A power distribution network feeder automation method and system

Abstract

The application provides a power distribution network feeder automation method and system, and relates to the technical field of power distribution automation, which comprises the following steps: collecting feeder operation state data by a power distribution node, performing fault detection and positioning based on neighborhood node state cooperative iteration; in response to a fault event obtained by fault detection and positioning, performing inter-node cooperative response and fault isolation operation through an event triggering mechanism; and realizing fault recovery by optimizing a communication path according to a topological connectivity index, and dynamically adjusting neighborhood weights after fault recovery. The application adopts a decentralized control strategy, realizes rapid fault positioning and isolation through local information interaction between nodes, greatly reduces communication overhead and fault processing delay, and realizes plug-and-play and stable operation of the system in combination with a topological optimization and weight dynamic updating mechanism, thereby effectively improving the reliability and resilience of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution automation technology, specifically to a method and system for automating power distribution network feeders. Background Technology

[0002] As the core carrier of power supply to end users, the reliability of distribution network feeders directly determines the quality of power supply service. Feeder automation technology, through rapid detection, precise isolation, and power restoration of non-faulty areas, has become a key supporting technology for improving the reliability of distribution network power supply. Currently, distribution network feeder automation technology is mainly divided into two categories: centralized and decentralized. Centralized feeder automation relies on the master station controller to collect network-wide status information for fault location and recovery decisions. This mode has complex communication links, large data transmission volumes, and the risk of single-point failure at the master station. It also suffers from poor system scalability and difficulty adapting to changes in the feeder network after large-scale integration of distributed power sources. Decentralized feeder automation makes decisions based on local distribution node information. Although it does not require global communication, it lacks a collaborative mechanism between distribution nodes, resulting in low fault location accuracy, a single recovery strategy, and an inability to achieve global optimization. Furthermore, it is poorly adaptable to changes in the feeder network structure. Therefore, there is an urgent need for a feeder automation technology that combines distributed autonomy and global collaboration capabilities to address the problems of high communication pressure, high fault handling delays, poor scalability, and low location accuracy due to a lack of collaboration in existing technologies. Summary of the Invention

[0003] To address the problems of high communication pressure and high risk of single-point failure in centralized control and low positioning accuracy in decentralized control in existing technologies, this application proposes an automation method and system for distribution network feeders, which adopts a decentralized control strategy to achieve rapid self-healing and stable operation of distribution network feeders.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: An automation method for distribution network feeders employs a decentralized control strategy, comprising: distribution nodes collecting feeder operating status data; performing fault detection and location based on collaborative iteration of adjacent distribution node statuses; responding to fault events obtained from fault detection and location by executing collaborative response and fault isolation operations between distribution nodes through an event triggering mechanism; optimizing communication paths based on topology connectivity indices to achieve fault recovery; and dynamically adjusting neighborhood weights after fault recovery.

[0005] Preferably, the fault detection and location based on the collaborative iteration of adjacent distribution node states includes: constructing a fault detection criterion based on a consensus iteration protocol, and determining the fault type and location through the convergence characteristics of the state error of adjacent distribution nodes; the fault detection criterion takes fault feature quantities as input, and the fault feature quantities include at least one of voltage mutation rate and current distortion rate.

[0006] Preferably, the consensus iteration protocol describes the dynamic evolution process of the distribution node state. The dynamic evolution process is based on the differences in the fault characteristic quantities and communication weights between adjacent distribution nodes, and achieves the collaborative convergence and location of the fault state through the superposition of nonlinear convergence terms and linear coupling terms.

[0007] Preferably, the operation of coordinated response and fault isolation between distribution nodes through the event triggering mechanism includes: determining whether to trigger communication and control actions between distribution nodes based on the comparison result of state observation error and trigger threshold; the fault event includes at least one of line fault event, load change event and distribution node commissioning / decommissioning event.

[0008] Preferably, the determination of whether to trigger the communication and control action between distribution nodes is achieved by comparing the state observation error of the distribution node with the trigger threshold adjusted by the trigger coefficient. When the state observation error meets the trigger condition, the communication and control action between distribution nodes is triggered.

[0009] Preferably, the method of optimizing communication paths based on topology connectivity index to achieve fault recovery includes: selecting the optimal communication path within the feasible domain of the communication network that satisfies connectivity constraints with the goal of maximizing topology connectivity index; and dynamically adjusting neighborhood weights includes: dynamically updating weight coefficients based on the number of connections between adjacent distribution nodes.

[0010] Preferably, the topology connectivity index is characterized by the second smallest eigenvalue of the Laplacian matrix corresponding to the communication topology graph. The optimization objective is to maximize the topology connectivity index and select the optimal communication path within the feasible domain of the communication network that satisfies the connectivity constraints. The weight coefficient is dynamically updated according to the number of connections between adjacent distribution nodes, wherein the weight between distribution nodes is inversely proportional to the number of connections between adjacent distribution nodes, and the weight of a distribution node is the complement of the sum of the weights of its neighbors, so as to achieve adaptive matching between the weight and the neighborhood size.

[0011] Furthermore, the present invention also provides a power distribution network feeder automation system, comprising: a distribution node layer, consisting of terminal equipment deployed at each distribution node of the feeder, configured to collect feeder operating status data and execute local control commands; a communication layer, configured to realize information interaction between distribution nodes; and a control layer, including a local autonomous sublayer and a network-wide coordination sublayer, wherein the local autonomous sublayer is configured to handle single-area faults, and the network-wide coordination sublayer is configured to realize cross-area fault recovery and operation optimization.

[0012] Preferably, the terminal equipment of the power distribution node layer includes: a status acquisition module configured to acquire voltage, current and switch status data; a local calculation module configured to store a consistency iteration protocol, an event triggering function and a weight update rule; a communication module configured to support information interaction between power distribution nodes; and a control execution module configured to drive the sectionalizing switches to perform opening and closing operations.

[0013] Preferably, the communication layer is configured to support dynamic adjustment of the communication topology and maintain communication connectivity through a weight update mechanism when power distribution nodes are put into operation or deactivated; the local autonomous sublayer of the control layer is configured to handle single-area faults through a consensus iteration protocol, and the network-wide coordination sublayer is configured to achieve cross-area fault recovery through the collaboration of multiple power distribution nodes.

[0014] Beneficial effects: This invention enables fault detection and location by using distribution nodes to perform collaborative iteration based on the states of adjacent distribution nodes. It utilizes local information interaction between distribution nodes to achieve rapid alignment of fault characteristics, replacing the traditional centralized master station decision-making, significantly reducing communication overhead and improving response speed. By executing collaborative response and fault isolation operations between distribution nodes through an event-triggered mechanism, the communication behavior is changed from periodic full transmission to on-demand triggering, further reducing network load. By optimizing communication paths and dynamically adjusting neighborhood weights through topology connectivity indicators, the system can maintain stable operation without manual reconstruction when distribution nodes are added or removed or the topology changes, achieving plug-and-play characteristics. The decentralized control strategy combining local autonomy and network-wide coordination ensures rapid self-healing of faults in a single area and achieves global optimization of cross-regional faults, effectively improving the reliability and resilience of the distribution network. Attached Figure Description

[0015] Figure 1 This is a flowchart of the power distribution network feeder automation method according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0018] Example 1: like Figure 1 As shown, this embodiment provides a method for automating distribution network feeders. This method employs a decentralized control strategy, eliminating reliance on a centralized master station for global decision-making. Instead, it achieves feeder automation through local information exchange and collaborative computation between distribution nodes. Specifically, the method in this embodiment includes steps S1 to S3.

[0019] Step S1: The power distribution node collects feeder operation status data and performs fault detection and location based on the collaborative iteration of adjacent power distribution node statuses.

[0020] Specifically, the distribution nodes collect real-time operational status data such as voltage, current, and switch positions at their location. In this embodiment, the distribution node can be a feeder terminal unit (FTU). Unlike traditional methods that upload all data to the master station for centralized decision-making, the distribution nodes in this embodiment only interact with their physically or communicatively connected neighboring distribution nodes. Each distribution node compares and iteratively calculates its own operational status characteristics with those of its neighboring distribution nodes based on a consensus iteration protocol. Under normal operating conditions, the status characteristics of each distribution node will tend to be consistent; when a fault occurs in a certain area, the status characteristics of the upstream and downstream distribution nodes at the fault point will show significant differences. Through the collaborative convergence characteristics of these differences, each distribution node can quickly identify the fault type and accurately locate the fault section using only local information. This mechanism of collaborative iteration of neighboring distribution node status transforms centralized global decision-making into distributed local collaboration, eliminating the risk of single-point faults at the master station and significantly reducing the latency of fault detection.

[0021] Step S2: In response to the fault event obtained from fault detection and location, perform coordinated response and fault isolation operations between power distribution nodes through the event triggering mechanism.

[0022] Specifically, after fault location is completed, the relevant distribution nodes enter the coordinated response phase. Traditional distributed control often employs a periodic broadcast communication mode, continuously consuming communication resources even when no fault occurs. This embodiment introduces an event-triggered mechanism, transforming the communication mode from time-driven to event-driven. Distribution nodes continuously monitor their own state observation errors, triggering communication and control actions only when the error exceeds a preset trigger threshold or a specific type of fault event is detected. For example, after detecting fault characteristics, distribution nodes on both sides of the fault point trigger an event, quickly exchanging isolation commands through neighborhood communication, and collaboratively driving sectionalizing switches to disconnect the faulty area. This mechanism significantly reduces redundant communication, lowers network load, and improves the system's response reliability in environments with limited communication bandwidth.

[0023] Step S3: Optimize the communication path based on the topology connectivity index to achieve fault recovery, and dynamically adjust the neighborhood weights after fault recovery.

[0024] Specifically, after fault isolation, the system needs to restore power to the non-faulty areas. This embodiment optimizes and filters communication paths based on topology connectivity indices to find the optimal path that satisfies connectivity constraints, enabling cross-regional load transfer or power supply support. During and after restoration, considering that the distribution network topology may change due to fault isolation or the commissioning / decommissioning of distributed generation, the system employs a dynamic weight adjustment mechanism. Each distribution node updates its communication weight coefficient with neighboring distribution nodes in real time based on the current number of connections to adjacent distribution nodes, allowing the control algorithm to adapt to the new network topology. This dynamic adjustment mechanism gives the system plug-and-play capability, allowing it to adapt to network changes without manual parameter reconfiguration, thus maintaining long-term operational stability.

[0025] Through the above scheme, the decentralized strategy avoids the communication bottleneck and single point of failure risk of centralized control, the neighborhood collaborative iteration and event triggering mechanism greatly improve the real-time performance and communication efficiency of fault handling, and the topology adaptation and dynamic weight adjustment ensure the robustness of the system in complex and ever-changing environments.

[0026] Example 2: This embodiment, based on Embodiment 1, provides a detailed explanation of the specific implementation process of fault detection and location. Specifically, the fault detection and location based on the collaborative iteration of adjacent distribution node states includes: constructing a fault detection criterion based on a consensus iteration protocol, and determining the fault type and location through the convergence characteristics of the state errors of adjacent distribution nodes; the fault detection criterion takes fault feature quantities as input, and the fault feature quantities include at least one of voltage mutation rate and current distortion rate.

[0027] In power distribution networks, different types of faults often exhibit different electrical characteristics. For example, short-circuit faults are usually accompanied by a sudden drop in voltage and a sudden rise in current, while ground faults may manifest as changes in zero-sequence voltage and current. To enable the consensus iteration protocol to handle these heterogeneous data, this embodiment first performs feature extraction and normalization on the collected raw electrical quantities. Specifically, the distribution node calculates the voltage mutation rate, which is the ratio of the difference between the voltage amplitude at the current moment and the voltage amplitude at the previous moment to the rated voltage; simultaneously, it calculates the current distortion rate, which is the ratio of the current harmonic content to the fundamental frequency content. After normalization, these two feature quantities are mapped to intervals. Furthermore, the fault detection criterion describes the dynamic evolution process of the distribution node state. This dynamic evolution process is based on the differences in the fault feature quantities between adjacent distribution nodes and the communication weights, achieving coordinated convergence and location of the fault state through the superposition of nonlinear convergence terms and linear coupling terms.

[0028] Specifically, in this embodiment, each distribution node compares and iteratively calculates its own operating state characteristics with the state characteristics of its neighboring distribution nodes based on a consensus iteration protocol. Specifically: By improving the consensus algorithm and combining the fault characteristics of the distribution node itself with the state of adjacent distribution nodes through collaborative iteration, the fault type and location can be determined. In the formula, For power distribution nodes i Rate of change of fault characteristic quantities For power distribution nodes i Fault characteristic quantities, For power distribution nodes j Fault characteristic quantities, For power distribution nodes i The set of adjacent distribution nodes, For power distribution nodes i and j Communication weight, This is the fault detection index coefficient. , This is the proportionality coefficient.

[0029] Set up power distribution nodes i The fault characteristic quantities are The set of adjacent distribution nodes is Distribution nodes i and j The communication weight is The fault detection index coefficient is The proportionality coefficient is , ,when ( When the threshold for fault detection is reached, a fault is determined to exist.

[0030] The consensus iteration protocol used in this embodiment comprises two key components: a nonlinear convergence term and a linear coupling term. The nonlinear convergence term utilizes a composite operation of a sign function and a power exponent. Its role is to provide a strong driving force in the early stages of iteration, when the state errors of distribution nodes are large, prompting the states of each distribution node to converge rapidly towards a consistent state. The linear coupling term utilizes the state differences between adjacent distribution nodes for linear adjustment. Its role is to provide fine-grained adjustment capabilities in the later stages of iteration, when the state errors of distribution nodes are small, ensuring the accuracy and stability of the final convergence. This combination of nonlinear-dominated rapid convergence and linear-dominated precise stability mechanism enables the system to reach a consistent state within a finite time when facing fault disturbances, rather than the asymptotic convergence of traditional linear algorithms. This finite-time convergence characteristic is crucial for distribution network fault handling, meaning that the system can complete the coordinated alignment of fault characteristics in a very short time, thereby gaining a valuable time window for subsequent fault isolation and effectively preventing the fault range from expanding.

[0031] To more clearly illustrate the operating mechanism of this protocol, key parameters in the protocol and their physical meanings are defined. The gain coefficient of the nonlinear term... The value determines the convergence speed; a larger value results in faster convergence, but also increases sensitivity to communication noise. Therefore, in practical applications, a trade-off must be made based on communication quality. An optimal value range is, for example, where, under normal operating conditions, the fault characteristic quantities of each distribution node tend to be consistent, meaning the states of all distribution nodes in the network converge to the same value. When a fault occurs in a section, the current in the upstream distribution node suddenly increases, causing its fault characteristic quantity to deviate significantly from its normal value. Downstream distribution nodes also experience changes in their characteristic quantities due to power loss. At this time, the distribution nodes on both sides of the fault section cannot reach consensus within a finite time during the consistency iteration process due to the large state differences, resulting in state tearing. By monitoring the convergence characteristics of the distribution node state error—that is, determining whether the distribution node state converges to a preset threshold range within a preset time—fault events can be identified. If the distribution node state does not converge within the preset time, it is determined that the distribution node is in the fault-affected area. Furthermore, by comparing the state differences between adjacent distribution nodes, the fault section can be accurately located. This consistency-based iterative detection mechanism requires no master station involvement and can be completed solely through local information exchange between adjacent power distribution nodes, significantly reducing fault detection latency and improving location accuracy.

[0032] Example 3: This embodiment, based on Embodiment 1, provides a detailed explanation of the specific triggering mechanism for coordinated response and fault isolation. Specifically, the execution of coordinated response and fault isolation operations between distribution nodes through an event triggering mechanism includes: determining whether to trigger communication and control actions between distribution nodes based on a comparison between the state observation error and the triggering threshold; the fault event includes at least one of line fault events, load change events, and distribution node commissioning / decommissioning events.

[0033] In traditional distributed control systems, distribution nodes typically employ a periodic broadcast communication mode. This means that regardless of system state changes, each distribution node sends its status information to neighboring nodes at fixed time intervals. While simple, this mode consumes significant communication bandwidth resources during long-term normal operation of the distribution network, increasing the risk of network congestion. This embodiment introduces an event-triggered mechanism, transforming the communication mode from time-driven to event-driven. Communication is triggered only when a specific event occurs or a state deviation exceeds a threshold.

[0034] Furthermore, the determination of whether to trigger inter-distribution node communication and control actions is achieved by comparing the state observation error of the distribution node with the trigger threshold adjusted by the trigger coefficient. When the state observation error meets the trigger condition, inter-distribution node communication and control actions are triggered.

[0035] Specifically, the distribution node continuously monitors its own operating status and calculates the current state observation error. This state observation error is defined as the norm distance between the actual state vector of the distribution node at the current moment and the state vector broadcast at the last trigger moment. The formula is expressed as: in For the state observation error of the distribution node, For power distribution nodes i No. k Observations after the trigger, For event threshold items, The triggering coefficient is used for the events, which include fault events, load surge events, and distribution node commissioning / discharge events, with corresponding triggering thresholds of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... , , This error reflects the degree of change in the status of the distribution node relative to the time of the last broadcast.

[0036] The logic for determining the trigger condition is as follows: The state observation error is considered. Compare with the trigger threshold. The trigger threshold is not a fixed value, but a dynamic threshold related to the current state, specifically determined by the trigger coefficient. With state threshold term The product is determined by the state observation error. When the trigger threshold after trigger coefficient adjustment is greater than or equal to the threshold value, the trigger condition is deemed met. At this point, the distribution node immediately initiates communication, sending the latest status information to adjacent distribution nodes and executing corresponding control actions, such as tripping the sectionalizing switch. If the condition is not met, the distribution node remains silent and does not send data, thus significantly reducing the communication frequency under normal conditions.

[0037] To more clearly illustrate the technical effectiveness of this mechanism, a specific fault scenario is used for comparative analysis. Assume a feeder contains 10 distribution nodes with a communication cycle of 10 milliseconds. In traditional periodic communication mode, the entire network needs to broadcast to the distribution nodes 100 times per second. However, under the event-triggered mechanism of this embodiment, the state changes of distribution nodes are slow during normal operation, and the state observation error remains below the threshold for a long time. The distribution nodes only trigger communication when a significant disturbance is detected. Actual measurement data shows that during normal operation, the triggering frequency can be reduced to 1-2 times per second, reducing communication overhead by more than 90%. When a short-circuit fault occurs, the current and voltage states of distribution nodes near the fault point change abruptly, and the state observation error instantly exceeds the threshold. The triggering mechanism responds within milliseconds, immediately activating neighborhood communication and coordinated control, ensuring real-time fault handling. This characteristic effectively resolves the contradiction between limited communication resources and high real-time requirements in distribution network automation systems.

[0038] Furthermore, this embodiment employs differentiated threshold setting strategies to adapt to varying control requirements for different types of fault events. For line fault events, due to their rapid development and significant impact, the system sets a relatively small trigger threshold and a high trigger coefficient to ensure extremely high sensitivity and achieve millisecond-level rapid response. For load change events, although they also cause state fluctuations, their impact is relatively low; the system sets a moderate threshold to ensure monitoring effectiveness while avoiding frequent false triggers. For distribution node commissioning / decommissioning events, the system sets specific thresholds to identify topology changes and trigger subsequent weight update processes. This hierarchical threshold strategy allows the system to dynamically adjust response resources based on the severity of the event, balancing sensitivity and stability.

[0039] Example 4: This embodiment, based on Embodiment 1, provides a detailed explanation of the communication path optimization during the fault recovery phase and the dynamic weight adjustment mechanism during long-term system operation. Specifically, the step of optimizing the communication path based on the topology connectivity index to achieve fault recovery includes: selecting the optimal communication path within the feasible domain of the communication network that satisfies connectivity constraints, with the goal of maximizing the topology connectivity index; the step of dynamically adjusting the neighborhood weights includes: dynamically updating the weight coefficients based on the number of connections to adjacent distribution nodes.

[0040] After a fault occurs in the distribution network and is isolated, the network topology often changes. For example, power loss in some sections or the operation of tie switches can cause changes in the ring network structure. At this time, the original communication path may no longer be the optimal path, and communication islands may even appear. To ensure that fault recovery commands can be transmitted quickly and reliably to non-faulty areas, this embodiment introduces a topology connectivity index as an optimization objective. Specifically, the topology connectivity index is characterized by the second smallest eigenvalue of the Laplace matrix corresponding to the communication topology graph. Maximizing the topology connectivity index is the optimization objective, and the optimal communication path is selected within the feasible region of the communication network that satisfies connectivity constraints.

[0041] The Laplacian matrix L is an important mathematical tool for describing the topological structure of a graph, and its second smallest eigenvalue is... This is called algebraic connectivity. The physical significance of algebraic connectivity lies in characterizing the robustness and synchronization capability of a network. A larger value indicates a tighter network connection, faster convergence speed of information propagation between distribution nodes, and a stronger ability of the network to resist distribution node failures or link breaks. This embodiment constructs an objective function... Under the constraint of satisfying network connectivity, feasible communication topologies are traversed, and the path with the highest algebraic connectivity is selected as the optimal communication path. Describe the objective function. Represents the communication topology graph G, This represents the set of all feasible topologies that satisfy the basic connectivity requirements. This indicates a constraint.

[0042] For example, in cross-regional load transfer scenarios, the system may face multiple alternative paths. By calculating the algebraic connectivity of each path, the path with the strongest connectivity is selected for command transmission, thereby effectively avoiding the risk of communication link congestion or interruption and ensuring the reliability of fault recovery.

[0043] Furthermore, considering that the actual operation of the distribution network is often accompanied by dynamic changes such as the commissioning and decommissioning of distributed power sources, load access, or maintenance of distribution nodes, a fixed weight configuration is difficult to maintain the long-term stability of the system. Therefore, this embodiment introduces a dynamic weight adjustment mechanism. The weight coefficients are dynamically updated based on the number of connections between adjacent distribution nodes, wherein the weight between distribution nodes is inversely proportional to the number of connections between adjacent distribution nodes, and the weight of a distribution node is the complement of the sum of the weights of its neighbors, to achieve adaptive matching between the weight and the size of the neighborhood.

[0044] Specifically, this embodiment uses the Metropolis rule for weight updates. The design logic of this rule is as follows: if a distribution node has a large number of neighboring distribution nodes, it indicates that the distribution node is in a hub position in the network and has strong information aggregation capabilities. Therefore, the weight assigned to a single neighboring distribution node should be appropriately reduced to avoid information overload; conversely, if the number of connections to a distribution node is small, the weight of a single link should be increased to ensure the effectiveness of information transmission. The formula is expressed as: in, , These are the distribution nodes i , j The number of adjacent distribution nodes, These are the elements of the updated neighborhood weight matrix.

[0045] To illustrate the effectiveness of this mechanism more clearly, let's take the scenario of power distribution node commissioning and decommissioning as an example. Suppose a new distributed power distribution node is added to the network. After this node is connected, the connection counts of both itself and its neighboring distribution nodes change. Without manual intervention or global reconfiguration, each relevant distribution node only needs to obtain the latest connection count information of its neighboring distribution nodes to automatically calculate and update the weight matrix locally according to the aforementioned rules. This adaptive adjustment mechanism based on local information enables the system to respond to topology changes in real time, maintain the convergence performance of the consistency iteration protocol, and thus achieve plug-and-play functionality, significantly reducing system operation and maintenance costs and expansion difficulty.

[0046] Example 5: This embodiment provides a distribution network feeder automation system. The system adopts a layered distributed architecture, divided into three layers based on both physical entities and logical functions: the distribution node layer, the communication layer, and the control layer. This layered architecture not only provides the physical carrier for the decentralized control method described in the previous embodiment, but also achieves rapid self-healing and optimized operation of the distribution network feeder faults through inter-layer coordination.

[0047] Specifically, the distribution node layer consists of terminal devices deployed at each distribution node of the feeder, serving as the smallest unit for system perception and execution. These terminal devices are widely distributed in key locations such as feeder sectionalizing switches, branch line distribution nodes, and distribution transformers, configured to collect feeder operating status data and execute local control commands. It should be understood that the form of the terminal devices is not limited to a specific type of hardware; they can be traditional feeder terminal units (FTUs), intelligent converged terminals integrating edge computing capabilities, or intelligent circuit breaker controllers with communication functions. The core role of the distribution node layer lies in localized processing. In the adjacent distribution node state collaborative iteration process mentioned in the previous embodiments, the terminal devices of the distribution node layer are not only data collectors but also participants in the computation. Each terminal device stores and runs consensus iteration protocols, event triggering functions, and other algorithmic rules locally, without needing to upload all raw data to the cloud or main station. This significantly reduces dependence on communication bandwidth and ensures local autonomy in extreme situations such as communication interruptions.

[0048] The communication layer is configured to enable information exchange between distribution nodes. Unlike the star topology in traditional centralized architectures where all distribution nodes communicate with the master station, the communication layer in this embodiment constructs a peer-to-peer communication network. The communication layer supports the convergence of multiple communication media, such as fiber optic communication, private wireless networks, or power line carrier communication (HPLC). Logically, the communication layer is responsible for maintaining the neighborhood relationships described in the previous embodiments, ensuring that each distribution node can exchange data in real-time and reliably with its physically connected or logically associated neighboring distribution nodes. The data flow of the communication layer exhibits typical lateral interaction characteristics; that is, the status information required for fault detection and location mainly flows laterally between adjacent distribution nodes, rather than converging vertically. This flattened communication architecture eliminates the communication bottleneck of the central distribution node, making the system highly robust in the face of single-point communication failures; the interruption of any single link will not affect the overall coordination of the entire network.

[0049] The control layer is the logical core of the system, comprising a local autonomous sublayer and a network-wide coordination sublayer. This hierarchical control design aims to balance the mutually constraining performance metrics of response speed and global optimization. The local autonomous sublayer is configured to handle single-area faults, and its control scope is typically limited to a finite neighborhood near the fault point. For example, when a short-circuit fault occurs in a section of a feeder, the local autonomous sublayer, through the aforementioned consensus iteration protocol, completes fault location within tens of milliseconds and directly drives adjacent switches to achieve fault isolation. This process relies entirely on local information and requires no cross-area coordination, thus ensuring extremely fast response. The network-wide coordination sublayer is configured to achieve cross-area fault recovery and operational optimization, handling complex problems that the local autonomous sublayer cannot solve. For example, when fault isolation leads to power loss in a large area of ​​non-faulty regions, requiring power restoration from other power sources via tie switches, the network-wide coordination sublayer intervenes, calculating the optimal power transfer path based on topology connectivity metrics and balancing the load capacity of each power source. It should be understood that the local autonomous sublayer and the network-wide coordination sublayer are not completely separate, but rather work collaboratively: the local autonomous sublayer acts as the first line of defense, quickly cutting off faults; the network-wide coordination sublayer acts as the second line of defense, optimizing recovery strategies. This collaborative mechanism of rapid local action followed by optimized network-wide action avoids the long delays of centralized control and overcomes the lack of a global perspective in decentralized control, achieving efficient self-healing of distribution network feeder automation.

[0050] Example 6: This embodiment, based on the aforementioned embodiments, provides a detailed description of the specific module configuration and functional implementation of the distribution network feeder automation system. Specifically, the terminal equipment at the distribution node layer includes: a status acquisition module configured to acquire voltage, current, and switch status data; a local calculation module configured to store a consistency iteration protocol, event triggering functions, and weight update rules; a communication module configured to support information exchange between distribution nodes; and a control execution module configured to drive sectionalizing switches to perform opening and closing operations.

[0051] As the system's sensing front end, the status acquisition module's core function is to acquire high-quality raw electrical quantity data. Specifically, this module typically integrates high-precision voltage transformers, current transformers, and analog-to-digital converter circuits, enabling it to capture, in real-time, the voltage amplitude, current amplitude, active power, reactive power, and switch position status of feeder distribution nodes at millisecond-level sampling frequencies. It should be understood that the types of data acquired are not limited to those listed above; depending on the specific application scenario, they may also include characteristic quantities such as zero-sequence voltage and zero-sequence current used for ground fault detection. After preprocessing such as filtering and normalization, this raw data forms the input characteristic quantities required for the fault detection criteria in the aforementioned embodiments, providing a data foundation for subsequent local calculations.

[0052] The local computing module is the core hardware carrier for implementing the decentralized control strategy. Unlike traditional terminal devices that only function as data transmission units, the local computing module in this embodiment possesses edge computing capabilities. It internally stores and runs the core algorithm rules defined in the aforementioned embodiments. Specifically, this module stores a consistency iteration protocol program, which can calculate the distribution node state error in real time based on data input from the state acquisition module, and perform the superposition operation of nonlinear convergence terms and linear coupling terms, thereby completing the collaborative convergence judgment of fault characteristics locally without relying on instructions issued by the master station. Simultaneously, this module also stores an event trigger function, which can continuously calculate the observation error between the current state and the last broadcast state, compare it with a dynamic threshold, and generate a trigger signal. Furthermore, the weight update rules are also embedded in the module's non-volatile memory. When receiving a topology change notification from a neighboring distribution node, this module can automatically call the Metropolis rules to calculate new weight coefficients and update the local routing table. This design, which pushes the algorithm down to the terminal, enables each distribution node to have independent decision-making capabilities, providing a key hardware guarantee for achieving local autonomy and rapid response. The hardware form of this module can be a digital signal processor (DSP), a field-programmable gate array (FPGA), or an embedded microcontroller (MCU).

[0053] The communication module is configured to support information exchange between power distribution nodes. Specifically, this module is responsible for building and maintaining the neighborhood communication links in the aforementioned embodiments. Unlike the star topology in traditional centralized architectures where terminals only communicate with the master station, the communication module in this embodiment supports peer-to-peer communication protocols, enabling it to directly exchange status data, trigger signals, and weight update information with physically connected or logically associated neighboring power distribution nodes. This module can support various communication media, such as fiber optic Ethernet, wireless private networks (LTE-230, 5G slicing), or power line carrier communication (HPLC). Under the event-triggered mechanism, the communication module is normally in a low-power listening state, activating the high-frequency data transmission mode only when the local computing module generates a trigger signal, thereby effectively reducing network energy consumption and bandwidth usage.

[0054] The control execution module is configured to drive the sectionalizing switch to perform opening and closing operations. This module is typically connected to the operating mechanism of the feeder sectionalizing switch. When the local calculation module determines, based on the consensus iteration protocol, that the fault is located in the section of this distribution node, or receives a coordinated isolation command from an adjacent distribution node, the control execution module outputs corresponding electrical control signals to drive the switch mechanism to operate, cutting off the fault current or isolating the faulty section. This module also has protection logic such as anti-pumping and interlocking to ensure the reliability and safety of the switch operation.

[0055] Furthermore, the communication layer is configured to support dynamic adjustment of the communication topology and maintain communication connectivity through a weight update mechanism when distribution nodes are put into operation or deactivated; the local autonomous sublayer of the control layer is configured to handle single-area faults through a consensus iteration protocol, and the network-wide coordination sublayer is configured to achieve cross-area fault recovery through the collaboration of multiple distribution nodes.

[0056] The dynamic adjustment capability of the communication layer is crucial for the system to adapt to topology changes. When a distribution node is added or removed from the network, the communication layer can automatically detect the change in link status and trigger the weight update mechanism described in the previous embodiments. Specifically, the communication layer broadcasts topology change information. Upon receiving the information, the relevant distribution nodes use their local computing modules to recalculate algebraic connectivity and neighborhood weights, thereby reconstructing the communication network without manual intervention and maintaining the system's connectivity and robustness. This mechanism gives the system plug-and-play characteristics, greatly reducing operation and maintenance costs.

[0057] The layered design of the control layer achieves a balance between response speed and global optimization. The local autonomous sublayer mainly relies on the local computing modules of the distribution node layer. Its processing logic is simple and closed-loop: when the fault is limited to a single area, the local autonomous sublayer quickly locates the fault through a consistency iteration protocol and directly drives the control execution module to complete the isolation. The entire process does not require cross-regional communication and has extremely low latency. The network-wide coordination sublayer is usually undertaken by aggregation distribution nodes or edge computing gateways with stronger configuration capabilities. Its function is to handle cross-regional fault recovery. For example, when fault isolation leads to a large-scale power outage and it is necessary to transfer the load from an adjacent feeder through a tie switch, the network-wide coordination sublayer intervenes, collects the status information of the distribution nodes in the relevant areas, calculates the optimal transfer path based on the topology connectivity index, and issues coordinated control commands. This layered coordination mode of fast local response and optimized network-wide response effectively solves the contradiction between the high latency of traditional centralized control and the lack of global vision in decentralized control.

[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention, such as adjusting the nonlinear convergence term parameters in the consensus iteration protocol, changing the physical transmission medium of the communication layer, or modifying the threshold determination logic of the event triggering mechanism, should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A power distribution network feeder automation method, characterized by, A decentralized control strategy is adopted, including: The power distribution nodes collect feeder operation status data and perform fault detection and location based on the collaborative iteration of the status of adjacent power distribution nodes. In response to fault events detected and located, coordinated response and fault isolation operations between power distribution nodes are executed through an event triggering mechanism. The communication path is optimized based on the topology connectivity index to achieve fault recovery, and the weights of connected power distribution nodes are dynamically adjusted after the fault is recovered.

2. The power distribution network feeder automation method of claim 1, wherein, The fault detection and location based on the collaborative iteration of adjacent power distribution node states includes: A consensus iteration protocol is constructed, and a fault detection criterion is built based on the consensus iteration protocol. The fault type and location are determined by the convergence characteristics of the state error of adjacent power distribution nodes. The fault detection criterion takes fault characteristic quantities as input, and the fault characteristic quantities include at least one of voltage mutation rate and current distortion rate.

3. The power distribution network feeder automation method of claim 2, wherein, The consensus iteration protocol describes the dynamic evolution process of the distribution node state. The dynamic evolution process is based on the differences in the fault characteristic quantities and communication weights between adjacent distribution nodes. It achieves the collaborative convergence and localization of the fault state through the superposition of nonlinear convergence terms and linear coupling terms.

4. The power distribution network feeder automation method of claim 1, wherein, The operation of coordinated response and fault isolation between power distribution nodes through an event-triggered mechanism includes: Based on the comparison between the state observation error and the trigger threshold, it is determined whether to trigger communication and control actions between power distribution nodes. The fault events include at least one of line fault events, load change events, and distribution node commissioning / decommissioning events.

5. The power distribution network feeder automation method of claim 4, wherein, The determination of whether to trigger communication and control actions between distribution nodes is achieved by comparing the state observation error of the distribution node with the trigger threshold adjusted by the trigger coefficient. When the state observation error meets the trigger condition, communication and control actions between distribution nodes are triggered.

6. The power distribution network feeder automation method of claim 1, wherein, The method of optimizing communication paths based on topological connectivity indices to achieve fault recovery includes: With the goal of maximizing topological connectivity, the optimal communication path is selected within the feasible domain of the communication network that satisfies connectivity constraints. The dynamic adjustment of neighborhood weights involves dynamically updating the weight coefficients based on the number of connections between adjacent power distribution nodes.

7. The electric distribution network feeder automation method of claim 6, wherein, The topological connectivity index is characterized by the second smallest eigenvalue of the Laplacian matrix corresponding to the communication topology graph. The optimization objective is to maximize the topological connectivity index and select the optimal communication path within the feasible domain of the communication network that satisfies the connectivity constraints. The weighting coefficients are dynamically updated based on the number of connections between adjacent distribution nodes. The weights between distribution nodes are inversely proportional to the number of connections between adjacent distribution nodes, and the weight of a distribution node is the complement of the sum of the weights of its neighbors, so as to achieve adaptive matching between the weights and the size of the neighborhood.

8. A power distribution grid feeder automation system adapted for use in the power distribution grid feeder automation method according to any one of claims 1-7, characterized by include: The distribution node layer consists of terminal equipment deployed at each distribution node of the feeder, configured to collect feeder operating status data and execute local control commands; The communication layer is configured to enable information exchange between power distribution nodes. The control layer includes a local autonomous sublayer and a network-wide coordination sublayer. The local autonomous sublayer is configured to handle single-region faults, and the network-wide coordination sublayer is configured to achieve cross-regional fault recovery and operation optimization.

9. The distribution network feeder automation system of claim 8, wherein, The terminal equipment at the power distribution node layer includes: The status acquisition module is configured to acquire voltage, current, and switch status data; The local computing module is configured to store the consistency iteration protocol, event triggering functions, and weight update rules. The communication module is configured to support information exchange between power distribution nodes; The control execution module is configured to drive the segmented switch to perform opening and closing operations.

10. The electric distribution network feeder automation system of claim 8, wherein, The communication layer is configured to support dynamic adjustment of the communication topology and maintain communication connectivity through a weight update mechanism when power distribution nodes are put into operation or decommissioned. The local autonomous sublayer of the control layer is configured to handle single-area faults through a consensus iteration protocol, and the network-wide coordination sublayer is configured to achieve cross-area fault recovery through the collaboration of multiple power distribution nodes.

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