A power distribution network dynamic repair method and system considering multi-fault interaction and network reconstruction

Abstract

The application discloses a kind of power distribution network dynamic repair method and system considering multiple fault interaction and network reconfiguration, the method collects power distribution network node, line parameter and extreme event after fault state data, constructs four-dimensional fault set;Dynamic repair collaborative optimization model of power distribution network is constructed, multiple setting constraints are added, and the model after constraint is obtained;Set initial time step and pre-set prediction time domain window and control step;With the goal of maximizing system load recovery efficiency, the model is solved using rolling horizon optimization algorithm, and the comprehensive repair and reconstruction action sequence is obtained;Intercept and execute first step instruction, verify topology and determine whether overload conflict is caused, adjust repair priority when conflict and output preventive reconstruction decision, and calculate load recovery efficiency in real time;After monitoring the repair completion signal, the real-time state is obtained, and the model is iteratively solved until the fault is repaired and the load is recovered.The application solves the problems of lack of adaptive adjustment capability in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power system post-disaster recovery and resilience enhancement technology, specifically to a dynamic emergency repair method and system for distribution networks that considers multi-fault interaction and network reconfiguration. Background Technology

[0002] As the final hub connecting the power system to users, the safe and stable operation of the distribution network is crucial to the socio-economic development. In recent years, extreme weather and natural disasters have occurred frequently, often causing severe physical damage to the distribution network and triggering large-scale power outages. To enhance the resilience of the power grid, post-disaster emergency repair scheduling and network topology reconstruction have become key means to quickly restore power supply. Compared with traditional single restoration methods, the industry has begun to explore the joint scheduling of repair personnel's emergency repair routes and distribution network switching operations in order to maximize the restoration of system load in the shortest possible time.

[0003] However, most existing post-disaster repair strategies employ static maintenance decision models, generating globally fixed repair and reconfiguration schemes after acquiring fault information once in the early post-disaster period. This ignores the fact that repair is a continuous spatiotemporal evolution process and lacks the ability to dynamically respond and re-optimize based on network topology changes during repair. Furthermore, current research often idealizes damaged components as single "disconnection faults" in fault modeling, neglecting "blocking faults" caused by relay protection device malfunctions or switch mechanical failures in real extreme disasters. Failure to differentiate the modeling of blocking faults leads to the system losing topology control over these forcibly connected components during repair, easily triggering complex "combination dilemmas" such as microgrid capacity exceeding limits, rendering the planned repair scheme completely infeasible at the physical execution level.

[0004] Therefore, there is an urgent need for a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, in order to solve the problems of existing technologies such as the neglect of blocking faults and the lack of adaptive adjustment capabilities. Summary of the Invention

[0005] To address these issues, this invention provides a dynamic emergency repair method and system for distribution networks that considers multi-fault interaction and network reconfiguration. This solves problems in existing technologies such as low efficiency of dynamic coordination of post-disaster recovery strategies, neglect of locked-in faults due to the single dimension of fault modeling, and lack of adaptive adjustment capability in environments with uncertain repair time and success rate.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dynamic emergency repair method for distribution networks considering multi-fault interaction and network reconfiguration, characterized in that it includes: Based on the collected distribution network node parameters, line parameters, and fault status data after extreme events, a four-element fault set including disconnection faults and blocking faults is constructed. Based on the aforementioned four-element fault set, a dynamic emergency repair collaborative optimization model for the distribution network is constructed. Power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state are added to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. Based on the constrained collaborative optimization model, an initial time step is set, and the prediction time domain window and control step are preset in combination with the distribution network scale and computational complexity. Based on the set time parameters and the constrained collaborative optimization model, with the objective function being to maximize the system load recovery efficiency and the decision variables being the geographical movement path of the emergency repair team and the distribution network switch variables, the constrained collaborative optimization model is solved by the rolling time domain optimization algorithm to obtain the comprehensive emergency repair and reconstruction action sequence. Based on the comprehensive emergency repair and reconstruction action sequence, the emergency repair and reconstruction instruction of the first control step is intercepted and executed; after execution, the network topology is checked to determine whether an overload conflict occurs due to forced conduction caused by a lockout-type fault; if a conflict exists, the emergency repair priority is adjusted to output a preventive reconstruction decision, and the current system load recovery efficiency is calculated in real time. Once a discrete event trigger signal indicating the completion of the emergency repair task is detected, the real-time status of the distribution network is obtained. Based on the real-time status of the distribution network, the constrained collaborative optimization model is iteratively solved until all faulty components are repaired and the load restoration process is completed.

[0007] As a preferred scheme for a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, the parameters of the distribution network nodes and lines include: network topology, branch impedance, upper limit of branch transmission capacity, load weight of each node, and maximum output limit of distributed power sources. The four-element fault set includes: a set of disconnection-type fault branches, a set of disconnection-type fault nodes, a set of interlocking-type fault branches, and a set of interlocking-type fault nodes.

[0008] As a preferred scheme for a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, the objective function expression of the constrained collaborative optimization model is:

[0009] In the formula, For system load recovery efficiency; This is the total recovery time; For the set of load nodes in the power distribution system; t For the current moment; j The node number; For nodes j At any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions. In the process of calculating the current system load recovery efficiency in real time, the formula for calculating the system load recovery efficiency is as follows:

[0010] In the formula, For time step t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodes j At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

[0011] As a preferred scheme for a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, the expression for the power balance constraint is:

[0012] In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively. The expression for the linearized power flow equation constraint is as follows:

[0013] In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches; The expression for the radial topological constraint is:

[0014] In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step t Power supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time step t Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

[0015] As a preferred scheme for a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, the expression for the hard constraint of the network topology state is: For branch circuit faults:

[0016] In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures:

[0017] In the formula, For nodes The load at time Is it powered? For nodes j At time step t Normal operating condition energized state variables; The geographical movement path decision variables of the emergency repair team satisfy the spatiotemporal transfer constraints of maintenance resources and the logical constraints of maintenance tasks. The expression for the spatiotemporal transfer constraint of the maintenance resources is:

[0018] In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number; F This is the set of all possible movement paths for the repair team. mThis represents the maximum number of moves a repair team can make during the entire repair cycle. R To assemble the emergency repair team; The expression for the logical constraint of the maintenance task is:

[0019] In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

[0020] This invention also provides a dynamic emergency repair system for distribution networks that considers multiple fault interactions and network reconfiguration. The system employs the aforementioned dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, comprising: The data perception and classification module is used to construct a four-element fault set, including disconnection faults and blocking faults, based on the collected distribution network node parameters, line parameters, and fault status data after extreme events. The model and constraint construction module is used to construct a dynamic emergency repair collaborative optimization model for the distribution network based on the four-element fault set; power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state are added to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. The parameter setting module is used to set the initial time step based on the constrained collaborative optimization model, and to preset the prediction time domain window and control step size in combination with the distribution network scale and computational complexity. The rolling optimization module is used to solve the constrained collaborative optimization model based on the set time parameters and the constrained collaborative optimization model, with the objective function of maximizing the system load recovery efficiency and the decision variables of the geographical movement path of the emergency repair team and the distribution network switch variables, and to obtain the comprehensive emergency repair and reconstruction action sequence by using the rolling time domain optimization algorithm. The conflict avoidance and execution module is used to intercept and execute the first control step's repair and reconstruction instruction based on the comprehensive repair and reconstruction action sequence; after execution, it verifies the network topology to determine whether an overload conflict is caused by forced conduction due to a lockout-type fault; if a conflict exists, it adjusts the repair priority, outputs a preventive reconstruction decision, and calculates the current system load recovery efficiency in real time. The event-driven closed-loop module is used to obtain the real-time status of the distribution network after detecting a discrete event trigger signal indicating the completion of the emergency repair task; and to iteratively solve the constrained collaborative optimization model based on the real-time status of the distribution network until all faulty components are repaired and the load restoration process is completed.

[0021] As a preferred solution for a dynamic emergency repair system for distribution networks that considers multiple fault interactions and network reconfiguration, the data perception and classification module includes the following parameters for distribution network nodes and lines: network topology, branch impedance, branch transmission capacity limit, load weight of each node, and maximum output limit of distributed power sources. The four-element fault set includes: a set of disconnection-type fault branches, a set of disconnection-type fault nodes, a set of interlocking-type fault branches, and a set of interlocking-type fault nodes.

[0022] As a preferred solution for a dynamic emergency repair system for distribution networks that considers multi-fault interaction and network reconfiguration, the objective function expression of the constrained collaborative optimization model in the rolling optimization module is:

[0023] In the formula, For system load recovery efficiency; This is the total recovery time; For the set of load nodes in the power distribution system; t For the current moment; j The node number; For nodes j At any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions. In the conflict avoidance and execution module, during the real-time calculation of the current system load recovery efficiency, the calculation formula for the system load recovery efficiency is as follows:

[0024] In the formula, For time step t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodesj At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

[0025] As a preferred solution for a dynamic emergency repair system for distribution networks that considers multi-fault interaction and network reconfiguration, the expression for the power balance constraint in the model and constraint construction module is as follows:

[0026] In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively. The expression for the linearized power flow equation constraint is as follows:

[0027] In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches; The expression for the radial topological constraint is:

[0028] In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step t Power supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time step t Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

[0029] As a preferred solution for a dynamic emergency repair system for distribution networks that considers multiple fault interactions and network reconfiguration, the expression for the hard constraint of the network topology state in the model and constraint construction module is as follows: For branch circuit faults:

[0030] In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures:

[0031] In the formula, For nodes The load at time Is it powered? For nodes j At time step t Normal operating condition energized state variables; The geographical movement path decision variables of the emergency repair team satisfy the spatiotemporal transfer constraints of maintenance resources and the logical constraints of maintenance tasks. The expression for the spatiotemporal transfer constraint of the maintenance resources is:

[0032] In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number; F This is the set of all possible movement paths for the repair team. m This represents the maximum number of moves a repair team can make during the entire repair cycle. R To assemble the emergency repair team; The expression for the logical constraint of the maintenance task is:

[0033] In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

[0034] The present invention has the following advantages: First, by constructing a four-element fault set that includes disconnection-type faults and blocking-type faults, a unified characterization of the various fault states of the distribution network under extreme events is achieved, realizing comprehensive coverage of fault interaction scenarios and avoiding the limitation of traditional methods that only consider a single fault type.

[0035] Second, by adopting event-driven logic, maintenance scheduling and network dynamic reconstruction are optimized in a closed loop. The sequence of emergency repair and reconstruction actions is adjusted in real time through a rolling time-domain optimization framework, which improves the dynamic adaptability of the fault recovery process and can adapt to the constantly changing network status and resource conditions during emergency repair.

[0036] Third, in response to the forced conduction characteristic of interlocking faults, hard constraints on topological state are introduced into the model, and overload conflict verification and preventive reconfiguration are added in the execution phase, which effectively avoids the safety risks caused by interlocking faults and ensures the operational safety of the emergency repair process.

[0037] Fourth, multiple constraints, such as power flow safety constraints, radial topology constraints, and spatiotemporal transfer constraints of maintenance resources, are integrated into the collaborative optimization model. Under the premise of satisfying physical operation rules and resource constraints, the system load recovery efficiency is maximized, and a balance between fault recovery effect and resource utilization efficiency is achieved.

[0038] Fifth, through a closed-loop iterative optimization mechanism, the optimization model is updated based on the real-time status feedback of the emergency repair completion event, and the emergency repair and reconfiguration strategies are continuously corrected, which improves the practicality and feasibility of the overall solution and is closer to the actual needs of power distribution network emergency repair under extreme events. Attached Figure Description

[0039] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0040] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0041] Figure 1 This is a flowchart illustrating a dynamic emergency repair method for a distribution network that considers multiple fault interactions and network reconfiguration, as provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the specific implementation process of a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, as provided in Embodiment 1 of the present invention. Figure 3 This is a schematic diagram of the load recovery curve in one possible embodiment provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the architecture of a dynamic emergency repair system for a distribution network that considers multiple fault interactions and network reconfiguration, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0042] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] See Figure 1 and Figure 2 Embodiment 1 of the present invention provides a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, comprising the following steps: S1. Based on the collected distribution network node parameters, line parameters, and fault status data after extreme events, construct a four-element fault set that includes disconnection faults and blocking faults. S2. Based on the aforementioned four-element fault set, construct a dynamic emergency repair collaborative optimization model for the distribution network; add power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. S3. Based on the constrained collaborative optimization model, set the initial time step, and combine the distribution network scale and computational complexity to preset the prediction time domain window and control step. S4. Based on the set time parameters and the constrained collaborative optimization model, with the objective function being to maximize the system load recovery efficiency and the decision variables being the geographical movement path of the emergency repair team and the distribution network switch variables, the constrained collaborative optimization model is solved by the rolling time-domain optimization algorithm to obtain the comprehensive emergency repair and reconstruction action sequence. S5. Based on the comprehensive emergency repair and reconstruction action sequence, intercept and execute the emergency repair and reconstruction instruction of the first control step; after execution, verify the network topology to determine whether overload conflict is caused by forced conduction due to a lockout-type fault; if a conflict exists, adjust the emergency repair priority, output a preventive reconstruction decision, and calculate the current system load recovery efficiency in real time. S6. After detecting the discrete event trigger signal of the completion of the emergency repair task, obtain the real-time status of the distribution network; based on the real-time status of the distribution network, iteratively solve the constrained collaborative optimization model until all faulty components are repaired and the load restoration process is completed.

[0045] In this embodiment, in step S1, based on the collected distribution network node parameters, line parameters, and fault status data after extreme events, a four-element fault set including disconnection faults and blocking faults is constructed.

[0046] Specifically, node parameters and line parameters of the distribution network nodes are collected through devices such as SCADA systems, fault indicators, and inspection terminals. Combined with fault status data after extreme events, such as typhoons and rainstorms, faults are classified into two categories: disconnection faults and blocking faults. These are further subdivided into sets of disconnection fault branches, disconnection fault nodes, blocking fault branches, and blocking fault nodes, forming a four-element fault set. This classification method comprehensively covers multi-fault interaction scenarios under extreme events, providing complete fault status input for the subsequent construction of collaborative optimization models.

[0047] The parameters of the distribution network nodes and lines include: network topology, branch impedance, upper limit of branch transmission capacity, load weight of each node, and maximum output limit of distributed power sources.

[0048] In this embodiment, in step S2, a dynamic emergency repair collaborative optimization model for the distribution network is constructed based on the four-element fault set; power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state are added to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model.

[0049] Specifically, the basic framework of a dynamic emergency repair collaborative optimization model is constructed with the dispatch path of the emergency repair team and the operation status of the distribution network switches as the core decision variables. Power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology status are added to the dynamic emergency repair collaborative optimization model in sequence. Through the addition of the above constraints, the model not only satisfies the physical rules of distribution network operation, but also adapts to fault characteristics and resource constraints.

[0050] Among them, a1, power balance constraint: ensures the balance between active and reactive power supply and demand at each node.

[0051] The expression for the power balance constraint is:

[0052] In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively.

[0053] a2. Linearized power flow equation constraints: A linearized method is used to handle nonlinear terms in power flow calculations, balancing computational efficiency with power flow safety verification.

[0054] The expression for the linearized power flow equation constraint is as follows:

[0055] In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches.

[0056] a3. Radial topology constraint: Ensure that the distribution network always operates in a radial pattern to avoid the formation of ring networks.

[0057] The expression for the radial topological constraint is:

[0058] In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step tPower supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time step t Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

[0059] a4. Spatial and temporal constraints on maintenance resources: Restricting the movement path, time, and number of tasks of emergency repair teams to conform to the actual rules of resource scheduling.

[0060] The expression for the spatiotemporal transfer constraint of the maintenance resources is:

[0061] In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number; F This is the set of all possible movement paths for the repair team. m This represents the maximum number of moves a repair team can make during the entire repair cycle. R The assembly of the emergency repair team.

[0062] The expression for the logical constraint of the maintenance task is:

[0063] In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

[0064] a5. Hard constraints on network topology status: For disconnection-type faults, the network must be locked to the disconnection state; for blocking-type faults, the network must be locked to the conduction state, to ensure the physical rationality of the fault status.

[0065] The expression for the hard constraint of the network topology state is: For branch circuit faults:

[0066] In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures:

[0067] In the formula, For nodes The load at time Is it powered? For nodes j At time step t The normal operating condition energized state variables.

[0068] In this embodiment, in step S3, based on the constrained collaborative optimization model, an initial time step is set, and the prediction time domain window and control step are preset in combination with the distribution network scale and computational complexity.

[0069] Specifically, based on the emergency repair cycle requirements of the distribution network, an initial time step is set to divide the time stages of the emergency repair process. Taking into account the actual situation such as the number of nodes, lines, and fault scale of the distribution network, and comprehensively considering the computational complexity of the model solution, a prediction time domain window and control step size are preset.

[0070] The prediction time-domain window determines the range of future repair stages to be considered in each optimization solution, while the control step size determines the length of the action sequence executed each time. Properly setting these parameters can balance computational efficiency with optimization effectiveness, avoiding excessively long solution times due to an overly large time-domain window, or excessively frequent strategy adjustments due to an overly small control step size.

[0071] In this embodiment, in step S4, based on the set time parameters and the constrained collaborative optimization model, with the objective function of maximizing the system load recovery efficiency and the decision variables of the geographical movement path of the emergency repair team and the distribution network switch variables, the constrained collaborative optimization model is solved by the rolling time domain optimization algorithm to obtain the comprehensive emergency repair and reconstruction action sequence.

[0072] Specifically, the objective function is to maximize system load restoration efficiency. This objective function comprehensively considers the total restoration time, actual node load, load priority weights, and total load under normal operating conditions, ensuring priority restoration of high-priority, high-capacity loads. The geographical movement path of the repair team, including location transfer, task allocation, and distribution network switch status variables, is used as decision variables, balancing repair scheduling and network reconfiguration. A rolling time-domain optimization algorithm is employed to solve the constraints in stages within a preset prediction time-domain window, resulting in a comprehensive repair and reconfiguration action sequence that includes the repair team's movement path and switch action commands, providing a decision-making basis for subsequent command execution.

[0073] The objective function expression of the constrained collaborative optimization model is as follows:

[0074] In the formula, For system load recovery efficiency; This is the total recovery time; For the set of load nodes in the power distribution system; t For the current moment; j The node number; For nodes j At any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions.

[0075] In this embodiment, in step S5, based on the comprehensive emergency repair and reconstruction action sequence, the emergency repair and reconstruction instruction of the first control step is intercepted and executed; after execution, the network topology is checked to determine whether an overload conflict occurs due to forced conduction caused by a lockout-type fault; if a conflict exists, the emergency repair priority is adjusted to output a preventive reconstruction decision, and the current system load recovery efficiency is calculated in real time.

[0076] Specifically, from the comprehensive action sequence obtained in step S4, the emergency repair and reconfiguration instructions of the first control step are extracted and executed, including dispatch instructions for the emergency repair team to the fault point and instructions for opening and closing distribution network switches. After the instructions are executed, the current network topology status of the distribution network is verified, with a focus on checking whether there are conflicts such as power overload or voltage exceeding limits on the lines that are forcibly connected due to blockage-type faults. If an overload conflict is detected, the priority of the emergency repair tasks is adjusted to prioritize the faulty components that cause the conflict, and preventive reconfiguration decisions are output, such as adjusting the switch status of non-faulty lines to divert load. At the same time, the current system load recovery efficiency is calculated in real time, and the recovery effect after the instruction execution is evaluated to provide feedback for subsequent iterative optimization.

[0077] The formula for calculating the system load recovery efficiency is as follows:

[0078] In the formula, For time step t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodes j At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

[0079] In this embodiment, in step S6, after detecting the discrete event trigger signal of the completion of the emergency repair task, the real-time status of the distribution network is obtained; based on the real-time status of the distribution network, the constrained collaborative optimization model is iteratively solved until all faulty components are repaired and the load restoration process is completed.

[0080] Specifically, real-time operational data of the distribution network is collected through status monitoring equipment. When a discrete event trigger signal indicating the completion of an emergency repair task is detected, such as the repair of a faulty component or the activation of a switch, real-time status data of the current distribution network is acquired, including updated fault repair status, network topology status, and load restoration status. This real-time status data is fed back to the constrained collaborative optimization model to update parameters such as fault status and resource status in the model. The process then returns to step S4 for iterative solution, generating a new sequence of emergency repair and reconfiguration actions. This closed-loop iterative process continues until all faulty components are repaired and all load nodes in the distribution network have resumed power supply, completing the entire emergency repair and load restoration process.

[0081] In one possible implementation, a verification example is provided as follows: The experiments of this invention are based on simulation analysis of a standard IEEE 33-node distribution network system. The example system is set to include five distributed generation sources, and critical load nodes with different weights are identified. To verify the advantages of the proposed method (DMRS-NR), traditional static maintenance strategy (SMRS) that does not consider dynamic reconfiguration and priority-based heuristic strategy (PBHS) are introduced as comparative benchmarks.

[0082] Through simulation verification, the load recovery curves of various methods are as follows: Figure 3 As shown in Table 1, the core performance indicators of the present invention (DMRS-NR), the heuristic strategy (PBHS), and the traditional static strategy (SMRS) are compared under multiple fault scales.

[0083] Table 1 Core performance metrics for each strategy

[0084] As shown in Table 1, for a scenario involving 30 large-scale concurrent faults (Case 3), simulation results demonstrate that the traditional static strategy (SMRS) suffers from an inability to handle the "combinatorial dilemma" caused by locked-in faults, resulting in a system load recovery efficiency (RES) of only 58.4%. In contrast, this invention (DMRS-NR) transforms locked-in faults into physical hard constraints and combines them with dynamic topology reconstruction, successfully unlocking recovery paths that traditional methods cannot discover, significantly improving the RES to 71.4%, representing a 22.42% performance leap compared to the static strategy. Furthermore, in the core metric of weighted recovery load, this invention achieves a remarkable 51.86% improvement over the static strategy, fully demonstrating the superior computational capability of quaternary fault modeling in extremely constrained environments.

[0085] In terms of the spatial geographic movement of maintenance teams, this invention also demonstrates significant advantages in resource utilization. In the aforementioned 30 fault scenarios, the total movement path of maintenance teams using the traditional static strategy was as long as 16.7 km; while this invention utilizes Rolling Time-Domain Optimization (RHO) to dynamically correct the spatiotemporal path, significantly reducing ineffective path transfers, resulting in a final maintenance path length of only 11.5 km, a reduction of 31.90%. Furthermore, in the validation of a quaternary complex fault model containing 30 faults, the overall load recovery efficiency of this invention reached 74.15%, significantly better than the heuristic strategy's 65.59% and the static strategy's 56.16%. Moreover, statistical analysis of variance (ANOVA) showed that this method has a significant statistical superiority in system recovery efficiency (p<0.0001).

[0086] The application scenarios of this invention are as follows: In scenarios where multiple faults occur concurrently in the power distribution network due to extreme weather disasters, this invention can coordinate multiple types of fault states, achieve synergistic optimization of emergency repair scheduling and network reconstruction, and improve the power supply recovery capability after disasters.

[0087] In scenarios where power distribution network protection devices fail to operate or switch mechanical failures lead to lockout-type faults, this invention can adapt to fault forced conduction characteristics, avoid overload conflicts, and ensure the stable progress of emergency repairs.

[0088] In scenarios where high-priority loads need to be restored quickly after a large-scale power outage, this invention can prioritize the restoration of important loads, balance resource allocation and restoration efficiency, and meet the electricity needs of people's livelihoods and critical needs.

[0089] In scenarios where distribution network faults are dispersed and emergency repair resources are limited, this invention can dynamically adjust emergency repair paths and reconfiguration strategies, make rational use of maintenance resources, and shorten the overall emergency repair cycle.

[0090] In scenarios where the network status changes dynamically during power distribution network emergency repairs, this invention can adapt to the uncertainties of the repair process and enhance the practicality of the solution by iteratively optimizing the scheme based on real-time status feedback.

[0091] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0092] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] Example 2

[0094] See Figure 4 Embodiment 2 of the present invention also provides a dynamic emergency repair system for distribution networks that considers multiple fault interactions and network reconfiguration, comprising: The data perception and classification module 001 is used to construct a four-element fault set, including disconnection faults and blocking faults, based on the collected distribution network node parameters, line parameters, and fault status data after extreme events. The model and constraint construction module 002 is used to construct a dynamic emergency repair collaborative optimization model for the distribution network based on the four-element fault set; and to add power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. The parameter setting module 003 is used to set the initial time step based on the constrained collaborative optimization model, and to preset the prediction time domain window and control step size in combination with the distribution network scale and computational complexity. The rolling optimization module 004 is used to solve the constrained collaborative optimization model based on the set time parameters and the constrained collaborative optimization model, with the objective function of maximizing the system load recovery efficiency and the decision variables of the geographical movement path of the emergency repair team and the distribution network switch variables, and to obtain the comprehensive emergency repair and reconstruction action sequence by using the rolling time domain optimization algorithm. The conflict avoidance and execution module 005 is used to intercept and execute the first control step's emergency repair and reconstruction instruction based on the comprehensive emergency repair and reconstruction action sequence; after execution, it verifies the network topology to determine whether an overload conflict is caused by forced conduction due to a lockout-type fault; if a conflict exists, it adjusts the emergency repair priority, outputs a preventive reconstruction decision, and calculates the current system load recovery efficiency in real time. The event-driven closed-loop module 006 is used to obtain the real-time status of the distribution network after detecting a discrete event trigger signal indicating the completion of the emergency repair task; and to iteratively solve the constrained collaborative optimization model based on the real-time status of the distribution network until all faulty components are repaired and the load restoration process is completed.

[0095] In this embodiment, the data perception and classification module 001 includes the following parameters for distribution network nodes and lines: network topology, branch impedance, branch transmission capacity limit, load weight of each node, and maximum output limit of distributed power sources. The four-element fault set includes: a set of disconnection-type fault branches, a set of disconnection-type fault nodes, a set of interlocking-type fault branches, and a set of interlocking-type fault nodes.

[0096] In this embodiment, the objective function expression of the constrained collaborative optimization model in the rolling optimization module 004 is:

[0097] In the formula, For system load recovery efficiency; This is the total recovery time; For the set of load nodes in the power distribution system; t For the current moment; j The node number; For nodes jAt any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions. In the conflict avoidance and execution module 005, during the real-time calculation of the current system load recovery efficiency, the calculation formula for the system load recovery efficiency is as follows:

[0098] In the formula, For time step t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodes j At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

[0099] In this embodiment, the expression for the power balance constraint in the model and constraint construction module 002 is:

[0100] In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively. The expression for the linearized power flow equation constraint is as follows:

[0101] In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches; The expression for the radial topological constraint is:

[0102] In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step t Power supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time stept Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

[0103] In this embodiment, the expression for the network topology state hard constraint in the model and constraint construction module 002 is: For branch circuit faults:

[0104] In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures:

[0105] In the formula, For nodes The load at time Is it powered? For nodes j At time step t Normal operating condition energized state variables; The geographical movement path decision variables of the emergency repair team satisfy the spatiotemporal transfer constraints of maintenance resources and the logical constraints of maintenance tasks. The expression for the spatiotemporal transfer constraint of the maintenance resources is:

[0106] In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number;F This is the set of all possible movement paths for the repair team. m This represents the maximum number of moves a repair team can make during the entire repair cycle. R To assemble the emergency repair team; The expression for the logical constraint of the maintenance task is:

[0107] In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

[0108] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0109] Example 3

[0110] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a dynamic emergency repair method for a distribution network that considers multiple fault interactions and network reconfiguration. The program code includes instructions for executing the dynamic emergency repair method for a distribution network that considers multiple fault interactions and network reconfiguration as described in Embodiment 1 or any possible implementation thereof.

[0111] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0112] Example 4

[0113] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor; The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute a dynamic emergency repair method for distribution networks that considers multiple fault interactions and network reconfiguration, as described in Embodiment 1 or any possible implementation thereof.

[0114] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0115] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0116] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0117] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A dynamic emergency repair method for distribution networks considering multiple fault interactions and network reconfiguration, characterized in that, include: Based on the collected distribution network node parameters, line parameters, and fault status data after extreme events, a four-element fault set including disconnection faults and blocking faults is constructed. Based on the aforementioned four-element fault set, a dynamic emergency repair collaborative optimization model for the distribution network is constructed. Power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state are added to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. Based on the constrained collaborative optimization model, an initial time step is set, and the prediction time domain window and control step are preset in combination with the distribution network scale and computational complexity. Based on the set time parameters and the constrained collaborative optimization model, with the objective function being to maximize the system load recovery efficiency and the decision variables being the geographical movement path of the emergency repair team and the distribution network switch variables, the constrained collaborative optimization model is solved by the rolling time-domain optimization algorithm to obtain the comprehensive emergency repair and reconstruction action sequence. Based on the comprehensive emergency repair and reconstruction action sequence, the emergency repair and reconstruction instruction of the first control step is intercepted and executed; after execution, the network topology is checked to determine whether an overload conflict occurs due to forced conduction caused by a lockout-type fault; if a conflict exists, the emergency repair priority is adjusted to output a preventive reconstruction decision, and the current system load recovery efficiency is calculated in real time. After detecting discrete event trigger signals indicating the completion of emergency repair tasks, the real-time status of the distribution network is obtained; The constrained collaborative optimization model is iteratively solved based on the real-time status of the distribution network until all faulty components are repaired and the load restoration process is completed.

2. The dynamic emergency repair method for distribution networks considering multiple fault interactions and network reconfiguration according to claim 1, characterized in that, The parameters of the distribution network nodes and lines include: network topology, branch impedance, branch transmission capacity limit, load weight of each node, and maximum output limit of distributed power sources. The four-element fault set includes: a set of disconnection-type fault branches, a set of disconnection-type fault nodes, a set of interlocking-type fault branches, and a set of interlocking-type fault nodes.

3. The dynamic emergency repair method for distribution networks considering multiple fault interactions and network reconfiguration according to claim 2, characterized in that, The objective function expression of the constrained collaborative optimization model is: In the formula, For system load recovery efficiency; This is the total recovery time; For the set of load nodes in the power distribution system; t For the current moment; j The node number; For nodes j At any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions. In the process of calculating the current system load recovery efficiency in real time, the formula for calculating the system load recovery efficiency is as follows: In the formula, For time steps t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodes j At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

4. The dynamic emergency repair method for distribution networks considering multiple fault interactions and network reconfiguration according to claim 3, characterized in that, The expression for the power balance constraint is: In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively. The expression for the linearized power flow equation constraint is as follows: In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches; The expression for the radial topological constraint is: In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step t Power supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time step t Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

5. A dynamic emergency repair method for distribution networks considering multiple fault interactions and network reconfiguration according to claim 4, characterized in that, The expression for the hard constraint of the network topology state is: For branch circuit faults: In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures: In the formula, For nodes The load at time Is it powered? For nodes j At time step t Normal operating condition energized state variables; The geographical movement path decision variables of the emergency repair team satisfy the spatiotemporal transfer constraints of maintenance resources and the logical constraints of maintenance tasks. The expression for the spatiotemporal transfer constraint of the maintenance resources is: In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number; F This is the set of all possible movement paths for the repair team. m This represents the maximum number of moves a repair team can make during the entire repair cycle. R To assemble the emergency repair team; The expression for the logical constraint of the maintenance task is: In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

6. A dynamic emergency repair system for a distribution network considering multiple fault interactions and network reconfiguration, employing the dynamic emergency repair method for a distribution network considering multiple fault interactions and network reconfiguration as described in any one of claims 1-5, characterized in that, include: The data perception and classification module is used to construct a four-element fault set, including disconnection faults and blocking faults, based on the collected distribution network node parameters, line parameters, and fault status data after extreme events. The model and constraint construction module is used to construct a dynamic emergency repair collaborative optimization model for the distribution network based on the four-element fault set; power balance constraints, linearized power flow equation constraints, radial topology constraints, spatiotemporal transfer constraints of maintenance resources, and hard constraints of network topology state are added to the dynamic emergency repair collaborative optimization model for the distribution network to obtain a constrained collaborative optimization model. The parameter setting module is used to set the initial time step based on the constrained collaborative optimization model, and to preset the prediction time domain window and control step size in combination with the distribution network scale and computational complexity. The rolling optimization module is used to solve the constrained collaborative optimization model based on the set time parameters and the constrained collaborative optimization model, with the objective function of maximizing the system load recovery efficiency and the decision variables of the geographical movement path of the emergency repair team and the distribution network switch variables, and to obtain the comprehensive emergency repair and reconstruction action sequence by using the rolling time domain optimization algorithm. The conflict avoidance and execution module is used to intercept and execute the first control step's repair and reconstruction instruction based on the comprehensive repair and reconstruction action sequence; after execution, it verifies the network topology to determine whether an overload conflict is caused by forced conduction due to a lockout-type fault; if a conflict exists, it adjusts the repair priority, outputs a preventive reconstruction decision, and calculates the current system load recovery efficiency in real time. The event-driven closed-loop module is used to obtain the real-time status of the distribution network after detecting a discrete event trigger signal indicating the completion of the emergency repair task; and to iteratively solve the constrained collaborative optimization model based on the real-time status of the distribution network until all faulty components are repaired and the load restoration process is completed.

7. A dynamic emergency repair system for distribution networks considering multi-fault interaction and network reconfiguration as described in claim 6, characterized in that, In the data perception and classification module, the parameters of the distribution network nodes and lines include: network topology, branch impedance, upper limit of branch transmission capacity, load weight of each node, and maximum output limit of distributed power sources. The four-element fault set includes: a set of disconnection-type fault branches, a set of disconnection-type fault nodes, a set of interlocking-type fault branches, and a set of interlocking-type fault nodes.

8. A dynamic emergency repair system for distribution networks considering multiple fault interactions and network reconfiguration according to claim 7, characterized in that, In the rolling optimization module, the objective function expression of the constrained collaborative optimization model is: In the formula, For system load recovery efficiency; This is the total recovery time; It refers to the set of load nodes in a power distribution system; t For the current moment; j The node number; For nodes j At any moment t The actual active load; For nodes j Load priority weights; This represents the total load level of the power distribution system under normal operating conditions. In the conflict avoidance and execution module, during the real-time calculation of the current system load recovery efficiency, the calculation formula for the system load recovery efficiency is as follows: In the formula, For time step t System load recovery efficiency; N It is the set of all load nodes in the distribution network; For nodes j At time step t Power supply status variables; For nodes j Load priority weights; For nodes j The rated active load power.

9. A dynamic emergency repair system for distribution networks considering multi-fault interaction and network reconfiguration according to claim 8, characterized in that, In the model and constraint construction module, the expression for the power balance constraint is: In the formula, For nodes i At time step t Active power output of distributed power sources / generators; For nodes i At time step t Reactive power output of distributed power sources / generators; For nodes The load at time Is it powered? For nodes i Active load; For nodes i reactive load; For the line l At time step t The active power; For the line l At time step t reactive power; and They are nodes Great merit and no merit in effort; and These are collections of substation nodes and distributed power generation nodes, respectively. The expression for the linearized power flow equation constraint is as follows: In the formula, , They are nodes i and i respectively. Voltage amplitude squared; and branch road Resistance and reactance; For the line l At time step t The topological state variables; M is a maximal positive number in the Big M method; and These are the minimum and maximum limits of the voltage amplitude; branch road At the rated power; L represents the set of branches; The expression for the radial topological constraint is: In the formula, branch road A set of binary variables representing connection states; The set of associated vectors and their polyhedra for generating the tree; For the line i At time step t Topological state variables; For the line i At time step t Decision variables for emergency repair / switching actions; For the line i At time step t Fault repair status variables; For nodes j At time step t Power supply status variables; For nodes j At time step t Decision variables for emergency repair / isolation actions; For nodes j At time step t Fault repair status variables; and They are nodes The set of inbound and outbound branches; for Linearized representation of; For nodes The number of connected branches; For disconnected fault branches; For disconnected fault nodes; This is a set of interlocking fault branches; This is a set of lockout-type fault nodes.

10. A dynamic emergency repair system for a distribution network considering multiple fault interactions and network reconfiguration as described in claim 9, characterized in that, In the model and constraint construction module, the expression for the hard constraint of the network topology state is: For branch circuit faults: In the formula, branch road At any moment Topological state variables, 1 for closed, 0 for open; For node failures: In the formula, For nodes The load at time Is it powered? For nodes j At time step t Normal operating condition energized state variables; The geographical movement path decision variables of the emergency repair team satisfy the spatiotemporal transfer constraints of maintenance resources and the logical constraints of maintenance tasks. The expression for the spatiotemporal transfer constraint of the maintenance resources is: In the formula, For maintenance personnel exist Is the time at the point of failure? If maintenance is performed, the value is 1; otherwise, it is 0. The maximum time for maintenance status; To move to the fault point and the total time required to complete its repair; It is a sufficiently large positive number; F This is the set of all possible movement paths for the repair team. m This represents the maximum number of moves a repair team can make during the entire repair cycle. R To assemble the emergency repair team; The expression for the logical constraint of the maintenance task is: In the formula, Fault At any moment Whether it has been repaired: 1 for yes, 0 for no; This serves as a start marker for maintenance; if at time... Start repairing the fault The value is 1 if it is 1, otherwise it is 0. To fix the fault The time.

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