Power distribution network self-healing method and device, electronic equipment and storage medium

Abstract

The invention discloses a power distribution network self-healing method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a digital twinborn model of the power distribution network, and determining an effective self-healing strategy from predetermined initial self-healing strategies through the digital twinborn model; in the process of performing self-healing of the power distribution network through the effective self-healing strategy, performing rationality detection on a self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result; and the self-healing line is adjusted according to the detection result, so that the self-healing process of the power distribution network can be smoothly carried out. According to the scheme, the initial self-healing strategy is verified by constructing the digital twinborn model of the power distribution network to obtain the effective self-healing strategy, and the self-healing line is detected and adjusted in the self-healing process of the power distribution network through the effective self-healing strategy, so that the problems in the self-healing line can be solved in time, the smooth self-healing process of the power distribution network is ensured, and the self-healing efficiency of the power distribution network is improved. Therefore, the self-healing capability of the power distribution network is improved, and support is provided for stable operation of the power distribution network.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a distribution network self-healing method, device, electronic equipment and storage medium. Background Art

[0002] With the continuous expansion of electricity demand, higher requirements are placed on the reliability and stability of distribution networks. In order to ensure the stable operation of distribution networks, distribution network self-healing technology is generally used to achieve fault response, troubleshooting and power supply restoration after a distribution network failure. However, the self-healing schemes in current distribution network self-healing technologies are usually formulated based on experience, historical data or expert knowledge. If there are defects or errors in the self-healing schemes, it may lead to the failure of distribution network self-healing, making it difficult to ensure the accuracy and reliability of distribution network self-healing. Summary of the invention

[0003] The present application provides a distribution network self-healing method, device, electronic device and storage medium, which can ensure the smooth progress of the distribution network self-healing process, thereby improving the self-healing capability of the distribution network and providing support for the stable operation of the distribution network.

[0004] According to one aspect of the present application, a distribution network self-healing method is provided, the method comprising:

[0005] Constructing a digital twin model of the distribution network, and determining an effective self-healing strategy from predetermined initial self-healing strategies through the digital twin model;

[0006] In the process of self-healing the distribution network by using the effective self-healing strategy, a rationality detection is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result;

[0007] The self-healing line is adjusted according to the detection result so that the self-healing process of the distribution network proceeds smoothly.

[0008] According to another aspect of the present application, a power distribution network self-healing device is provided, the device comprising:

[0009] An effective strategy determination module is used to construct a digital twin model of the distribution network and determine an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model;

[0010] A self-healing line detection module is used to perform a rationality detection on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme during the process of self-healing the distribution network through the effective self-healing strategy, and obtain a detection result;

[0011] The self-healing line adjustment module is used to adjust the self-healing line according to the detection result so that the self-healing process of the distribution network proceeds smoothly.

[0012] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network self-healing method described in any embodiment of the present invention.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distribution network self-healing method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present application is to construct a digital twin model of the distribution network, and determine an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model; in the process of self-healing the distribution network through the effective self-healing strategy, the self-healing line corresponding to the effective self-healing strategy is tested for rationality based on a preset detection scheme to obtain a detection result; the self-healing line is adjusted according to the detection result to ensure the smooth progress of the self-healing process of the distribution network. The technical solution of the embodiment of the present application is to verify the initial self-healing strategy by constructing a digital twin model of the distribution network to obtain an effective self-healing strategy, and to detect and adjust the self-healing line during the process of self-healing the distribution network through the effective self-healing strategy, so as to promptly solve the problems existing in the self-healing line, ensure the smooth progress of the self-healing process of the distribution network, thereby improving the self-healing ability of the distribution network and providing support for the stable operation of the distribution network.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in 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 creative work.

[0020] Figure 1is a flow chart of a distribution network self-healing method provided according to Embodiment 1 of the present application;

[0021] Figure 2 is a flow chart of a distribution network self-healing method provided according to Embodiment 2 of the present application;

[0022] Figure 3 It is a schematic diagram of a process for constructing a digital twin model of a distribution network according to the second embodiment of the present application;

[0023] Figure 4 It is a partial schematic diagram of a simplified distribution network digital twin model provided according to the second embodiment of the present application;

[0024] Figure 5 It is a structural schematic diagram of a distribution network self-healing device provided according to Embodiment 3 of the present application;

[0025] Figure 6 It is a structural schematic diagram of an electronic device for implementing the distribution network self-healing method of an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1A flow chart of a distribution network self-healing method is provided for the first embodiment of the present application. This embodiment is applicable to the case of self-healing a distribution network that has a fault. The method can be executed by a distribution network self-healing device. The distribution network self-healing device can be implemented in the form of hardware and / or software. The distribution network self-healing device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] S110, constructing a digital twin model of the distribution network, and determining an effective self-healing strategy from predetermined initial self-healing strategies through the digital twin model.

[0031] The distribution network is a key component of the power system, responsible for transmitting electricity from the high-voltage transmission network or power station to the user end, usually including medium-voltage and low-voltage power grids, which are directly connected to the user to provide the required electricity to the user. The digital twin model is to create a virtual model for the physical object in a digital way, simulating its behavioral characteristics in the real environment, so as to achieve continuous monitoring, future prediction and performance optimization of the physical object.

[0032] Among them, the initial self-healing strategy refers to a series of strategies pre-set in the distribution network to deal with faults or abnormal situations. These strategies are usually formulated based on experience, historical data or expert knowledge, and aim to quickly restore the normal operation of the distribution network. The effective self-healing strategy refers to the strategy with high rationality and success rate selected from the initial self-healing strategy with the assistance of the digital twin model.

[0033] In the embodiment of the present application, a digital twin model of the distribution network can be constructed, and the predetermined initial self-healing strategy can be simulated through the constructed digital twin model to screen out effective self-healing strategies with high rationality and success rate from the initial self-healing strategies, thereby ensuring the stable operation of the distribution network. By constructing a digital twin model of the distribution network, not only can the real-time status of the distribution network be truly reflected, but also the rationality of the initial self-healing strategy can be verified without interfering with the actual operation, thereby improving the safety of the self-healing of the distribution network.

[0034] Optionally, the process of predetermining the initial self-healing strategy includes: for a target distribution network fault that meets the self-healing conditions, determining a target self-healing operation corresponding to each link in the distribution network self-healing process corresponding to the target distribution network fault; integrating the target self-healing operations to determine the initial self-healing strategy corresponding to the target distribution network fault.

[0035] Among them, distribution network self-healing is a continuous process that automatically detects, isolates faults and quickly restores power supply when a distribution network fails through an automated monitoring and control mechanism. Its purpose is to quickly respond to various fault conditions, minimize power outage time, and improve the reliability of the distribution network. The distribution network self-healing process includes the following links: 1. Fault detection: Through the key parameters such as voltage, current, and load collected by sensors, after data analysis, the possible fault type or abnormal state of the fault is identified to provide a basis for subsequent links. 2. Fault location: Using relevant positioning methods or artificial intelligence algorithms, the specific location of the fault is accurately and quickly located. The accuracy of fault location is the key to ensuring the implementation of self-healing. 3. Fault isolation: After the fault location is completed, the system will isolate the fault according to the type, location and severity of the fault, as well as the current operating status and topology of the distribution network, to ensure that the faulty part is separated from the healthy part of the distribution network, prevent the fault from spreading, and protect the stable operation of the power grid. 4. Fault recovery strategy generation: By analyzing the impact of the fault on the operation of the distribution network, a variety of possible fault recovery strategies are generated, such as load transfer and power supply path switching. 5. Execution of fault recovery strategy. After the fault recovery strategy is generated, the fault recovery strategy is executed by remotely controlling related devices through the distribution automation system (DAS). It should be noted that if the fault is recovered after the first distribution network self-healing, there is no need to perform the second distribution network self-healing. Otherwise, the second distribution network self-healing is performed. If the fault cannot be recovered after two distribution network self-healings, further analysis is required, such as updating the fault recovery strategy.

[0036] In an embodiment of the present application, for various target distribution network faults that meet the self-healing conditions, the target self-healing operations corresponding to each link when restoring the target distribution network fault can be determined according to the above-mentioned distribution network self-healing links based on experience, historical data or expert knowledge, and the target self-healing operations of each link can be integrated to determine the initial self-healing strategy corresponding to the target distribution network fault.

[0037] S120. In the process of performing self-healing of the distribution network by using an effective self-healing strategy, a rationality detection is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result.

[0038] Among them, the preset detection schemes include self-healing line configuration detection, self-healing line equipment detection, and self-healing line topology detection. A self-healing line refers to a power line related to an effective self-healing strategy during the process of self-healing the distribution network through an effective self-healing strategy. The self-healing line configuration detection is to detect the consistency of the configuration attributes of the self-healing line, the controllability of the self-healing switch, and the integrity of the point meter data. The self-healing line equipment detection is to detect the operating status of the distribution network equipment related to the self-healing line. The self-healing line topology detection is to detect the trunk line topology relationship of the self-healing line.

[0039] In the embodiment of the present application, in the process of self-healing the distribution network through an effective self-healing strategy, the self-healing line corresponding to the effective self-healing strategy can be rationally detected based on a preset detection scheme, that is, the self-healing line configuration detection, self-healing line equipment detection and self-healing line topology detection are performed on the self-healing line corresponding to the effective self-healing strategy to obtain a detection result. By performing rationality detection on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme, possible problems or performance bottlenecks in the self-healing line can be discovered in a timely manner, and then the self-healing line can be adjusted and optimized to improve the self-healing ability of the distribution network.

[0040] S130: adjusting the self-healing line according to the detection result, so that the self-healing process of the distribution network proceeds smoothly.

[0041] In an embodiment of the present application, after a rationality detection is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme and the detection result is obtained, the self-healing line can be adjusted according to the detection result. Specifically, the detection result can be analyzed to clarify the problems and deficiencies of the self-healing line, and then a targeted adjustment plan can be formulated to adjust the self-healing line so that the self-healing process of the distribution network can proceed smoothly.

[0042] The technical solution of the embodiment of the present application constructs a digital twin model of the distribution network, and determines an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model; in the process of self-healing the distribution network through the effective self-healing strategy, the self-healing line corresponding to the effective self-healing strategy is tested for rationality based on a preset detection scheme to obtain a detection result; the self-healing line is adjusted according to the detection result to ensure the smooth progress of the self-healing process of the distribution network. The technical solution of the embodiment of the present application constructs a digital twin model of the distribution network to verify the initial self-healing strategy to obtain an effective self-healing strategy, and detects and adjusts the self-healing line during the process of self-healing the distribution network through the effective self-healing strategy, which can promptly solve problems existing in the self-healing line, ensure the smooth progress of the self-healing process of the distribution network, and then improve the self-healing ability of the distribution network, and provide support for the stable operation of the distribution network.

[0043] Embodiment 2

[0044] Figure 2 This is a flow chart of a distribution network self-healing method provided in the second embodiment of the present application. The present embodiment is optimized based on the above embodiment. For solutions not described in detail in the present embodiment, please refer to the above embodiment. Figure 2 As shown, the method includes:

[0045] S210: construct a digital twin model of the distribution network, and determine an effective self-healing strategy from predetermined initial self-healing strategies through the digital twin model.

[0046] Optionally, a digital twin model of the distribution network is constructed, and an effective self-healing strategy is determined from a predetermined initial self-healing strategy through the digital twin model, including: acquiring static data and dynamic data of the distribution network equipment, and performing physical modeling of the distribution network equipment according to the static data to obtain a distribution network equipment model; implementing simulation operation of the distribution network equipment model according to the dynamic data, as well as interactive collaboration between each distribution network equipment model, to obtain a digital twin model of the distribution network; simulating the initial self-healing strategy according to the digital twin model, and determining an effective self-healing strategy from the initial self-healing strategy according to the simulation results.

[0047] Among them, static data can be the geometric dimensions, material properties, etc. of distribution network equipment, and dynamic data can be the operating data of distribution network equipment, such as real-time current, voltage, etc.

[0048] In the embodiment of the present application, the static data and dynamic data of the distribution network equipment can be obtained respectively, and the distribution network equipment is first modeled based on the static data, and the entity of the distribution network equipment is converted into a digital model to obtain the distribution network equipment model, and then the simulation operation of the distribution network equipment model is realized based on the dynamic data to ensure that the distribution network equipment model can reflect the characteristics of the distribution network equipment. Finally, by fusing and integrating the distribution network equipment model, the interactive coordination between the various distribution network equipment models is realized, and the digital twin model of the distribution network is obtained to simulate the system behavior of the distribution network. For example, Figure 3 A schematic diagram of the process of building a digital twin model of a distribution network is shown in FIG. Figure 3 As shown, after the digital twin model of the distribution network is built, the digital twin model can also be verified and updated through the actual operation data of the distribution network to improve the accuracy of the digital twin model. Figure 4 A partial schematic diagram of a simplified distribution network digital twin model is shown. Figure 4 As shown in the figure, K1-K10 represent different feeder switches, and u1-u8 represent different users. Afterwards, the initial self-healing strategy can be simulated according to the digital twin model, and an effective self-healing strategy can be determined from the initial self-healing strategy according to the simulation results.

[0049] S220: When a distribution network fault that meets the self-healing condition is detected in the distribution network, an effective self-healing strategy is executed based on the distribution automation system to perform self-healing of the distribution network.

[0050] In an embodiment of the present application, when a distribution network fault that meets the self-healing conditions is detected in the distribution network, an effective self-healing strategy corresponding to the type of distribution network fault that has occurred can be selected, and the distribution network self-healing can be performed based on the remote control of the relevant distribution network equipment by the distribution automation system.

[0051] S230. In the process of self-healing the distribution network through an effective self-healing strategy, a rationality detection is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result.

[0052] Optionally, a rationality check is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result, including: repeatedly performing a self-healing line configuration check on the self-healing line through an AC automaton algorithm to obtain a verification result of the configuration attribute consistency, the controllability of the self-healing switch, and the integrity of the point table data of the self-healing line; continuously performing a self-healing line equipment check on the self-healing line through an equipment status detection model based on a machine learning algorithm to obtain the operating status of the distribution network equipment corresponding to the self-healing line; regularly performing a self-healing line topology check on the self-healing line through a depth-first search algorithm to obtain an inspection result of the topological relationship of the self-healing line; and using the verification result, the operating status, and the inspection result as the detection result.

[0053] Among them, the AC automaton algorithm is a prefix-based, deterministic finite automaton principle-based string multi-mode matching algorithm, which can efficiently find the existence of multiple pattern strings in a given text string, including whether they exist, how many times they exist, and where they exist. In the embodiment of the present application, the AC automaton algorithm can quickly locate and verify preset patterns in a large amount of data, such as configuration attribute consistency.

[0054] Among them, the depth-first search algorithm is an algorithm for traversing or searching a tree or graph, which traverses the nodes of the tree along the depth of the tree and searches the branches of the tree as deeply as possible. In the embodiment of the present application, the self-healing line topology detection can be implemented by the depth-first search algorithm, which can comprehensively inspect all nodes and connections to ensure that nothing is missed.

[0055] In an embodiment of the present application, the self-healing line configuration detection can be repeatedly performed on the self-healing line by the AC automaton algorithm. Specifically, the consistency of the configuration attributes of the feeder and the switch can be verified, the controllability of the self-healing switch can be verified, and the point table data integrity of the distribution network equipment involved in the self-healing process can be verified to obtain the verification result. The self-healing line equipment detection can be continuously performed on the self-healing line by the equipment state detection model obtained after training the machine learning algorithm (such as decision tree, random forest, support vector machine, etc.). Specifically, the operation data of the distribution network equipment can be analyzed by the equipment state detection model to identify the behavior or performance degradation that does not meet the expectations, and obtain the operation status of the distribution network equipment. The self-healing line topology detection can be performed regularly on the self-healing line by the depth-first search algorithm. Specifically, it can include the full self-healing line topology scheduled inspection, the specified self-healing line topology inspection, the model association problem inspection and the topology problem inspection, etc., to obtain the inspection result of the topological relationship of the self-healing line. Afterwards, the verification result, the operation status and the inspection result are used as the detection result.

[0056] S240. Adjust the self-healing line according to the detection result to ensure smooth self-healing of the distribution network.

[0057] Optionally, the self-healing line is adjusted according to the detection result to ensure smooth self-healing of the distribution network, including: if the configuration attributes of the self-healing line are inconsistent, the self-healing switch is uncontrollable, or the point meter data is incomplete, adjusting the relevant parameters of the self-healing line; if the operating status of the distribution network equipment of the self-healing line is offline, triggering an alarm to remind the operation and maintenance personnel to handle the offline distribution network equipment; if the topological relationship of the self-healing line is abnormal, recording and reporting the topological relationship abnormality for the operation and maintenance personnel to adjust the abnormal topological relationship.

[0058] In an embodiment of the present application, if the configuration attributes of the self-healing line are inconsistent, the self-healing switch is uncontrollable, or the point table data is incomplete according to the verification results in the detection results, the relevant parameters of the self-healing line are adjusted to ensure that the configuration of the self-healing line meets the requirements of the effective self-healing strategy. If the operating state of the distribution network equipment is found to be offline according to the operating state in the detection results, an alarm prompt is triggered to remind the operation and maintenance personnel to handle the offline distribution network equipment. Optionally, for problems such as remote control failure, abnormal battery power, voltage transformer disconnection, or removal of the protection pressure plate in the distribution network equipment on the self-healing line, the distribution network system will start the intelligent alarm mechanism, automatically prohibit the execution of the self-healing function, and provide an alarm notification. If the topological relationship of the self-healing line is found to be abnormal according to the inspection results in the detection results, the abnormal topological relationship is recorded and reported for the operation and maintenance personnel to adjust the abnormal topological relationship. Through the above process, adjustments can be made to the possible problems and deficiencies on the self-healing line so that the self-healing process of the distribution network can proceed smoothly.

[0059] S250: Obtaining operation data and detection results of the distribution network during the process of self-healing the distribution network through an effective self-healing strategy.

[0060] In an embodiment of the present application, in the process of self-healing of the distribution network through an effective self-healing strategy, the distribution network operation data generated in the process and the detection results obtained by rationality detection of the self-healing line can be collected to provide data support for the visualization of the distribution network self-healing process and the self-healing line rationality detection process.

[0061] S260. After edge computing is performed on the operation data and the detection results, the edge computing results are displayed through a graphical interface to achieve a visual display of the distribution network self-healing process and the rationality detection process.

[0062] Among them, edge computing uses computing resources on edge devices (such as routers, switches, IoT devices, etc.) to process, analyze and store data to reduce data transmission delays, alleviate network pressure, and provide faster and more flexible services.

[0063] In an embodiment of the present application, the operating data and the detection results can be analyzed and processed on the edge computing node, the processed data and the analysis results can be sent to the central server, and the processed data and the analysis results can be converted into a graphical interface for display using visualization technology. At the same time, the digital twin model constructed in step S210 can be combined to realize the visualization of the distribution network self-healing process and the rationality detection process.

[0064] The technical solution of the embodiment of the present application constructs a digital twin model of the distribution network, and determines an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model; when a distribution network fault that meets the self-healing conditions is detected in the distribution network, the effective self-healing strategy is executed based on the distribution automation system to perform self-healing of the distribution network; in the process of self-healing the distribution network through the effective self-healing strategy, the rationality of the self-healing line corresponding to the effective self-healing strategy is detected based on a preset detection scheme to obtain a detection result; the self-healing line is adjusted according to the detection result to ensure a smooth process of self-healing of the distribution network; the operation data and detection results of the distribution network are obtained in the process of self-healing the distribution network through the effective self-healing strategy; after edge computing the operation data and the detection results, the edge computing results are displayed through a graphical interface to achieve a visual display of the distribution network self-healing process and the rationality detection process. The technical solution of the embodiment of the present application verifies the initial self-healing strategy by constructing a digital twin model of the distribution network to obtain an effective self-healing strategy, and detects and adjusts the self-healing line in the process of self-healing the distribution network through the effective self-healing strategy, which can promptly solve the problems existing in the self-healing line, ensure the smooth progress of the self-healing process of the distribution network, and then improve the self-healing ability of the distribution network, and provide support for the stable operation of the distribution network. At the same time, by visually displaying the self-healing process of the distribution network and the rationality detection process, the operation and maintenance personnel can enhance their perception and control of the state of the distribution network, thereby making the decision-making process of adjusting the self-healing line more scientific and reasonable.

[0065] Embodiment 3

[0066] Figure 5 This is a schematic diagram of the structure of a distribution network self-healing device provided in Example 3 of the present application. Figure 5 As shown, the device comprises:

[0067] An effective strategy determination module 310 is used to construct a digital twin model of the distribution network, and determine an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model;

[0068] A self-healing line detection module 320 is used to perform a rationality detection on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result during the process of self-healing the distribution network through the effective self-healing strategy;

[0069] The self-healing line adjustment module 330 is used to adjust the self-healing line according to the detection result so that the self-healing process of the distribution network proceeds smoothly.

[0070] Optionally, the effective strategy determination module 310 includes:

[0071] A target self-healing operation determination unit, configured to determine, for a target distribution network fault that meets a self-healing condition, a target self-healing operation corresponding to each link in a distribution network self-healing process corresponding to the target distribution network fault;

[0072] The initial self-healing strategy determining unit is used to integrate the target self-healing operation and determine the initial self-healing strategy corresponding to the target distribution network fault.

[0073] Optionally, the effective strategy determination module 310 includes:

[0074] A distribution network equipment model determination unit, used to obtain static data and dynamic data of distribution network equipment, and perform entity modeling on the distribution network equipment according to the static data to obtain a distribution network equipment model;

[0075] A digital twin model determination unit, used to implement simulation operation of a distribution network equipment model according to the dynamic data, as well as interactive collaboration between various distribution network equipment models, to obtain a digital twin model of the distribution network;

[0076] An effective self-healing strategy determination unit is used to simulate the initial self-healing strategy according to the digital twin model, and determine an effective self-healing strategy from the initial self-healing strategy according to the simulation result.

[0077] Optionally, the device further comprises:

[0078] The distribution network self-healing module is used to execute the effective self-healing strategy based on the distribution automation system to perform distribution network self-healing when a distribution network fault that meets the self-healing condition is detected in the distribution network.

[0079] Optionally, the preset detection scheme includes self-healing line configuration detection, self-healing line equipment detection and self-healing line topology detection; the self-healing line detection module 320 includes:

[0080] A verification result determination unit, used to repeatedly perform self-healing line configuration detection on the self-healing line through an AC automaton algorithm to obtain verification results of the consistency of configuration attributes of the self-healing line, the controllability of the self-healing switch, and the integrity of the point table data;

[0081] An operation status determination unit, configured to continuously perform self-healing line equipment detection on the self-healing line through an equipment status detection model based on a machine learning algorithm, and obtain an operation status of a distribution network equipment corresponding to the self-healing line;

[0082] A patrol result determination unit, used for periodically performing self-healing line topology detection on the self-healing line through a depth-first search algorithm to obtain a patrol result of the topological relationship of the self-healing line;

[0083] The detection result determination unit is used to use the verification result, the operating status and the inspection result as the detection result.

[0084] Optionally, the self-healing line adjustment module 330 includes:

[0085] A self-healing line configuration adjustment unit, configured to adjust relevant parameters of the self-healing line if the configuration attributes of the self-healing line are inconsistent, the self-healing switch is uncontrollable, or the point table data is incomplete;

[0086] A self-healing line equipment adjustment unit, configured to trigger an alarm prompt to remind operation and maintenance personnel to process the offline distribution network equipment if the operation state of the distribution network equipment of the self-healing line is offline;

[0087] The self-healing line topology adjustment unit is used to record and report the abnormal topology relationship if the topology relationship of the self-healing line is abnormal, so that the operation and maintenance personnel can adjust the abnormal topology relationship.

[0088] The device further comprises:

[0089] An operation data acquisition module, used to acquire the operation data of the distribution network and the detection result during the process of self-healing the distribution network through the effective self-healing strategy;

[0090] The self-healing panoramic monitoring module is used to display the edge computing results through a graphical interface after edge computing is performed on the operation data and the detection results, so as to realize the visual display of the distribution network self-healing process and the rationality detection process.

[0091] The distribution network self-healing device provided in the embodiment of the present application can execute the distribution network self-healing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0092] Embodiment 4

[0093] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0094] like Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0096] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a distribution network self-healing method.

[0097] In some embodiments, the distribution network self-healing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the distribution network self-healing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the distribution network self-healing method in any other appropriate manner (e.g., by means of firmware).

[0098] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0102] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0103] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document is not limited here.

[0105] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of this application.

Claims

1. A distribution network self-healing method, characterized in that: The method comprises: Constructing a digital twin model of the distribution network, and determining an effective self-healing strategy from predetermined initial self-healing strategies through the digital twin model; In the process of self-healing the distribution network by using the effective self-healing strategy, a rationality detection is performed on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme to obtain a detection result; The self-healing line is adjusted according to the detection result so that the self-healing process of the distribution network proceeds smoothly.

2. The method according to claim 1, characterized in that A process for pre-determining an initial self-healing strategy, including: For a target distribution network fault that meets the self-healing condition, determine a target self-healing operation corresponding to each link in the distribution network self-healing process corresponding to the target distribution network fault; The target self-healing operations are integrated to determine an initial self-healing strategy corresponding to the target distribution network fault.

3. The method according to claim 1, characterized in that A digital twin model of the distribution network is constructed, and an effective self-healing strategy is determined from a predetermined initial self-healing strategy through the digital twin model, including: Obtaining static data and dynamic data of a distribution network device, and performing entity modeling on the distribution network device according to the static data to obtain a distribution network device model; Implementing simulation operation of the distribution network equipment model and interactive collaboration between the distribution network equipment models according to the dynamic data to obtain a digital twin model of the distribution network; The initial self-healing strategy is simulated according to the digital twin model, and an effective self-healing strategy is determined from the initial self-healing strategy according to the simulation result.

4. The method according to claim 1, characterized in that: After building a digital twin model of the distribution network and determining an effective self-healing strategy from predetermined initial self-healing strategies through the digital twin model, the method further includes: When a distribution network fault that meets the self-healing condition is detected in the distribution network, the effective self-healing strategy is executed based on the distribution automation system to perform self-healing of the distribution network.

5. The method according to claim 1, characterized in that The preset detection scheme includes self-healing line configuration detection, self-healing line equipment detection and self-healing line topology detection; Based on the preset detection scheme, the rationality of the self-healing line corresponding to the effective self-healing strategy is detected to obtain the detection results, including: Repeating the self-healing line configuration detection on the self-healing line by using an AC automaton algorithm to obtain verification results of the consistency of configuration attributes of the self-healing line, the controllability of the self-healing switch, and the integrity of the point table data; Continuously performing self-healing line equipment detection on the self-healing line through an equipment status detection model based on a machine learning algorithm to obtain the operating status of the distribution network equipment corresponding to the self-healing line; Regularly performing self-healing line topology detection on the self-healing line by using a depth-first search algorithm to obtain inspection results of the topological relationship of the self-healing line; The verification result, the operating status and the inspection result are used as the detection result.

6. The method according to claim 1, characterized in that The self-healing line is adjusted according to the detection result so that the self-healing process of the distribution network proceeds smoothly, including: If the configuration attributes of the self-healing line are inconsistent, the self-healing switch is uncontrollable, or the point table data is incomplete, adjust the relevant parameters of the self-healing line; If the operating state of the distribution network equipment of the self-healing line is offline, trigger an alarm prompt to remind the operation and maintenance personnel to handle the offline distribution network equipment; If the topological relationship of the self-healing line is abnormal, the abnormal topological relationship is recorded and reported so that the operation and maintenance personnel can adjust the abnormal topological relationship.

7. The method according to claim 1, characterized in that The method further comprises: Acquiring the operation data of the distribution network and the detection result during the process of self-healing the distribution network using the effective self-healing strategy; After edge computing is performed on the operating data and the detection results, the edge computing results are displayed through a graphical interface to achieve a visual display of the distribution network self-healing process and the rationality detection process.

8. A distribution network self-healing device, characterized in that: The device comprises: An effective strategy determination module is used to construct a digital twin model of the distribution network and determine an effective self-healing strategy from a predetermined initial self-healing strategy through the digital twin model; A self-healing line detection module is used to perform a rationality detection on the self-healing line corresponding to the effective self-healing strategy based on a preset detection scheme during the process of self-healing the distribution network through the effective self-healing strategy, and obtain a detection result; The self-healing line adjustment module is used to adjust the self-healing line according to the detection result so that the self-healing process of the distribution network proceeds smoothly.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the power distribution network self-healing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distribution network self-healing method according to any one of claims 1 to 7 when executed.

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