Intelligent power distribution layered self-healing fault isolation and power supply recovery method

Abstract

The invention discloses an intelligent power distribution layered self-healing fault isolation and power supply recovery method, which is suitable for realizing rapid fault isolation and power supply recovery of a non-fault area when a power distribution system has a fault. According to the method, a hierarchical control framework is adopted, and intelligent switch equipment on the lower layer is responsible for collecting the current and voltage states of a line in real time and immediately completing local identification and fault section removal after a preset fault signal is detected; the fault information is then uploaded to a power distribution automation master station on the upper layer, the master station analyzes a recoverable power supply area in combination with the current network topology and generates a network reconstruction scheme, and a non-fault area is switched to a standby power supply path by controlling an interconnection switch; the system supports the backtracking analysis of historical data such as waveform distortion and partial discharge signals before a fault, identifies a potential abnormal trend, optimizes an operation strategy, and achieves a self-adaptive early warning function. The method has the advantages of being rapid in response, clear in structure and high in autonomy, and the reliability and continuity of the power distribution system can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an intelligent power distribution hierarchical self-healing fault isolation and power supply recovery method. Background Art

[0002] In the existing technology, the distribution network usually relies on the distribution automation system to achieve fault detection, isolation and power supply restoration. This type of system mostly adopts a centralized control structure. The master station analyzes the fault location by collecting terminal data and sends operating instructions to the on-site switch equipment to complete fault isolation and network reconstruction. In addition, some systems integrate feeder automation or small-scale self-healing functions, which can achieve fault response and recovery control in local areas under specific conditions, thereby improving power supply reliability.

[0003] However, existing technologies have problems such as slow response, insufficient information loop and single recovery strategy when dealing with complex topologies or multi-point failures. The centralized control method is highly dependent on communication and is limited by data transmission and processing delays, making it difficult to complete large-scale adaptive power supply restoration in a short period of time. At the same time, the lack of an effective operating status early warning mechanism makes it impossible for the system to identify potential risks in time before failure, limiting the further improvement of self-healing capabilities.

[0004] Therefore, it is urgent to propose an intelligent power distribution fault isolation and power supply restoration method with faster response, more flexible structure and adaptive early warning capability. Summary of the invention

[0005] The present application provides an intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method to improve the reliability and continuity of the power distribution system.

[0006] The present application provides a method for intelligent power distribution hierarchical self-healing fault isolation and power supply restoration, comprising: Establishing a hierarchical control architecture in the power distribution system, the hierarchical control architecture comprising a lower-layer intelligent switch device and an upper-layer distribution automation master station; The intelligent switch device collects the current and voltage status of the connected line. When a signal that meets the preset fault criteria is detected, the fault section is identified locally and the corresponding line section is cut off, so as to achieve fault isolation in the first time. The intelligent switch device uploads fault information and key electrical data to the distribution automation master station, which determines the recoverable power supply area based on the network topology information and dynamically generates a reconstruction plan; The distribution automation master station controls a group of tie switches to perform operations according to the reconstruction plan, switching the non-fault area to other power supply paths to achieve power supply recovery; While power restoration is being executed, a retrospective analysis is performed on the current waveform, harmonic characteristics or partial discharge signal before the fault occurred. If an abnormal trend is identified, the abnormal event is recorded and the operating strategy is updated for early warning and identification of similar faults in the future.

[0007] The beneficial effects of the technical solution provided by this application include: (1) By establishing a hierarchical control architecture, the intelligent switch equipment and the distribution automation master station can be coordinated and cooperated, which can quickly complete fault identification and isolation locally, effectively shorten the fault response time, and improve the instant processing capability of the distribution system. (2) The automated master station is used to perform network topology analysis and reconstruct path generation, and the rapid power supply restoration of non-fault areas is completed without human intervention, which significantly reduces the scope and duration of power outages and ensures power supply continuity. (3) The introduction of a retrospective analysis mechanism for electrical abnormal signals before faults enables the system to have adaptive early warning capabilities, identify potential risks in advance and optimize operation strategies, thereby improving the operation safety and fault resistance of the distribution network. (4) The entire method has a highly automated feature and can realize distributed collaborative control and flexible power supply path switching under a variety of complex working conditions, improving the intelligence level and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of an intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0009] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0010] The first embodiment of the present application provides a method for intelligent power distribution hierarchical self-healing fault isolation and power supply restoration. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 The first embodiment of the present application provides a method for intelligent power distribution hierarchical self-healing fault isolation and power supply restoration, which is described in detail.

[0011] Step S101: Establishing a hierarchical control architecture in a power distribution system, wherein the hierarchical control architecture includes a lower-layer intelligent switch device and an upper-layer distribution automation master station.

[0012] In the intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in this embodiment, step S101 is a basic preparation step for the entire method, which aims to build a power distribution system structure with hierarchical perception, rapid response and coordinated control capabilities to support the efficient execution of subsequent fault processing and power supply restoration operations. Step S101 is described in detail below.

[0013] The "establishment of a hierarchical control architecture in the power distribution system" in step S101 means that in an existing or newly built medium-voltage or low-voltage distribution network, the control architecture is divided into at least two logical levels according to the functional division and information processing capabilities: the lower layer is the field intelligent control layer, and the upper layer is the centralized coordination control layer, so as to realize the functional decoupling and distributed deployment of data collection, fault detection, decision analysis and control execution.

[0014] The lower-level intelligent switchgear refers to the power switchgear installed at key nodes (such as feeder switches, branch switches, ring network cabinets, etc.). The equipment has the real-time acquisition function of basic electrical parameters such as current and voltage, and has built-in edge computing capabilities and fault identification logic, and can independently complete fault feature identification and disconnection actions. Preferably, such equipment supports the IEC 61850 communication protocol and is equipped with components such as circuit breaker control unit (BCU), current transformer (CT), voltage transformer (PT) and programmable logic controller (PLC). The sampling frequency should be no less than 5 kHz to ensure accurate capture of fast-changing events such as short circuits and overloads. The device should support at least one digital signal processor (DSP) or equivalent embedded computing unit to implement the operation of local fault identification algorithms, such as short-circuit judgment based on current mutation rate ΔI / Δt or zero-sequence current analysis.

[0015] The upper-level distribution automation master station is usually located in the dispatching center or control room. It is composed of high-performance servers or industrial control computing platforms, runs the distribution automation master station software, and has the ability to model the topology of the entire network, manage communications, generate fault handling strategies, and control power supply path reconstruction. The master station maintains a stable connection with each intelligent switch device through a communication network (such as optical fiber Ethernet, wireless public network, LoRa, NB-IoT, etc.). The master station system needs to integrate the network topology diagram, SCADA interface, real-time database and expert control module, and support telemetry, telesignaling and remote control operations of external devices.

[0016] The specific process of establishing the hierarchical control architecture includes the following implementation links. First, the distribution network structure is digitally modeled, including the geographical information and electrical connection relationship of each feeder, node, substation and switch location. Secondly, intelligent switching equipment is installed at each key node, and on-site debugging and communication network access are completed. Subsequently, the corresponding distribution topology map and equipment address information are loaded in the master station system, and the fault response strategy template is configured. The configuration of message format, communication cycle, heartbeat mechanism, encryption protocol, etc. should be completed between the master station and each intelligent device to ensure data security and real-time performance.

[0017] The hierarchical control architecture established in the above way not only has the front-end distributed collection and local response capabilities, but also has the global coordination and control capabilities of the center end, which can realize the collaborative control mode of information concentration upward, decision distribution downward, and execution at the edge. The setting of this architecture provides basic support for subsequent fault detection, isolation, reconstruction and early warning steps, ensuring that the entire system has significant advantages in response speed, processing accuracy and operational reliability.

[0018] In summary, step S101 not only requires the establishment of a hierarchical structure at the hardware deployment level, but also emphasizes the hierarchical division of functional logic and control flow.

[0019] Furthermore, the establishment of a hierarchical control architecture in the power distribution system includes: In the initialization stage, the distribution automation master station generates a logical hierarchical structure of multi-level power supply areas based on the geographic information system GIS and historical operation topology data, and allocates corresponding switch equipment addresses, fault judgment templates and communication strategies to each level node to achieve synchronous matching of topological structure and functional configuration; Through the configuration information sent down, the operation roles and action permissions of each intelligent switch device are synchronized, so that it has different levels of fault response priority, coordination waiting time and reporting window parameters according to the network layer, so as to achieve adaptive coordinated response among multiple devices in concurrent fault scenarios; After the hierarchical structure deployment is completed, the distribution automation master station performs chain mutual recognition operations on the intelligent switching devices in each group of zones, allowing adjacent switching devices to complete identity authentication and communication handshakes locally, and establish self-organizing boundary recognition capabilities between devices to support regional autonomous fault handling when the master station is offline.

[0020] In this embodiment, further limitations on how to establish a hierarchical control architecture are proposed, with the focus on enabling the distribution system to not only have hierarchical control capabilities but also have structured, self-organizing and adaptive operating characteristics through a series of configurations and collaborations in the system initialization phase.

[0021] Before the distribution system is put into operation, it first enters the initialization stage, which is led by the distribution automation master station, using its built-in geographic information system GIS platform and long-term accumulated historical operation topology data to perform hierarchical modeling of the entire distribution network. GIS data usually includes spatial location information of substations, switch stations, feeders, branch lines, load points, communication nodes, etc., while topology data reflects electrical connection relationships, circuit breaker status, switch logic and historical load paths. Based on this information, the master station automatically divides the multi-level power supply area logical levels, such as substation level, main feeder level, branch line level, terminal level, etc., based on regional division, feeder ownership, transformer node, load density, etc.

[0022] After completing the hierarchical structure definition, the master station will assign exclusive configuration parameters to the key nodes in each level. These parameters include the unique address code bound to the intelligent switch device (such as the logical node LN identifier), the preset fault judgment template (such as short-circuit current threshold, duration judgment window, waveform distortion characteristics, etc.), and the communication mechanism (such as reporting cycle, receiving window size, congestion control strategy, etc.). The above configuration not only ensures the accurate logical positioning of each intelligent switch device in the structure, but also forms a matching relationship with the overall system scheduling strategy in terms of function allocation.

[0023] These configuration information generated by the master station will be sent to intelligent switch devices at all levels through communication networks (such as fiber-optic Ethernet, wireless public networks, industrial-grade 5G, etc.). After receiving the information, the switch device will automatically load the configuration content and update the local parameters, thus forming a multi-level division of labor and collaborative operation mode. Specifically, the intelligent switch devices at the upper level will obtain a higher fault response priority, and can be the first to judge and isolate the fault segment at this level when multiple faults occur simultaneously; while the lower-level devices need to collect the status information of neighboring devices within the coordination waiting time before making action decisions. In addition, the reporting window of each device can also be set according to the level. For example, the main feeder node reports more frequently to ensure the synchronization of the main path information, while the branch line equipment uses event triggering to reduce communication pressure.

[0024] After all configurations are issued and the deployment is confirmed to be effective, the master station further starts the chain mutual recognition mechanism to enhance the self-organizing control capabilities within each partition. The operation is initiated by the master station issuing a mutual recognition start command, triggering adjacent intelligent switch devices in the same area to perform identity authentication and communication handshake processes in turn. Identity authentication is based on device identification, key verification or configuration version consistency verification, and communication handshake establishes a local low-latency communication channel, such as using RS-485, CAN bus or dedicated wireless frequency hopping link, to achieve local state exchange without central scheduling.

[0025] Through chain mutual recognition operation, each group of intelligent switch devices will form an adaptive small autonomous domain. In the case of a master station communication link interruption or master station offline operation, these autonomous domains can rely on the established inter-device collaborative relationship to autonomously determine local faults and execute corresponding isolation and recovery strategies, thereby significantly improving the system's ability to resist communication failures and autonomous response efficiency.

[0026] In summary, the steps described above, from topology modeling to parameter configuration to autonomous mutual recognition, form a highly adaptive, well-structured, self-organizing hierarchical control architecture. This architecture not only breaks through the excessive reliance on the master station in traditional distribution automation systems, but also effectively supports the distributed response and coordinated control between lower-level intelligent devices, enabling the present invention to achieve significant improvements in self-healing ability, response speed, system elasticity, etc.

[0027] Furthermore, the chain mutual recognition operation includes: After receiving the chain mutual recognition instruction issued by the distribution automation master station, the intelligent switch device conducts mutual recognition negotiation with other physically adjacent intelligent switch devices through a low-latency local communication protocol, and determines the fault response priority of each intelligent switch device in the link based on the device identification, geographic location information and historical response performance indicators; After completing mutual recognition, each intelligent switch device periodically broadcasts its own state summary information to adjacent intelligent switch devices. The state summary information includes the current load state, fault judgment summary and received control instruction identification, which is used to establish an operation state consistency judgment mechanism in a local area; In the event that communication with the distribution automation master station is interrupted or unreachable, the mutually recognized intelligent switching devices will independently operate the reconstruction strategy in the local area according to the pre-negotiated response sequence, including delayed closing control, cascade fault isolation and communication chain integrity self-check, to achieve autonomous self-healing operation within a limited range.

[0028] In this embodiment, the specific technical process of the chain mutual recognition operation is further defined. This operation is intended to enhance the local coordination capability between intelligent switchgear, so that when the distribution automation master station cannot issue instructions in time or communication is abnormal, it can still rely on the local mechanism to complete autonomous fault handling and power supply reconstruction.

[0029] After the hierarchical control structure configuration is completed in the system initialization phase, the distribution automation master station will issue a chain mutual recognition instruction to a group of intelligent switch devices in a predefined area. This instruction contains the mutual recognition range identification, communication handshake rules, response parameter templates, and mutual recognition algorithm configuration. After receiving the instruction, the intelligent switch device will perform two-way identification and negotiation with other physically adjacent intelligent switch devices through a local low-latency communication protocol. The communication protocol can be based on CAN bus, RS-485, low-power wireless (such as Zigbee) or industrial-grade wireless Ethernet, and it is necessary to ensure that the two-way handshake is completed within 10~50 milliseconds.

[0030] During the mutual recognition process, the intelligent switch devices will actively exchange their respective identities (such as device addresses, unique serial numbers), geographic location information (provided by GIS data or GPS modules), and historical response performance indicators (such as the most recent action response time, false operation rate, recovery success rate, etc.). Based on these data, the devices will dynamically establish a priority table in the local link through a negotiation mechanism. This table defines which device will perform isolation or reconstruction operations first when a fault occurs in the future, and which device needs to wait for other devices to complete preliminary judgments before responding, thereby preventing multiple devices from acting at the same time and causing linkage conflicts or misjudgments.

[0031] After completing the mutual recognition negotiation, each intelligent switch device will enter the information synchronization state. The device will broadcast a set of status summary information to the adjacent intelligent switch devices in its link at a set period (for example, every 500 milliseconds to 2 seconds). The status summary information is generated locally by the device and includes the current load status (such as current size, load rate), real-time fault judgment summary (such as fault type, confidence), and the control instruction identification code recently received or issued. This mechanism allows the device to quickly compare the judgment of the neighboring device status when the master station does not issue a unified command, so as to determine whether there is a local abnormality or whether to enter the self-healing mode.

[0032] Redundant compression encoding should be used for broadcast data to ensure communication throughput and avoid transmission conflicts. Polling broadcast or frequency hopping-based short frame communication mechanism is recommended. When any device in the link detects that the distribution automation master station is unreachable (for example, no heartbeat packet is received for multiple consecutive communication cycles or the master station address PING fails), it enters the autonomous control mode. In this case, the intelligent switching devices that have completed mutual recognition can independently start the local fault isolation and power supply restoration process according to the response sequence table generated by negotiation.

[0033] In autonomous mode, the first operation to be performed includes the delayed closing mechanism, that is, after detecting that the backup path has the closing conditions, it will not be executed immediately, but will wait for the preset delay time and confirm that the neighboring device status is consistent to avoid closed-loop conflicts. Secondly, the device will implement a cascade fault isolation strategy. For example, when the upstream device does not act, the downstream device speculates that the upstream may have an unresolved fault and will actively expand the isolation range to ensure power supply safety. In addition, the intelligent switching device will also perform a communication chain self-check operation, and verify whether it is still connected to other devices on its mutual recognition chain by broadcasting a response packet to determine whether it has complete autonomous control conditions.

[0034] The entire autonomous process is completed locally and maintains a synchronized interface with the master station’s data records, ensuring seamless integration of device status and control history once the master station resumes communication.

[0035] In summary, the chain mutual recognition operation not only builds a dynamically updateable local response priority mechanism, but also introduces device status sharing and fault emergency autonomous process, which significantly improves the robustness and recovery capability of the system in abnormal communication environment.

[0036] Based on the chain mutual recognition mechanism proposed in this embodiment, the intelligent switching devices in the link can not only realize autonomous fault response in the traditional sense, but can also be further expanded to more complex and higher-level scenarios, such as emergency dispatch linkage control, distributed power supply access management, load dynamic reconstruction negotiation, etc., to build a decentralized, scenario-adaptive distribution intelligent collaboration system.

[0037] In the emergency dispatch scenario, the chain-based mutual recognition of intelligent switchgear is not limited to responding to local fault information, but dynamically evaluates the power supply safety margin based on the status of the entire link. When the distribution automation master station issues early warning instructions for the possible risk of extreme weather, short-term load shocks or power outages at the upper level, each intelligent switchgear in the link can actively enter the "pre-coordination state". In this state, in addition to conventional status information, the devices also share parameters such as risk estimates, adjustable load ratios, and backup path feasibility scores calculated based on preset models. The device can establish a distributed event voting mechanism on the chain. If most nodes in the chain predict that the short-term power shortage reaches the warning threshold, the open-loop isolation, load sinking, and backup contact pre-closing actions are executed in advance to form a regional defensive dispatch response, which significantly improves the overall response capability to sudden risks. This process is different from the existing centralized control of the master station, but is driven by link collaboration, with stronger local rapidity and prediction accuracy.

[0038] In the distributed power access scenario, the chain structure can be expanded to a power-aware mutual recognition chain. Once the intelligent switching device at the access point detects that the distributed power source has the conditions for grid connection, such as stable inverter output, voltage frequency matching, and sufficient grid harmonic tolerance, it can automatically send a grid connection request to other devices in the chain. The request will be accompanied by key parameters such as injectable power, maximum response delay, and planned operating time. Other devices in the link will dynamically feedback the acceptability based on data such as the current load status, topological node voltage sensitivity, and fault response priority. Ultimately, the on-chain consensus algorithm (such as the minimum response delay path calculation based on device priority weighting) determines whether to accept the power access. If a local power source is connected to the grid, the link will automatically generate a local energy flow reconstruction plan and adjust the fault strategy within the link, such as setting the power node isolation priority to ensure that the power side can be quickly separated in the event of a fault, while not affecting the self-healing logic of the main power supply path. This method breaks through the traditional fixed power access point or master station approval mechanism, allowing devices to locally collaborate to complete access negotiation and power supply mode switching, and improve the adaptability of the distribution system to the dynamic access of distributed power sources.

[0039] In addition, the chain coordination mechanism can also be extended to multi-path load distribution optimization. When multiple backup paths are available at the same time, the link device can actively initiate path sharing negotiation based on the real-time load status. Each device constructs a local power supply path score based on the local load margin, the current bus voltage level, and the historical operation reliability. Through chain propagation and convergence, the score result will automatically form a power supply optimization recommendation. For example, for the same load point, if the two contact paths come from different feeders, the link will recommend a load splitting mode, and the load will be shared by the two paths in proportion, and the distribution ratio will be automatically adjusted according to real-time feedback, thereby achieving dynamic power supply balancing at the link level. This type of self-balancing logic is significantly different from the traditional master station fault-based reconstruction strategy, and has stronger scenario adaptability and inter-device negotiation capabilities.

[0040] Through the above mechanism, this embodiment not only expands the real-time collaboration capability between devices on the basis of chain mutual recognition, but also realizes the structural transition of the distribution system from "control command chain" to "autonomous intelligent chain". The key is that the equipment has dynamic mutual recognition identity, can propagate context status, can establish operation consensus on its own in the absence of a master station, and can adaptively reconstruct local control strategies according to different scenario goals (such as safety, efficiency, and flexibility) in various types of power events. This architecture is fundamentally different from traditional hierarchical centralized master-slave control. It does not rely on central computing or static configuration, but is an embedded, linked, and evolving chain intelligent collaboration mechanism.

[0041] In terms of implementation, the above functions can be realized by deploying controllers with chain state management, dynamic consensus negotiation and adaptive strategy execution modules in intelligent switch devices. With the communication protocol stack that supports link state broadcasting and collaborative decision-making, this mechanism can be directly embedded in the existing distribution automation architecture, with good scalability and practical deployment prospects.

[0042] Step S102: The intelligent switch device collects the current and voltage status of the connected line. When a signal that meets the preset fault criteria is detected, the fault section is identified locally and the corresponding line section is cut off to achieve fault isolation in the first place.

[0043] In this embodiment, step S102 is the core link to quickly isolate the fault and reduce the scope and duration of power outage. The basic goal of this step is to identify and remove the fault locally by relying on the intelligent switch equipment deployed at the lower level when a fault occurs in the power distribution system.

[0044] First, the intelligent switchgear should have the ability to monitor the current and voltage status of the connected distribution lines in real time. To ensure detection accuracy and response timeliness, the device should integrate current transformers (CT) and voltage transformers (PT) or high-precision sensor modules, and be equipped with a microprocessor or digital signal processor (DSP) with edge computing capabilities. The device should acquire three-phase current and voltage data at a sampling frequency of not less than 5 kHz to ensure high-fidelity recording of the initial state of the fault, while generating real-time characteristic parameters such as effective value, phase angle, and zero-sequence component.

[0045] On the basis of real-time data collection, the intelligent switching device needs to run a set of preset fault identification criteria, which can make comprehensive judgments based on parameters such as sudden change in fault current amplitude, duration, change in current direction, zero-sequence current amplitude, and phase voltage drop. For example, if the system current amplitude exceeds three times the rated current threshold in a very short time and lasts for more than two sampling cycles, accompanied by a corresponding voltage drop or an increase in the imbalance factor, it can be determined as a typical short-circuit fault. In order to improve the robustness of identification, multi-condition fusion criteria can be introduced and priorities can be set to achieve the distinction between transient disturbances and permanent faults.

[0046] After determining that a fault exists, the device needs to locate and determine the faulty section. Intelligent switch equipment can use the current direction method, longitudinal difference method, negative sequence current method or artificial intelligence-assisted algorithm to analyze the current direction and current amplitude relationship between upstream and downstream switches, so as to infer whether the fault is located in this section of the line. If the current at the current switch outlet increases significantly and there is no significant current input at the inlet, it can be preliminarily determined that the fault is located downstream of this section; if fault current flows both upstream and downstream, it is necessary to compare and determine the data of adjacent switches.

[0047] Once the line is determined to be the source of the fault, the device will issue a control command to perform a physical disconnection operation through the internal circuit breaker drive mechanism. The circuit breaker can be a vacuum circuit breaker, SF6 circuit breaker or solid-state switch, etc. The disconnection time is required to be controlled within the millisecond range to ensure that the fault current is cut off before the zero transition point to prevent arc propagation or equipment damage. While performing the disconnection, the device should also record the current event timestamp, current and voltage curves before and after the fault, switch action status and other information to form a complete local fault event record for subsequent upload to the master station system for further analysis.

[0048] It is worth noting that step S102 emphasizes that fault identification and isolation operations are completed locally on the intelligent switch device and do not rely on external instructions from the power distribution master station. Therefore, the system has a significant response speed advantage and can complete local self-healing processing in the first time when there is a communication delay or the master station is unreachable.

[0049] In summary, step S102 realizes the rapid identification and isolation of the faulty section without human intervention after the fault occurs by deploying intelligent switching equipment with real-time data collection and local fault identification capabilities, laying a key foundation for subsequent power supply restoration and system reconstruction, and significantly improving the operating stability and reliability of the distribution system.

[0050] Furthermore, the intelligent switch device collects the current and voltage status of the connected line, and when a signal that meets the preset fault criterion is detected, the fault section is identified locally and the corresponding line section is cut off to achieve fault isolation in the first time, including: Intelligent switchgear adopts a dynamic judgment adjustment mechanism. According to the topological position of the device, the fluctuation characteristics of the historical load curve and the coordinated parameters of other intelligent switchgear in the link, the threshold range of local fault identification is adjusted in real time during operation, so that fault identification is closer to the scene characteristics and avoids false operation or refusal to operate. When a designated intelligent switch device detects that the electrical status of the line it is connected to meets the preliminary fault characteristics, it does not immediately remove the fault, but synchronizes the preliminary fault information to other adjacent intelligent switch devices in the link through a chain communication mechanism. Multiple devices jointly complete multi-point synchronous verification and judgment to confirm that it is a consistent fault, and then select the device with the highest priority to perform isolation action, and other devices enter a waiting response state to prevent repeated removal; After the removal action is completed, the post-fault data caching mechanism is executed. The intelligent switching device that performs the isolation action records the complete electrical waveform and judgment parameters before and after the fault, and saves the recorded data in a structured manner in the chain communication network for subsequent master station backtracking analysis or other equipment linkage strategy adjustment to achieve event cascade closed-loop management.

[0051] In order to achieve fast, accurate fault response without false triggering, this embodiment provides a highly intelligent dynamic criterion adjustment and multi-device collaborative confirmation mechanism, and combines the structured caching and sharing of event data to construct an adaptive local fault handling logic that can adapt to the complex operating environment of the distribution network.

[0052] First, in terms of the fault identification strategy of the intelligent switching device, different from the method of using static fixed current and voltage thresholds as judgment criteria in the existing distribution automation system, this embodiment introduces a dynamic criterion adjustment mechanism. Specifically, the intelligent switching device will refer to the historical load fluctuation characteristics of its long-term operation process, such as the morning and evening load change law, periodic peaks, etc., based on its current network topology position, such as a trunk feeder node, branch node or terminal node, and receive real-time operating status parameters from other devices in the link, such as load rate changes, voltage drop frequency, response delay, etc., to dynamically calculate the criterion window applicable to the current scene. The window includes but is not limited to the short-circuit current judgment threshold, the voltage drop judgment time window, the zero-sequence current amplitude and duration, etc. This dynamic adjustment method enables the device to have a stronger ability to suppress false operation in high-load cycles or high-disturbance sections, and can appropriately improve the response sensitivity in low-interference and sensitive areas, effectively avoiding false operations and leakage phenomena.

[0053] Secondly, after detecting a signal that may meet the preliminary fault characteristics, the intelligent switch device does not immediately perform the circuit breaking operation, but instead sends the preliminary judgment result to other adjacent intelligent switch devices in the link synchronously through a low-latency chain communication mechanism. The preliminary judgment information includes the detection time, current and voltage mutation parameters, judgment trigger details, and the current status label of the device. After receiving the information, the adjacent device performs a quick comparison with its own monitoring data to confirm whether a similar fault trend is detected, or whether the fault may be located in this section of the line. Through this collaborative verification mechanism, multi-point synchronous judgment can be achieved. Once the response consistency threshold set in the link is exceeded (for example, more than two devices report matching fault trends at the same time), the device with the highest negotiation priority initiates the isolation action. The priority can be generated based on preset indicators such as device response delay, topological location weight, and action stability record, while other devices enter a waiting state and make subsequent responses when the isolation is confirmed to be successful or the link is abnormal. This chain consistency confirmation method effectively prevents multiple points from being repeatedly removed or triggered due to signal reflection, transient disturbances, etc., which is conducive to maintaining the stability of the system and restoring the integrity of the path.

[0054] After the fault isolation action is completed, the intelligent switch device that performs the action will also automatically trigger the local data cache and reporting mechanism. The device not only records the action time and switch state changes, but also packages the full waveform current, voltage data, dynamic judgment values, communication interaction records, etc. within a few seconds before and after the fault, and writes them into the local data module in a structured format, and uploads them to the link shared cache area through the chain communication interface. Other intelligent switching devices and upper-level master station systems can call this data for retrospective analysis, event verification, strategy optimization, etc. during subsequent fault recovery or system reconstruction. The structured data can be recorded in JSON or compressed tag format (such as CBOR), and the event label and link node number can be embedded to achieve cross-device and cross-link timing comparison and behavior consistency auditing.

[0055] Through the above mechanism, this embodiment has achieved substantial breakthroughs in the prior art in three dimensions: the intelligence of fault identification, the coordination of execution actions, and the closed-loop nature of data processing. The intelligent switching device is no longer a single-point static response unit, but an autonomous node with environmental perception, adaptive collaborative judgment, and data traceability capabilities, forming an intelligent self-healing unit network with evolutionary capabilities. The functions described in this step can be realized by simply embedding a dynamic threshold adjustment module, a lightweight chain communication protocol stack, a fault collaborative judgment algorithm, and a structured data recording module on the basis of the conventional intelligent switching device control logic.

[0056] Furthermore, the dynamic criterion adjustment mechanism of the intelligent switch device includes calculating the local short-circuit current action threshold based on the following formula 1: : ; in, The maximum load current in the last 24 hours; is the standard deviation of the load current in the last hour, used to characterize the current load fluctuation; is the average phase voltage within the current 15 minutes; It is the target voltage value issued by the distribution automation master station; Indicates the topological level parameters of the intelligent switch device. The value of the main feeder device is 1.0, the value of the branch device is 0.5, and the value of the terminal node device is 0; is the empirical coefficient.

[0057] In the intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in this embodiment, in order to achieve a more sensitive and stable fault identification function, the intelligent switch device no longer uses a fixed short-circuit current threshold for judgment, but introduces a dynamic judgment adjustment mechanism. This mechanism combines the historical load operation status, voltage stability and topological structure characteristics of the equipment to calculate the current short-circuit current action threshold in real time, so as to adaptively adjust the judgment standard according to the actual situation on site, and avoid false operation or refusal to operate due to different equipment locations or changes in operating status.

[0058] This embodiment uses formula 1 to dynamically calculate the short-circuit current action threshold , This formula is constructed based on physical meaning and actually available data, and has good engineering feasibility. Now we explain each parameter in the formula item by item: in, It is the maximum load current in the past 24 hours, in amperes (A), which is used to characterize the maximum power load of the current device access point in the past complete operation cycle. This value is recorded by the built-in sampling module of the intelligent switch device for historical current sampling data, and the maximum value is generated in the hourly update or sliding time window as the operating scale benchmark of the current device.

[0059] parameter It is the standard deviation of the load current in the past hour (unit: A), which is used to reflect the current fluctuation degree of the device access point in the short term, that is, whether the load is stable. By collecting current data once a minute, the standard deviation can be calculated in the sliding window. The larger the standard deviation, the more severe the load fluctuation, so the action threshold of the device should be increased accordingly to avoid false action caused by short-term spikes.

[0060] Indicates the average phase voltage within the current 15 minutes, in volts (V), which is calculated by continuous sampling of the voltage measurement module inside the device. This value reflects whether the current power supply voltage is stable and is suitable for judging whether the system is in an undervoltage, light load or local fluctuation state.

[0061] It is the target voltage value sent by the master station. The unit is also volts (V). It is generally 220V, 230V or the rated phase voltage set by the system. It is used as a reference for the device to judge voltage offset. The device obtains and periodically refreshes this parameter from the master station through the communication interface.

[0062] It is a topological level factor, which is used to reflect the structural position of the intelligent switch device in the entire power distribution network. Considering that the main feeder equipment has a greater impact on the global power supply, its action needs to be more cautious. Set to 1.0; set to 0.5 for branch line equipment and 0 for end-user access node equipment. This parameter is generated by the system in combination with GIS and topology diagrams during network initialization and written into the configuration table of each device.

[0063] The empirical coefficients k1k_1, k2k_2, and k3k_3 are weighting factors that can be set based on the system debugging results. The recommended values ​​are: : Indicates the sensitivity to load fluctuations. The recommended value is adjustable between 0.5 and 1.0. The more severe the fluctuation, the higher the action threshold; : Indicates the sensitivity to voltage offset. The recommended value is adjustable between 1.0 and 1.5, reflecting that the device needs to improve the tolerance threshold during undervoltage / overvoltage. : Indicates the importance weight of the topological location. The recommended value is adjustable between 0.3 and 0.7. The protection threshold of the backbone equipment is slightly increased to avoid false operation.

[0064] According to the calculation results of the above formula, the short-circuit action current threshold at the current moment is The value will be slightly higher than the historical maximum load current, and the value will be adjusted dynamically according to the real-time load fluctuation, voltage stability and topological position. For example, when a device is at the end of a branch line, the load is stable and the voltage is normal, the threshold is close to The threshold value will be appropriately raised to avoid false operation when the device is in the middle of the main feeder, the load fluctuation is large, and the voltage fluctuation is obvious. This action criterion based on scene weight correction is completely different from the static setting method commonly used in the prior art.

[0065] In summary, the dynamic criterion adjustment mechanism has significant engineering implementation value. The parameters required by the above formula can be directly realized through standard modules such as sampling function, standard deviation operation, voltage average calculation, topology identification and master station communication interface in the intelligent switch device, and the threshold update can be performed in the device embedded logic or edge controller as a scheduled task. This mechanism is particularly suitable for adaptive protection judgment of multi-level and heterogeneous nodes in large-scale distribution networks, effectively improving the robustness and false operation suppression capabilities of the system.

[0066] Step S103: The intelligent switch device uploads the fault information and key electrical data to the distribution automation master station, which determines the recoverable power supply area based on the network topology information and dynamically generates a reconstruction plan.

[0067] In this embodiment, step S103 mainly involves information uploading, global situation awareness, topology analysis, and generation of power supply reconstruction schemes, and is a key bridge link between local fault identification and network-wide recovery scheduling. The core goal of this step is to enable the distribution automation master station to accurately obtain fault-related data at the first time, and intelligently analyze feasible power supply recovery paths based on the current network operation status and equipment topology. The following is a detailed description of its implementation method.

[0068] When the intelligent switch device completes the identification and removal of the fault section locally, it will upload the fault-related information to the distribution automation master station through the distribution automation communication network. The information includes at least the timestamp of the fault, the identified fault type (such as single-phase grounding, phase-to-phase short circuit, etc.), the current and voltage waveform data before and after the action, the unique identification code of the device, the current state of the switch, and the electrical state parameters of the adjacent lines. The communication method can adopt standard protocols such as IEC 60870-5-104, DNP3 or IEC 61850, and realize stable transmission of information through optical fiber, Ethernet, 4G / 5G or LoRa networks. In high-reliability application scenarios, redundant channels and time synchronization mechanisms can also be set to ensure that the master station receives fault information in a timely and accurate manner.

[0069] After the master station system receives the uploaded data, it will first update the received device status in its distribution network information management module to complete the real-time refresh of the distribution network topology. The master station system should have a complete network topology map and node connection relationship database. By comparing the original structure with the current state changes, it can automatically determine the topological location of the fault section, the isolation range and its impact on the remaining loads.

[0070] Next, the master station system will call in the topology analysis and path reconstruction algorithm to analyze the current power layout, line operation status, and accessibility of the backup power path, determine which power supply areas are not directly affected by the fault but lose power due to network structure interruption, and then mark the "recoverable power supply area". During this analysis process, it is necessary to comprehensively consider factors such as feeder load capacity, switch operation constraints, busbar segmentation status, tie switch availability, backup power capacity, safety margin, etc., to avoid overload, reverse power supply or system instability caused by blind closing.

[0071] Subsequently, the master station system dynamically generates a power supply reconstruction plan based on the calculation results. The plan includes operation instructions such as which interconnecting switches to close, which original feeders no longer bear the power supply task, how to redistribute the load, and whether the voltage control point needs to be adjusted. In order to achieve rapid deployment, the plan should be issued in the form of an instruction queue or scripted operation, and a safety logic verification mechanism should be set up to avoid further deterioration of the system due to misoperation. When necessary, the master station can also perform multi-objective optimization on the reconstruction plan, such as selecting the plan with the least operation steps, the smallest voltage fluctuation, or the best load balancing among multiple feasible paths to improve the network's adaptability and operating efficiency.

[0072] It is worth noting that the execution of the entire step must be based on real-time performance, which generally requires data upload, topology identification, and reconstruction solution generation to be completed within a few seconds to ten seconds after the fault occurs. Therefore, the master station system should have multi-threaded processing capabilities and a high-performance data processing architecture to support asynchronous event response and dynamic visualization.

[0073] In summary, the key to the implementation of step S103 is: on the one hand, it relies on the complete upload of fault information by the intelligent switch device, and on the other hand, it relies on the global perception and fast computing ability of the master station system for the distribution network. Through this step, it is possible to achieve rapid reconstruction preparation for the power supply capacity of the non-fault area, create conditions for the subsequent automatic operation of the contact switch and power supply restoration, and thus ensure that the system has strong self-healing ability and operational resilience in most fault situations.

[0074] Furthermore, the intelligent switch device uploads fault information and key electrical data to the distribution automation master station, which determines the recoverable power supply area based on the network topology information and dynamically generates a reconstruction plan, including: Before uploading fault information, the intelligent switch device performs time series segmentation and feature extraction on the current and voltage waveform data within the specified time before and after the fault. Through principal component analysis and standard deviation filter compression algorithm, it extracts low-dimensional feature vectors including the core content of fault trend, waveform disturbance amplitude, and spectrum distortion characteristics, and packages them together with the switch status and equipment identification to form structured compressed data and upload them to the distribution automation master station; After receiving the structured compressed data, the distribution automation master station constructs a weighted graph model based on the topological location of the fault node, the historical power supply path record and the current contact switch status. The edge weight comprehensively considers the path resistance, remaining load capacity, switch status, power supply priority and safety margin, and searches for the minimum cost path on the graph to determine a set of multiple alternative power supply paths. After generating multiple feasible power supply reconstruction schemes, the master station calculates the comprehensive score of each scheme in terms of power supply coverage, load balancing and control efficiency through a multi-objective optimization algorithm based on the local operation evaluation data returned by each intelligent switching device in the link, including the expected switching load, switch response delay, and adjustable voltage capability. The scheme with the highest score is selected as the final power supply reconstruction strategy, and a fault recovery operation log is written before execution to achieve traceable management of the decision-making process.

[0075] First, after completing the fault identification and isolation operation, the intelligent switch device does not directly upload the full amount of original sampling data, but pre-processes the current and voltage waveforms in the specified time period before and after the fault. This time period usually includes sampling data from 1 second before the fault to 3 seconds after the fault, and the sampling frequency is recommended to be no less than 2 kHz to cover the common fault transient process. During the data processing process, the device first divides the waveform data into multiple equal-length segments according to the time axis, and performs principal component analysis (PCA) on each segment to identify the most representative waveform change characteristics. On this basis, the local disturbance segment, that is, the waveform distortion mutation area, is identified by the standard deviation filter, and the corresponding disturbance amplitude and spectrum energy change and other statistical characteristics are extracted. Finally, a set of low-dimensional feature vectors containing fault trends, disturbance characteristics, frequency domain information, etc. are formed, which are further compressed into structured data blocks. At the same time, the device packages and merges metadata such as the current switch status (such as whether it is tripped, action delay, etc.), device unique identification, timestamp, etc. to form a complete structured compressed data unit, which is uploaded to the distribution automation master station through the communication link.

[0076] After receiving the compressed data, the master station will quickly locate the faulty node from the network topology model it maintains based on the location of the faulty device identified in the received data. The master station combines the historical power supply path records (including the operation log and success rate of each contact switch) with the real-time status of the currently available contact switches (opening and closing positions, telesignaling effectiveness, etc.), and builds a regional weighted graph with the faulty node as the root. The nodes of the graph represent the positions of each power supply unit or switch, and the edge weights comprehensively consider the following dimensions: line resistance value, remaining load capacity of the equipment in the path (inferred from the real-time load rate of the equipment), controllability of the switch status (whether automatic switching is allowed), historical power supply priority of the path (if it is an important load channel, the score can be improved), and topological safety margin (such as whether there is a risk of branch linkage in the branch, etc.). Based on the graph structure, the master station uses a minimum cost path search algorithm (such as Dijkstra or improved The algorithm traverses the graph structure and generates a set of alternative power supply reconstruction paths with low path costs and feasible structures.

[0077] After completing the generation of alternative paths, the master station further enters the path evaluation stage. In this process, the master station will query the local operation evaluation data reported by all relevant intelligent switch devices in the link, including but not limited to the expected load capacity (calculated based on the difference between the current operating current and the rated capacity of the device), the expected switching response time (which can be combined with the historical average response of the switch device and the dynamic prediction of the current action queue), whether it has the voltage regulation capability (such as equipped with on-load voltage regulation control or reactive power compensation device), and use these data as path adaptability indicators for scoring.

[0078] After the reconstruction plan is determined, the master station will also write the plan generation time, scoring model, selected path number, pre-execution state snapshot and other information into the fault recovery operation log, and form a traceable record for future system maintenance, strategy correction and operation tracking. This log can not only be used for post-review by operation and maintenance personnel, but also as a data source for the system's self-learning mechanism to promote the evolution and optimization of future solution selection models.

[0079] In summary, this embodiment combines edge device intelligent compression with central system global analysis to build an intelligent power restoration framework with clear structure, flexible response, path evaluation, and traceable execution. In actual deployment, it only relies on conventional sampling hardware, embedded controllers, and master station control platforms, and can be evolved and implemented based on current distribution automation technology.

[0080] Furthermore, the comprehensive scoring function used to evaluate the quality of the power supply reconstruction path in the multi-objective optimization algorithm is Given by the following formula 2: ; The goal of this scoring function is to convert the performance of multiple paths on key operating indicators into a unified scoring value, where The smaller the value, the better the path is and the more suitable it is for power restoration.

[0081] Indicates the number of loads that the current candidate path can cover, that is, the number of users or nodes that can be re-powered once the path is put into operation. The unit can be the number (number of nodes) or the total kilowatt load. This value can be calculated by the master station based on the power supply simulation results of the network topology diagram.

[0082] The corresponding Indicates the total number of loads in all current fault-cutoff areas; measured in the same unit. The ratio of this item measures the ability of the path to restore power supply coverage. The closer the value is to 1, the stronger the restoration ability is, so take its complementary value Indicates the “unrecovered ratio”. The smaller the ratio, the better the path.

[0083] Indicates the remaining capacity of the device at the node with the largest load in the candidate path, in kilovolt-amperes (kVA), which can be estimated by the difference between the current operating current and the rated capacity of the device. For example, if the rated capacity of a device is 100 kVA and the current operating load is 70 kVA, then its It is 30 kVA. It is the theoretical maximum power supply capacity of the path, which can be determined comprehensively by the equipment capacity and line transmission capacity of all power supply paths.

[0084] Indicates the average response delay returned by all intelligent switch devices participating in the communication operation in the path, in milliseconds (ms). This value can be obtained by sending a query command from the master station to the device or estimated based on historical operation records, including communication transmission delay and the time required for the device to execute control commands.

[0085] It is the reference maximum allowed response time defined in the system and is recommended to be set within the range of 500 ms to 1000 ms.

[0086] It is the comprehensive reliability score calculated by each device in the path according to its local voltage regulation capability, historical stability score and device action accuracy, and the value range is 0 to 1; for example, it can be set as: ; in is the correct rate of actions in the last 10 operations; It is the stability score of the equipment within three months (generated by the main station operation and maintenance module based on the fault reporting rate); Indicates the device's ability to support voltage regulation or reactive power compensation (1 for support and 0 for non-support). The higher the score, the more reliable the device in the path.

[0087] are weighting factors, and their recommended values ​​are 0.4, 0.3, 0.2 and 0.1 respectively.

[0088] The master station uses this scoring function to calculate the score for each alternative path. , and finally select the path with the lowest score as the execution plan for power restoration. The master station will also record the score results and the parameters involved in the calculation and write them into the operation log for future retrospective analysis or model optimization.

[0089] Step S104: the distribution automation master station controls a group of tie switches to perform operations according to the reconstruction plan, switching the non-fault area to other power supply paths to achieve power supply recovery.

[0090] In the intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in this embodiment, step S104 is the key link in realizing power supply reconstruction. The core of the method is to control the execution of the reconstruction plan through the distribution automation master station so that power supply to areas not involved in the fault can be restored as soon as possible, thereby reducing the impact scope of the power outage and improving system resilience.

[0091] In step S103, the distribution automation master station determines the recoverable power supply area based on the fault information and network topology, and generates a complete power supply reconstruction plan. The plan clarifies the topological structure of the backup power supply path, the control actions of each switch device, the load distribution strategy, etc. In step S104, the master station will issue instructions for the reconstruction plan and complete the automatic switching of the power supply path by accurately controlling the closing action of the tie switch.

[0092] The distribution automation master station first needs to establish a stable connection with each field switchgear through a communication system. The communication network can be in the form of optical fiber, dedicated wireless, LTE public network, 5G or industrial Ethernet, depending on the geographical distribution and communication needs of the distribution network. The communication protocol preferably uses standard protocols such as IEC 60870-5-104, IEC 61850 or DNP3 to ensure efficient transmission and execution confirmation of instructions.

[0093] On the basis of stable communication link, the master station will send control instructions to the target interconnection switch equipment in sequence according to the reconstruction plan. These interconnection switches are usually installed between adjacent feeders or at the junction of the distribution ring network structure, and have remote control capabilities and status feedback capabilities. Each instruction usually includes the device identification address, the expected action type (such as closing or keeping locked), execution timing control parameters, etc. In order to prevent misoperation or electrical shock, the master station must also perform multiple checks before sending, including confirmation of the online status of the equipment, verification of the current electrical parameters, and judgment of the operation interlocking conditions. For example, in a scenario where multiple interconnection switches need to be operated at the same time, the master station can set the time sequence according to the path logic relationship to avoid parallel closing causing parallel operation of the power supply.

[0094] Before executing the closing operation, the master station can also determine whether the load capacity of the backup power supply is sufficient based on real-time voltage, current and other data to prevent power supply voltage drops or system protection actions due to sudden load increases. When it is confirmed that the power supply path conditions are met, the master station issues a closing command, and the contact switch will execute the switching action after receiving the command to complete the closing of the power supply path.

[0095] After each switch operation, the master station also needs to read back the telesignaling and telemetry data to confirm whether the operation was successfully executed. For example, after the tie switch is closed, the master station can read the switch status value "closed" and cross-verify it in combination with the downstream current changes. Once it is confirmed that the path has been successfully established and the load has returned to normal, the master station will update the network topology map and device status information to put the system in a new stable operating state.

[0096] In scenarios where multiple contact points need to be operated simultaneously or in stages, the master station can also use operation queues and sequential control logic to ensure that the power supply restoration process complies with electrical safety regulations, such as avoiding improper operations such as no-voltage closing and closed-loop closing. In addition, during the path switching process, the master station can coordinate with voltage regulation devices (such as reactive compensation equipment and tap transformers) to control the voltage quality to ensure the stability of the power supply after restoration.

[0097] During the entire process, the operation does not require human intervention and can be completed automatically within seconds after a fault occurs, significantly improving the power supply reliability and self-healing capability of the distribution system.

[0098] In summary, step S104 realizes dynamic reconstruction of the power supply path through precise control of the tie switch by the automated master station. This process not only relies on stable communication and control mechanisms, but also relies on the rationality of the reconstruction scheme and the rigor of the operation logic, so as to achieve fast, safe and efficient power supply restoration in non-faulty areas.

[0099] Furthermore, the distribution automation master station controls a group of tie switches to perform operations according to the reconstruction scheme to switch the non-fault area to other power supply paths to achieve power supply recovery, including: Before issuing a power supply reconstruction command, the distribution automation master station pre-calculates the operation risk factor based on the current load status, voltage stability and successful operation history of all the tie switches involved in the optimal path determined by the comprehensive scoring function, and marks the nodes with operation risks greater than a preset threshold as nodes requiring confirmation, delays issuing execution commands for the nodes requiring confirmation, and broadcasts confirmation requests at the same time; After receiving the confirmation request, the intelligent switch device marked as the node to be confirmed will negotiate the status with its adjacent intelligent switch devices in the link, obtain the voltage fluctuation at both ends, load prediction after switching, equipment temperature rise or response saturation status information through chain mutual recognition communication, and make local operability judgment; if it is confirmed that the switching conditions are met, it will return the switching status flag allowed, otherwise it will return the refusal to execute with the negotiation reason; After receiving feedback from all key nodes, the distribution automation master station dynamically modifies the original path plan, and issues control instructions to all interconnecting switch groups according to the updated path sequence, and controls the power supply reconstruction process in sequence using a step-by-step delayed closure method. After each step, the interconnection node status is confirmed in real time, and the unsatisfied part enters the retry mechanism and is written into the operation log.

[0100] In order to ensure safe, stable and controllable path switching during the power supply reconstruction process after fault isolation, this embodiment also provides a control process of the distribution automation master station for the tie switch. This process not only takes into account the scoring priority of path planning, but also introduces the operational risk assessment, local feasibility negotiation and status feedback mechanism of the tie node, thereby realizing the whole process management of the orderly closing, segmented confirmation and dynamic adjustment of the tie switch, significantly improving the risk resistance and intelligence level of the system during the fault recovery process.

[0101] First, before the master station generates the power supply reconstruction path and prepares to issue the switching control command, it will prioritize the status assessment of all interconnecting switch devices involved in the selected path. This assessment is based on the current real-time load data of each intelligent switching device, the stability trend of the voltage at the device, and the device's previous action execution records, including the action success rate, false operation rate, and timeout frequency. Through this multi-dimensional information, the master station calculates an operation risk factor for each device, which indicates the safety and reliability of the device in performing switching operations under the current working conditions. If the operation risk factor of a device exceeds the preset threshold set by the system, the master station will mark it as a "node requiring confirmation" and will not immediately issue a closing command to it, but will first broadcast a confirmation request.

[0102] After receiving the confirmation request, the intelligent switch device marked as a node to be confirmed will not make an independent judgment, but will exchange status information with other adjacent intelligent switch devices through a chain mutual recognition mechanism. Specifically, the device will actively request the upstream and downstream switches to check the real-time voltage fluctuation amplitude at both ends, whether there is transient waveform distortion, the expected load change trend after switching, whether there is equipment overheating or action queue congestion, etc. These collaborative data are integrated to determine whether the device has the on-site conditions to perform the closing operation. If the judgment result is positive, the device will return a feedback sign of "switching allowed"; if an abnormality is found in the electrical environment or equipment status, it will return "rejection of execution" and attach negotiation reasons, such as "voltage fluctuation exceeds the limit" or "expected load excess" and other information for the master station to use in decision-making.

[0103] After collecting feedback from all nodes that need to be confirmed, the master station dynamically adjusts the original power supply reconstruction path plan based on the returned results. If some nodes refuse to switch or fail to respond after a timeout, the master station will give priority to local detours, node replacement, or adopt a segmented power supply strategy based on the original path; if the power supply target cannot be met, it will automatically switch to the power supply path with the second best comprehensive score and restart the control preparation process. After confirming the final path, the master station sends control instructions to all the contact switches involved in the switch in sequence, and executes them in a step-by-step delayed closing manner, that is, the action of each switch is initiated separately after the set safety delay. After the action is completed, the master station confirms its status feedback in real time, including whether the closure is successful, whether the current rises to the load level, and whether the voltage is stable within the allowable range.

[0104] If a node fails to close or returns to an abnormal state within the specified time, the master station will suspend subsequent control instructions to avoid power supply path interruption or parallel shock. Such abnormal nodes will be automatically included in the retry queue and retry the closing operation within the maximum number of retries set by the system. Each abnormal response or retry operation will be recorded in detail in the master station operation log, including device ID, action timestamp, abnormality type, retry rounds and final results, to facilitate subsequent operation and maintenance analysis and algorithm optimization.

[0105] In summary, this embodiment constructs a highly reliable power supply reconstruction control scheme with adaptive adjustment capability by introducing risk-based control sequence planning, link-level state negotiation feedback mechanism, sequential delay control and failure retry logic. This scheme realizes intelligent coordinated closing control of multi-path interconnection switches without relying on manual intervention, significantly improving system recovery speed and power supply safety.

[0106] Furthermore, the distribution automation master station generates a control sequence and a control delay according to the following rules when controlling the tie switch to perform an operation: The control sequence of the tie switches is comprehensively sorted according to the topological level of the equipment, the expected load increment and the operational risk level, with priority given to controlling equipment with a lower level, small load change and lower risk; The control instruction of each tie switch sets an independent delay time, and the delay time is dynamically adjusted according to the basic safety interval and in combination with the load mutation amplitude and operation risk level of the equipment; The distribution automation master station performs status confirmation for each closing operation. If the response is abnormal or timed out, subsequent instructions are suspended and the abnormal device is included in the retry queue. The retry operation has a maximum number of attempts and is automatically recorded in the operation log.

[0107] This embodiment also provides a closing sequence and time control strategy used when the distribution automation master station controls the tie switch to perform the power supply path switching operation. This strategy aims to ensure that the tie switch completes the closing operation one by one in the optimal sequence and reasonable rhythm under the premise of ensuring safety and power supply continuity, thereby achieving accurate power supply restoration in non-fault areas and avoiding the occurrence of overvoltage, current shock or system oscillation.

[0108] Before the master station determines the final power supply reconstruction path and prepares to issue the tie switch closing control command, it will sort all the tie switch devices to be operated. The sorting is not based on the traditional physical location order, but on a comprehensive consideration of three key parameters. The first is the hierarchical position of the device in the topology. Generally, the devices close to the end or low-voltage branch are considered to be at a lower level because their actions have less impact on the overall system. Closing these devices first can reduce the risk of system disturbances. The second is the expected load increment, that is, the amount of load change that each device may bear after closing. The system will give priority to closing those nodes with a smaller expected load jump amplitude to achieve a distributed power supply strategy of "gradual loading and gradual recovery". Finally, the operational risk level of each device can be determined by the device's action history, the device health status assessment results, and the stability of the electrical environment. The master station weights these three parameters to derive the sorting priority and generates a complete control command issuance sequence based on the priority.

[0109] The control instructions for each interconnecting switch are not issued at the same time, but with independent delay time. This delay time setting is based on the basic safety interval time pre-defined by the system, and is dynamically adjusted in combination with the load change amplitude and operation risk level of the equipment. For example, for a certain device, it needs to bear a large load transition after closing, or it has a history of malfunction, then the master station will allocate a longer delay time for it, so as to ensure that after the previous node is closed, the system has enough time to complete the self-adjustment of voltage stability and load distribution. This dynamic delay mechanism can not only effectively prevent system fluctuations caused by concurrent equipment actions, but also make the power supply reconstruction process more flexible and controllable.

[0110] After each tie switch receives a closing command and executes the operation, the master station will immediately monitor its status changes, usually confirming the closed loop through indicators such as telesignal signals, switch position indications, load current rising trends, etc. If the device fails to return to the expected state within the specified time, or an abnormal feedback occurs, such as closing failure, load failure to respond, and drastic voltage fluctuations, the master station will immediately suspend the issuance of subsequent control instructions to prevent the fault path from continuing to expand or causing system interlocking errors.

[0111] At this point, the abnormal node will be automatically added to the retry queue. The master station will arrange for it to try to execute the closing action again at a later time period based on the current system load status and device stability. Usually, each device has a maximum allowable number of retries, and it is generally recommended not to exceed three times. The results of each attempt, including success and failure, feedback signal status, execution time, abnormality type, etc., will be fully recorded in the system operation log. These logs can be used for operation and maintenance audits, equipment health tracking, or iterative learning of subsequent power supply strategy models, building a high-reliability control system of "state perception-action execution-feedback recording-risk closed loop" for the entire system.

[0112] Through the above control sequence generation and delay strategy setting, this embodiment not only realizes a refined power supply recovery execution mechanism, but also significantly improves the system's operational stability, response flexibility and abnormal response capabilities. Different from the traditional centralized control method of unified command and indifferent action, this embodiment uses hierarchical topology perception and device behavior prediction model to realize self-healing control logic that is executed on demand, in sequence, and according to feedback in full-path communication, which is of great practical value.

[0113] Step S105: While executing power restoration, a retrospective analysis is performed on the current waveform, harmonic characteristics or partial discharge signal before the fault occurs. If an abnormal trend is identified, the abnormal event is recorded and the operation strategy is updated for early warning and identification of similar faults in the future.

[0114] In the intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in this embodiment, step S105 not only undertakes the system backtracking analysis function after the fault, but also builds a future-oriented adaptive optimization foundation. Its role is to give the system the ability to continuously learn and evolve through abnormal trend identification and strategy updates, thereby improving the self-healing and prediction level of the entire distribution network in the face of complex operating environments.

[0115] After the power restoration operation begins, the distribution automation master station immediately starts a historical data backtracking mechanism to analyze the evolution of key electrical parameters in the period before the fault occurs. These parameters mainly include current waveform, voltage waveform, harmonic distribution, zero-sequence component, negative sequence current, partial discharge characteristics, power quality indicators, etc. The analysis time window can be flexibly set according to the fault type and system response characteristics. It is usually recommended to trace back 5 to 30 seconds of data to cover the precursor change stage before the fault.

[0116] The sources of retrospective data include data cached locally by the intelligent switchgear and historical sampling data stored in the master station database. To achieve high-resolution analysis, the system should use a sampling frequency of no less than 5kHz and support transient waveform extraction when necessary. In response to these data, the master station uses a variety of algorithms for signal analysis and pattern recognition. For example, the fast Fourier transform (FFT) is used to analyze the changing trend of harmonic distribution to identify whether the surge in high-order harmonics is an unstable precursor to a fault; the sliding window zero-sequence current mean and standard deviation are used to extract abnormal disturbances; for partial discharge signals, high-frequency noise signals and pulse counting statistics can be combined to realize the mining of early signs of cable insulation degradation.

[0117] After completing the extraction of various features, the system compares the current fault case with the historical known fault model or expert knowledge base to determine whether the fault has a recognizable precursor trend. If a statistically significant abnormal trend is identified, the master station will associate the trend with the fault event and archive it, and record key information such as the relevant lines, equipment, time points, parameter change curves, etc., to form a structured abnormal event record.

[0118] Furthermore, the system can automatically adjust the operation strategy based on the characteristics of the abnormal event. For example, when it is identified that a feeder has experienced the same type of harmonic distortion before multiple historical faults, the system can appropriately increase the monitoring frequency for the line, lower the abnormal waveform trigger threshold, or suggest that the dispatcher arrange maintenance in advance. Similarly, for cable sections with frequent abnormal partial discharge signals, the system can include them in key inspections or reconfigure the power supply path to reduce the load.

[0119] The above strategy update not only affects the handling process of this fault, but will also be written into the operation strategy database of the main station, becoming the knowledge basis for judging similar faults in the future. The strategy may include dynamic threshold adjustment, warning level setting, equipment priority sorting or linkage switch strategy changes, etc., to enhance the accuracy of warning and response efficiency of the system when facing similar working conditions in the future.

[0120] It should be emphasized that the entire process does not require human intervention. The system automatically completes data extraction, analysis, matching and strategy updates to ensure that the early warning mechanism is real-time, adaptable and sustainable.

[0121] To sum up, step S105 is not only a retrospective mining of the pre-fault state, but also a future-oriented adaptive risk control mechanism. It enables the system to have self-learning, self-optimization and self-defense capabilities by deeply analyzing historical waveforms, identifying abnormal trends and dynamically optimizing operation strategies, thereby greatly improving the early warning capability and self-healing level of the intelligent distribution system.

[0122] Furthermore, the retrospective analysis of the current waveform, harmonic characteristics or partial discharge signal before the fault occurs includes: After a fault is detected, the distribution automation master station retrieves the original sampled data cached by each relevant intelligent switch device in the pre-fault period, and establishes a time-continuous waveform set covering multiple devices. Each data sequence in the time-continuous waveform set includes a timestamp, current value, voltage value, zero-sequence quantity, and amplitude of each harmonic, and uniformly aligns the reference time node to form a comparable analysis basis; The distribution automation master station compares and analyzes the characteristic parameters in the time-continuous waveform set to identify whether the following three types of indicators have a continuous change trend before the fault: first, whether the amplitude of a specific subharmonic increases in a number of consecutive sampling cycles; second, whether the zero-sequence component of the current shows a stable offset trend; third, whether the voltage fluctuation amplitude repeatedly exceeds the set fluctuation threshold in a short period; if any of the above indicators meets the trend change condition, it is determined that the fault has abnormal precursor characteristics; The distribution automation master station compares fault events with abnormal precursor characteristics with the current response process records, and automatically updates the operation strategy data, including: weight parameters added to the corresponding precursor characteristics, adjustment range of threshold sensitivity in future judgment criteria, or sends early warning configuration parameter update instructions to related intelligent switching devices to improve the early identification and response capabilities of subsequent similar events.

[0123] This embodiment also proposes a system-implementable electrical anomaly prediction mechanism based on sampling data trend judgment and strategy update. This mechanism not only focuses on the reuse of historical data after a fault occurs, but also emphasizes the coherent coordination between data organization structure, trend judgment method and strategy update logic, which can significantly improve the distribution system's ability to identify potential faults and the evolution of operating strategies.

[0124] When the system detects a fault in the line, the distribution automation master station will immediately call the local cache data in each intelligent switch device related to the fault. During normal operation, each intelligent switch device will continuously record the electrical quantity data of the line to which it is connected at a fixed sampling period and cache it in the local storage module. When the master station issues a data retrieval command, it will require each device to upload its original sampling data within a few seconds before the fault. These data include the timestamp of each sampling point, three-phase current value, three-phase voltage value, zero-sequence current component, and voltage and current amplitudes of at least the first five harmonics. To facilitate subsequent comparison and analysis, the master station performs unified time alignment processing on the data sequences reported by all devices, selects the fault trigger time as a unified reference point, and converts the time information in each data sequence into relative time relative to the reference point to ensure that the analysis window is consistent and synchronized in time.

[0125] Based on the above-mentioned set of time-continuous waveforms, the distribution automation master station then performs parameter-level trend analysis. First, the master station detects the amplitude of each harmonic, especially whether the third, fifth and seventh harmonics show an increasing trend in multiple consecutive sampling cycles before the fault occurs. If the amplitude of a harmonic component continues to rise in two or more cycles, it can be preliminarily determined that there is a possibility of potential power quality disturbance. Secondly, the master station checks whether the zero-sequence current component slowly deviates from zero or a normal small amplitude state, and continues to show obvious asymmetric load characteristics, which is usually a precursor to low-level ground faults or insulation degradation. Thirdly, the master station counts the frequency and amplitude of voltage fluctuations in unit time to determine whether it continuously exceeds the set short-period fluctuation threshold, such as more than three fluctuations exceeding ±10% of the rated value in one second. When any of the above indicators meets the trend change conditions, the master station marks the fault as "having abnormal precursor characteristics", that is, not only an electrical fault has occurred, but also a clear physical precursor signal can be traced back from the historical data before the fault.

[0126] In order to incorporate this abnormal event into the knowledge-based operation strategy system, the master station will correlate and compare such faults with their response processes. For example: whether this fault can be quickly isolated within the specified time, whether there is a false operation or refusal to operate, whether a specific communication path is used, etc. In combination with these response records, the master station adjusts the current operation strategy library. Specifically, for the parameters that successfully identify the precursor characteristics, the master station will automatically increase its weight parameters in the future fault judgment criteria, and increase the sensitivity of this feature in the local judgment criteria of the intelligent switch device. At the same time, for events that have experienced trend anomalies but have not been responded to in time, the master station can appropriately lower the action thresholds in some criteria, so that the equipment can intervene and judge earlier. In addition, the master station can also directly send the warning parameters to the relevant intelligent switch devices, so that once similar trends are detected in future operations, they can issue an alarm signal in advance, or preset a power-off action under permitted conditions.

[0127] Through the above-mentioned retrospective analysis and strategy adaptive update mechanism, the present invention not only realizes the reverse modeling of the cause of the fault that has occurred, but also establishes a self-learning closed loop with "trend identification-behavior association-strategy evolution" as the core, improving the system's ability to prevent hidden faults or marginal disturbances. Different from the traditional single-point waveform threshold judgment or purely artificial experience rules, this embodiment provides a cross-node, cross-time, and accumulative distribution anomaly recognition logic, providing a solid technical foundation for realizing self-healing control under hierarchical collaboration.

[0128] The second embodiment of the application provides an electronic device, the electronic device comprising: processor; The memory is used to store a program, and when the program is read and executed by the processor, it executes an intelligent power distribution hierarchical self-healing fault isolation and power supply recovery method provided in the first embodiment of the present application.

[0129] The third embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program executes an intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method provided in the first embodiment of the present application.

[0130] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. An intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method, characterized in that: include: Establishing a hierarchical control architecture in the power distribution system, the hierarchical control architecture comprising a lower-layer intelligent switch device and an upper-layer distribution automation master station; The intelligent switch device collects the current and voltage status of the connected line. When a signal that meets the preset fault criteria is detected, the fault section is identified locally and the corresponding line section is cut off, so as to achieve fault isolation in the first time. The intelligent switch device uploads fault information and key electrical data to the distribution automation master station, which determines the recoverable power supply area based on the network topology information and dynamically generates a reconstruction plan; The distribution automation master station controls a group of tie switches to perform operations according to the reconstruction plan, switching the non-fault area to other power supply paths to achieve power supply recovery; While power restoration is being executed, a retrospective analysis is performed on the current waveform, harmonic characteristics or partial discharge signal before the fault occurred. If an abnormal trend is identified, the abnormal event is recorded and the operating strategy is updated for early warning and identification of similar faults in the future.

2. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 1 is characterized in that: The establishment of a hierarchical control architecture in the power distribution system includes: In the initialization stage, the distribution automation master station generates a logical hierarchical structure of multi-level power supply areas based on the geographic information system GIS and historical operation topology data, and allocates corresponding switch equipment addresses, fault judgment templates and communication strategies to each level node to achieve synchronous matching of topological structure and functional configuration; Through the configuration information sent down, the operation roles and action permissions of each intelligent switch device are synchronized, so that it has different levels of fault response priority, coordination waiting time and reporting window parameters according to the network layer, so as to achieve adaptive coordinated response among multiple devices in concurrent fault scenarios; After the hierarchical structure deployment is completed, the distribution automation master station performs chain mutual recognition operations on the intelligent switching devices in each group of zones, allowing adjacent switching devices to complete identity authentication and communication handshakes locally, and establish self-organizing boundary recognition capabilities between devices to support regional autonomous fault handling when the master station is offline.

3. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 2 is characterized in that: The chain mutual recognition operation includes: After receiving the chain mutual recognition instruction issued by the distribution automation master station, the intelligent switch device conducts mutual recognition negotiation with other physically adjacent intelligent switch devices through a low-latency local communication protocol, and determines the fault response priority of each intelligent switch device in the link based on the device identification, geographic location information and historical response performance indicators; After completing mutual recognition, each intelligent switch device periodically broadcasts its own state summary information to adjacent intelligent switch devices. The state summary information includes the current load state, fault judgment summary and received control instruction identification, which is used to establish an operation state consistency judgment mechanism in a local area; In the event that communication with the distribution automation master station is interrupted or unreachable, the mutually recognized intelligent switching devices will independently operate the reconstruction strategy in the local area according to the pre-negotiated response sequence, including delayed closing control, cascade fault isolation and communication chain integrity self-check, to achieve autonomous self-healing operation within a limited range.

4. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 3 is characterized in that: The intelligent switch device collects the current and voltage status of the connected line, and when a signal that meets the preset fault criterion is detected, the fault section is identified locally and the corresponding line section is cut off to achieve fault isolation in the first time, including: Intelligent switchgear adopts a dynamic judgment adjustment mechanism. According to the topological position of the device, the fluctuation characteristics of the historical load curve and the coordinated parameters of other intelligent switchgear in the link, the threshold range of local fault identification is adjusted in real time during operation, so that fault identification is closer to the scene characteristics and avoids false operation or refusal to operate. When a designated intelligent switch device detects that the electrical status of the line it is connected to meets the preliminary fault characteristics, it does not immediately remove the fault, but synchronizes the preliminary fault information to other adjacent intelligent switch devices in the link through a chain communication mechanism. Multiple devices jointly complete multi-point synchronous verification and judgment to confirm that it is a consistent fault, and then select the device with the highest priority to perform isolation action, and other devices enter a waiting response state to prevent repeated removal; After the removal action is completed, the post-fault data caching mechanism is executed. The intelligent switching device that performs the isolation action records the complete electrical waveform and judgment parameters before and after the fault, and saves the recorded data in a structured manner in the chain communication network for subsequent master station backtracking analysis or other equipment linkage strategy adjustment to achieve event cascade closed-loop management.

5. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 4 is characterized in that: The dynamic criterion adjustment mechanism of the intelligent switch device includes calculating the local short-circuit current action threshold based on the following formula 1 : ； in, The maximum load current in the last 24 hours; is the standard deviation of the load current in the last hour, used to characterize the current load fluctuation; is the average phase voltage within the current 15 minutes; It is the target voltage value issued by the distribution automation master station; Indicates the topological level parameters of the intelligent switch device. The value of the main feeder device is 1.0, the value of the branch device is 0.5, and the value of the terminal node device is 0; is the empirical coefficient.

6. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 5 is characterized in that: The intelligent switch device uploads fault information and key electrical data to the distribution automation master station, which determines the recoverable power supply area based on the network topology information and dynamically generates a reconstruction plan, including: Before uploading fault information, the intelligent switch device performs time series segmentation and feature extraction on the current and voltage waveform data within the specified time before and after the fault. Through principal component analysis and standard deviation filter compression algorithm, it extracts low-dimensional feature vectors including the core content of fault trend, waveform disturbance amplitude, and spectrum distortion characteristics, and packages them together with the switch status and equipment identification to form structured compressed data and upload them to the distribution automation master station; After receiving the structured compressed data, the distribution automation master station constructs a weighted graph model based on the topological location of the fault node, the historical power supply path record and the current contact switch status. The edge weight comprehensively considers the path resistance, remaining load capacity, switch status, power supply priority and safety margin, and searches for the minimum cost path on the graph to determine a set of multiple alternative power supply paths. After generating multiple feasible power supply reconstruction schemes, the master station calculates the comprehensive score of each scheme in terms of power supply coverage, load balancing and control efficiency through a multi-objective optimization algorithm based on the local operation evaluation data returned by each intelligent switching device in the link, including the expected switching load, switch response delay, and adjustable voltage capability. The scheme with the highest score is selected as the final power supply reconstruction strategy, and a fault recovery operation log is written before execution to achieve traceable management of the decision-making process.

7. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 6 is characterized in that: Comprehensive scoring function used to evaluate the quality of power supply reconstruction path in the multi-objective optimization algorithm Given by the following formula 2: ； in, Indicates the amount of load that the current candidate path can cover; Indicates the total load quantity in all current fault cut-off areas; The remaining available capacity of the device where the heaviest-loaded node in the path is located; The theoretical maximum power supply capacity of this path; Indicates the average response delay returned by all intelligent switch devices participating in the liaison operation in the path; is the reference maximum allowed response time defined in the system; It is the comprehensive reliability score of each device in the path calculated based on its local voltage regulation capability, historical stability score and device action accuracy, with a value range of 0 to 1; is the weighting factor.

8. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 7 is characterized in that: The distribution automation master station controls a group of tie switches to perform operations according to the reconstruction scheme, switching the non-fault area to other power supply paths to achieve power supply recovery, including: Before issuing a power supply reconstruction command, the distribution automation master station pre-calculates the operation risk factor based on the current load status, voltage stability and successful operation history of all the tie switches involved in the optimal path determined by the comprehensive scoring function, and marks the nodes with operation risks greater than a preset threshold as nodes requiring confirmation, delays issuing execution commands for the nodes requiring confirmation, and broadcasts confirmation requests at the same time; After receiving the confirmation request, the intelligent switch device marked as the node to be confirmed will negotiate the status with its adjacent intelligent switch devices in the link, obtain the voltage fluctuation at both ends, load prediction after switching, equipment temperature rise or response saturation status information through chain mutual recognition communication, and make local operability judgment; if it is confirmed that the switching conditions are met, it will return the switching status flag allowed, otherwise it will return the refusal to execute with the negotiation reason; After receiving feedback from all key nodes, the distribution automation master station dynamically modifies the original path plan, and issues control instructions to all interconnecting switch groups according to the updated path sequence, and controls the power supply reconstruction process in sequence using a step-by-step delayed closure method. After each step, the interconnection node status is confirmed in real time, and the unsatisfied part enters the retry mechanism and is written into the operation log.

9. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 8, characterized in that: When controlling the tie switch to perform an operation, the distribution automation master station generates a control sequence and a control delay according to the following rules: The control sequence of the tie switches is comprehensively sorted according to the topological level of the equipment, the expected load increment and the operational risk level, with priority given to controlling equipment with a lower level, small load change and lower risk; The control instruction of each tie switch sets an independent delay time, and the delay time is dynamically adjusted according to the basic safety interval and in combination with the load mutation amplitude and operation risk level of the equipment; The distribution automation master station performs status confirmation for each closing operation. If the response is abnormal or timed out, subsequent instructions are suspended and the abnormal device is included in the retry queue. The retry operation has a maximum number of attempts and is automatically recorded in the operation log.

10. The intelligent power distribution hierarchical self-healing fault isolation and power supply restoration method according to claim 9, characterized in that: The retrospective analysis of the current waveform, harmonic characteristics or partial discharge signal before the fault occurs includes: After a fault is detected, the distribution automation master station retrieves the original sampled data cached by each relevant intelligent switch device in the pre-fault period, and establishes a time-continuous waveform set covering multiple devices. Each data sequence in the time-continuous waveform set includes a timestamp, current value, voltage value, zero-sequence quantity, and amplitude of each harmonic, and uniformly aligns the reference time node to form a comparable analysis basis; The distribution automation master station compares and analyzes the characteristic parameters in the time-continuous waveform set to identify whether the following three types of indicators have a continuous change trend before the fault: first, whether the amplitude of a specific subharmonic increases in a number of consecutive sampling cycles; second, whether the zero-sequence component of the current shows a stable offset trend; third, whether the voltage fluctuation amplitude repeatedly exceeds the set fluctuation threshold in a short period; if any of the above indicators meets the trend change condition, it is determined that the fault has abnormal precursor characteristics; The distribution automation master station compares fault events with abnormal precursor characteristics with the current response process records, and automatically updates the operation strategy data, including: weight parameters added to the corresponding precursor characteristics, adjustment range of threshold sensitivity in future judgment criteria, or sends early warning configuration parameter update instructions to related intelligent switching devices to improve the early identification and response capabilities of subsequent similar events.

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