Intelligent power distribution network self-healing control method and system

Abstract

The invention discloses an intelligent power distribution network self-healing control method and system, and relates to the technical field of power grid self-healing control, and the method comprises the steps: carrying out the data collection and processing based on a first method; real-time fault detection and preliminary diagnosis are carried out through the first equipment; and controlling the self-healing operation to execute feedback optimization. According to the intelligent power distribution network self-healing control method provided by the invention, through wired and wireless multi-mode data acquisition, and through real-time fault detection and preliminary diagnosis, the system can accurately position the problem and quickly identify the fault type at the initial stage of fault occurrence, and the fault response time is greatly shortened. According to the method, effective identification of fault types is ensured, the subsequent self-healing strategy of the system is more accurate, the protection and recovery capability of the power grid is enhanced, and better effects are achieved in the aspects of accurate fault positioning and response, safety and resource utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid self-healing control, and in particular to a smart distribution network self-healing control method and system. Background Art

[0002] The innovation and application of information technology constantly affects people's production and life. Especially in the multiple constructions of globalization, modernization and urbanization, the demand for electricity in all walks of life has increased accordingly, and it is particularly necessary to build power grids in line with the times. Distribution networks equipped with intelligent technology have become one of the effective means to solve today's energy challenges. The self-healing control system of the intelligent distribution network is an important part of it, and it is the core technology for realizing automatic control of distribution networks using intelligent technology.

[0003] Precisely because of the great impact of power system operation, strengthening the intelligence of power system operation can improve the safety and reliability of the current power grid operation. In the application of self-healing control technology of distribution network, technicians can realize all-weather detection and management, which is very beneficial to improving the practicality of the current power infrastructure.

[0004] The self-healing control technology of intelligent distribution network has experienced a long development. With the continuous innovation and development of power technology, this technology has gradually been applied to the construction of some large and medium-sized cities, and some localized transformations and research have been carried out, providing strong support for urban construction. With the increasing innovation of information technology, more and more technicians have realized the importance and necessity of intelligent power grid construction, and many new types of power intelligent equipment have been developed and produced. This development status has also laid a good foundation for the large-scale promotion and application of self-healing control technology of intelligent distribution network. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the existing smart distribution network self-healing control method is not conducive to achieving efficient, reliable and self-healing control of the distribution network, and is not conducive to reducing the impact of faults on the power grid.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a self-healing control method for an intelligent distribution network, comprising data collection and processing based on a first method; real-time fault detection and preliminary diagnosis through a first device; and controlling self-healing operations to perform feedback optimization.

[0008] As a preferred solution of the smart distribution network self-healing control method described in the present invention, the data collection and processing based on the first method includes using the first method to connect the data collection device with the dispatching center and transmit the sensor data to the cloud server.

[0009] As a preferred solution of the smart distribution network self-healing control method described in the present invention, wherein: the data collection and processing based on the first method includes cleaning the collected data and converting the format of the data after cleaning.

[0010] As a preferred solution of the self-healing control method for the smart distribution network of the present invention, wherein: the real-time fault detection and preliminary diagnosis by the first device includes detecting the fault by the first device;

[0011] After the abnormality is detected, the fault type is identified by comparison.

[0012] As a preferred solution of the self-healing control method of the intelligent distribution network described in the present invention, wherein: the real-time fault detection and preliminary diagnosis by the first device includes performing a comprehensive evaluation, generating a self-healing strategy for the power grid state according to a preset expert strategy library and template, and outputting preliminary control instructions;

[0013] When the grid operation status or fault information changes, the self-healing strategy is dynamically updated.

[0014] As a preferred solution of the self-healing control method of the smart distribution network of the present invention, wherein: the control of the self-healing operation execution feedback optimization includes sending the generated control instruction to the device;

[0015] After the fault is isolated, reconfigure the equipment status and adjust switches or customer loads as needed.

[0016] As a preferred solution of the smart distribution network self-healing control method described in the present invention, wherein: the control of self-healing operation execution feedback optimization includes continuously monitoring and collecting execution results during the execution of the self-healing operation, including device feedback data and self-healing operation completion degree.

[0017] Another object of the present invention is to provide a smart distribution network self-healing control system, which can perform real-time fault detection and preliminary diagnosis through a first device, thereby solving the problem that the current smart distribution network self-healing control technology is not conducive to reducing the impact of faults on the power grid.

[0018] As a preferred solution of the smart distribution network self-healing control system described in the present invention, it includes: a data acquisition and processing module, a fault detection and self-healing strategy generation module, and a self-healing operation execution and feedback optimization module; the data acquisition and processing module is used for data acquisition and data cleaning and standardization processing; the fault detection and self-healing strategy generation module is used for real-time fault detection and preliminary diagnosis; the self-healing operation execution and feedback optimization module is used for control instruction execution and equipment reconfiguration.

[0019] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a self-healing control method for a smart distribution network.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a self-healing control method for a smart distribution network.

[0021] Beneficial effects of the present invention: The intelligent distribution network self-healing control method provided by the present invention collects data in multiple ways, builds a multi-level data collection channel, improves the accuracy and coverage of data acquisition, and provides a solid foundation for the dynamic response and real-time adjustment of the self-healing strategy, ensuring the real-time and reliability of distribution network monitoring, removing redundant and invalid data through data cleaning, ensuring the quality of data source, and enabling the system to have anti-interference and high real-time response capabilities through data cleaning and standardization processing, which is conducive to the stability of the system in a complex operating environment. Through real-time fault detection and preliminary diagnosis, the system can accurately locate the problem and quickly identify the fault type at the early stage of the fault, greatly shortening the fault response time. Ensure the effective identification of the fault type, make the subsequent self-healing strategy of the system more accurate, and enhance the protection and recovery capabilities of the power grid. The adaptability of the strategy is achieved through the dynamic adjustment of the strategy, ensuring the intelligent generation and real-time optimization of the self-healing strategy, and enhancing the recovery capability of the power grid in various fault scenarios. The dynamic adjustment mechanism provides the system with extremely strong adaptability and resilience, thereby minimizing the fault outage time of the power grid and maintaining the stability and reliability of the power supply system. Through efficient execution of control instructions and equipment reconfiguration, the system ensures rapid response after a fault occurs, reducing the impact of power consumption outside the fault area. Equipment reconfiguration further improves the utilization efficiency of power grid resources, allowing the system to flexibly adjust and ensure power supply continuity under abnormal circumstances. The present invention achieves better results in terms of accurate fault positioning and response, safety, and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 An overall flow chart of a smart distribution network self-healing control method provided for the first embodiment of the present invention.

[0024] Figure 2A system schematic diagram of a smart distribution network self-healing control method provided for the first embodiment of the present invention.

[0025] Figure 3 An overall flow chart of a smart distribution network self-healing control system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0027] Example 1, reference Figure 1-Figure 2 , is an embodiment of the present invention, and provides a smart distribution network self-healing control method, comprising:

[0028] S1: Data collection and processing based on the first method.

[0029] Furthermore, a smart distribution network self-healing control system is provided, comprising a dispatching center, smart devices, a network communication unit, a self-healing logic diagram software, a fault detection and positioning unit, a device reconfiguration unit, and a fault recovery unit;

[0030] The dispatch center is the core of the entire system, responsible for the collection, analysis and processing of distribution network related data, and generating corresponding control instructions according to different situations;

[0031] Smart devices are connected to the system as sensors or power equipment, transmitting relevant data to the dispatch center;

[0032] The network communication unit is responsible for data transmission and the delivery of control instructions;

[0033] The self-healing logic diagram software can automatically generate and adjust the self-healing strategy according to the real-time operating status and fault information of the distribution network. The self-healing logic diagram software comprehensively considers the long-term investment cost and short-term operating economy of the distribution network to improve the asset utilization rate of the energy storage system and lines in the power grid.

[0034] The fault detection and location unit monitors and analyzes the distribution network status, detects fault events and locates the fault location by integrating multivariate data and internal algorithms;

[0035] The equipment reconfiguration unit adjusts the state of distribution network equipment and transfers faults by controlling switches, line connections and customer loads;

[0036] The fault recovery unit repairs the equipment and distribution network affected by the fault on the basis of equipment reconfiguration and equipment recovery, and restores the normal operation of the distribution network.

[0037] It should be noted that the first method can be multiple, examples include wired data acquisition, wireless data acquisition, cloud data acquisition and edge computing acquisition, etc.; the acquisition methods in this embodiment include wired data acquisition, wireless data acquisition, and cloud data acquisition; wired data acquisition: connecting the sensor and the data collector through a wired connection to achieve stable and reliable data transmission, which is suitable for long-distance transmission and large data volume transmission. Common wired data acquisition methods include Modbus, Profibus, and Ethernet; wireless data acquisition: using a wireless network to connect the sensor and the data collector, which is easy and flexible to install and suitable for complex environments and mobile devices. Common wireless data acquisition methods include TPUNB, Wi-Fi, Bluetooth, and Zigbee.

[0038] It should be noted that in another feasible embodiment, the first method adopts edge computing collection, including: deploying edge computing equipment at the edge of the distribution network (such as substations, distribution cabinets), combining sensors to collect data in real time and perform preliminary processing locally, such as data filtering, anomaly detection, etc. Edge computing reduces data transmission delays and is suitable for application scenarios that require rapid response. It also reduces the computing load of the central server and improves the overall system efficiency. It is suitable for power grid monitoring scenarios with high real-time requirements, especially in environments with large data volumes, and can significantly reduce transmission bandwidth requirements.

[0039] Furthermore, cloud data collection connects the data collector to the cloud server and stores and manages the data through the cloud platform. This method facilitates centralized management of data, real-time monitoring, and remote control. Common cloud data collection methods include MQTT and HTTP. Data analysis uses big data analysis technology to conduct in-depth analysis of the large amount of collected data to find out the correlation and patterns between the data. Data processing ensures the precision and accuracy of the data through data cleaning, data quality detection, data clarity, and data integration.

[0040] S2: Perform real-time fault detection and preliminary diagnosis through the first device.

[0041] Furthermore, the first device may be of multiple types, for example, various sensors and intelligent devices in the distribution network, as well as multifunctional intelligent gateways, wireless sensor nodes, etc.; the acquisition method in this embodiment includes that the self-healing logic diagram software can automatically generate and adjust the self-healing strategy according to the real-time operating status and fault information of the distribution network, and the implementation method is as follows:

[0042] The self-healing logic diagram software collects the grid’s operating data in real time, including key parameters such as voltage, current, power factor, and temperature, through various sensors and smart devices integrated in the distribution network;

[0043] The collected raw data first needs to be integrated and cleaned to ensure the accuracy and consistency of the data, which involves data standardization, deduplication, and missing value processing operations;

[0044] The software uses built-in analysis algorithms and models to monitor and evaluate the real-time operation status of the power grid, including evaluating the load level, stability, and safety indicators of the power grid;

[0045] By comparing the operating data under normal conditions with the real-time data, the software can automatically detect potential faults or abnormal conditions in the power grid and make preliminary diagnosis and location;

[0046] The self-healing logic diagram software usually has multiple self-healing strategies and templates built in, which are pre-set based on the experience and knowledge of experts. According to the real-time operation status and fault information of the power grid, the software will automatically match the most suitable self-healing strategy and template and generate corresponding control instructions. As the operation status of the power grid changes and the fault information is updated, the software will continuously evaluate and adjust the self-healing strategy to ensure its effectiveness and adaptability.

[0047] The generated self-healing strategy control instructions will be sent to the corresponding equipment or system in the distribution network to perform the self-healing operation; the software will continuously monitor the execution process of the self-healing operation and collect the execution results and feedback information; this information will be used to further evaluate the effectiveness and optimization direction of the self-healing strategy;

[0048] With the continuous application of self-healing logic diagram software in distribution networks, a large amount of actual operation data and experience will be accumulated; these data and experience can be used to update and optimize the software's built-in analysis algorithms, self-healing strategies and templates; self-healing logic diagram software should have the ability to continuously learn and improve, and be able to continuously adapt to changes in the distribution network operating environment and new failure modes.

[0049] It should be noted that in another feasible embodiment, the first device adopts a multifunctional intelligent gateway and a wireless sensor node, including: the functional intelligent gateway integrates a variety of sensor interfaces (such as current, voltage and temperature sensors), and has data processing and edge computing functions, which can pre-process, compress and forward data locally to reduce the data transmission load. It is suitable for power distribution scenarios where equipment is relatively concentrated, especially in places where real-time data processing is required. Wireless sensor nodes usually use low-power wide area networks (such as LoRa or NB-IoT) for data transmission, which can monitor parameters such as temperature, current and voltage in real time, and are suitable for distributed monitoring. It is suitable for long-distance distributed power grid monitoring needs, especially for places with complex environments and difficult wiring.

[0050] S3: Control the self-healing operation to perform feedback optimization.

[0051] Furthermore, the fault detection and location unit monitors and analyzes the distribution network status by integrating multivariate data and internal algorithms, detects fault events and locates the fault location. The implementation method is as follows:

[0052] The fault detection and location unit will integrate a variety of sensors and data acquisition equipment to collect multiple data such as voltage, current, power factor, temperature, etc. of the distribution network; clean, integrate and standardize the collected raw data to ensure the quality and accuracy of the data; understand the real-time operating status of the distribution network by real-time monitoring of key parameters such as voltage and current of the distribution network; use internal algorithms to process and analyze real-time data to detect abnormal conditions in the distribution network, such as voltage fluctuations and current anomalies;

[0053] Extract fault features from abnormal data, such as fault frequency, amplitude, and duration; compare the extracted fault features with the preset fault mode library to identify the specific fault type;

[0054] According to the fault characteristics and the topological structure of the distribution network, the fault location is initially located by using algorithms such as fault description, trial and error correction, substitution, segmentation, reverse tracking or system monitoring. For example, active online monitoring can be used to send out detection signals and monitor return loss and standing wave values ​​to achieve high-precision fault location.

[0055] Based on the fault detection results and location information, corresponding control instructions are generated, such as disconnecting the power supply in the fault area and starting the backup equipment; the control instructions are sent to the relevant equipment in the distribution network to perform fault isolation and recovery operations, and the execution results and feedback information are collected to evaluate the effectiveness of fault handling.

[0056] It should be noted that the realization of the self-healing control system is based on the local control method of timing coordination or the centralized control method based on the distribution automation master station; the local control method based on timing coordination: does not rely on the distribution automation master station or the distribution substation, through the on-site distribution automation terminal, protection device or automatic switching device to coordinate with each other in timing logic, when a fault occurs in the distribution network, it will automatically isolate the faulty area, restore power supply to the non-faulty area, and report the processing process and results; the centralized control method based on the distribution automation master station: when a feeder fault occurs, the master station automatically calculates and automatically implements remote control to achieve fault handling.

[0057] Example 2, an embodiment of the present invention, provides a self-healing control method for an intelligent distribution network. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0058] First of all, this experiment aims to verify the performance advantages of fault detection, location and self-healing operation of the smart distribution network self-healing control system under high load conditions. The test environment simulates the actual distribution network operation scenario, which includes a dispatching center, smart devices, network communication units, self-healing logic diagram software, fault detection and location units, equipment reconfiguration units and fault recovery units. Each subsystem is connected to a simulated distribution line, and key operating data such as voltage, current, power factor, temperature, etc. of the line are collected in real time through wired, wireless and cloud data collection methods. The specific steps are as follows:

[0059] First, the dispatch center sets the target parameters under high load conditions (such as voltage maintained at 110±5V and power factor above 0.95), and connects the sensors of the trunk line through wired data acquisition methods (such as Modbus protocol) to ensure long-distance and high-precision data transmission. Branch lines use wireless networks (such as Zigbee) for flexible data acquisition, and temperature and load distribution monitoring use cloud data acquisition (MQTT protocol). Data is transmitted to the dispatch center through a network communication unit and uploaded to the self-healing logic diagram software in real time.

[0060] Under high-load operating conditions, the self-healing logic diagram software collects data such as voltage, current, power factor, temperature, etc. in real time, and combines internal algorithms and big data analysis technology to clean and standardize the data to eliminate abnormal noise and redundant data. The self-healing logic diagram software detects abnormal current fluctuations in a branch line (continuous deviation of ±10%) by comparing it with the preset normal state, and automatically triggers the response of the fault detection and positioning unit. The unit uses fault feature extraction and comparison algorithms to identify the overload fault on the line and locate its position.

[0061] The self-healing logic diagram software refers to the fault type and power grid topology, and selects the self-healing strategy suitable for the current load from the built-in strategy library. According to the specific strategy, the self-healing logic diagram software automatically generates load transfer instructions, controls the device reconfiguration unit to switch the switch on the overloaded line, and distributes the load to the adjacent line. The network communication unit quickly transmits instructions to related equipment, executes line reconfiguration, and realizes the isolation of the fault area and the restoration of power supply to the non-fault area. The operation records and feedback results of the entire process are uploaded to the self-healing logic diagram software to optimize the system's response strategy.

[0062] After the fault area is isolated and the load is transferred, the system starts the fault recovery unit, gradually restores the normal state of the equipment, and repairs the affected distribution lines. The self-healing logic diagram software continues to evaluate the effect of the recovery process through data monitoring, adjusts the load of related equipment, and ensures the load balance and safety of the entire system. During the entire test process, the time records of each step and the parameter data during the recovery process are used as test results to further verify the response speed and self-healing efficiency of the system under high load conditions.

[0063] Table 1 Experimental data table

[0064]

[0065] It can be seen from the above data that this embodiment fully verifies the self-healing ability and response efficiency of the smart distribution network self-healing control system in a high-load environment. First, the data acquisition process demonstrates the stability of the system under multi-source data acquisition. The changes in voltage, current, power factor and other parameters of the trunk line and branch lines 1 and 2 are all within the normal fluctuation range, while branch line 3 has current and temperature anomalies at 35 seconds, indicating that the line is overloaded. The self-healing logic diagram software successfully detected the anomaly and started the fault detection and positioning unit, completing the fault isolation and load transfer operations in only 3 seconds, showing excellent response speed.

[0066] Secondly, the "fault recovery time" in the table further proves the self-healing efficiency of the present invention. After the fault occurs, the self-healing logic diagram software automatically selects the best load transfer strategy, reconfigures the line to complete the load takeover in 38 seconds, and the standby line re-shares part of the load of branch 3 in 39 seconds, achieving rapid recovery of the system. Under the premise that the power factor is maintained above 0.95 and the voltage is around 110V, the load of each line is restored to balance, ensuring the continuous operation of the power grid.

[0067] Compared with the prior art, the system innovation of the present invention is reflected in many aspects. First, based on the real-time data collection and accurate analysis, the self-healing logic diagram software can timely diagnose and isolate faults, providing a reliable basis for subsequent load distribution; second, through the built-in diversified self-healing strategy library, the system can automatically select and adjust the most appropriate strategy, reduce manual operations, and improve the efficiency and adaptability of the self-healing process; third, the fault recovery unit effectively coordinates the status of each device to ensure a smooth transition of load transfer. These technical innovations significantly shorten the fault handling time, allowing the distribution network to maintain safe and stable operation under complex high-load conditions.

[0068] In summary, it can be clearly seen from the data of this embodiment that the smart distribution network self-healing control system has shown significant innovation and superiority under high load conditions. The system realizes real-time monitoring and rapid fault recovery of the distribution network, significantly improving the self-healing ability and power supply stability of the distribution network. These superior performances make it a key technology in future smart distribution network fault management.

[0069] Example 3, reference Figure 3 , which is an embodiment of the present invention, provides a smart distribution network self-healing control system, including a data acquisition and processing module, a fault detection and self-healing strategy generation module, and a self-healing operation execution and feedback optimization module.

[0070] The data acquisition and processing module is used for data acquisition, data cleaning and standardization; the fault detection and self-healing strategy generation module is used for real-time fault detection and preliminary diagnosis; the self-healing operation execution and feedback optimization module is used for control instruction execution and equipment reconfiguration.

[0071] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), 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 disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 self-healing control method for a smart distribution network, characterized in that: include: Performing data collection and processing based on the first method; Performing real-time fault detection and preliminary diagnosis through the first device; Controls self-healing operations to perform feedback optimization.

2. The self-healing control method for a smart distribution network according to claim 1, characterized in that: The data collection and processing based on the first method includes using the first method to connect the data collection device with the dispatching center and transmit the sensor data to the cloud server.

3. The self-healing control method for a smart distribution network according to claim 2, characterized in that: The data collection and processing based on the first method includes cleaning the collected data and converting the format of the data after cleaning.

4. The smart distribution network self-healing control method according to claim 3, characterized in that: The real-time fault detection and preliminary diagnosis by the first device includes detecting the fault by the first device; After the abnormality is detected, the fault type is identified by comparison.

5. The smart distribution network self-healing control method according to claim 4, characterized in that: The real-time fault detection and preliminary diagnosis by the first device includes performing a comprehensive assessment, generating a self-healing strategy for the power grid state according to a preset expert strategy library and template, and outputting preliminary control instructions; When the grid operation status or fault information changes, the self-healing strategy is dynamically updated.

6. The smart distribution network self-healing control method according to claim 5, characterized in that: The controlling the self-healing operation to perform feedback optimization includes sending the generated control instructions to the device; After the fault is isolated, reconfigure the equipment status and adjust switches or customer loads as needed.

7. The self-healing control method for a smart distribution network according to claim 6, characterized in that: The controlling of the self-healing operation execution feedback optimization includes continuously monitoring and collecting execution results during the execution of the self-healing operation, including device feedback data and the completion degree of the self-healing operation.

8. A system using the smart distribution network self-healing control method according to any one of claims 1 to 7, characterized in that: It includes data collection and processing module, fault detection and self-healing strategy generation module, and self-healing operation execution and feedback optimization module; The data acquisition and processing module is used for data acquisition, data cleaning and standardization processing; The fault detection and self-healing strategy generation module is used for real-time fault detection and preliminary diagnosis; The self-healing operation execution and feedback optimization module is used to control instruction execution and device reconfiguration.

9. 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 smart distribution network self-healing control method according to any one of claims 1 to 7 are implemented.

10. 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 the smart distribution network self-healing control method according to any one of claims 1 to 7 are implemented.

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