Inspection scheduling method and system for power grid insurance supply in extreme weather

Abstract

The invention provides an inspection scheduling method and system for power grid supply guarantee in extreme weather, a power grid supply guarantee inspection scheduling model constructed by the method considers the influence of three typical extreme weather of icing, dust raising and strong wind on power grid supply, and a combined configuration and scheduling mode of an unmanned aerial vehicle and an operation and maintenance team is adopted. The characteristics of low working cost of the unmanned aerial vehicle and comprehensive working coverage of an operation and maintenance team are considered, the advantages of the unmanned aerial vehicle and the operation and maintenance team are fully utilized for advantage complementation, the influence of the conventional inspection frequency on the fault occurrence frequency and supply guarantee workload is comprehensively considered, and the comprehensive supply guarantee cost is minimized on the premise that the power supply reliability of a power grid is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of power system inspection and dispatching technology, specifically relating to an inspection and dispatching method and system for ensuring power grid supply under extreme weather conditions. Background Technology

[0002] With rapid economic and social development and the deepening of energy transition, the scale of the power system continues to expand, and the power grid structure is becoming increasingly complex. Traditional manual inspection methods are no longer sufficient to meet the management needs of modern power grids, facing not only high costs and low efficiency but also limitations in coverage, precision, and response speed. Therefore, intelligent technologies such as drone inspections are being gradually promoted and applied. Through a "human-machine collaboration" model, these technologies complement each other, effectively increasing inspection efficiency several times over and providing more intelligent technical support for the safe and stable operation of the power grid.

[0003] In recent years, global climate change has intensified, leading to frequent extreme weather events such as icing, dust storms, and strong winds, which have significantly impacted power supply lines and renewable energy power plants. Extreme weather not only causes physical damage to transmission lines, such as line breaks and tower tilting, but also causes drastic fluctuations in renewable energy power output. For example, strong winds can damage wind turbine blades, dust storms can affect the efficiency of photovoltaic panels, and high temperature and humidity can reduce the insulation performance of equipment. These factors pose serious challenges to the safe and stable supply of the power system, and inspections and maintenance during extreme weather events further increase the workload and costs associated with ensuring power supply. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for inspection and dispatching to ensure power grid supply under extreme weather conditions.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] Firstly, this invention proposes a method for inspection and dispatching to ensure power grid supply under extreme weather conditions, including:

[0007] S1. Construct a power grid supply guarantee inspection and scheduling model. This model considers three extreme weather conditions: icing, dust, and strong winds, as well as the joint configuration and scheduling of drones and maintenance teams, with the goal of minimizing the overall cost of power supply guarantee.

[0008] S2. Solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

[0009] The objective function of the power grid supply guarantee inspection and scheduling model includes:

[0010] ;

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction;

[0016] The constraints include drone-maintenance team configuration constraints and extreme weather constraints.

[0017] The configuration constraints for the drone-maintenance team include:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] In the above formula, For nodes The location variables for drone configuration points, For nodes The location variables for configuring maintenance teams. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection.

[0026] The extreme weather constraints include:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line Decision variables for deploying drones For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line Decision variables for dispatching maintenance teams For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for deploying drones For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for dispatching maintenance teams.

[0047] The power grid supply guarantee inspection and scheduling model also considers the impact of regular inspection frequency on the frequency of fault occurrence and the workload of supply guarantee. The model's constraints also include fault troubleshooting constraints.

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line Decision variables for dispatching maintenance teams For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time.

[0057] The constraints of the power grid supply guarantee inspection and dispatch model also include conventional inspection constraints:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance.

[0069] Secondly, this invention proposes an inspection and dispatch system for power grid supply assurance under extreme weather conditions, including a model building module and a model solving module;

[0070] The model building module is used to build a power grid supply guarantee inspection and scheduling model. The model takes into account three extreme weather conditions: icing, dust and strong winds, as well as the joint configuration and scheduling of drones and maintenance teams, with the goal of minimizing the overall cost of power supply guarantee.

[0071] The model solving module is used to solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

[0072] The model building module includes an objective function building unit and a constraint condition building unit;

[0073] The objective function construction unit is used to construct the following objective function:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction;

[0080] The constraint construction unit is used to construct the configuration constraints for the UAV-maintenance team and the extreme weather constraints.

[0081] The configuration constraints for the drone-maintenance team include:

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] In the above formula, For nodes The location variables for drone configuration points, For nodes The location variables for configuring maintenance teams. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection.

[0090] The extreme weather constraints include:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line Decision variables for deploying drones For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line Decision variables for dispatching maintenance teams For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for deploying drones For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for dispatching maintenance teams.

[0111] The constraint construction unit is also used to construct troubleshooting constraints:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line Decision variables for dispatching maintenance teams For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time.

[0121] The constraint construction unit is also used to construct routine inspection constraints:

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] ;

[0132] In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance.

[0133] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0134] 1. The grid supply inspection and scheduling model proposed in this invention, which is a method for ensuring grid supply under extreme weather conditions, considers the impact of three typical extreme weather events—icing, dust storms, and strong winds—on the power grid. Icing on power lines in winter can affect the reliability of power supply, dust storms may affect the output of photovoltaic power plants, and strong winds may affect the blades of wind turbines in wind farms, resulting in additional workload. On the other hand, it adopts a joint configuration and scheduling mode of drones and maintenance teams, taking into account the low operating cost of drones and the comprehensive coverage of maintenance teams. It makes full use of the advantages of both to complement each other, and aims to minimize the overall cost of power supply, thereby minimizing the overall cost of power supply while ensuring the reliability of grid supply.

[0135] 2. The inspection and dispatch method for power grid supply guarantee under extreme weather conditions proposed in this invention comprehensively considers the impact of regular inspection frequency on the frequency of fault occurrence and the workload of supply guarantee. Regular inspection can eliminate some potential power supply hazards and reduce the possibility of power supply faults, but it will also increase the workload of supply guarantee. Taking these two factors into account, the overall supply guarantee cost is reduced. Attached Figure Description

[0136] Figure 1 This is a topology diagram of the 20-node power grid in Example 1.

[0137] Figure 2 This is a schematic diagram of the routine inspection process in this invention.

[0138] Figure 3 This is a schematic diagram of the fault handling process in this invention.

[0139] Figure 4 This is a schematic diagram of the extreme weather patrol and maintenance process in this invention.

[0140] Figure 5 The diagram shows the planning results of the drone-maintenance team configuration points obtained in Example 1.

[0141] Figure 6 This is a schematic diagram of the system described in Example 2. Detailed Implementation

[0142] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0143] Example 1:

[0144] This embodiment addresses topologies such as... Figure 1 The 20-node power grid shown is implemented using an inspection and dispatching method for ensuring power supply under extreme weather conditions. The specific steps are as follows:

[0145] 1. Construct a power grid supply guarantee inspection and dispatch model. This model considers three extreme weather conditions: icing, dust storms, and strong winds; the joint configuration and dispatch of drones and maintenance teams; and the impact of routine inspection frequency on the frequency of faults and the workload of supply guarantee. It involves routine inspections, fault handling, and extreme weather inspections and maintenance (specific processes are as follows). Figures 2-4 (As shown).

[0146] The power grid supply guarantee inspection and dispatch model aims to minimize the comprehensive cost of power supply guarantee. This comprehensive cost includes the configuration cost of drones and maintenance teams, the operational cost of the maintenance teams, and the energy loss cost caused by faults. The specific objective function is as follows:

[0147] (1)

[0148] (2)

[0149] (3)

[0150] (4)

[0151] (5)

[0152] In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction.

[0153] The constraints of the power grid supply guarantee inspection and dispatch model include:

[0154] (1) Unmanned Aerial Vehicle (UAV) - Maintenance Team Configuration Constraints

[0155] (6)

[0156] (7)

[0157] (8)

[0158] (9)

[0159] (10)

[0160] (11)

[0161] (12)

[0162] Equations (6)-(7) are constraints on the configuration points of drones and maintenance teams; Equations (8)-(11) are constraints on the number of drones and maintenance teams; Equation (12) is a constraint on the total workload.

[0163] In the above formula, For nodes The drone configuration point location variable, when set to 1, indicates a node This is the drone configuration point; a value of 0 indicates a node. Not a drone deployment point. For nodes The location variable for the operation and maintenance team configuration point is set to 1, indicating that the node is selected. This is the configuration point for the operations and maintenance team; a value of 0 indicates a node. It is not a configuration point for the maintenance team. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection.

[0164] (2) Routine inspection constraints:

[0165] Routine inspections are a regular task in the process of ensuring power supply. Both drones and maintenance teams are capable of performing routine inspections. However, due to their functional limitations, drones cannot fully cover the inspection work of some lines. Therefore, maintenance teams are needed to complete the inspection of these lines.

[0166] (13)

[0167] (14)

[0168] (15)

[0169] (16)

[0170] (17)

[0171] (18)

[0172] (19)

[0173] (20)

[0174] (twenty one)

[0175] (twenty two)

[0176] Equations (13)-(14) are constraints on the amount of routine inspection tasks; Equation (15) is a constraint on the dispatch scheme, where only one drone or maintenance team is dispatched from one node to perform the task; Equation (15) is a constraint on the coverage of drones, where drones cannot completely inspect some lines; Equations (17)-(18) are constraints on the number of drones dispatched, where only one drone is dispatched for the inspection of a single line; Equation (19) is a constraint on the service range of drones; Equations (20)-(21) are constraints on the number of maintenance teams dispatched, where only one maintenance team is dispatched for the inspection of a single line; Equation (22) is a constraint on the service range of maintenance teams.

[0177] In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance.

[0178] (3) Troubleshooting constraints:

[0179] Routine inspections can eliminate some potential power supply problems, thereby reducing the likelihood of power supply failures. This embodiment considers the impact of the frequency of routine inspections on the frequency of failures; that is, increasing the frequency of routine inspections can reduce the frequency of failures. Since power supply failure troubleshooting involves manual operation, all troubleshooting tasks are performed by the maintenance team.

[0180] (twenty three)

[0181] (twenty four)

[0182] (25)

[0183] (26)

[0184] (27)

[0185] (28)

[0186] (29)

[0187] (30)

[0188] Equations (23)-(24) are constraints on the frequency of fault occurrence; Equations (25)-(26) are constraints on the workload of the maintenance team under fault conditions; Equations (27)-(28) are constraints on the number of maintenance teams dispatched under fault conditions; Equation (29) is constraints on the service scope of the maintenance team under fault conditions; Equation (30) is constraints on the energy loss caused by fault.

[0189] In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line The decision variable for dispatching maintenance teams, where a value of 1 indicates a node To the line Dispatch maintenance teams, with 0 representing the node. To the line Dispatch maintenance teams. For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time.

[0190] (4) Extreme weather constraints:

[0191] This embodiment primarily considers three typical extreme weather conditions: icing, dust storms, and strong winds. In winter, icing on power lines can impact power supply reliability; the solution is to use electric current to melt the ice after identifying the icing points through inspection. Dust storms may mainly affect the output of photovoltaic power plants, while strong winds may impact the blades of wind turbines in wind farms. For both of these extreme scenarios, drones are used in conjunction with the maintenance team for inspections and simultaneous maintenance.

[0192] (31)

[0193] （32）

[0194] （33）

[0195] （34）

[0196] （35）

[0197] （36）

[0198] （37）

[0199] （38）

[0200] （39）

[0201] （40）

[0202] （41）

[0203] （42）

[0204] （43）

[0205] （44）

[0206] （45）

[0207] （46）

[0208] （47）

[0209] （48）

[0210] （49）

[0211] Equations (31)-(32) represent the workload constraints for patrols during icy weather; Equation (33) represents the dispatch plan constraints during icy weather; Equation (34) represents the coverage constraints for drones during icy weather; Equations (35)-(36) represent the number of drones dispatched during icy weather; Equation (37) represents the service range constraints for drones; Equations (38)-(39) represent the number of maintenance teams dispatched during icy weather; Equation (40) represents the service range constraints for maintenance teams during icy weather; Equation ( Equation (41)-(43) represents the constraint on the number of drones dispatched in dusty and windy weather scenarios; Equation (44) represents the constraint on the service range of drones; Equation (45) represents the constraint on the workload of maintenance teams in dusty and windy weather scenarios; Equation (46) represents the constraint on the workload of maintenance teams in dusty and windy weather scenarios; Equation (47)-(48) represents the constraint on the number of maintenance teams dispatched in dusty and windy weather scenarios; Equation (49) represents the constraint on the service range of maintenance teams in dusty and windy weather scenarios.

[0212] In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line The decision variable for dispatching drones, where a value of 1 indicates a node To the line Dispatch drones, and take 0 to represent a node. No line Dispatch drones, For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line The decision variable for dispatching maintenance teams, where a value of 1 indicates a node To the line Dispatch maintenance teams, with 0 representing the node. No line Dispatch maintenance teams. For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants The decision variable for dispatching drones, where a value of 1 indicates a node Towards new energy plants Dispatch drones, and take 0 to represent a node. Not to new energy plants Dispatch drones, For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants The decision variable for dispatching maintenance teams, where a value of 1 indicates a node Towards new energy plants Dispatch maintenance teams, with 0 representing the node. Not to new energy plants Dispatch maintenance teams.

[0213] 2. Solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

[0214] The parameters in the model of this embodiment are set as follows:

[0215] The discount rate is 0.05; the investment period is 5 years; the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment of the maintenance team, and the unit energy loss cost are 300,000 yuan, 1,000,000 yuan, 1,000,000 yuan, and 15 yuan / kWh, respectively; the maximum total number of drones and maintenance teams is 10 and 30, respectively; the maximum number of deployment points for drones and maintenance teams is 4 and 6, respectively; the maximum number of deployments per drone and maintenance team per year is 300 and 200, respectively; the maximum service distance for drones and maintenance teams is 20 kilometers and 10 kilometers, respectively; the impact coefficient of inspection frequency on failure frequency is 2×10. -4 The total operating time in a year is 8760 hours.

[0216] The solution was run on the MATLAB / CPLEX platform, with the following hardware parameters: Intel Core i7-9750H, 32GB RAM, 2.6GHz. The resulting UAV-maintenance team configuration point planning map is as follows. Figure 5 As shown.

[0217] To verify the effectiveness of the method described in this invention, the inspection and scheduling scheme obtained in Example 1 was used as Strategy 1. Based on the 20-node power grid described in Example 1, a scheduling strategy obtained by relying solely on maintenance teams for independent power supply was adopted as Strategy 2. The annual comprehensive power supply guarantee costs of these two strategies were compared, and the results are shown in Table 1.

[0218]

[0219] As can be seen, compared with Strategy 2, Strategy 1 reduces the annualized configuration cost of the line by 5.60%, the annual operating cost by 15.76%, the annual energy loss cost by 43.54%, and the annual comprehensive supply guarantee cost by 18.76%.

[0220] Example 2:

[0221] like Figure 6 As shown, an inspection and dispatch system for ensuring power grid supply under extreme weather conditions includes a model building module and a model solving module.

[0222] The model building module is used to build a power grid supply guarantee inspection and scheduling model. The model considers three extreme weather conditions: icing, dust, and strong winds, as well as the joint configuration and scheduling of drones and maintenance teams. The goal is to minimize the overall cost of power supply guarantee. The model includes an objective function building unit and a constraint condition building unit.

[0223] The objective function construction unit is used to construct the following objective function:

[0224] ;

[0225] ;

[0226] ;

[0227] ;

[0228] ;

[0229] In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction;

[0230] The constraint construction unit is used to construct:

[0231] Unmanned Aerial Vehicle (UAV) Maintenance Team Configuration Constraints

[0232] ;

[0233] ;

[0234] ;

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] In the above formula, For nodes The location variables for drone configuration points, For nodes The location variables for configuring maintenance teams. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection.

[0240] Routine inspection constraints:

[0241] ;

[0242] ;

[0243] ;

[0244] ;

[0245] ;

[0246] ;

[0247] ;

[0248] ;

[0249] ;

[0250] ;

[0251] In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance;

[0252] Troubleshooting constraints

[0253] ;

[0254] ;

[0255] ;

[0256] ;

[0257] ;

[0258] ;

[0259] ;

[0260] ;

[0261] In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line Decision variables for dispatching maintenance teams For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time;

[0262] Extreme weather constraints

[0263] ;

[0264] ;

[0265] ;

[0266] ;

[0267] ;

[0268] ;

[0269] ;

[0270] ;

[0271] ;

[0272] ;

[0273] ;

[0274] ;

[0275] ;

[0276] ;

[0277] ;

[0278] ;

[0279] ;

[0280] ;

[0281] ;

[0282] In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line Decision variables for deploying drones For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line Decision variables for dispatching maintenance teams For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for deploying drones For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for dispatching maintenance teams.

[0283] The model solving module is used to solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

Claims

1. A method for inspection and dispatching to ensure power grid supply under extreme weather conditions, characterized in that, The method includes: S1. Construct a power grid supply guarantee inspection and scheduling model. This model considers three extreme weather conditions: icing, dust, and strong winds, as well as the joint configuration and scheduling of drones and maintenance teams, with the goal of minimizing the overall cost of power supply guarantee. S2. Solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

2. The inspection and dispatching method for ensuring power grid supply under extreme weather conditions according to claim 1, characterized in that, The objective function of the power grid supply guarantee inspection and scheduling model includes: ； ； ； ； ； In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction; The constraints include drone-maintenance team configuration constraints and extreme weather constraints.

3. The inspection and dispatching method for ensuring power grid supply under extreme weather conditions according to claim 2, characterized in that, The configuration constraints for the drone-maintenance team include: ； ； ； ； ； ； ； In the above formula, For nodes The location variables for drone configuration points, For nodes The location variables for configuring maintenance teams. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection. The extreme weather constraints include: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line Decision variables for deploying drones For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line Decision variables for dispatching maintenance teams For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for deploying drones For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for dispatching maintenance teams.

4. The inspection and dispatching method for ensuring power grid supply under extreme weather conditions according to claim 2, characterized in that, The power grid supply guarantee inspection and scheduling model also considers the impact of regular inspection frequency on the frequency of fault occurrence and the workload of supply guarantee. The model's constraints also include fault troubleshooting constraints. ； ； ； ； ； ； ； ； In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line Decision variables for dispatching maintenance teams For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time.

5. The inspection and dispatching method for ensuring power grid supply under extreme weather conditions according to claim 2, characterized in that, The constraints of the power grid supply guarantee inspection and dispatch model also include conventional inspection constraints: ； ； ； ； ； ； ； ； ； ； In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance.

6. A dispatching and inspection system for power grid supply assurance under extreme weather conditions, characterized in that: The system includes a model building module and a model solving module; The model building module is used to build a power grid supply guarantee inspection and scheduling model. The model takes into account three extreme weather conditions: icing, dust and strong winds, as well as the joint configuration and scheduling of drones and maintenance teams, with the goal of minimizing the overall cost of power supply guarantee. The model solving module is used to solve the power grid supply guarantee inspection and scheduling model to obtain the inspection and scheduling scheme of drones and maintenance teams.

7. A power grid inspection and dispatching system for ensuring power supply under extreme weather conditions, as described in claim 6, is characterized in that... The model building module includes an objective function building unit and a constraint condition building unit; The objective function construction unit is used to construct the following objective function: ； ； ； ； ； In the above formula, , The configuration costs for drones and maintenance teams are respectively. The annual labor cost for the maintenance team, Cost of annual energy loss , These are the discount rate and the investment period, respectively. , , , These are the unit configuration cost of the drone, the unit configuration cost of the maintenance team, the cost per deployment by the maintenance team, and the unit energy loss cost. , They are nodes The number of drones and maintenance teams deployed at the site. Configure nodes from the maintenance team during a single routine inspection. Dispatch to Line The number of maintenance teams Configure nodes from the operations and maintenance team when handling faults. Dispatch to Line The number of maintenance teams Configure nodes from the maintenance team during a single icing weather inspection. Dispatch to Line The number of maintenance teams Configure nodes for drones in dusty and windy weather scenarios Sent to new energy plant The number of drones, For the routine inspection routes throughout the year frequency, Patrol routes during icing weather throughout the year frequency, For the impact of dust and strong winds on new energy power plants throughout the year The frequency of inspection and maintenance The total running time in a year. For the line The actual frequency of failures, For the line Energy loss during a malfunction; The constraint construction unit is used to construct the configuration constraints for the UAV-maintenance team and the extreme weather constraints.

8. A power grid supply inspection and dispatching system for extreme weather conditions as described in claim 7, characterized in that, The configuration constraints for the drone-maintenance team include: ； ； ； ； ； ； ； In the above formula, For nodes The location variables for drone configuration points, For nodes The location variables for configuring maintenance teams. , These are the maximum number of drone deployment points and maintenance team deployment points, respectively. For the large M constant, , These represent the maximum total number of drones and maintenance teams, respectively. , These are the routine inspection routes throughout the year. Patrol routes during icy weather Total workload For the route of the year The workload of troubleshooting Inspecting new energy plants during dusty and windy weather throughout the year Total workload , These represent the maximum number of times a single drone and a single maintenance team can be dispatched within a year, respectively. , These represent the workload that a single drone or a single maintenance team can complete during a single inspection. The extreme weather constraints include: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, For a single patrol route The workload, For single-time icing weather patrols from the node Dispatch to Line The number of drones, During a single patrol in icing weather, the node Whether to send to the line Decision variables for deploying drones For nodes With the line distance, , These are the service distance limits for drones and maintenance teams, respectively. During a single patrol in icing weather, the node Whether to send to the line Decision variables for dispatching maintenance teams For dust and windy weather scenarios, from node Sent to new energy plant The number of drones, For nodes With new energy plants distance, For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for deploying drones For new energy power plants The workload of a single maintenance session For dusty and windy weather scenarios, nodes Whether to provide new energy plants Decision variables for dispatching maintenance teams.

9. A dispatching and inspection system for power grid supply assurance under extreme weather conditions, as described in claim 7, is characterized in that... The constraint construction unit is also used to construct troubleshooting constraints: ； ； ； ； ； ； ； ； In the above formula, For the line The original fault frequency, The coefficient representing the impact of inspection frequency on failure frequency. For the route of the year The workload of troubleshooting For the line The workload of troubleshooting a single fault, This refers to the amount of work that a single maintenance team can complete during a single inspection. When handling faults, nodes Whether to send to the line Decision variables for dispatching maintenance teams For the large M constant, For nodes With the line distance, This is the upper limit of the service distance for the maintenance team. For the line Energy loss during a malfunction , The lines are respectively Power loss during a fault and power loss from renewable energy sources. For the line The fault recovery time.

10. A power grid supply inspection and dispatching system for extreme weather conditions as described in claim 7, characterized in that, The constraint construction unit is also used to construct routine inspection constraints: ； ； ； ； ； ； ； ； ； ； In the above formula, For the routine inspection routes throughout the year Total workload For a single patrol route The workload, For a single routine inspection from the node Dispatch to Line The number of drones, , These represent the workload that a single drone and a single maintenance team can complete during a single inspection, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for deploying drones , These are the service distance limits for drones and maintenance teams, respectively. For a single routine inspection, the node Whether to send to the line Decision variables for dispatching maintenance teams For nodes With the line The distance.

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