Distributed resource collaborative reconstruction method, system and device for power distribution network fault self-recovery and storage medium

Abstract

The invention relates to the technical field of power distribution network intelligent control, in particular to a power distribution network fault self-healing distributed resource collaborative reconstruction method, system and device and a storage medium. The method comprises the following steps: collecting multi-source fault information of a power distribution network, constructing a fault information set, determining a fault section based on the fault information set through confidence-weighted information consistency verification, and generating a hierarchical isolation strategy to minimize a power failure range of a non-fault area; generating a fault isolation operation sequence according to the position of the fault section, and evaluating the photovoltaic output capacity, the energy storage charge and discharge capacity and the connection line transmission capacity; establishing a coupling relationship between network topology adjustment and distributed resource output adjustment, and determining a switching operation sequence and a resource scheduling scheme by solving a multi-constraint optimization problem; and calculating a dynamic importance score according to the load type, the current power utilization state and the social influence factor, sorting the loads according to the dynamic importance score, sequentially accessing the loads, and verifying the voltage and the line load rate.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for distribution networks, and in particular to a method, system, device, and storage medium for distributed resource collaborative reconfiguration for self-healing of distribution network faults. Background Technology

[0002] As the final link of the power system, the distribution network directly supplies power to users, and the quality of its operation also affects the user experience. As the load density increases, some problems have emerged. For example, in the traditional distribution network fault handling method, dispatchers mainly rely on manual judgment based on fault signals and user reports. It usually takes 30 minutes to 2 hours from the occurrence of a fault to its accurate location, resulting in low fault recovery efficiency.

[0003] In recent years, distribution automation technology has been gradually promoted and applied. By configuring sensing devices such as fault indicators and smart meters, richer information on the operation of the distribution network can be obtained, and clearer fault data and operational quality can be provided to maintenance personnel. However, how to quickly and accurately locate the fault section from massive amounts of multi-source information still faces challenges.

[0004] Currently, the following problems still exist in the field of power distribution network fault recovery: First, the ability to integrate and process multi-source fault information is insufficient. Information reported by different devices has time deviations and reliability differences, and there is a lack of consistency verification methods, resulting in low fault location accuracy. Second, the network reconfiguration scheme is too simplistic. Existing methods mainly restore power supply by changing the state of tie switches and sectionalizing switches. The reconfiguration goal usually only considers minimizing network losses or load balancing, failing to fully utilize the regulation capabilities of new resources such as distributed photovoltaics and energy storage. Third, the load restoration priority is fixed. Power supply is restored according to the preset load level order, which cannot adapt to the dynamic changes in the importance of loads in actual operation. When available capacity is limited, it is difficult to achieve the optimal load restoration combination. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to quickly and accurately locate faults, maximize load restoration while ensuring power supply safety, shorten power restoration time, and improve the self-healing capability of the distribution network.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a distributed resource collaborative reconfiguration method for self-healing of distribution network faults, which includes: collecting multi-source fault information of the distribution network and constructing a fault information set; determining fault sections based on the fault information set through confidence-weighted information consistency verification; and generating a hierarchical isolation strategy to minimize the power outage range of non-faulty areas. A fault isolation operation sequence is generated based on the location of the fault section. The fault isolation operation sequence is arranged in the order of isolating the fault point first and then transferring the load, and the photovoltaic output capacity, energy storage charging and discharging capacity and interconnection line transmission capacity are evaluated. Establish the coupling relationship between network topology adjustment and distributed resource output regulation, and determine the switching operation sequence and resource scheduling scheme by solving a multi-constraint optimization problem; Calculate the dynamic importance score based on load type, current power consumption status and social influencing factors, sort the loads according to the dynamic importance score, connect the loads in sequence and verify the voltage and line load rate, and when the verification is successful, include the load in the recovery plan and update the remaining capacity.

[0008] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the step of determining the fault section based on the fault information set and through confidence-weighted information consistency verification includes: performing time alignment and type classification on the collected multi-source fault information, wherein the multi-source fault information includes protection action signals, switch change signals, fault indicator alarm information, and voltage sag events; and assigning confidence weights to each piece of information according to the reliability and historical accuracy of different information sources. The consistency of fault information is determined by cross-validation mechanism, which increases the probability of fault in the same fault segment when multiple information sources point to the same fault segment. The final location of the faulty section is determined based on the weighted consistency score.

[0009] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the generation of the hierarchical isolation strategy includes identifying the upstream protection equipment and adjacent interconnection switches of the fault section. Based on network topology, determine the minimum set of switching operations required for fault isolation; The set of switching operations is sorted in the order of isolating the fault point first and then transferring the load to form a hierarchical isolation operation sequence; Verify the safety and effectiveness of the isolation operation sequence.

[0010] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the step of establishing the coupling relationship between network topology adjustment and distributed resource output regulation includes: analyzing the power flow distribution and voltage distribution under different topology states; obtaining the adjustability margin of distributed power sources and the state of charge of energy storage; setting voltage deviation constraints, line capacity constraints, and resource ramp-up rate constraints; and, under all constraints, solving for the switching state and resource output combination that maximizes load recovery and minimizes resource regulation costs.

[0011] The beneficial effects of this preferred technical solution are as follows: by analyzing the power flow distribution and voltage distribution under different topology states, the adjustability margin of distributed power sources and the state of charge of energy storage can be obtained, and the current resource status of the distribution network can be accurately grasped; by setting voltage deviation constraints, line capacity constraints and resource ramp-up rate constraints, boundary conditions for safe network operation can be established; under all constraints, the switching state and resource output combination that maximizes load recovery and minimizes resource adjustment cost can be solved, realizing deep coupling between network topology adjustment and distributed resource regulation, giving full play to the supporting role of new resources such as photovoltaics and energy storage, while ensuring voltage quality during power supply.

[0012] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the step of solving for the combination of switch states and resource outputs that maximizes load recovery and minimizes resource regulation costs includes: Determine the network topology formed after the switching operation; calculate the output allocation of each distributed power source and energy storage under the current topology. Energy storage is used to provide voltage and frequency support; distributed power sources are controlled to adjust their output at a set rate to avoid power oscillations.

[0013] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the step of calculating the dynamic importance score based on load type, current power consumption status, and social impact factors includes: setting basic weights for primary loads, secondary loads, and tertiary loads respectively; determining a time period coefficient based on the peak power consumption level of the current time period; determining an impact coefficient based on the social impact range of the load; and multiplying the basic weights, time period coefficients, and impact coefficients to obtain the dynamic importance score.

[0014] The beneficial effects of this preferred technical solution are as follows: By setting basic weights for primary, secondary, and tertiary loads respectively, a basic framework for load classification is established; a time period coefficient is determined based on the peak electricity consumption level of the current time period, enabling dynamic identification of the importance of loads at different times; an impact coefficient is determined based on the social impact range of the load, incorporating social benefits into the evaluation system; and a dynamic importance score is obtained by multiplying the basic weights, time period coefficients, and impact coefficients, so that the load restoration priority is no longer fixed but dynamically adjusted according to the operating status. When available capacity is limited, important loads are given priority. Compared with the fixed priority method, in practical applications, this shortens the power restoration time for critical loads such as hospitals and communication base stations and improves the social benefits of distribution network fault restoration.

[0015] As a preferred embodiment of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults described in this invention, the step of sequentially connecting loads and verifying voltage and line load rate includes selecting loads to be connected in descending order of dynamic importance score. Calculate the voltage value of each node and the load rate of each line after connecting the candidate load; determine whether the voltage value is within the allowable range and whether the load rate is less than the limit; when the determination result is yes, connect the candidate load and deduct the power of the connected load from the remaining capacity.

[0016] Secondly, embodiments of the present invention provide a distributed resource collaborative reconfiguration system for self-healing of distribution network faults, which includes an information acquisition module for acquiring multi-source fault information of the distribution network and constructing a fault information set. The fault location and isolation module is used to determine the fault segment based on the fault information set through confidence-weighted information consistency verification, and generate a hierarchical isolation strategy. The resource assessment module is used to assess the available resource capacity of the distribution network; The collaborative optimization module is used to establish resource collaboration strategies, taking into account the coupling relationship between network topology adjustment and distributed resource regulation. The dynamic decision-making module is used to build a dynamic recovery decision-making model and adjust the recovery order based on the real-time assessment results of load importance. The execution coordination module is used to coordinate the execution of fault isolation, network reconstruction, and load recovery.

[0017] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as described in the first aspect of the present invention.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as described in the first aspect of the present invention.

[0019] The beneficial effects of this invention are as follows: This invention collects multi-source fault information from the distribution network and constructs a fault information set. It performs timestamp correction and type classification on the collected information, assigns confidence weights to different information sources based on their reliability, and determines the consistency of fault information through a cross-validation mechanism. This solves the problem of low accuracy in traditional methods where fault location relies on a single information source, achieving rapid and accurate identification of faulty sections. When generating a hierarchical isolation strategy, it identifies the boundary switches of the faulty section and determines the minimum set of switch operations, forming an operation sequence according to the order of isolating the fault point first and then transferring the load, controlling the power outage range of non-faulty areas. It also evaluates photovoltaic power output. After determining the capacity, energy storage charging and discharging capacity, and interconnection line transmission capacity, a coupling relationship between network topology adjustment and distributed resource output regulation is established. By solving multi-constraint optimization problems, the switching operation sequence and resource scheduling scheme are determined, fully mobilizing the supporting role of new resources such as photovoltaics and energy storage. Compared with the traditional method of relying solely on switching operations, this improves the load recovery capability. Dynamic importance scores are calculated based on load type, current power consumption status, and social impact factors. Loads are sorted according to the scores and connected for verification in sequence, realizing dynamic adjustment of load recovery priority. This ensures that important loads are restored first when available capacity is limited, thereby improving the overall fault self-healing capability of the distribution network. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of a distributed resource collaborative reconfiguration method for self-healing of distribution network faults; Figure 2 Computer equipment diagram for a distributed resource collaborative reconfiguration method for self-healing of distribution network faults; Figure 3 Another flowchart for a distributed resource collaborative reconfiguration method for self-healing of distribution network faults. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0025] Example 1 Reference Figure 1 - Figure 2 This is the first embodiment of the present invention, which provides a distributed resource collaborative reconfiguration method for self-healing of distribution network faults, including: S100: Collects multi-source fault information of the distribution network and constructs a fault information set. Based on the fault information set, it determines the fault section through confidence-weighted information consistency verification and generates a hierarchical isolation strategy to minimize the power outage range of non-faulty areas.

[0026] S200: Generates a fault isolation operation sequence based on the location of the fault section. The fault isolation operation sequence is arranged in the order of isolating the fault point first and then transferring the load, and evaluates the photovoltaic power output capacity, energy storage charging and discharging capacity and interconnection line transmission capacity.

[0027] S300: Establish the coupling relationship between network topology adjustment and distributed resource output regulation, and determine the switching operation sequence and resource scheduling scheme by solving a multi-constraint optimization problem.

[0028] S400: Calculates dynamic importance score based on load type, current power consumption status and social impact factors, sorts loads according to dynamic importance score, connects loads in sequence and verifies voltage and line load rate, and when verification is successful, includes the load in the recovery plan and updates the remaining capacity.

[0029] It should be noted that when a distribution network fault occurs, the fault information reported by different sensing devices has timestamp deviations and reliability differences. Direct fusion will lead to a decrease in the accuracy of fault location. Traditional network reconfiguration schemes mainly restore power supply by adjusting the status of tie switches and sectional switches, which fails to make full use of the control capabilities of new resources such as distributed photovoltaics and energy storage. Load restoration is carried out according to a preset fixed priority, which cannot adapt to the actual changes in the importance of loads at different times.

[0030] Therefore, through steps S100-S400, multi-source fault information of the distribution network is collected, and the fault location is quickly and accurately achieved through confidence-weighted consistency verification. The fault section is determined within seconds, and the optimal isolation strategy is generated. The photovoltaic power output capacity, energy storage charging and discharging capacity, and tie line transmission capacity are evaluated to establish the coupling relationship between network topology adjustment and distributed resource output regulation. The switching operation sequence and resource scheduling scheme are determined by solving a multi-constraint optimization problem to achieve the coordinated cooperation between distribution network topology reconfiguration and distributed resources. Dynamic importance scores are calculated based on load type, current power consumption status, and social influencing factors. Loads are sorted according to the scores and connected for verification in sequence to achieve intelligent load recovery.

[0031] Example 2 Reference Figure 1 - Figure 3 This is the second embodiment of the present invention.

[0032] In this embodiment, step S100 involves collecting multi-source fault information from the distribution network and constructing a fault information set. Based on the fault information set, fault sections are determined through confidence-weighted information consistency verification, and a hierarchical isolation strategy is generated to minimize the power outage range in non-faulty areas. This includes the following steps A1-A2: A1: Based on the fault information set, the fault section is determined by information consistency verification with confidence weighting, including time alignment and type classification of the collected multi-source fault information, which includes protection action signals, switch position change signals, fault indicator alarm information and voltage sag events. Based on the reliability and historical accuracy of different information sources, each piece of information is assigned a confidence weight. The consistency of fault information is determined by cross-validation mechanism, which increases the probability of fault in the same fault segment when multiple information sources point to the same fault segment. The final location of the faulty section is determined based on the weighted consistency score.

[0033] Specifically, when a fault occurs in the distribution network, the distribution automation system acquires fault-related information through multiple acquisition points. This information comes from different types of equipment, including protection action signals generated by protection devices, position change signals of circuit breakers and switches, alarm signals from fault indicators, and voltage dip events recorded by smart meters. The distribution automation terminal uploads operating data every 2 seconds, and immediately uploads fault waveform data when an anomaly is detected. The fault indicator sends an alarm signal within 1 second after detecting a fault current, carrying detailed information such as the fault direction and fault type.

[0034] Since clocks on different devices may deviate, the collected information first needs to be timestamped. GPS time synchronization or a network time protocol is used to achieve time synchronization between devices, with time accuracy controlled within 10 milliseconds. The timestamps reported by the devices are then corrected using the following formula: (1) In the formula, For the timestamp reported by device i, This refers to the clock skew of the device (obtained through periodic clock synchronization measurements). This is the corrected timestamp.

[0035] This correction method unifies the timestamps of all information sources to the same time base, providing an accurate time alignment basis for subsequent information fusion analysis.

[0036] After time correction, the information is categorized and weighted with confidence levels. Based on the information source type, the collected information is divided into four main categories: protection action, switch position change, voltage monitoring, and fault indication. Since the reliability of different information sources varies, each piece of information is assigned a confidence level weight. The weights are determined based on the reliability of the information source: protection device information. Fault indicator information Voltage monitoring information User repair information These categorized and weighted information will be passed to the fault location module in the second step for fusion analysis.

[0037] Assuming the power distribution network has n sections and m information collection points, construct the correlation matrix. , of which Indicates a section Information collection point when a fault occurs i Whether an alarm signal will be generated is defined as follows: (2) When an actual fault occurs, the alarm status of each acquisition point forms an observation vector. ,in This indicates that data collection point i is triggering an alarm. This indicates no alarm was triggered. By comparing the observation vector with each column of the correlation matrix, Calculate the fault confidence for each segment. Fault confidence The calculation formula is: (3) In the formula, For section Fault confidence The confidence weights of information source i determined in the first step are... For consistency function, when hour ,otherwise .

[0038] After calculating the fault confidence of all sections, the section with the highest confidence is selected as the fault section. The selection formula is as follows: (4) To further improve positioning accuracy, the system also checks the confidence levels of adjacent segments. If the highest confidence level is reached... With the second highest confidence level If the difference is less than the threshold (0.2), it is determined to be a boundary fault, and further detailed positioning is required.

[0039] A2: Generate a hierarchical isolation strategy, including identifying upstream protection devices and adjacent interconnecting switches in the faulty section; Based on network topology, determine the minimum set of switching operations required for fault isolation; The set of switching operations is sorted in the order of isolating the fault point first and then transferring the load, forming a hierarchical isolation operation sequence; Verify the safety and effectiveness of the isolation operation sequence.

[0040] Specifically, in determining the faulty section Subsequently, a fault isolation scheme needs to be generated quickly, while minimizing the power outage range in non-faulty areas. The distribution network topology is represented using graph theory, with an undirected graph G=(V,E), where V is the set of nodes representing buses and E is the set of edges representing lines. Each edge is equipped with a sectionalizing switch or tie switch, and the fault section... One or more edges in the corresponding edge set.

[0041] The isolation strategy first identifies the boundary switches of the faulty section, traverses the network topology, and locates the segment switches at both ends of the faulty section, denoted as... and These two switches are key devices for fault isolation. Immediately issuing a trip command to them will electrically isolate the faulty section after the switches trip, but at the same time, it will cause some areas downstream of the faulty section to lose power.

[0042] Further assess the extent of the power outage, tracing downstream from the faulty section to identify all nodes that lost power due to isolation operations. Set of outage nodes. The method for determining it is as follows: (5) for For each node in the circuit, check whether it is possible to connect to other energized areas via a tie switch.

[0043] When multiple tie-off switch options exist, it is necessary to compare the overall performance of each option. Evaluation metrics include load restoration capacity. Number of switch operations The formula for selecting the optimal switching operation scheme is: (6) In the formula, For the optimal switching operation scheme, and This is a weighting coefficient (determined based on the system's preference for recovery speed and operational complexity, typically set to a certain value).

[0044] The weighting coefficients are set to reflect the preference for recovery speed and operational complexity. Typically, α is set to 0.8 and β to 0.2, reflecting the principle of prioritizing load recovery.

[0045] Based on the above calculations, the optimal combination of interconnecting switches is determined. Following the order of isolating the fault point first and then transferring the load, a hierarchical isolation operation sequence is formed. The first level of operation involves tripping the switches at both ends of the faulty section. The first operation is to immediately disconnect the fault; the second operation is to close the selected interconnecting switch to establish a backup power supply path for the power outage area; the third operation is to open the redundant switch on the original path to optimize the network topology.

[0046] After performing the isolation operation, record two key node sets: the set of nodes that are still powerless after isolation. and the set of nodes that have had their power restored .

[0047] In this embodiment, step S200 generates a fault isolation operation sequence based on the location of the fault section. The fault isolation operation sequence is arranged in the order of isolating the fault point first and then transferring the load. The photovoltaic power output capacity, energy storage charging and discharging capacity, and interconnection line transmission capacity are evaluated, including the following step B1: B1: Generate a fault isolation operation sequence based on the location of the fault section. The fault isolation operation sequence is arranged in the order of isolating the fault point first and then transferring the load, and evaluates the photovoltaic power output capacity, energy storage charging and discharging capacity and interconnection line transmission capacity.

[0048] Specifically, the adjustable output range of distributed photovoltaic (PV) power sources is first evaluated. The output power of PV power sources is directly affected by the current light intensity, and their adjustability depends on the margin between the real-time output and the maximum available output. The current real-time output of each distributed PV power source i is then obtained. and rated capacity Simultaneously, an independent weather forecasting module is invoked to predict the available output of the photovoltaic power source within the next 15 minutes based on real-time weather data. The formula for calculating the up-adjustment capacity of photovoltaic power sources is: (7) In the formula, In order to increase capacity, In order to reduce capacity, The available power output for the next 15 minutes is predicted based on real-time meteorological data (provided by a separate forecasting module). Minimum technical output (typically 10% of rated capacity).

[0049] When the real-time output of the photovoltaic power source is close to the available output, the room for upward adjustment is small; while when the lighting conditions are good but the current output is limited, the room for upward adjustment is large.

[0050] The formula for calculating the down-regulation capacity of photovoltaic power sources is as follows: (8) The capacity reduction reflects the ability of photovoltaic power sources to lower their output to cooperate with regulation when necessary. By calculating the upward and downward capacity reduction of all distributed photovoltaic power sources in the distribution network, the overall adjustability margin of photovoltaic resources can be determined.

[0051] Next, the charge / discharge capacity of the energy storage is evaluated. The available capacity of the energy storage is constrained by both the current state of charge (SOC) and power limitations. The rated charge / discharge power of each energy storage unit i is obtained. Energy storage capacity Current state of charge and the maximum and minimum permissible states of charge. and ,generally Set to 0.9 Set to 0.1 to protect battery life. The formula for calculating the discharge power of energy storage is: (9) The min function in the formula means taking the smaller of the two terms to ensure that the discharge power does not exceed the rated power limit and does not cause the state of charge to fall below the safe lower limit.

[0052] The formula for calculating the rechargeable power of energy storage is: (10) In the formula, and These represent the rechargeable power and dischargeable power of energy storage system i, respectively. Rated charge and discharge power, For energy storage capacity, The current state of charge, and These represent the maximum and minimum permissible states of charge (typically 0.9 and 0.1), respectively. Use a time window (15 minutes).

[0053] The assessment of energy storage charging and discharging capabilities provides a flexible energy buffering mechanism that can smooth power fluctuations and provide voltage and frequency support during load recovery.

[0054] Finally, assess the main grid interconnection capacity. Query the reserve capacity of adjacent substations to determine the maximum power that can be transmitted to the fault area via interconnection lines. The interconnection capacity is constrained by transformer capacity and line thermal stability; the safe transmission margin is determined based on real-time power flow calculations.

[0055] After aggregating all available resources, a resource capacity vector is formed. This vector fully describes all the resources available for fault recovery in the current distribution network. The resource capacity vector will serve as an important constraint in subsequent collaborative optimization steps to ensure that the generated reconfiguration scheme is feasible within the resource capacity range.

[0056] In this embodiment, step S300 establishes the coupling relationship between network topology adjustment and distributed resource output adjustment, and determines the switching operation sequence and resource scheduling scheme by solving a multi-constraint optimization problem, including the following steps C1-C2: C1: Establish the coupling relationship between network topology adjustment and distributed resource output regulation, including analyzing power flow distribution and voltage distribution under different topology states; Obtain the adjustability margin of distributed power sources and the state of charge of energy storage; set voltage deviation constraints, line capacity constraints, and resource ramp-up rate constraints. Under all constraints, find the combination of switching states and resource outputs that maximizes load recovery and minimizes resource adjustment costs.

[0057] Specifically, for areas that still lose power after isolation The process of intelligent network reconfiguration is initiated, coordinating the use of distributed photovoltaic power sources, energy storage, and network topology adjustments to maximize load recovery.

[0058] First, assess the distribution of distributed resources within the power outage area and inspect the power outage area. Does the area contain distributed photovoltaic (PV) power sources or energy storage? If so, the area has the potential to form a microgrid for self-powering. Calculate the total available output of all distributed PV power sources within the power outage area. Total dischargeable capacity of energy storage At the same time, the total load demand of the power outage area is calculated. .

[0059] when In this situation, the power outage area can achieve self-sufficiency entirely through internal resources, without the need for external support. At this time, the distributed power generation and energy storage are switched to islanded operation mode. The energy storage provides a voltage source to establish a stable voltage and frequency reference, and the distributed photovoltaic power generation adjusts its output according to load changes.

[0060] when At that time, the resources within the power outage area are insufficient to support the entire load, requiring a combination of network topology adjustments and selective load restoration. A collaborative optimization model is established, with decision variables including the states of each switch (the switching states between nodes i and j). Indicates that the circuit is closed. (Indicates the tripping) and the recovery status of each load node (load of node k, This indicates that power has been restored. (Indicates that the power is still out).

[0061] The goal of collaborative optimization is to maximize load recovery while minimizing network loss. The objective function is: (11) In the formula, The load importance weight for node k (provided by the dynamic priority evaluation module in step six) Let k be the load power. For network power loss, This is the loss penalty coefficient (value 0.1).

[0062] The first term of the objective function represents the weighted load recovery amount, ensuring that critical loads are restored first; the second term represents the network loss penalty, encouraging the selection of topology schemes with lower losses.

[0063] The optimization model needs to satisfy several constraints. The radial topology constraint ensures that the reconstructed network does not contain loops; the node voltage constraint ensures that the voltage of all nodes that have resumed power supply is within the allowable range, typically requiring that the voltage deviation does not exceed ±7% of the rated voltage; the line capacity constraint ensures that the line current does not exceed the thermal stability limit to avoid line overload; and the resource capacity constraint limits the output of distributed power sources and energy storage to the available capacity evaluated in the previous steps.

[0064] C2: Solve for the combination of switching states and resource output that maximizes load recovery and minimizes resource adjustment costs, including: Determine the network topology formed after the switching operation; calculate the output allocation of each distributed power source and energy storage under the current topology. Energy storage is used to provide voltage and frequency support; distributed power sources are controlled to adjust their output at a set rate to avoid power oscillations.

[0065] Specifically, an improved particle swarm optimization algorithm is used to solve the collaborative optimization model. The core idea of ​​the algorithm is to treat each candidate solution as a particle in the search space. The particle continuously adjusts its position based on its own experience and the experience of the group, and eventually converges to the optimal solution.

[0066] In this problem, the particle's position vector includes all switching states and load recovery states, forming a high-dimensional decision vector. The particle's velocity vector represents the adjustment direction and magnitude of the decision variable. During the algorithm iteration, each particle i maintains three key pieces of information: its current position. Current speed and historical best position Meanwhile, the algorithm maintains the globally optimal position. , representing the optimal solution found by all particles.

[0067] The particle velocity update formula is: (12) In the formula, Let i be the velocity of particle i in the t-th iteration. For location, The inertia weight (value 0.6) and The learning factor (all values ​​are 1.8). and A random number in the interval [0,1]. The historical best position of particle i, This is the globally optimal position.

[0068] The velocity update formula consists of three parts: the first term ω· The inertial term keeps the particle in its original search direction; the second term c1·r1·( -) represents the cognitive term, guiding the particle to move towards its historical optimal position; the third term c2·r2·( -) represents the social term, guiding the particle towards the global optimum. Through the combined effect of these three terms, the particle can both conduct extensive exploration in the search space and converge towards the optimal region.

[0069] The particle position is updated based on the velocity. Since the decision variables include discrete switching states and load recovery states, the position needs to be discretized after the update, mapping the continuous values ​​to 0 or 1.

[0070] Determine the network topology structure formed after the switching operation, draw a topology diagram to clarify the new power supply path and node connection relationship, calculate the output distribution of each distributed photovoltaic power source and energy storage under the current topology structure, determine the precise output power of each device through power flow calculation, and ensure that the power flow distribution meets the voltage and line capacity requirements.

[0071] Energy storage is used to provide voltage and frequency support. During load recovery, the energy storage operates in voltage source mode to maintain the stability of the voltage amplitude and frequency of the distribution network. Distributed photovoltaic power sources are controlled to adjust their output at a set rate to avoid power oscillations. A slope control strategy is used to smoothly transition to the target output value. These optimization results and control parameters are passed to the execution coordination module to guide actual fault recovery operations and achieve deep collaboration between network topology and distributed resources.

[0072] In this embodiment, step S400 calculates a dynamic importance score based on the load type, current power consumption status, and social influencing factors. Loads are then sorted according to their dynamic importance scores, and the loads are connected sequentially. Voltage and line load rates are verified. When verification is successful, the load is included in the recovery plan, and the remaining capacity is updated. This includes the following steps D1-D2: D1: Calculate the dynamic importance score based on load type, current power consumption status and social influencing factors, including setting basic weights for primary load, secondary load and tertiary load respectively; The time period coefficient is determined based on the peak electricity consumption level of the current period; The impact coefficient is determined based on the scope of the social impact of the load. The dynamic importance score is obtained by multiplying the base weight, the time period coefficient, and the influence coefficient.

[0073] Specifically, when available resources cannot restore all lost loads, it is necessary to determine the priority order of load restoration. Traditional methods use a preset fixed priority, simply dividing the load into level one, level two, and level three, and restoring it in order of level.

[0074] Load importance is determined by three core factors: load type Current period characteristics (H) and social impact. A comprehensive importance assessment model is established, calculated using the following formula: (13) In the formula, The overall importance weight of load k, The weighting coefficients for each factor (determined based on expert experience) ), This is the load type coefficient, for Level 1 loads (hospitals, government offices, etc.). Secondary load (schools, businesses, etc.) Level 3 load (ordinary residents, etc.) , Load utilization rate represents the proportion of the actual electricity consumption of a load to its installed capacity during the current period.

[0075] The time period characteristic H reflects the current time's requirements for power supply reliability. During peak electricity consumption periods, such as 13:00-16:00 in summer and 17:00-20:00 in winter, the load demand is strong and lasts for a long time, so the H value is 1.2. During normal consumption periods, such as 9:00-12:00 in the morning and 20:00-22:00 in the afternoon, the H value is 1.0. During off-peak electricity consumption periods, such as 22:00 at night to 6:00 the next day, the load demand is low, so the H value is 0.8.

[0076] Basic weights are set for primary loads, secondary loads, and tertiary loads, respectively, which are reflected in... In the coefficients, the time period coefficient H is determined based on the peak electricity consumption level of the current period, reflecting the impact of time factors on load importance. The influence coefficient is determined based on the social impact range of the load, capturing priority adjustment needs under special circumstances. The basic weight, time period coefficient, and influence coefficient are comprehensively calculated using a weighted summation formula to obtain the dynamic importance score. .

[0077] After calculating the importance weights of all power outage loads, according to The values ​​are sorted from largest to smallest to form a dynamic priority sequence. ,in As the highest priority load, This is the lowest priority load.

[0078] D2: Connect the loads sequentially and verify the voltage and line load rate, including selecting the loads to be connected in order of dynamic importance score from high to low; Calculate the voltage value of each node and the load rate of each line after the candidate load is connected; Determine whether the voltage value is within the allowable range and whether the load rate is less than the limit; If the judgment result is yes, the candidate load is connected and the power of the connected load is deducted from the remaining capacity.

[0079] Specifically, the load restoration process adopts a step-by-step verification strategy. First, loads to be restored are selected in descending order of dynamic importance score. Starting from the first load in the priority sequence, it is checked whether the load has been restored in the previous steps. If it has been restored, the load is skipped and the next load is selected; if it has not been restored, it is verified as a load to be restored.

[0080] For the load node k to be connected, calculate the voltage value of each node and the load rate of each line after the load is connected, and determine whether the voltage value is within the allowable range. The voltage quality requirement of the distribution network is that the voltage deviation of the node does not exceed ±7% of the rated voltage.

[0081] Iterate through the voltage calculation results of all nodes and check if any node voltage exceeds the limit; if all node voltages meet the constraints, the voltage verification passes; if any node voltage exceeds the limit, the voltage verification fails.

[0082] When the judgment result is yes (i.e., the voltage value is within the allowable range and the load rate is less than the limit), the candidate load k is connected, the load recovery status is updated, and it is set to 1 to indicate that the load has been restored to power supply, and the recovery time is recorded; at the same time, the power of the connected load is deducted from the remaining capacity, and the available resource status is updated. If the connected load is powered by distributed power sources and energy storage, the available capacity of distributed power sources and energy storage is reduced accordingly; if the connected load is powered by the main grid interconnection line, the capacity that the main grid can provide is reduced accordingly.

[0083] If the judgment result is negative, i.e., the voltage exceeds the limit or the line is overloaded, the load k is abandoned and marked as a load that cannot be restored under the current conditions. The load is skipped, and the next load in the priority sequence is selected for further testing. This step-by-step verification strategy ensures that each load connection does not compromise voltage quality or line safety.

[0084] Repeat the above selection, verification, access, or skip process until the remaining capacity is insufficient or all loads have been evaluated. The criterion for insufficient remaining capacity is that the sum of the remaining capacity of all available resources is less than the power requirement of the next load to be restored in the priority sequence. At this point, stop the load restoration process and output the final load restoration plan, including the set of restored loads, the set of unrestored loads, and the restoration time of each load.

[0085] During load restoration, voltage and frequency stability are continuously monitored. After each load is restored, the voltage fluctuation amplitude and frequency deviation are monitored. If the voltage deviation exceeds the allowable range (e.g., ±7%), or the frequency deviation exceeds the allowable range (e.g., ±0.2Hz), the load restoration process is immediately paused. At this time, the output of distributed resources is adjusted to increase the voltage support and frequency regulation capabilities of energy storage, or the power of the connected loads is appropriately reduced. Load restoration continues only after the situation stabilizes. After load restoration is completed, the system enters steady-state operation, and various operating parameters are continuously monitored to ensure the safe and stable power supply of the distribution network.

[0086] The formula for calculating the load recovery rate is: (14) In the formula, For load recovery rate, The set of nodes for power restoration (updated in real time during execution), This represents the initial set of power-depleted nodes.

[0087] The load recovery rate reflects the overall effectiveness of the fault self-healing scheme and is an important indicator for evaluating the quality of the scheme.

[0088] The formula for calculating recovery time is: (15) In the formula, The time when the fault occurred (obtained from the timestamp), This is the moment of the last load recovery (obtained from the execution log).

[0089] Recovery time reflects how quickly a fault can heal itself; the shorter the time, the faster the response.

[0090] Stability is assessed by monitoring voltage fluctuations after recovery and calculating the standard deviation of voltage over a period of time (e.g., 5 minutes) after recovery, using the following formula: (16) In the formula, For voltage standard deviation, Let be the voltage at the i-th sampling time. Where is the rated voltage, and N is the number of samplings. If The system is determined to be stable.

[0091] In summary, by unifying the timestamps of different devices to the same time base through a timestamp correction formula, the information fusion error caused by clock deviation is resolved, providing accurate time alignment for fault location. By constructing an association matrix and calculating a weighted consistency score, the fault confidence of a segment increases when multiple high-reliability information sources simultaneously point to that segment, and complementary verification enhances the anti-interference capability of fault location. When generating a hierarchical isolation strategy, the optimal scheme is selected by comparing the load restoration amount and number of switching operations of different tie switch combinations, maximizing the restoration of power supply to non-faulty areas while quickly isolating faults. By evaluating the up- and down-adjustment capacity of photovoltaic power sources and the charging and discharging capacity of energy storage systems, the current resource status of the distribution network is accurately grasped. A coupling relationship between network topology adjustment and distributed resource output regulation is established, and a particle swarm optimization algorithm is used to solve multi-constraint optimization problems to determine the switching operation sequence and resource scheduling scheme, achieving deep synergy between network reconfiguration and resource regulation. Dynamic importance scores are calculated comprehensively based on load type, time period characteristics, and social impact, enabling the load restoration order to be dynamically adjusted according to the actual operating status, achieving priority protection of important loads and maximizing load restoration amount under limited resources.

[0092] Example 3 This example is based on a simulation verification of an IEEE 33-node distribution network system. This system comprises 33 nodes, 32 feeders, 5 distributed photovoltaic generators (total capacity 3.2MW), 2 energy storage systems (500kWh / 250kW each), and several tie switches. The distribution network has a rated voltage of 10kV and a total load capacity of approximately 4.8MW. A detailed distribution network model, including line impedance, load characteristics, and distributed power supply control system, is built on the MATLAB / Simulink platform.

[0093] The simulation scenario is set as follows: a three-phase short-circuit fault occurs at node 18, with a fault impedance of 0.01 ohms. The fault occurs at 14:00 (peak electricity consumption period), when the photovoltaic output is at a high level (approximately 80% of rated capacity), and the energy storage system's state of charge is 65% and 70%, respectively. During this period, node 10 is the site of an ongoing community activity, and its load importance is temporarily increased.

[0094] The performance differences between this technical solution and traditional methods are compared through simulation. Traditional methods employ a fixed fault handling process: manual fault location determination (simulation time 15 minutes), manual fault isolation (5 minutes), and gradual load restoration according to preset priorities (10 minutes), without considering the collaborative utilization of distributed resources. This technical solution automatically executes the entire process according to the aforementioned eight steps.

[0095] 2. Validity Verification Form Table 1: Comparison of Fault Location Performance

[0096] This technical solution integrates multi-source information such as distribution automation data, fault indicator signals, and voltage monitoring data, and utilizes a confidence-weighted information consistency verification algorithm to shorten fault location time to 3.2 seconds and improve location accuracy to 97.8%. In complex topology scenarios (including multiple branches and multiple power sources), traditional methods are prone to misjudgment, while this technical solution can accurately identify faulty sections through correlation matrices and consistency functions.

[0097] Table 2: Comparison of Load Recovery Effects

[0098] This technical solution, through the synergistic utilization of distributed photovoltaics, energy storage systems, and network topology adjustments, increases the load recovery rate from 59.4% to 94.2% compared to traditional methods, and reduces the recovery time from 30 minutes to 4.8 minutes. Due to the adoption of dynamic priority assessment, all critical loads (Level 1 and Level 2 loads) are fully restored, with only a small number of non-critical Level 3 loads failing to recover due to capacity limitations. Simultaneously, by optimizing power flow distribution, system losses are reduced to 42.1 kW, a 46.4% reduction compared to traditional methods.

[0099] Table 3: Distributed Resource Utilization

[0100] Traditional methods do not consider the control capabilities of distributed resources, which remain idle or operate as before and do not participate in fault recovery. This technical solution, through resource capacity assessment in the third step and collaborative optimization in the fifth step, fully mobilizes various distributed resources, achieving a total utilization rate of 92.0%, contributing 3220kW of capacity support for load recovery, accounting for 71.2% of the total restored load, and significantly improving the self-healing capability of the distribution network.

[0101] Table 4: Evaluation Effect of Dynamic Priority

[0102] The dynamic priority assessment method adjusted the load restoration order based on the characteristics of the current time period (afternoon peak electricity consumption H=1.2), actual load utilization, and social impact. Although Node 10 was a secondary load, its dynamic weight was increased to 0.92 due to ongoing community activities (social impact), moving it from 8th to 2nd in the fixed priority ranking. Power was restored within 32 seconds of the fault, ensuring the smooth operation of the activities. Node 15, the school, had lower electricity consumption in the afternoon (utilization rate 53.1%), resulting in a corresponding decrease in its priority. This dynamic adjustment mechanism ensured that limited resources were used for the loads most in need of power, improving social benefits.

[0103] Table 5: System Stability Indicators

[0104] This technical solution provides voltage and frequency support through an energy storage system and employs ramp control (Formula 14) to smoothly adjust the output of distributed power sources, significantly improving system stability. During the recovery process, the voltage deviation is controlled within 4.2%, meeting national standards, while traditional methods suffer voltage deviations of up to 9.8% due to sudden load input, exceeding the allowable range. The frequency deviation is reduced from 0.35Hz to 0.12Hz, the number of voltage fluctuations is reduced from 8 to 2, and the power oscillation amplitude of the distributed power source is reduced by 73.8%, ensuring power quality.

[0105] Example 4 The above is a schematic scheme of a distributed resource collaborative reconfiguration method for self-healing of distribution network faults. It should be noted that the technical solution of this distributed resource collaborative reconfiguration system for self-healing of distribution network faults belongs to the same concept as the technical solution of the aforementioned distributed resource collaborative reconfiguration method for self-healing of distribution network faults. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned distributed resource collaborative reconfiguration method for self-healing of distribution network faults.

[0106] This embodiment also provides a distributed resource collaborative reconfiguration system for self-healing of distribution network faults, including: The information acquisition module is used to collect multi-source fault information of the power distribution network and construct a fault information set; The fault location and isolation module is used to determine the fault segment based on the fault information set through confidence-weighted information consistency verification, and generate a hierarchical isolation strategy. The resource assessment module is used to assess the available resource capacity of the distribution network; The collaborative optimization module is used to establish resource collaboration strategies, taking into account the coupling relationship between network topology adjustment and distributed resource regulation. The dynamic decision-making module is used to build a dynamic recovery decision-making model and adjust the recovery order based on the real-time assessment results of load importance. The execution coordination module is used to coordinate the execution of fault isolation, network reconstruction, and load recovery.

[0107] This embodiment also provides an electronic device suitable for distributed resource collaborative reconfiguration for self-healing of distribution network faults, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as proposed in the above embodiment.

[0108] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as proposed in the above embodiments.

[0109] The storage medium proposed in this embodiment and the distributed resource collaborative reconfiguration method for realizing self-healing of distribution network faults proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0110] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed resource collaborative reconfiguration method for self-healing faults in distribution networks, characterized in that: This includes collecting multi-source fault information from the distribution network and constructing a fault information set; based on the fault information set, determining the fault section through confidence-weighted information consistency verification; and generating a hierarchical isolation strategy to minimize the power outage range in non-faulty areas. A fault isolation operation sequence is generated based on the location of the fault section. The fault isolation operation sequence is arranged in the order of isolating the fault point first and then transferring the load, and the photovoltaic output capacity, energy storage charging and discharging capacity and interconnection line transmission capacity are evaluated. Establish the coupling relationship between network topology adjustment and distributed resource output regulation, and determine the switching operation sequence and resource scheduling scheme by solving a multi-constraint optimization problem; Calculate the dynamic importance score based on load type, current power consumption status and social influencing factors, sort the loads according to the dynamic importance score, connect the loads in sequence and verify the voltage and line load rate, and when the verification is successful, include the load in the recovery plan and update the remaining capacity.

2. The distributed resource collaborative reconfiguration method for self-healing faults in a distribution network as described in claim 1, characterized in that: The method of determining fault segments based on a fault information set and through confidence-weighted information consistency verification includes: performing time alignment and type classification on the collected multi-source fault information, which includes protection action signals, switch position change signals, fault indicator alarm information, and voltage dip events; and assigning confidence weights to each piece of information based on the reliability and historical accuracy of different information sources. The consistency of fault information is determined by cross-validation mechanism, which increases the probability of fault in the same fault segment when multiple information sources point to the same fault segment. The final location of the faulty section is determined based on the weighted consistency score.

3. The distributed resource collaborative reconfiguration method for self-healing distribution network faults as described in claim 2, characterized in that: The generation of the hierarchical isolation strategy includes identifying the upstream protection equipment and adjacent interconnection switches of the faulty section; Based on network topology, determine the minimum set of switching operations required for fault isolation; The set of switching operations is sorted in the order of isolating the fault point first and then transferring the load to form a hierarchical isolation operation sequence; Verify the safety and effectiveness of the isolation operation sequence.

4. The distributed resource collaborative reconfiguration method for self-healing of distribution network faults as described in claim 3, characterized in that: The establishment of the coupling relationship between network topology adjustment and distributed resource output regulation includes analyzing the power flow distribution and voltage distribution under different topology states; and obtaining the adjustability margin of distributed power sources and the state of charge of energy storage. Set voltage deviation constraints, line capacity constraints, and resource ramp-up rate constraints; under all constraints, solve for the switching state and resource output combination that maximizes load recovery and minimizes resource regulation cost.

5. The distributed resource collaborative reconfiguration method for self-healing distribution network faults as described in claim 4, characterized in that: The solution for the combination of switching states and resource output that maximizes load recovery and minimizes resource adjustment costs includes, Determine the network topology formed after the switching operation; Calculate the output allocation of each distributed power source and energy storage under the current topology; Energy storage is used to provide voltage and frequency support; distributed power sources are controlled to adjust their output at a set rate to avoid power oscillations.

6. The distributed resource collaborative reconfiguration method for self-healing distribution network faults as described in claim 5, characterized in that: The calculation of dynamic importance score based on load type, current power consumption status and social impact factors includes: setting basic weights for primary loads, secondary loads and tertiary loads respectively; determining time period coefficients based on the peak power consumption level of the current time period; determining impact coefficients based on the social impact range of the load; and multiplying the basic weights, time period coefficients and impact coefficients to obtain the dynamic importance score.

7. The distributed resource collaborative reconfiguration method for self-healing distribution network faults as described in claim 6, characterized in that: The sequential connection of loads and verification of voltage and line load rate includes selecting loads to be connected in descending order of dynamic importance score; Calculate the voltage value of each node and the load rate of each line after connecting the candidate load; determine whether the voltage value is within the allowable range and whether the load rate is less than the limit; when the determination result is yes, connect the candidate load and deduct the power of the connected load from the remaining capacity.

8. A distributed resource collaborative reconfiguration system for self-healing distribution network faults, based on the distributed resource collaborative reconfiguration method for self-healing distribution network faults as described in any one of claims 1 to 7, characterized in that: It also includes an information acquisition module, used to collect multi-source fault information of the distribution network and construct a fault information set; The fault location and isolation module is used to determine the fault segment based on the fault information set through confidence-weighted information consistency verification, and generate a hierarchical isolation strategy. The resource assessment module is used to assess the available resource capacity of the distribution network; The collaborative optimization module is used to establish resource collaboration strategies, taking into account the coupling relationship between network topology adjustment and distributed resource regulation. The dynamic decision-making module is used to build a dynamic recovery decision-making model and adjust the recovery order based on the real-time assessment results of load importance. The execution coordination module is used to coordinate the execution of fault isolation, network reconstruction, and load recovery.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the distributed resource collaborative reconfiguration method for self-healing of distribution network faults as described in any one of claims 1 to 7.

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