finite element analysis, and optimize the electromagnetic field performance indicators to obtain the optimization results; the evaluation and calculation module is used to initialize the particle swarm, each particle represents a device connection relationship and operating parameter configuration, evaluate the fitness of each particle through non-dominated sorting and crowding distance calculation, and update the individual and global optimal solutions to obtain the optimized device connection relationship and operating parameters, wherein the speed and position of the particles are dynamically adjusted during the calculation process, and the operating data of the distributed energy equipment is used as a constraint condition; the processing module is used to obtain the effectiveness evaluation based on the optimized device connection relationship and operating parameters through electromagnetic field performance verification processing, and perform real-time monitoring and research and analysis on the effectiveness evaluation to obtain the final power supply optimization plan.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned low-voltage topology intelligent generation and analysis method are implemented.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned low-voltage topology intelligent generation and analysis method.

[0019] The beneficial effects of the present invention are as follows: the present invention uses finite element analysis technology to conduct a comprehensive electromagnetic field performance analysis of the electromagnetic topology structure of the distributed energy system, and by decomposing the complex system into multiple discrete units, accurately calculates key performance indicators such as electric field strength, magnetic field strength, power loss, etc., effectively identifies potential problems in the system, such as excessive power loss or insufficient voltage stability, and provides a quantitative basis for subsequent optimization; adopts a multi-objective optimization algorithm to optimize the electromagnetic field performance indicators, and by defining optimization goals (such as minimizing power loss, maximizing voltage stability), and combining actual operating data, dynamically adjusts the connection relationship and operating parameters of the equipment; by identifying key nodes and merging non-key nodes, the electromagnetic topology structure is significantly simplified, the calculation complexity is reduced, and the construction and maintenance costs of the system are reduced; by optimizing the connection relationship and operating parameters of the equipment, the voltage stability and anti-interference ability of the system are improved, and the stability and reliability of the system are further enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0021] Figure 1 This is a flow chart of a low-voltage topology intelligent generation and analysis method in Example 1.

[0022] Figure 2 This is a schematic diagram of the module structure of a low-voltage topology intelligent generation and analysis system in Example 2.

[0023] Figure 3 This is a structural diagram of a low-voltage topology intelligent generation and analysis device in Example 3.

[0024] In the figure: 701, construction and identification module; 702, optimization module; 703, evaluation and calculation module; 704, processing module; 800, low-voltage topology intelligent generation and analysis equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. 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.

[0027] Example 1, with reference to Figure 1 , which is the first embodiment of the present invention, provides a low-voltage topology intelligent generation and analysis method including: Figure 1 As shown:

[0028] S100. Use smart sensors and Internet of Things technology to collect operating data and topological information of distributed energy equipment, build a complete electromagnetic topological map, and use the depth-first search algorithm in graph theory to identify and merge non-critical nodes to obtain an identified topological structure. The critical nodes include important load nodes, nodes with complete data collection, and distributed energy access nodes. The operating data includes power generation equipment data, load demand data, energy storage system data, and environmental data. The topological information includes device connection relationships and geographic location information.

[0029] It should be noted that in this step, the power generation equipment data includes the light intensity, temperature and power generation of the solar photovoltaic panels; the wind speed, wind direction, rotation speed and output power of the wind turbine; the load demand data includes the power load demand in different regions and at different time periods; the energy storage system data includes the state of charge (SOC), charge and discharge current, and voltage of the battery; and the environmental data includes meteorological information such as temperature, humidity, and air pressure.

[0030] It can be understood that step S100 includes steps S101, S102, and S103, wherein:

[0031] S101. Define the attributes of each node according to the type and location of the device, wherein the attributes of the node include the node type and location coordinates;

[0032] S102: Define the edge connection mode based on the connection relationship between devices, where each edge Representation node and nodes electrical connections between.

[0033] S103, constructing a topological graph using the adjacency matrix, where Representation node and nodes If there is a connection between .

[0034] It's important to note that in a distributed energy system, each device (such as a solar photovoltaic panel, wind turbine, or energy storage device) can be considered a node. Node attributes are defined as follows: Node type: Identifies the node's function, such as power generation, energy storage, or load node. Location coordinates: Indicates the node's geographic location, typically expressed in latitude and longitude in the format (xi, yi). Therefore, by defining the node type and location coordinates, we can clearly identify the role and physical location of each device in the system, providing a foundation for subsequent topology analysis and optimization.

[0035] In step S102, the edge connection mode is defined according to the connection relationship between the devices, where each edge Representation node and nodes The electrical connection between them. The connection relationship can be expressed by the adjacency matrix A as follows:

[0036] ,

[0037] Among them, the adjacency matrix A is an n×n matrix, where n is the total number of nodes. Therefore, by defining the connection method of the edges, a complete electromagnetic topology graph can be constructed, providing the necessary topological information for the subsequent depth-first search algorithm.

[0038] This step S100 also includes S104, S105 and S106, wherein:

[0039] S104. Starting from a starting node, search along the path until it is impossible to continue, and then backtrack. During the depth-first search algorithm, the access order and access status of each node are recorded, and the nodes that have been visited are marked. Through the depth-first search traversal, the node access order and the preliminary identification results of the key nodes are obtained.

[0040] S105. According to the access order of the nodes and the preliminary identification results, after node importance evaluation processing, classification results of key nodes and non-key nodes are obtained. In the search process, key nodes are identified and non-key nodes are merged.

[0041] S106 , according to the classification results of key nodes and non-key nodes, a node merging process is performed to obtain an identified topological structure.

[0042] It should be noted that in steps S104-S106, the depth-first search (DFS) algorithm starts from a starting node and searches as deeply as possible along the path until it cannot continue, and then backtracks. The recursive process of the DFS algorithm is as follows: The first step is to mark the current node has been visited; the second step is to traverse the nodes All adjacent nodes of , if the node If it has not been visited, recursively call the DFS algorithm and set for The predecessor node; the third step is when the node After all adjacent nodes of are visited, mark the node It is understood that through the DFS algorithm, the entire topology graph can be systematically traversed, the access order of each node can be recorded, and the basis for the subsequent node importance evaluation can be provided.

[0043] Among them, identifying key nodes includes:

[0044] Key nodes are identified by calculating the local and global impacts of the nodes, where the local impact The calculation formula is expressed as,

[0045] ,

[0046] in, Representation node The degree, that is, the number of connected edges, Represents the total number of nodes in the graph.

[0047] Global impact The calculation formula is expressed as,

[0048] ,

[0049] in, Representation node The degree, Representation node and nodes The shortest path length between is a constant used to adjust the weight of the global influence.

[0050] Total impact The calculation formula is expressed as,

[0051] ,

[0052] By calculating the LGC value of each node, the key nodes can be identified.

[0053] It is understandable that in this embodiment, based on the identified key nodes and non-key nodes, the non-key nodes are merged. The merging process is as follows:

[0054] Node merging condition: If non-critical nodes If all the adjacent nodes of are key nodes, Merge into its adjacent key node.

[0055] Merge operation: Update the adjacency matrix A and The connection relationship is transferred to its adjacent key node and deleted Through the above steps, non-critical nodes are merged to obtain a streamlined topology structure, which provides a basis for subsequent optimization analysis.

[0056] Therefore, based on the above description, the above method can be applied to the optimization scheduling and fault diagnosis of distributed energy systems in practical research. For example, in distributed energy systems in remote mountainous areas, using smart sensors and IoT technology to collect device operating data and topology information, and using the DFS algorithm to identify key nodes and merge non-key nodes, can effectively simplify the topology and improve the system's operational efficiency and reliability.

[0057] S200, based on the identified topological structure, finite element analysis is performed to obtain electromagnetic field performance indicators, and the electromagnetic field performance indicators are optimized to obtain optimization results.

[0058] It is understandable that in this step, the system is simplified into a finite element model based on the identified topology. In the model, each device (such as power generation equipment, energy storage equipment, etc.) is regarded as a unit, and the connection relationship between devices is represented by meshing. The accuracy of the meshing directly affects the accuracy of the analysis results. Therefore, in the finite element model, corresponding excitations (such as voltage and current) and boundary conditions (such as grounding and insulation) are applied. These conditions are set based on actual operating data, such as the output power of power generation equipment and the charge state of energy storage equipment. That is, through finite element analysis, electromagnetic field performance indicators such as electric field strength, magnetic field strength, power loss, voltage stability, etc. are calculated to reflect the operating status of the system under the current topology.

[0059] In this step, optimization objectives are defined based on electromagnetic field performance indicators. For example, minimizing power loss or maximizing voltage stability can be achieved. These objectives can be adjusted to meet the optimization needs of different scenarios. An appropriate optimization algorithm, such as a genetic algorithm (GA) or particle swarm optimization (PSO), is then selected to iteratively search for the optimal topology and device parameter configuration. The optimization algorithm then adjusts the topology and device parameters based on the defined objective function. For example, this can be achieved by adjusting the connectivity between devices or changing their operating parameters to optimize electromagnetic field performance indicators.

[0060] S300. Initialize the particle swarm. Each particle represents a device connection relationship and operating parameter configuration. Evaluate the fitness of each particle through non-dominated sorting and crowding distance calculation, and update the individual and global optimal solutions to obtain the optimized device connection relationship and operating parameters. During the calculation process, the speed and position of the particles are dynamically adjusted, and the operating data of the distributed energy equipment is used as a constraint.

[0061] In this example, the operating data of the distributed energy devices in this scenario (such as power generation and state of charge) is used as constraints to ensure that the optimization results meet both the optimization objectives and actual operating conditions. The resulting optimized device connections and operating parameter configurations provide a basis for subsequent verification and adjustment, reducing computational complexity and lowering system construction and maintenance costs. By optimizing device connections and operating parameters, the system's voltage stability and anti-interference capabilities are improved, further enhancing its stability and reliability.

[0062] S400: Based on the optimized equipment connection relationship and operating parameters, after electromagnetic field performance verification, an effectiveness evaluation is obtained, and the effectiveness evaluation is monitored, evaluated, and analyzed in real time to obtain the final power supply optimization plan.

[0063] It should be noted that, based on the optimized device connections and operating parameters obtained in the above steps, finite element analysis techniques are re-used to verify the electromagnetic field performance of the optimized topology. The effectiveness of the optimization results is evaluated by comparing performance indicators before and after optimization (such as improved power supply efficiency and reduced voltage fluctuations). Furthermore, the feasibility and stability of the optimization scheme in actual operation are further verified by combining real-time operational data from the distributed energy system in this scenario, such as the real-time power generation of the equipment and changes in load demand. If the optimization results do not meet the expected goals, the optimization algorithm parameters or optimization objectives need to be adjusted and the optimization process repeated. Next, a real-time monitoring system is used to continuously collect operating status and environmental data from the distributed energy devices. Based on this real-time data, dynamic adjustment strategies are implemented, such as adjusting the charge and discharge power of energy storage devices according to real-time load demand and adjusting the operating parameters of power generation equipment according to changes in light intensity and wind speed. Through dynamic adjustment, the optimized topology and operating parameters are ensured to adapt to complex operating environments, ensuring the stability and reliability of the power supply system. Ultimately, an optimized power supply solution suitable for distributed energy systems in remote mountainous areas is developed, ensuring that power supply needs are met in complex environments.

[0064] This embodiment proposes a distributed energy system electromagnetic topology optimization method based on finite element analysis and optimization algorithms. This method uses intelligent sensors and Internet of Things (IoT) technology to collect device operating data and topology information, constructing a complete electromagnetic topology map. Finite element analysis is then used to perform detailed electromagnetic field performance analysis of the topology, calculating key performance indicators such as electric field strength, magnetic field strength, and power loss. Based on this, a multi-objective optimization algorithm (such as particle swarm optimization) is combined to optimize system scheduling, adjusting device connectivity and operating parameters to improve overall system performance. Verification and adjustment ensure the feasibility and effectiveness of the optimization scheme in practical applications. This effectively addresses existing technology challenges and enables efficient operation and optimized scheduling of distributed energy systems in complex environments.

[0065] Example 2, reference Figure 2 , which is the second embodiment of the present invention, which is different from the first embodiment in that: a system for low-voltage topology intelligent generation and analysis method, such as Figure 2 As shown, it includes a construction and identification module 701, an optimization module 702, an evaluation and calculation module 703 and a processing module 704.

[0066] The identification module 701 is constructed to collect the operating data and topological information of distributed energy equipment using smart sensors and Internet of Things technology, construct a complete electromagnetic topological map, identify and merge non-critical nodes through the depth-first search algorithm in graph theory, and obtain an identified topological structure, where the critical nodes include important load nodes, nodes with complete data collection, and distributed energy access nodes. The operating data includes power generation equipment data, load demand data, energy storage system data, and environmental data, and the topological information includes device connection relationships and geographic location information.

[0067] The optimization module 702 is used to obtain electromagnetic field performance indicators based on the identified topological structure through finite element analysis, and optimize the electromagnetic field performance indicators to obtain optimization results.

[0068] The evaluation and calculation module 703 is used to initialize the particle swarm. Each particle represents a device connection relationship and operating parameter configuration. The fitness of each particle is evaluated through non-dominated sorting and crowding distance calculation, and the individual and global optimal solutions are updated to obtain the optimized device connection relationship and operating parameters. The speed and position of the particles are dynamically adjusted during the calculation process, and the operating data of the distributed energy equipment is used as a constraint condition.

[0069] The processing module 704 is used to obtain an effectiveness evaluation based on the optimized equipment connection relationship and operating parameters through electromagnetic field performance verification processing, and to perform real-time monitoring, research and analysis on the effectiveness evaluation to obtain the final power supply optimization plan.

[0070] The construction identification module 701 further includes a first definition unit, a second definition unit and a construction unit.

[0071] The first definition unit is used to define the attributes of each node according to the type and location of the device, wherein the attributes of the node include the node type and location coordinates.

[0072] The second definition unit is used to define the edge connection mode according to the connection relationship between devices, where each edge Representation node and nodes electrical connections between.

[0073] The construction unit is used to construct a topological graph using the adjacency matrix, where Representation node and nodes If there is a connection between .

[0074] The construction recognition module 701 further includes a traversal unit, a processing unit and a classification unit.

[0075] The traversal unit is used to start from a starting node and search along the path until it can no longer continue, and then backtrack. During the depth-first search algorithm, the access order and access status of each node are recorded, and the visited nodes are marked. Through the depth-first search traversal, the node access order and the preliminary identification results of the key nodes are obtained.

[0076] The processing unit is used to obtain classification results of key nodes and non-key nodes based on the node access order and preliminary identification results after node importance evaluation processing. During the search process, key nodes are identified and non-key nodes are merged.

[0077] The classification unit is used to obtain the identified topological structure based on the classification results of key nodes and non-key nodes through node merging processing.

[0078] The processing unit further includes a first computing unit and a second computing unit.

[0079] The first calculation unit is used to identify key nodes by calculating the local influence and global influence of the nodes, wherein the local influence The calculation formula is expressed as,

[0080] ,

[0081] in, Representation node The degree, that is, the number of connected edges, Represents the total number of nodes in the graph.

[0082] Global impact The calculation formula is expressed as,

[0083] ,

[0084] in, Representation node The degree, Representation node and nodes The shortest path length between is a constant used to adjust the weight of the global influence.

[0085] Total impact The calculation formula is expressed as,

[0086] ,

[0087] The second calculation unit is used to calculate the LGC value of each node, thereby identifying the key nodes.

[0088] Example 3, reference Figure 3 , which is the third embodiment of the present invention, which is different from the first two embodiments in that: a low-voltage topology intelligent generation and analysis device, such as Figure 3 As shown, the low-voltage topology intelligent generation and analysis device 800 includes: a processor 801 and a memory 802. The low-voltage topology intelligent generation and analysis device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0089] The processor 801 is used to control the overall operation of the low-voltage topology intelligent generation, analysis, and judgment device 800 to complete all or part of the steps in the low-voltage topology intelligent generation, analysis, and judgment method described above. The memory 802 is used to store various types of data to support the operation of the low-voltage topology intelligent generation, analysis, and judgment device 800. This data may include, for example, instructions for any application or method operating on the low-voltage topology intelligent generation, analysis, and judgment device 800, as well as application-related data, such as contact information, sent and received messages, images, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, a magnetic disk, or an optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse or buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the low-voltage topology intelligent generation and analysis device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: a Wi-Fi module, a Bluetooth module or an NFC module.

[0090] In an exemplary embodiment, the low-voltage topology intelligent generation and analysis device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned low-voltage topology intelligent generation and analysis method.

[0091] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the above-described low-voltage topology intelligent generation and analysis method. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the low-voltage topology intelligent generation and analysis device 800 to implement the above-described low-voltage topology intelligent generation and analysis method.

[0092] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0093] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0094] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0095] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0096] 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. A method for intelligent generation and analysis of low-voltage topology, characterized by: include, Utilize smart sensors and IoT technology to collect operational data and topological information from distributed energy devices, construct a complete electromagnetic topological map, and use a depth-first search algorithm from graph theory to identify and merge non-critical nodes to obtain an identified topological structure. Key nodes include important load nodes, nodes with complete data collection, and distributed energy access nodes. Operational data includes power generation equipment data, load demand data, energy storage system data, and environmental data. Topological information includes device connection relationships and geographic location information. Based on the identified topological structure, the electromagnetic field performance index is obtained through finite element analysis, and the electromagnetic field performance index is optimized to obtain the optimization result; Initialize a particle swarm, where each particle represents a device connection relationship and operating parameter configuration. Use non-dominated sorting and crowding distance calculations to evaluate the fitness of each particle, and update the individual and global optimal solutions to obtain the optimized device connection relationship and operating parameters. Dynamically adjust the particle's speed and position during the calculation process, using the operating data of distributed energy devices as constraints. Based on the optimized equipment connection relationship and operating parameters, after electromagnetic field performance verification, an effectiveness evaluation is obtained, and the effectiveness evaluation is monitored, evaluated and analyzed in real time to obtain the final power supply optimization plan.

2. The method for intelligently generating and analyzing low-voltage topology according to claim 1, wherein: The construction of a complete electromagnetic topology map includes: Define the attributes of each node according to the type and location of the device, where the node attributes include node type and location coordinates; According to the connection relationship between devices, the edge connection method is defined, where each edge Representation node and nodes Electrical connections between; The topological graph is constructed using the adjacency matrix, where Representation node and nodes If there is a connection between .

3. The method for intelligently generating and analyzing low-voltage topology according to claim 2, wherein: The depth-first search algorithm in graph theory is used to identify and merge non-critical nodes to obtain the identified topological structure, which includes: Starting from a starting node, search along the path until it is impossible to continue, and then backtrack. During the depth-first search algorithm, the access order and access status of each node are recorded, and the nodes that have been visited are marked. Through the depth-first search traversal, the node access order and the preliminary identification results of the key nodes are obtained; According to the access order of nodes and the preliminary identification results, after the node importance evaluation process, the classification results of key nodes and non-key nodes are obtained. In the search process, key nodes are identified and non-key nodes are merged; According to the classification results of key nodes and non-key nodes, the identified topological structure is obtained through node merging.

4. The method for intelligently generating and analyzing low-voltage topology according to claim 3, wherein: The identifying of key nodes includes: Key nodes are identified by calculating the local and global impacts of the nodes, where the local impact The calculation formula is expressed as, ， in, Representation node The degree, that is, the number of connected edges, Represents the total number of nodes in the graph; Global impact The calculation formula is expressed as, ， in, Representation node The degree, Representation node and nodes The shortest path length between is a constant used to adjust the weight of global influence; Total impact The calculation formula is expressed as, ， By calculating the LGC value of each node, the key nodes can be identified.

5. A low-voltage topology intelligent generation and analysis system, applying a low-voltage topology intelligent generation and analysis method according to any one of claims 1 to 4, characterized in that: It includes a construction recognition module (701), an optimization module (702), an evaluation calculation module (703) and a processing module (704); The construction and identification module (701) is used to collect the operation data and topology information of distributed energy equipment using smart sensors and Internet of Things technology, construct a complete electromagnetic topology map, identify and merge non-critical nodes through a depth-first search algorithm in graph theory, and obtain an identified topological structure, wherein the critical nodes include important load nodes, nodes with complete data collection, and distributed energy access nodes, wherein the operation data includes power generation equipment data, load demand data, energy storage system data, and environmental data, and the topology information includes equipment connection relationships and geographical location information; The optimization module (702) is used to obtain electromagnetic field performance indicators based on the identified topological structure through finite element analysis, and optimize the electromagnetic field performance indicators to obtain optimization results; The evaluation calculation module (703) is used to initialize the particle swarm, each particle represents a device connection relationship and operating parameter configuration, evaluate the fitness of each particle through non-dominated sorting and crowding distance calculation, and update the individual and global optimal solutions to obtain the optimized device connection relationship and operating parameters, wherein the speed and position of the particles are dynamically adjusted during the calculation process, and the operating data of the distributed energy equipment is used as a constraint condition; The processing module (704) is used to obtain an effectiveness evaluation based on the optimized equipment connection relationship and operating parameters through electromagnetic field performance verification processing, and to perform real-time monitoring, research and analysis processing on the effectiveness evaluation to obtain a final power supply optimization plan.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the low-voltage topology intelligent generation and analysis method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a low-voltage topology intelligent generation and analysis method according to any one of claims 1 to 4 are implemented.

Images