Method and system for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing

Abstract

The present invention discloses a method and system for improving the resilience of a distribution network taking into account drone de-icing in an ice disaster scenario, which belongs to the technical field of power systems. The method and system include: calculating the ice thickness and failure probability of each line through a pre-built ice thickness prediction model and a fault probability calculation model, and generating a set of severely iced lines; calculating the multi-dimensional resilience evaluation index of the distribution network in combination with a pre-built ice disaster scenario distribution network load change curve, and calculating a comprehensive resilience evaluation index; generating the optimal flight path and operation sequence of the drone, and scheduling the drone to perform de-icing operations; generating a recovery strategy based on real-time distribution network status data and a pre-built dynamic recovery optimization model, updating the comprehensive resilience evaluation index, and dynamically adjusting the weight coefficient distribution of the comprehensive resilience evaluation index based on the update result, synchronously updating the objective function weight coefficient of the dynamic recovery model, and iteratively executing the above steps until the comprehensive resilience evaluation index is continuously higher than the preset threshold.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and more specifically, relates to a disaster recovery technology that uses drones to de-ice when lines are severely iced in extreme ice disaster scenarios to eliminate failure risks and thus improve the resilience of distribution networks. Background Art

[0002] In recent years, extreme weather events have become more frequent, and power grid disasters caused by severe weather conditions have intensified. Ice disasters, a typical example of extreme disasters, can cause line breakages, insulator flashovers, and tower collapses, significantly impacting power systems. Repairs during extreme ice storms require de-energizing severely affected lines to protect repair personnel. This significantly impacts the de-icing efficiency of repair teams, increases power outage duration, and results in significant economic losses.

[0003] The existing technical document (CN118693820A) discloses a method for improving the resilience of distribution networks by coordinating multiple resources including source, grid and load in ice disaster scenarios. It constructs a multi-resource collaborative scheduling model and a penalty-based Gauss-Seidel method solution strategy. However, its line icing model does not consider the dynamic impact of line temperature rise caused by line loss heat on ice thickness, resulting in insufficient accuracy in ice thickness prediction; its resilience assessment only uses the reduction of load loss as a single indicator, and there is a problem of being unable to fully characterize the synergy between the distribution network's defense capability and recovery efficiency; although the traditional emergency repair team and multi-department collaborative scheduling strategy are adopted, there are problems such as low de-icing efficiency during the disaster, complex post-disaster recovery operations and high economic costs. Summary of the Invention

[0004] To address the deficiencies in the existing technology, the present invention provides a method for improving the resilience of distribution networks in extreme ice scenarios by considering drone de-icing. First, drones are used to locate faults, and a model for calculating ice thickness during ice disasters is established to identify lines with severe ice thickness during the disaster. Then, reasonable resilience assessment indicators are developed based on the characteristics of distribution network faults in ice disaster scenarios. Finally, a drone line de-icing model is established, which includes locating severely iced lines in the distribution network and dispatching drones for de-icing, minimizing load outages and thereby improving the resilience of the distribution network in ice disaster scenarios.

[0005] The present invention adopts the following technical solutions.

[0006] A first aspect of the present invention provides a method for improving the resilience of a distribution network in an ice disaster scenario, taking into account drone de-icing, comprising the following steps:

[0007] Real-time collection of distribution network meteorological data, line operating parameters, and equipment attribute data in ice disaster environments. Pre-built ice thickness prediction models and fault probability calculation models are used to calculate the ice thickness and fault probability of each line, generating a collection of severely iced lines.

[0008] Combined with the pre-built distribution network load change curve for ice disaster scenarios, the multi-dimensional resilience evaluation index of the distribution network is calculated, and the comprehensive resilience evaluation index is calculated. When the comprehensive resilience evaluation value falls below the preset threshold, the drone collaborative de-icing command is triggered;

[0009] The pre-built UAV de-icing recovery model generates the optimal flight path and operation sequence for UAVs, and dispatches UAVs to perform de-icing operations.

[0010] During the UAV de-icing operation, a recovery strategy is generated based on the real-time distribution network status data and the pre-built dynamic recovery optimization model. The comprehensive resilience assessment index is updated according to the distribution network operation data after the recovery strategy is executed. The weight coefficient distribution of the comprehensive resilience assessment index is dynamically adjusted based on the updated result, and the objective function weight coefficient of the dynamic recovery model is synchronously updated. The above steps are iteratively performed until the comprehensive resilience assessment index is continuously higher than the preset threshold.

[0011] Optionally, calculating the ice thickness of each line using a pre-built ice thickness prediction model includes:

[0012] Calculate the line surface temperature change based on the Joule heating effect generated by the line current and the ambient temperature;

[0013] The critical ice-free time before ice formation is determined using the transient heat balance equation for the conductor surface during an ongoing ice disaster. The transient heat balance equation characterizes the dynamic changes in the line surface temperature during an ongoing ice disaster by multiplying the specific heat capacity of the line material by the rate of change of the line surface temperature. It also includes the heat balance relationships of Joule heat generated by line operation, convective heat loss between the line and the environment, heat loss from supercooled water droplets heated to the condensation point, latent heat absorption from evaporating raindrops on the line surface, and radiative heat dissipation.

[0014] According to the real-time freezing rain intensity, wind speed and air moisture content, the ice thickness growth is calculated in segments after the start of icing. The ice thickness is zero before the critical no-icing time is reached, and the ice accumulation is dynamically calculated after the critical no-icing time is exceeded.

[0015] Optionally, generating a set of severely icing lines includes:

[0016] Preset ice thickness safety threshold and failure probability safety threshold;

[0017] Real-time monitoring of ice thickness and failure probability of each line. When both ice thickness and failure probability exceed their safety thresholds, the corresponding line is dynamically marked as a severely iced line and added to the severely iced line set.

[0018] Periodically clear lines that meet natural recovery conditions, where the natural recovery conditions include ice thickness being lower than a first recovery threshold and a failure probability being lower than a second recovery threshold.

[0019] Optionally, the calculation of the multi-dimensional resilience evaluation index of the distribution network includes:

[0020] The total load recovery index is calculated based on the time-integrated ratio of the actual load recovery during the ice disaster to the theoretical maximum load;

[0021] Fault recovery time indicator, calculated based on the ratio of the time taken to recover from the fault to the total duration of the ice disaster;

[0022] The network loss index is calculated based on the time-integrated ratio of the actual line network loss to the benchmark network loss.

[0023] Optionally, generating an optimal flight path and operation sequence for the UAV using a pre-built UAV deicing recovery model and scheduling the UAV to perform the deicing operation includes:

[0024] Obtain coordinate information, UAV cluster parameters, and thermodynamic characteristic parameters of the severely iced line set;

[0025] The pre-built UAV deicing recovery model calculates the deicing energy consumption and operation time of each line and generates the optimal collaborative operation plan;

[0026] Dispatch drone swarms to perform de-icing operations according to the optimal collaborative operation plan, and monitor and update the line icing status in real time.

[0027] Optionally, the pre-built UAV deicing recovery model includes:

[0028] The UAV deicing recovery model takes the maximization of the comprehensive resilience evaluation index during the duration of the ice disaster as the objective function;

[0029] The constraints of the UAV deicing recovery model include:

[0030] Global operation time constraint, requiring the total de-icing operation time of all lines to not exceed the preset maximum allowable operation time;

[0031] Power safety constraints ensure that the drone's remaining power meets the minimum requirements for returning to the nearest charging station;

[0032] The personnel quantity constraint requires that the number of operators configured for each route should not be less than the number of allocated drones;

[0033] Resource capacity constraints are used to verify that the de-icing equipment capacity carried by the drone meets the needs of line ice removal.

[0034] Optionally, generating a restoration strategy based on real-time distribution network status data and a pre-built dynamic restoration optimization model includes:

[0035] Real-time collection of distribution network node voltage, branch current and drone de-icing progress data, and update of line on / off status parameters;

[0036] The dynamic recovery optimization model is called and converted into a second-order cone programming model, and the objective function is set as a weighted comprehensive index that maximizes load recovery efficiency and operating economy;

[0037] Solve the second-order cone programming model to generate a recovery strategy, including network reconfiguration instructions and distributed generation output plans. The instructions include switching action sequences and new energy inverter control parameters.

[0038] Verify whether the recovery strategy meets the node voltage safety range and line current carrying capacity constraints. If so, issue an execution instruction.

[0039] Optionally, the pre-built dynamic recovery optimization model includes:

[0040] The objective function of the dynamic recovery optimization model is the weighted sum of the load recovery efficiency index and the operation economy index in the post-disaster recovery phase;

[0041] The constraints of the dynamic recovery optimization model include node power balance constraints, voltage safety operating range constraints, line current carrying capacity upper limit constraints, new energy output limit constraints, state linkage constraints for forced disconnection of non-deiced lines, and progress feedback constraints that meet a preset minimum closure ratio.

[0042] Optionally, updating the comprehensive resilience evaluation index according to the distribution network operation data after executing the restoration strategy, and dynamically adjusting the weight coefficient distribution of the comprehensive resilience evaluation index based on the update result includes:

[0043] When the updated comprehensive resilience evaluation index is lower than the preset threshold or the average improvement of multiple consecutive iterations is lower than the set threshold, the weight adjustment mechanism is activated;

[0044] Based on the degree of deviation between the actual performance of each sub-indicator and the benchmark value, the weight distribution ratio is adjusted in the opposite direction. When the sub-indicator value is lower than the preset benchmark value, the weight coefficient of the indicator is increased;

[0045] The total sum of weight coefficients is maintained constant through normalization processing, and the objective function weight of the dynamic recovery optimization model is updated synchronously to form a collaborative optimization closed loop of evaluation and recovery strategies.

[0046] A second aspect of the present invention provides a system for improving the resilience of a distribution network in ice disaster scenarios, taking into account drone de-icing. Based on the method for improving the resilience of a distribution network in ice disaster scenarios, taking into account drone de-icing, as described in the first aspect of the present invention, the system includes:

[0047] Data acquisition module, used to collect meteorological data, line operation parameters and equipment attribute data in real time under ice disaster environment;

[0048] An icing monitoring module is used to generate a set of severely iced lines using an icing thickness prediction model and a failure probability calculation model;

[0049] The resilience assessment module is used to calculate multi-dimensional resilience assessment indicators and comprehensive resilience assessment indicators based on the load change curve of the distribution network in ice disaster scenarios, and trigger the UAV collaborative de-icing command when the indicator falls below the threshold;

[0050] The drone scheduling module is used to generate the optimal flight path and operation sequence of drones and schedule drones to perform de-icing operations;

[0051] Dynamic recovery module, used to generate recovery strategies based on real-time distribution network status data and dynamically adjust evaluation index weight coefficients and recovery model parameters;

[0052] The status update module is used to update the set of severely iced lines and the operating status of the distribution network based on the feedback from the drone deicing operation.

[0053] Compared with the prior art, the beneficial effects of the present invention include at least:

[0054] 1. This invention solves the technical defects of traditional methods that ignore the dynamic changes in line operating temperature and the influence of the complex mechanical environment by constructing a multi-factor icing prediction model that takes into account the thermal effect of distribution network load changes and the coupling of vertical ice force and wind force. It significantly improves the accuracy of ice thickness prediction and provides a precise line status assessment basis for ice disaster prevention strategies.

[0055] 2. This invention overcomes the limitation of existing technologies that rely solely on power supply reliability indicators by establishing a comprehensive resilience assessment index system that integrates load recovery, fault recovery time, and network loss. It comprehensively quantifies the defense and recovery capabilities of the distribution network in ice disaster scenarios, and provides a multi-dimensional decision-making basis for formulating differentiated recovery strategies.

[0056] 3. This invention breaks through the technical bottleneck of traditional manual de-icing requiring power outages through a dynamic recovery mechanism that combines drone-assisted de-icing during disasters with human-machine collaborative repairs after disasters. It achieves rapid de-icing without stopping the line, significantly shortens the fault recovery cycle, and reduces the risk of personnel working at heights.

[0057] 4. The present invention solves the technical defect of the existing technology that separates resilience improvement and economic cost control by constructing a dynamic recovery model that coordinates the optimization of load recovery efficiency and operational economy. On the premise of ensuring node voltage safety and line current carrying capacity constraints, it significantly reduces network losses and new energy power generation costs in the ice disaster recovery phase, and realizes the unity of technical feasibility and economic optimality of the resilience improvement strategy.

[0058] 5. The present invention solves the technical problem that traditional static weight allocation cannot adapt to the dynamic changes of multi-objective priorities in the ice disaster recovery phase by establishing a dynamic feedback mechanism for the weight coefficients of comprehensive resilience assessment indicators. Based on the real-time operating data after the execution of the recovery strategy, the weight ratios of load recovery, fault time and network loss are reversely corrected to achieve closed-loop collaborative optimization of resilience assessment and recovery strategy, effectively improving the adaptability and recovery efficiency of the distribution network recovery strategy in the middle and late stages of ice disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the method for improving the resilience of a distribution network of the present invention;

[0060] Figure 2 It is the load variation curve of the system affected by ice disaster;

[0061] Figure 3 It is the modified IEEE33 node power distribution network diagram;

[0062] Figure 4 It is a 24-hour data chart of the total load power, wind turbine and photovoltaic output of each node;

[0063] Figure 5 This is a comparison chart of the recovery load of different schemes;

[0064] Figure 6 This is a comparison chart of resilience index values for different schemes. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0066] In embodiment 1, the present invention provides a method for improving the resilience of a distribution network in an ice disaster scenario taking into account drone de-icing, such as Figure 1 As shown, the following steps are included:

[0067] Step 1: Real-time collection of distribution network meteorological data, line operating parameters, and equipment attribute data under an ice disaster environment. The ice thickness and failure probability of each line are calculated using a pre-built ice thickness prediction model and a fault probability calculation model to generate a set of severely iced lines.

[0068] Preferably, in step 1, calculating the ice thickness of each line using a pre-built ice thickness prediction model includes:

[0069] (1) Calculate the circuit surface temperature:

[0070] (1)

[0071] Where: T s is the circuit surface temperature; T c is the ambient temperature; I is the line current; r is the resistance of the transmission line; v is the wind speed at the location of the transmission line before the ice disaster; d is the diameter of the transmission line.

[0072] (2) Calculate the time that the conductor is not covered with ice under line surface ice disaster, taking into account line loss, including:

[0073] When ice begins to form, the heat balance equation on the surface of the transmission line can be expressed as follows:

[0074] (2)

[0075] Where: q z It is the Joule heat generated by the operation of the transmission line; q c is the convection heat loss between the circuit and the environment; q w It is the heat loss caused by supercooled water droplets being heated to the condensation point; q e It is the latent heat absorbed by evaporation of raindrops on the line surface; q s It is radiative heat dissipation;

[0076] The calculation formula of the transient heat balance equation on the conductor surface during the continuous ice disaster is:

[0077] (3)

[0078] Where: m is the mass per unit length of the transmission line; C p is the specific heat capacity of the transmission line material itself; q z0 yes T s = Line loss at 0℃, T s is the circuit surface temperature.

[0079] The time for the conductor to remain ice-free under ice disasters, taking line loss into account, is obtained from equations (1) to (3) and is expressed as follows:

[0080] (4)

[0081] Where: It is the time that the conductor is not covered with ice considering line loss under ice disaster, and T0 represents the surface temperature of the line during stable operation before the disaster.

[0082] (3) Calculate the ice thickness of the line under ice disaster:

[0083] (5)

[0084] Where: D ice (t) is the ice thickness of the line at time t; ρ I and ρ W

[0085] are the densities of ice and water respectively; φ (t) is t The amount of freezing rain at the time; v w (t) is the wind speed at the location of the transmission line at time t; A (t) is the water content of the ambient air.

[0086] It is worth noting that in order to address the technical defect that the icing prediction model in the existing technology ignores the dynamic coupling between the Joule heating effect of the line operating current and the ambient temperature, the present invention constructs a multi-physics field coupling model that includes the transient thermal balance equation of the line surface, and calculates the critical non-icing time and icing growth rate before icing is formed in real time, which significantly improves the spatiotemporal resolution of icing thickness prediction and provides a reliable basis for the precise scheduling of defense resources before disasters.

[0087] Preferably, in step 1, calculating the failure probability of each line using a pre-built failure probability calculation model includes:

[0088] (1) Calculate the ice and wind loads on each line under ice disasters, including:

[0089] According to the ice thickness of the line at time t, the ice load per unit length of the line can be obtained: L I (t), can be expressed by the following formula:

[0090] (6)

[0091] Where: d is the line diameter;

[0092] Wind load per unit length of line L W (t) is:

[0093] (7)

[0094] Where: C is a constant, taking 6.964×10 -3 ; S is the span factor;

[0095] From equations (6) and (7), we can get the ice and wind load per unit length of transmission line: L IW (t), can be expressed by the following formula:

[0096] (8)

[0097] (2) Calculate the line disconnection failure rate, including:

[0098] According to the design load and ultimate load of the line for ice coverage, the failure rate of line disconnection per unit length at time t can be obtained as follows:

[0099] (9)

[0100] Where: P f (t) is the failure rate of line disconnection per unit length at time t; a 1W and b 1W are the design load and ultimate load of the line's ice-bearing capacity respectively;

[0101] The failure rate model of the entire line can be expressed as follows:

[0102] (10)

[0103] Where: P l,i (t) is the failure rate of the entire line i, l is the corresponding line length, P f (t) is the failure rate of line break per unit length at time t.

[0104] Preferably, in step 1, generating a set of severely icing lines includes:

[0105] Preset thresholds for ice thickness and failure probability. When the ice thickness and failure probability of a line are detected to exceed the safety threshold, the line will be automatically marked as a severely iced line and added to the collection. ;

[0106] For example, the threshold value may be set as:

[0107] Ice thickness threshold: ;

[0108] Failure probability threshold: ;

[0109] Update the route every 5 minutes and , if a line satisfies both and , then add it to the collection ; Clear the naturally recovered lines every 15 minutes, and the natural recovery threshold is set to and ; The natural recovery mentioned above refers to the situation where the ambient temperature rises and the ice slowly melts, which requires continuous monitoring.

[0110] It should be noted that in the subsequent steps, the UAV will immediately trigger the ice thickness detection after de-icing. If it meets the standard (≤5mm), it will be removed from the assembly in real time. .

[0111] The generation logic of severely iced lines is real-time monitoring - automatic marking - dynamic update.

[0112] Step 2: Based on the distribution network operation data and the time series data of the line disconnection probability output in step 1, combined with the pre-built distribution network load change curve for the ice disaster scenario, the multi-dimensional resilience evaluation index of the distribution network is calculated, and the comprehensive resilience evaluation index is calculated. When the comprehensive resilience evaluation value is lower than the preset threshold, the drone collaborative de-icing command is triggered.

[0113] Preferably, in step 2, the pre-built distribution network load change curve for the ice disaster scenario includes:

[0114] like Figure 2 As shown, the figure contains the distribution network load curve under normal conditions f 0(t) and distribution network load change curve under the influence of ice disaster f (t), where the corresponding working conditions at each moment are;

[0115] At time t0, the ice disaster begins to affect the distribution network;

[0116] At time t1, the line was covered with ice, and the dispatching center dispatched a drone to the severely iced line for de-icing;

[0117] At time t2, the drone arrived at the scene to perform de-icing operations. At this time, some lines failed due to excessive ice thickness. The dispatch center actively disconnected the faulty lines through switch operation.

[0118] At t3, as the temperature rose and the ice disaster receded, the emergency repair team and drones began to de-ice the ice-covered lines that had been proactively disconnected.

[0119] At t4, all lines were de-iced and the emergency repair team restored the lines that had broken down.

[0120] At t5, the distribution network returns to normal operation.

[0121] In view of the characteristics of ice disasters and the dynamic changes in the load of the distribution network during the disaster, the present invention first constructs a distribution network load change curve under the ice disaster scenario. The transmission lines are designed to have a certain ice resistance, so in the early stage of ice and snow weather, the power grid usually does not immediately experience line breakage failures. However, as the ice and snow weather continues, the thickness of ice covering the line gradually increases. When it exceeds its design load limit, the line may fail. Further development will cause more lines to be shut down due to line breakage failures, thereby significantly reducing the load level of the distribution network. Therefore, after detecting that the line is severely iced, the emergency repair team will repair the damaged line, so as not to affect the normal power supply of the distribution network, and the load level will gradually return to normal. The above-mentioned change process intuitively reflects the impact of ice disasters on the load of the distribution network, and provides a theoretical basis for formulating resilience improvement strategies.

[0122] Preferably, in step 2, the multi-dimensional resilience evaluation index includes a total load recovery index, a fault recovery time index, and a network loss index.

[0123] Preferably, the calculation of the total load recovery index of the distribution network in an ice disaster environment includes:

[0124] This indicator is designed to build resilience in power grid defenses. During disasters, drones equipped with de-icing tools were used to de-ice severely iced lines. This indicator is used to assess the load recovery of distribution networks during ice disasters. A larger value indicates a greater recovery in the distribution network load. It can be expressed using the following formula:

[0125] (11)

[0126] Where: R ice is the total load recovery index of the distribution network under the ice disaster, W is the load of the distribution network without line failure during the entire ice disaster process, W0 is the total load of the distribution network in this period when no failure occurs, t0 is the time when the ice disaster begins to affect the distribution network, and t5 is the time when the distribution network resumes normal operation.

[0127] Preferably, the calculation of the time index for fault recovery of the distribution network in an ice disaster environment includes:

[0128] This indicator focuses on building a resilient power grid. During disasters, drones equipped with deicing tools are used to de-ice severely iced lines. After disasters, drones are combined with traditional deicing measures to remove ice faster.

[0129] This indicator is used to evaluate the recovery speed of the distribution network during an ice disaster. The larger the indicator value, the faster the distribution network will recover. It can be expressed by the following formula:

[0130] (12)

[0131] Where: R time It is the time indicator for fault recovery. T’ The time it takes to recover from a fault during an ice disaster. T 0 is the total duration of the ice disaster.

[0132] Preferably, the calculating of the network loss index of the distribution network in an ice disaster environment includes:

[0133] The power loss in the form of heat during power transmission is mainly related to the current flowing through the line and the line resistance, which can be expressed by the following formula:

[0134] (13)

[0135] Where: R loss is the network loss indicator; I ( t ) is the current flowing through the distribution network branch, is the total branch network loss during normal operation of the distribution network, is the line loss of the distribution network in the ice disaster scenario, is the resistance of branch ij.

[0136] It is worth noting that in order to address the technical limitations of existing technologies where the single resilience assessment indicator leads to a tendency towards local optimization of recovery strategies, the present invention integrates a three-dimensional dynamic assessment system of load recovery, failure time, and network loss, and combines it with the hierarchical analysis method to dynamically allocate weight coefficients. This achieves the coordinated quantification of defense and resilience throughout the entire ice disaster cycle, providing a multi-objective optimization benchmark for the formulation of differentiated recovery strategies.

[0137] Preferably, the integration of the comprehensive resilience assessment model into comprehensive resilience assessment indicators includes:

[0138] To improve the resilience of the distribution network under extreme ice disasters, the distribution network resilience assessment model constructed in this paper uses a comprehensive resilience assessment index as the objective function. The comprehensive resilience assessment index consists of three parts: total load recovery, fault recovery time, and network loss. Its mathematical model can be expressed as follows:

[0139] (14)

[0140] Where R is the comprehensive resilience evaluation index; ω1, ω2, and ω3 are the weights corresponding to each index, and their initial values are obtained by the hierarchical analysis method; for example, the initial values can be set to 0.6, 0.3, and 0.1, respectively.

[0141] Preferably, the triggering of the UAV collaborative de-icing instruction includes:

[0142] When the comprehensive resilience evaluation index is lower than the preset threshold, a UAV de-icing decision instruction is generated, triggering the de-icing task scheduling in step three; illustratively, the preset threshold is set to 0.7, which can be dynamically adjusted according to the ambient temperature and the proportion of critical loads.

[0143] Step 3: Based on the spatial distribution of the collection of severely iced lines, the pre-built drone deicing recovery model is used to generate the optimal flight path and operation sequence of the drones, and a group of drones equipped with laser deicers are dispatched to perform non-stop deicing operations on the target lines.

[0144] Preferably, the step 3 includes:

[0145] Step 3.1: Obtain the coordinate information, UAV cluster parameters, and thermodynamic characteristic parameters of the severely iced line set;

[0146] Step 3.2: Calculate the deicing energy consumption and operation time of each line through the pre-built UAV deicing recovery model to generate the optimal collaborative operation plan;

[0147] Further preferably, the UAV deicing recovery model includes:

[0148] (1) Calculation of ice mass:

[0149] (15)

[0150] Where, m ij,i It is a line waiting to melt ice ij ice cover quality; d ij It is a line waiting to melt ice ij diameter; ρ i is the density of ice, take 900 kg / m 3 ; lij,i It is a line waiting to melt ice ij Ice cover length; D ij,ice For lines waiting to melt ice ij Thickness of ice cover;

[0151] (2) Calculation of de-icing heat requirements, including:

[0152] Calculation of heating heat:

[0153] (16)

[0154] Where, Q ij,1 is the amount of heat required to raise the temperature of ice to its critical melting temperature: C i is the specific heat capacity of ice, which is 2100 J / (kg·K); ∆ T i is the temperature difference between the initial ambient temperature and the critical temperature of the ice cover;

[0155] Calculation of latent heat of phase change:

[0156] (17)

[0157] Where, Q ij,2 is the heat required for the phase transition from critical ice to critical water: L i is the latent heat of phase change of ice, taken as 335000 J / kg;

[0158] Heat loss calculation:

[0159] (18)

[0160] Where, Q ij,3 is the heat conduction loss in the whole process: the loss factor μ is set according to the actual laser ice melting situation and is set to 0.5 in the present invention;

[0161] Total calorie calculation:

[0162] (19)

[0163] Where, Q ij is the total amount of heat required by the laser to melt a specific mass of ice;

[0164] (3) Operation time constraints, including:

[0165] (20)

[0166] (twenty one)

[0167] Where, It is a line waiting to melt ice ij Time required for ice melting, P is the actual working power of laser deicing, The preset maximum allowed operating time;

[0168] It is a global constraint used to ensure that de-icing operations on all lines are completed before the ice disaster worsens to prevent the spread of faults.

[0169] (4) Resource constraint verification, including:

[0170] Operator quantity constraints:

[0171] (twenty two)

[0172] Resource capacity constraints:

[0173] (twenty three)

[0174] Where, N person,j It's a line j Number of operators; N drone,j It's a line j Number of drones deployed; D j Fault point j resource needs; C drone,j For the line j Allocate the resource capacity carried by the drone.

[0175] It should be noted that the double subscript ij in the formula represents the physical line from node i to node j and the associated line topology; the single subscript j represents the j-th line in the ice-covered line set and is used to identify the line number.

[0176] Further preferably, generating the optimal collaborative operation plan includes:

[0177] (1) Task package division

[0178] According to the maximum flight time of the drone and battery capacity, and gather the severely iced lines Divide into several task packages , each task package satisfies:

[0179] (twenty four)

[0180] in, For the line jDe-icing time;

[0181] (2) Path optimization

[0182] For each task package , the shortest path algorithm is used to calculate the flight trajectory of the UAV, satisfying:

[0183] (25)

[0184] in, For the m The flight time of each path segment is calculated based on the flight distance and the cruising speed of the drone; is the deicing time of the mth line;

[0185] (3) Battery replacement strategy

[0186] When the drone has remaining battery Below the safety threshold , plan the route to the nearest charging station ,satisfy:

[0187] (26)

[0188] Where v is the cruising speed of the UAV;

[0189] (4) Instruction generation

[0190] Output a dispatch instruction set containing the following:

[0191] The take-off and landing coordinate sequence of each UAV;

[0192] The operating time window for each line;

[0193] The location and schedule of battery replacement points.

[0194] It should be noted that in the complete instruction set, the time window constraint ensures that the de-icing operation is synchronized with the grid restoration plan, and the battery replacement strategy avoids mission interruption and improves operation continuity.

[0195] It is worth noting that in order to solve the technical bottleneck of traditional manual de-icing that requires power outages and leads to a long recovery cycle, the present invention generates the optimal flight trajectory and operation sequence under the global operation time constraint through the collaborative design of the drone cluster path optimization model and the battery safety replacement strategy, thereby realizing continuous de-icing of ice-covered lines without power outages, and greatly improving the de-icing efficiency of key lines.

[0196] Step 3.3: dispatch the drone swarm to perform de-icing operations according to the plan, and monitor and update the line icing status in real time.

[0197] Further preferably, the real-time monitoring and updating of line icing status includes:

[0198] Use the infrared thermal imager carried by the drone to detect the temperature changes on the line surface and verify the de-icing status;

[0199] When the ice thickness is detected When the line is restored, it is marked as restored and removed from the set of severely icing lines.

[0200] Preferably, the objective function of the UAV deicing recovery model based on maximizing the comprehensive toughness evaluation index includes:

[0201] The objective function is:

[0202] (27)

[0203] Where: The time span of the ice disaster duration is a set, and R is the comprehensive resilience assessment indicator.

[0204] With the goal of maximizing the comprehensive resilience index throughout the ice disaster process, the use of drones equipped with laser de-icers to de-ice faulty lines without stopping operations can reduce the number of faulty and shut-down lines, reduce the lost load, and thus improve the resilience of the distribution network.

[0205] Drone deicing operations primarily rely on laser deicers. Both the drone and the laser deicer require power, so the deicing process is limited by resources such as the drone's battery level, the laser deicer, and the number of operators. The laser deicer's deicing time is related to the thickness and length of the ice covering the line. Therefore, the drone and the laser deicer share a common battery. The operator must remain on standby directly below the drone's flight path and replace the battery when it runs low, regardless of the battery replacement time.

[0206] Step 4: During the UAV de-icing operation, a recovery strategy is generated based on the real-time distribution network status data and the pre-built dynamic recovery optimization model. The comprehensive resilience evaluation index is updated according to the distribution network operation data after the recovery strategy is executed. The weight coefficient distribution of the comprehensive resilience evaluation index is dynamically adjusted based on the update result, and the objective function weight coefficient of the dynamic recovery model is synchronously updated. Steps 2-4 are iteratively executed until the comprehensive resilience evaluation index is continuously higher than the preset threshold.

[0207] Preferably, step 4 includes:

[0208] Step 4.1: Real-time collection of distribution network node voltage, branch current, and drone de-icing progress data;

[0209] Step 4.2: call the pre-built dynamic recovery optimization model and convert it into a second-order cone programming model;

[0210] Further preferably, the dynamic restoration optimization model includes an objective function and a distribution network Distflow constraint, wherein:

[0211] The objective function is:

[0212] During the ice disaster recovery phase ( t 3≤ t ≤ t 5) By dynamically adjusting the network topology and distributed generation (DG) output, the comprehensive index of post-disaster recovery efficiency and grid operation economy is maximized. Its mathematical expression is:

[0213] (28)

[0214] Where, , the recovery phase time set after the ice disaster ends; is the load recovery efficiency index at time t; is the economic operation indicator at time t; and is the weight coefficient, which is generated by proportional mapping of the comprehensive resilience evaluation index weights ω1 and ω2. The specific mapping rule is: , , can be adjusted dynamically according to the recovery progress. For example, they can be set to 0.7 and 0.3 respectively. =0.7, indicating that load restoration is prioritized and power supply to key loads such as hospitals and communication base stations is quickly restored. =0.3, indicating optimized economic operation, reduced network losses and renewable energy power generation costs;

[0215] Specifically, the load recovery efficiency index include:

[0216] It is used to measure the proportion of critical loads that have been restored during the recovery phase and is calculated using the following formula:

[0217] (29)

[0218] in, is a set of critical load nodes. For example, the division of critical load nodes is as shown in Table 2 in Example 3; is the restored load power of node j at time t; is the original load power of node j;

[0219] The economic performance indicators include:

[0220] Used to comprehensively evaluate grid losses and DG power generation costs during the restoration phase:

[0221] (30)

[0222] in, is the baseline cost, i.e. the total cost without optimization, including network losses and DG generation costs; is the actual cost at time t.

[0223] It should be noted that the objective function of the dynamic recovery optimization model is to maximize the comprehensive indicators of load recovery efficiency and operating economy in the post-disaster recovery stage. It balances the supply of key loads and cost control through dual weight coefficients to ensure that the distribution network recovers to normal quickly and economically after the disaster.

[0224] The Distflow constraints of the distribution network include:

[0225] (1) Node power balance constraints, including:

[0226] Active power balance:

[0227] (31)

[0228] Reactive power balance:

[0229] (32)

[0230] Node power decomposition:

[0231] (33)

[0232] (34)

[0233] Voltage-current relationship:

[0234] (35)

[0235] (36)

[0236] (37)

[0237] (38)

[0238] Where: Represents a node The node branch head end node set; Represents a node The node headed by the branch is the end node; P ki,t and Q ki,t They represent the active power and reactive power flowing from node k to node i at time t respectively; r ki and x ki Represent the resistance and reactance of branch ki respectively; I ki,trepresents the current flowing from node k to node i at time t; express Time from node Flow Node Active power; express Time from node Flow Node Reactive power; express Time Node Flow Node The current value; Representation node exist Active power injected from the outside at all times; Representation node exist Reactive power injected from the outside at all times; Representation node exist The voltage amplitude at the moment; V j,t Indicates that node j is in The voltage amplitude at the moment; and Respectively represent branches resistance and reactance; and express At the node at all times Active power output from wind and photovoltaic power generation; 、 express At the node at all times Reactive power output from wind and photovoltaic power generation; 、 Respectively expressed in Time SOP to node Active and reactive power provided; and Respectively expressed in Time Node The active power and reactive power consumed; M represents the relaxation constant, which is an arbitrarily large positive number; α ij,t Indicates the on / off status of branch ij.

[0239] (2) Operational safety constraints

[0240] (39)

[0241] (40)

[0242] Where: express The voltage amplitude at the moment; express Time branch the current flowing; and Respectively represent the upper and lower limits of voltage; Indicates the upper limit of the current in branch ij.

[0243] (3) Line current carrying capacity constraints

[0244] (41)

[0245] Where: express Timeline Active power flowing through; express Timeline Reactive power flowing through; Indicates a branch The maximum apparent power allowed to flow.

[0246] (4) Constraints on new energy output

[0247] (42)

[0248] Where: 、 Represents nodes respectively exist The upper limit of wind power and photovoltaic active output at any moment.

[0249] Further preferably, the dynamic restoration optimization model further includes line state linkage constraints, including:

[0250] (43)

[0251] in, is the on / off state of branch ij at time t, 0=open, 1=closed; is the set of lines that have completed deicing. The physical meaning of the line status linkage constraint is that if line ij is a severely iced line and has not yet completed deicing, the line is forcibly disconnected to prevent the recovery strategy from powering on the undeiced line and causing a secondary fault.

[0252] Further preferably, the dynamic recovery optimization model further includes de-icing progress feedback constraints, including:

[0253] (44)

[0254] Wherein, K is the de-icing completion threshold, which can be set to 0.8 for example; is the total number of de-iced lines. The physical meaning of the de-icing progress feedback constraint is to close at least 80% of the de-iced lines to ensure that the recovery strategy prioritizes the use of safe lines to reconstruct the network and avoid power supply islanding caused by excessive line disconnection.

[0255] Further preferably, converting the dynamic recovery optimization model into a second order cone programming (SOCP) model includes:

[0256] (1) Decomposition of line current carrying capacity constraints

[0257] (45)

[0258] (2) Variable substitution

[0259] (46)

[0260] (3) Reconstruction of the tidal flow equation

[0261] (47)

[0262] (48)

[0263] (49)

[0264] (50)

[0265] (4) Introduction of second-order cone constraints

[0266] (51)

[0267] (5) Security constraint update

[0268] (52)

[0269] (53)

[0270] It is worth noting that in order to address the technical deficiencies of existing recovery strategies that separate operational safety and economic goals, the present invention constructs a dynamic recovery model that collaboratively optimizes load recovery efficiency and network loss costs, and combines the second-order cone programming relaxation method to solve the global optimal solution for network reconstruction and distributed power output. Under the premise of meeting the node voltage safety constraints, the comprehensive economic cost of the post-disaster recovery stage is reduced.

[0271] Step 4.3, solving the second-order cone programming model and generating a restoration strategy, including network reconstruction instructions and distributed generation output plan;

[0272] Further preferably, the step 4.3 includes:

[0273] Call the solver, input the second-order cone programming (SOCP) model into CPLEX, and output the optimal branch state and optimal DG output;

[0274] Generate a network reconfiguration instruction and a DG output plan, wherein the network reconfiguration instruction includes generating a switch action sequence according to the optimal branch state, including closing and opening, and the DG output plan includes sending the optimal DG output to the inverter controller.

[0275] It should be noted that by controlling the switchgear to adjust the network topology and combining it with the distributed power generation output plan, the distribution network is actively divided into multiple power supply islands. Through active island division, areas not directly affected by the ice disaster can be independently powered by distributed power sources, reducing load losses.

[0276] Step 4.4: Verify and execute the recovery strategy.

[0277] Further preferably, the step 4.4 includes:

[0278] Verify whether the restoration strategy given in step 4.3 satisfies the operation safety constraints and new energy processing constraints in the distribution network Distflow constraints;

[0279] If the conditions are met, the relevant equipment is controlled to execute the recovery strategy, including controlling the switchgear to adjust the topology according to the network reconstruction instructions to form a new power supply island; writing the DG output plan into the inverter control protocol;

[0280] Step 4.5: Update the comprehensive resilience evaluation index based on the distribution network operation data after executing the recovery strategy, adjust the weight coefficient distribution of the comprehensive resilience evaluation index based on the update result, and synchronously update the objective function weight coefficient of the dynamic recovery model.

[0281] Further preferably, updating the comprehensive resilience evaluation index according to the distribution network operation data after executing the restoration strategy includes:

[0282] Calculate the new comprehensive resilience evaluation index:

[0283] (54)

[0284] in, It is the comprehensive resilience assessment index value in the post-disaster recovery stage;

[0285] is the load recovery indicator in the post-disaster recovery phase, and the calculation formula is:

[0286] (55)

[0287] Where, To actually restore the load, is the theoretical maximum load, The moment when the ice disaster begins;

[0288] is the recovery time indicator, and the calculation formula is:

[0289] (56)

[0290] Where, is the baseline recovery time, which is the expected recovery time when no optimization strategy is adopted; is the actual recovery time, that is, the actual recovery time after the optimization strategy is adopted;

[0291] is the network loss index in the post-disaster recovery phase, and the calculation formula is:

[0292] (57)

[0293] Where, is the current value of branch ij during normal operation before the disaster, is the resistance value of branch ij.

[0294] Further preferably, the adjusting the weight coefficient distribution of the comprehensive toughness evaluation index according to the update result includes:

[0295] When the updated comprehensive resilience evaluation index Or the average improvement of the indicator in the last three iterations When , the weight adjustment mechanism is started; specifically, , is the comprehensive resilience evaluation index of the previous iteration cycle. The dynamic weight adjustment is achieved through the gradient feedback mechanism. The weight update formula is:

[0296] (58)

[0297] in, For the learning rate, for example, set It should be noted that the direction of weight adjustment is inversely proportional to the actual performance of the sub-item indicators. Lower, its gradient If the gradient is small, the weight increase is limited; otherwise, the gradient is large and the weight increase is significant;

[0298] After executing the gradient feedback mechanism to adjust the comprehensive resilience evaluation index weights ω1, ω2 and ω3, the objective function weight coefficient of the dynamic recovery optimization model is updated synchronously. and ,achieving the coordinated optimization of assessment and recovery strategies.,Step 4.6,iteratively execute the above steps until the comprehensive,resilience assessment index is continuously higher than the preset threshold.

[0299] Further preferably, the step 4.6 further comprises, if Three consecutive cycles , lock the weight coefficient;

[0300] The process is terminated when the weight coefficient is locked or the maximum number of iterations is reached. For example, the maximum number of iterations is set to 10.

[0301] It should be noted that the weights are updated proportionally according to the gradient of the sub-indicators, and inefficient indicators are improved first; this can ensure that the model always focuses on the resilience dimensions that most need to be optimized, and ultimately maximize the comprehensive resilience of the distribution network.

[0302] It is worth noting that in order to address the technical problem of inaccurate multi-objective optimization caused by the rigidification of traditional weight allocation strategies, the present invention introduces a dynamic adjustment mechanism of weight coefficients based on gradient feedback, and reversely corrects the indicator weights according to the real-time load recovery rate and network loss deviation, so that the recovery strategy can adapt to the priority changes in different stages of ice disasters, thereby improving the convergence speed and optimization accuracy of strategy iteration.

[0303] The present invention aims to improve the resilience of the distribution network. Combining the development of drone technology, it proposes an extreme scenario in which ice is applied to the line during an ice disaster. It rationally analyzes the characteristics of line failures under ice-covered conditions, proposes an evaluation index for the resilience of the distribution network under ice disaster scenarios, and establishes a fault recovery model that considers drone de-icing under extreme ice disasters. Simulation results show that the introduction of drones as a tool for de-icing during ice disasters can effectively remove ice disasters, restore loads to the greatest extent, reduce power outage time and network losses, and make the entire recovery process have a lower economic cost, which corresponds to the resilience evaluation index proposed by the invention and effectively improves the resilience of the distribution network under extreme ice disaster scenarios. In addition, the present invention uses drones for de-icing, which can, on the one hand, accurately, quickly and efficiently remove ice from the surface of the line, and on the other hand, greatly save manpower and material resources and avoid unnecessary risks.

[0304] In Example 2, the present invention provides a system for improving the resilience of a distribution network in ice disaster scenarios, taking into account drone de-icing. Based on the method for improving the resilience of a distribution network in ice disaster scenarios, taking into account drone de-icing, described in Example 1, the system includes:

[0305] Data acquisition module, used to collect meteorological data, line operation parameters and equipment attribute data in real time under ice disaster environment;

[0306] An icing monitoring module is used to generate a set of severely iced lines using an icing thickness prediction model and a failure probability calculation model;

[0307] The resilience assessment module is used to calculate multi-dimensional resilience assessment indicators and comprehensive resilience assessment indicators based on the load change curve of the distribution network in ice disaster scenarios, and trigger the UAV collaborative de-icing command when the indicator falls below the threshold;

[0308] The drone scheduling module is used to generate the optimal flight path and operation sequence of drones and schedule drones to perform de-icing operations;

[0309] Dynamic recovery module, used to generate recovery strategies based on real-time distribution network status data and dynamically adjust evaluation index weight coefficients and recovery model parameters;

[0310] The status update module is used to update the set of severely iced lines and the operating status of the distribution network based on the feedback from the drone deicing operation.

[0311] Preferably, the system further comprises:

[0312] The human-computer interaction module is used to visualize the ice distribution heat map, UAV flight trajectory, and toughness index change curve, and provides a monitoring and manual intervention interface for the entire method process;

[0313] The data storage module is used to store a historical ice disaster case library, a drone performance parameter library, and a distribution network topology database, supporting the parameter calling requirements of all models in the method embodiment.

[0314] In order to more clearly introduce the outstanding essential features of the present invention and the significant progress it brings to the prior art, an application example of implementing the present invention is introduced in Example 3.

[0315] The following describes an embodiment of the present invention in detail with reference to the accompanying drawings. The application example specifically includes:

[0316] The third embodiment of the present invention is as follows Figure 3 The modified IEEE33-node system shown in the figure is used for calculation verification. It contains 33 nodes, 32 branches, and 5 tie switches. The head-end reference voltage is 12.66kV, and the three-phase power reference value is 10MV·A. The red nodes are critical loads, and the black nodes are ordinary loads. The distributed photovoltaic and wind turbines are located at nodes 7, 13, 27 and nodes 10 and 24 respectively. The system node voltage safety range is [0.95, 1.05]. The load power, wind turbine, and photovoltaic output data are shown in Figure 1. Figure 4 As shown in the figure, the calculation assumes that icing occurs at 4:00 AM in a heavy snowstorm environment. Lines 6, 12, 18, 21, 24, and 32 may be affected by icing. The load curtailment compensation cost is 1.10 yuan / kW·h.

[0317] Table 1 DG access nodes and capacity

[0318]

[0319] Table 2 Node load levels and weights

[0320]

[0321] In order to verify the effectiveness of the toughness improvement method proposed in this invention, three different schemes are set up:

[0322] Solution 1: Consider network reconstruction but not the line repair and restoration process;

[0323] Option 2: Consider network reconstruction and use traditional repair teams to de-ice lines during disasters.

[0324] Option 3: Considering network reconstruction, the de-icing function of drones is considered during the disaster, and the number of drones involved is 6;

[0325] After simulation calculation, the fault recovery effects corresponding to the three solutions are shown in Table 3:

[0326] Table 3 Fault recovery effects of three solutions

[0327]

[0328] Table 3 shows that the load shedding for Schemes 2 and 3 is 3144.1 kWh and 1792.2 kWh, respectively, both significantly less than the 6790.2 kWh for Scheme 1. This demonstrates that de-icing measures can effectively reduce load shedding during extreme ice disasters. Scheme 3's load shedding is less than that of Scheme 2, indicating that dispatching drones for de-icing can accelerate de-icing efficiency within a limited timeframe. Meanwhile, Scheme 1's total line loss reaches 2619.7 kWh, significantly higher than Schemes 2 and 3. While the loss for Scheme 2 is reduced, it remains significantly higher than the scenario using drone de-icing, further demonstrating the effectiveness of drone de-icing. Incorporating drones into distribution network fault recovery can significantly reduce manpower and material inputs. Furthermore, given the high labor costs and potential safety risks associated with post-disaster repair teams, drones offer significant economic advantages in fault recovery.

[0329] Depend on Figure 5As can be seen, at 4:00 a.m., the line was covered in ice. To avoid line disconnection, Option 2 uses a switch to disconnect the heavily iced line. After the disaster, a repair team is dispatched to perform de-icing and line repairs, restoring power through the switch. Option 3 deploys drones to de-ice the line without interrupting operation, significantly reducing the number of lines disconnected during the disaster. Due to the extreme snow and ice, the de-icing efficiency of human repair teams is very limited. Furthermore, the varying thickness of ice on different lines requires different materials and time for de-icing. Therefore, considering the use of drones in de-icing during ice disasters can more quickly restore lines to normal, effectively supplying power to the distribution network and minimizing load shedding during the recovery process.

[0330] At the same time, according to the proposed resilience evaluation index, the resilience index values of Scheme 2 and Scheme 3 are shown in Table 4:

[0331] Table 4 Toughness index corresponding to two deicing schemes

[0332]

[0333] From Table 4 and Figure 6 As shown, it is known that R ice 、R time、 R loss The three corresponding sub-resilience assessment indicators in this article show a significant increase in the load level associated with drone-based line deicing. This is because drones can de-ice lines without interrupting power during a disaster and assist repair teams in more rapidly removing ice from lines afterward, allowing the distribution network to operate at near-normal conditions and achieving higher levels of system resilience. Therefore, using drones for line deicing can significantly improve the resilience of the distribution network during extreme ice disasters, consistent with the objectives of this invention.

[0334] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0335] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for improving the resilience of distribution networks in ice disaster scenarios taking into account drone de-icing, characterized in that: The steps include: Real-time collection of distribution network meteorological data, line operating parameters, and equipment attribute data in an ice disaster environment. The ice thickness and failure probability of each line are calculated using a pre-built ice thickness prediction model and a fault probability calculation model to generate a set of severely iced lines. The multi-dimensional resilience assessment index of the distribution network is calculated using a pre-built ice disaster scenario distribution network load change curve, along with a comprehensive resilience assessment index. When the comprehensive resilience assessment value falls below a preset threshold, a drone collaborative de-icing command is triggered. The optimal flight path and operation sequence of the drone are generated through a pre-built drone de-icing recovery model, and the drone is scheduled to perform de-icing operations. During the drone de-icing operation, a recovery strategy is generated based on the real-time distribution network status data and the pre-built dynamic recovery optimization model. The comprehensive resilience assessment index is updated according to the distribution network operation data after the recovery strategy is executed, and the weight coefficient distribution of the comprehensive resilience assessment index is dynamically adjusted based on the updated result. The objective function weight coefficient of the dynamic recovery optimization model is synchronously updated, and the above steps are iteratively executed until the comprehensive resilience assessment index is continuously higher than the preset threshold.

2. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 1 is characterized by: Calculating the ice thickness of each line using the pre-built ice thickness prediction model includes: Calculate the line surface temperature change based on the Joule heating effect generated by the line current and the ambient temperature; The critical ice-free time before ice formation is determined using the transient heat balance equation for the line surface during an ongoing ice disaster. The transient heat balance equation for the line surface characterizes the dynamic changes in the line surface temperature during an ongoing ice disaster by multiplying the specific heat capacity of the line material by the rate of change of the line surface temperature. It also includes the heat balance relationships of Joule heat generated by line operation, convective heat transfer losses between the line and the environment, heat loss from supercooled water droplets heated to the condensation point, latent heat absorption from evaporating raindrops on the line surface, and radiative heat dissipation. According to the real-time freezing rain intensity, wind speed and air moisture content, the ice thickness growth is calculated in segments after the start of icing. The ice thickness is zero before the critical no-icing time is reached, and the ice accumulation is dynamically calculated after the critical no-icing time is exceeded.

3. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 1 is characterized by: Generating a set of severely icing lines includes: Preset ice thickness safety threshold and failure probability safety threshold; Real-time monitoring of ice thickness and failure probability of each line. When both ice thickness and failure probability exceed their safety thresholds, the corresponding line is dynamically marked as a severely iced line and added to the severely iced line set. Periodically clear lines that meet natural recovery conditions, where the natural recovery conditions include ice thickness being lower than a first recovery threshold and a failure probability being lower than a second recovery threshold.

4. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 1 is characterized by: The multi-dimensional resilience evaluation index of the distribution network is calculated as follows: The total load recovery index is calculated based on the time-integrated ratio of the actual load recovery during the ice disaster to the theoretical maximum load; Fault recovery time indicator, calculated based on the ratio of the time taken to recover from the fault to the total duration of the ice disaster; The network loss index is calculated based on the time-integrated ratio of the actual line network loss to the benchmark network loss.

5. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 1 is characterized by: The method of generating the optimal flight path and operation sequence of the UAV by using the pre-built UAV deicing recovery model and scheduling the UAV to perform the deicing operation includes: Obtain coordinate information, UAV cluster parameters, and thermodynamic characteristic parameters of the severely iced line set; The pre-built UAV deicing recovery model calculates the deicing energy consumption and operation time of each line and generates the optimal collaborative operation plan; Dispatch drone swarms to perform de-icing operations according to the optimal collaborative operation plan, and monitor and update the line icing status in real time.

6. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 5 is characterized by: The pre-built UAV de-icing recovery model includes: The UAV deicing recovery model takes the maximization of the comprehensive resilience evaluation index during the duration of the ice disaster as the objective function; The constraints of the UAV deicing recovery model include: Global operation time constraint, requiring the total de-icing operation time of all lines to not exceed the preset maximum allowable operation time; Power safety constraints ensure that the drone's remaining power meets the minimum requirements for returning to the nearest charging station; The personnel quantity constraint requires that the number of operators configured for each route should not be less than the number of allocated drones; Resource capacity constraints are used to verify that the de-icing equipment capacity carried by the drone meets the needs of line ice removal.

7. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 6 is characterized by: The generation of a restoration strategy based on real-time distribution network status data and a pre-built dynamic restoration optimization model includes: Real-time collection of distribution network node voltage, branch current and drone de-icing progress data, and update of line on / off status parameters; The dynamic recovery optimization model is called and converted into a second-order cone programming model, and the objective function is set as a weighted comprehensive index that maximizes load recovery efficiency and operating economy; Solve the second-order cone programming model to generate a recovery strategy, including network reconfiguration instructions and distributed generation output plans. The instructions include switching action sequences and new energy inverter control parameters. Verify whether the recovery strategy meets the node voltage safety range and line current carrying capacity constraints. If so, issue an execution instruction.

8. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 7 is characterized by: The pre-built dynamic recovery optimization model includes: The objective function of the dynamic recovery optimization model is the weighted sum of the load recovery efficiency index and the operation economy index in the post-disaster recovery phase; The constraints of the dynamic recovery optimization model include node power balance constraints, voltage safety operating range constraints, line current carrying capacity upper limit constraints, new energy output limit constraints, state linkage constraints for forced disconnection of non-deiced lines, and progress feedback constraints that meet a preset minimum closure ratio.

9. The method for improving distribution network resilience in ice disaster scenarios taking into account drone de-icing according to claim 1 or 8, characterized in that: The updating of the comprehensive resilience evaluation index according to the distribution network operation data after executing the restoration strategy, and the dynamic adjustment of the weight coefficient distribution of the comprehensive resilience evaluation index based on the updated result include: When the updated comprehensive resilience evaluation index is lower than the preset threshold or the average improvement of multiple consecutive iterations is lower than the set threshold, the weight adjustment mechanism is activated; Based on the degree of deviation between the actual performance of each sub-indicator and the benchmark value, the weight distribution ratio is adjusted in the opposite direction. When the sub-indicator value is lower than the preset benchmark value, the weight coefficient of the indicator is increased; The total sum of weight coefficients is maintained constant through normalization processing, and the objective function weight of the dynamic recovery optimization model is updated synchronously to form a collaborative optimization closed loop of evaluation and recovery strategies.

10. A distribution network resilience improvement system taking into account drone de-icing in ice disaster scenarios, based on a distribution network resilience improvement method taking into account drone de-icing in ice disaster scenarios according to any one of claims 1 to 9, characterized in that: The system includes: Data acquisition module, used to collect meteorological data, line operation parameters and equipment attribute data in real time under ice disaster environment; An icing monitoring module is used to generate a set of severely iced lines using an icing thickness prediction model and a failure probability calculation model; The resilience assessment module is used to calculate multi-dimensional resilience assessment indicators and comprehensive resilience assessment indicators based on the load change curve of the distribution network in ice disaster scenarios, and trigger the UAV collaborative de-icing command when the indicator falls below the threshold; The drone scheduling module is used to generate the optimal flight path and operation sequence of drones and schedule drones to perform de-icing operations; Dynamic recovery module, used to generate recovery strategies based on real-time distribution network status data and dynamically adjust evaluation index weight coefficients and recovery model parameters; The status update module is used to update the set of severely iced lines and the operating status of the distribution network based on the feedback from the drone deicing operation.

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