Power transmission line digital inspection investment effect analysis method based on unmanned aerial vehicle

Abstract

The invention relates to a power transmission line digital inspection investment effect analysis method based on an unmanned aerial vehicle, and the method comprises the steps: firstly constructing a fault compensation cost calculation module, and obtaining a manual inspection line fault probability; predicting the fault probability after the unmanned aerial vehicle is adopted for routing inspection according to the existing region routing inspection fault condition; on the basis of a fault load shedding amount calculation model, calculating compensation cost after the fault occurs; comparing the inspection efficiency of the manual inspection and the inspection efficiency of the digital inspection based on the unmanned aerial vehicle to obtain the labor cost difference; based on the benefit analysis model, the input-output of the power grid enterprise introduced unmanned aerial vehicle inspection is calculated through an input-output analysis model; and finally, the annual return rate of the unmanned aerial vehicle for digital inspection is given as an index, and decision support is provided for power grid investment. The comprehensive, dynamic and multi-dimensional cost benefit evaluation of the digital inspection and manual inspection modes of the unmanned aerial vehicle is realized; the method has the advantages that the direct and indirect cost is comprehensively analyzed, the cost-benefit evaluation is more comprehensive, and the optimal decision is ensured to be made.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission lines, and in particular relates to a method for analyzing investment effectiveness of digital inspection of power transmission lines based on unmanned aerial vehicles. Background Art

[0002] With the rapid development of the digital economy, power grid companies are accelerating their digital transformation to improve operational efficiency and management level. Transmission line inspection is an important part of power grid maintenance. Traditional manual inspection methods are unable to meet the efficient and safe operation needs of modern power grids due to low efficiency, limited coverage, and great influence of geographical conditions. For this reason, drone-based digital inspection technology has become an important direction for the digital transformation of power grid inspection with its high efficiency, accuracy, and flexibility. The existing drone-based digital inspection investment effectiveness analysis method mainly compares the costs of drone inspection and manual inspection to evaluate the economic benefits of the two inspection methods. The model usually includes cost accounting in multiple aspects such as equipment purchase cost, operation and maintenance cost, and personnel training cost, and obtains the economic benefits of digital inspection in long-term operation through comprehensive calculation; the existing cost-benefit analysis model mainly focuses on the cost-effectiveness of the digital inspection based on the cost-effectiveness of the inspection. The direct costs of UAV digital inspection and manual inspection in terms of equipment purchase, operation and maintenance, and personnel training are considered, but the consideration of indirect costs such as inspection efficiency, fault detection rate, and inspection quality is relatively limited, making it difficult to comprehensively evaluate the comprehensive benefits of different inspection modes; under complex terrain and harsh climatic conditions, the existing model's evaluation of the benefits of digital inspection is relatively weak, and fails to fully consider the differences in adaptability and reliability of different inspection modes in complex environments. For example, in different terrain environments, the inspection efficiency of digital inspection will be different, which leads to the limitations of the model in practical applications and makes it difficult to provide accurate investment guidance for enterprises in a variety of environments; therefore, it is very necessary to provide a UAV-based transmission line digital inspection investment effectiveness analysis method that comprehensively analyzes direct and indirect costs, has a more comprehensive cost-benefit evaluation, analyzes the inspection benefits in complex environments, and ensures the best decision. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for analyzing the investment effectiveness of digital inspection of power lines based on drones, which comprehensively analyzes direct and indirect costs, conducts a more comprehensive cost-benefit evaluation, analyzes the benefits of inspections in complex environments, and ensures that the best decision is made.

[0004] The object of the present invention is achieved by: a method for analyzing the investment effectiveness of digital inspection of power transmission lines based on unmanned aerial vehicles, the method comprising the following steps:

[0005] Step 1: First, build a fault compensation cost calculation module, consider the difference between the line failure probability of manual inspection and drone-based inspection, and obtain the line failure probability under the manual inspection mode by analyzing the historical data of transmission line failures;

[0006] Step 2: Using the failure conditions before and after drone inspections in existing areas, predict the failure probability after drone inspections;

[0007] Step 3: Based on the fault load shedding calculation model, calculate the load that needs to be shedding after the fault is stabilized, and use this as the amount of electricity required for compensation for the fault. Calculate the compensation cost after the fault occurs based on the scope and duration of the power outage.

[0008] Step 4: Compare the inspection efficiency of manual inspection and drone-based digital inspection, calculate the human resource input of the two, and find the difference in labor costs;

[0009] Step 5: Consider the difference in inspection efficiency, analyze the number of vehicles and round trips required for line inspection, and compare and analyze the costs incurred by vehicle transportation; at the same time, calculate the additional costs incurred by digital inspection, including the power and maintenance costs of drones;

[0010] Step 6: Based on the benefit analysis model, calculate the additional benefits brought by digital inspection, use it as input, and calculate the input-output of introducing drone inspection by power grid enterprises through the input-output analysis model;

[0011] Step 7: Finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment.

[0012] The fault-based load shedding calculation model in step 3 calculates the load that needs to be shelved after the fault becomes stable, and uses this as the amount of electricity required to compensate for the fault. The compensation cost after the fault occurs is calculated based on the power outage scope and power outage time. Specifically, by inputting the grid parameter information after the line fault based on the fault-based load shedding calculation model, the maximum load that the system can supply after the line fails can be calculated; the minimum load shedding under different fault conditions of the system can be known from the load demand when the line does not fail and the maximum supplyable load after the failure; data support is provided for the next step of calculating the effectiveness of deploying drones for inspections on transmission lines.

[0013] The fault load shedding calculation model in step 3 is specifically as follows: the power outage caused by the line fault is selected, and the compensation that the power grid company needs to pay to the power user is used as the cost of the line fault. After the fault occurs, the amount of load cut should be reduced as much as possible, the scope and impact of the power outage should be reduced, the inconvenience and economic losses caused to the user should be reduced, and the compensation cost of the power grid company to the power outage user should also be reduced. Therefore, the fault load shedding amount should be as small as possible, which can be equivalent to the maximum remaining supplyable load after the fault. The objective function of the fault load shedding calculation model of the present invention is to maximize the remaining supplyable load of the system:

[0014] In the formula, Dnum represents the number of loads connected to the system; d i represents the size of the i-th load after the fault load shedding; Const1 represents the power balance constraint of the power system; Gnum is the number of generator sets; g j represents the output of the jth unit; Const2 represents the power flow transmission constraint; PL max is the maximum transmission power matrix of the line; LF represents the power transmitted on the line; SF is the transformation factor matrix; KP is the node association matrix of the generator set; KD is the node association matrix of the load; Const3 represents the maximum and minimum output constraints of the generator set; G j,min represents the minimum output of the jth generator set; G j,max represents the maximum output of the jth generator set; Const4 represents the maximum and minimum load demand constraints. The maximum value of the remaining available load after a fault occurs is the load value when no load needs to be cut off, and the minimum value is the load value when all loads need to be cut off; d i,max is the i-th load demand value when there is no fault in the line.

[0015] The relevant costs in step 4 and step 5 are implemented using an effectiveness analysis model. Specifically, based on the effectiveness analysis model, the two different inspection modes of manual inspection and drone digital inspection are analyzed for effectiveness. Compared with manual inspection, the benefits of drone digital inspection mostly come from the reduced inspection costs. Therefore, the effectiveness analysis model includes: line fault compensation cost, line failure probability and labor cost.

[0016] The line fault compensation costs include: ① Line fault compensation costs for manual inspection: In the formula, is the line failure compensation cost in manual inspection mode; Lnum is the number of lines; represents the probability of failure of the ath line in the manual inspection mode; Δd a It indicates the load that needs to be removed by the system when the ath line fails, that is, the scope of power outage of the accident; ② The line failure compensation cost based on drone inspection: in, Compensation costs for line failures in drone digital inspection mode; represents the probability of failure of the ath line in the UAV digital inspection mode; K fault It is the load shedding compensation coefficient per megawatt.

[0017] The line failure probability is specifically as follows: the required load removal amount during a fault is given by the fault load shedding calculation model; the line failure probability under the manual inspection mode is obtained from the historical data given by the power grid; the line failure probability of the UAV digital inspection is obtained from the line failure historical data of the demonstration project that has implemented the UAV digital inspection. It can be used to estimate the lines that have not been put into UAV inspection and predict the line failure probability after the UAV inspection is put into use.

[0018] The labor cost includes: ① Labor cost under manual inspection mode: in, is the labor cost in manual inspection mode; L i is the length of the i-th line; E m K is the patrol efficiency of manual inspection, which means the patrol distance per person per day; labor ②Labor cost of UAV digital inspection: in, E is the labor cost in the UAV inspection mode; UAV is the patrol efficiency of drone inspection, which means the patrol distance that each drone can cover every day; N is the number of staff equipped with each drone.

[0019] The effectiveness analysis model also considers the vehicle transportation costs required during the line inspection process, mainly the vehicle power costs. Considering that the inspection path planning in different transmission line scenarios is different, the vehicle transportation distance is also different, including: ① Vehicle transportation cost for manual inspection: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol work point to the base camp in the manual patrol mode; m The number of days required for a manual inspection cycle; K tr The cost per kilometer of vehicle travel; 1. The number of vehicles required for line inspection in manual inspection mode; 2. The vehicle transportation cost in the UAV digital inspection mode: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol point to the base camp in the UAV inspection mode; UAVThe number of days it takes for a drone to complete an inspection cycle; The number of vehicles required for line inspection in manual inspection mode.

[0020] Compared with manual inspection, the vehicle transportation cost in the UAV digital inspection mode also needs to consider the cost of using the UAV, including the power cost of the UAV: ​​C UAV =L UAV ×K UAV +K re ×N UAV (8), where C UAV is the cost of using the drone; L UAV K is the total distance that the drone needs to fly during the drone inspection; UAV K represents the cost of drone inspection per kilometer, that is, the power cost per kilometer; re The cost of repairing the drone equipment; N UAV is the number of drones equipped; In summary, the total inspection cost under the manual inspection mode is: The total inspection cost of the drone digital inspection mode is:

[0021] The input-output analysis model in step 6 is specifically as follows: the present invention uses two methods, an annualized rate of return without considering the interest rate and an annualized rate of return with considering the interest rate, to analyze the input-output, wherein ① the annualized rate of return without considering the interest rate is: In the formula, Y represents the annualized rate of return without considering the interest rate; P is the income brought by the investment one year later; CI is the total investment cost; n is the full life cycle of the investment equipment, which is also the investment year; ② Annualized rate of return considering the interest rate: Where Y r represents the annualized rate of return taking into account the interest rate; α is the annual interest rate that can be obtained by using the investment cost for other financial management.

[0022] Beneficial effects of the invention: The invention is a method for analyzing the investment effectiveness of digital inspection of transmission lines based on drones, which mainly solves the limitations of existing cost-benefit analysis models in investment decisions for digital inspections based on drones. In use, firstly, the existing model only considers the direct costs of drone-based inspections and manual inspections (such as equipment purchase, operation and maintenance, and personnel training), but ignores indirect costs such as inspection efficiency, fault detection rate, and inspection quality. The invention provides a more comprehensive cost-benefit evaluation by comprehensively analyzing direct and indirect costs, so that enterprises can accurately judge the comprehensive economic benefits of different inspection modes; the existing model is relatively weak in evaluating the benefits of drone-based inspections under complex terrain and harsh climatic conditions, and fails to fully consider the differences in adaptability and reliability of different inspection modes in complex environments. The invention particularly conducts a detailed analysis of the inspection benefits in complex environments, provides more targeted investment guidance, and ensures that enterprises can make the best decisions in diversified environments; the invention has the advantages of comprehensive analysis of direct and indirect costs, more comprehensive cost-benefit evaluation, and inspection benefit analysis for complex environments to ensure the best decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] Example 1

[0026] like Figure 1 As shown, a method for analyzing the investment effectiveness of digital inspection of power transmission lines based on drones comprises the following steps:

[0027] Step 1: First, build a fault compensation cost calculation module, consider the difference between the line failure probability of manual inspection and drone-based inspection, and obtain the line failure probability under the manual inspection mode by analyzing the historical data of transmission line failures;

[0028] Step 2: Using the failure conditions before and after drone inspections in existing areas, predict the failure probability after drone inspections;

[0029] Step 3: Based on the fault load shedding calculation model, calculate the load that needs to be shedding after the fault is stabilized, and use this as the amount of electricity required for compensation for the fault. Calculate the compensation cost after the fault occurs based on the scope and duration of the power outage.

[0030] Step 4: Compare the inspection efficiency of manual inspection and drone-based digital inspection, calculate the human resource input of the two, and find the difference in labor costs;

[0031] Step 5: Consider the difference in inspection efficiency, analyze the number of vehicles and round trips required for line inspection, and compare and analyze the costs incurred by vehicle transportation; at the same time, calculate the additional costs incurred by digital inspection, including the power and maintenance costs of drones;

[0032] Step 6: Based on the benefit analysis model, calculate the additional benefits brought by digital inspection, use it as input, and calculate the input-output of introducing drone inspection by power grid enterprises through the input-output analysis model;

[0033] Step 7: Finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment.

[0034] The fault-based load shedding calculation model in step 3 calculates the load that needs to be shelved after the fault becomes stable, and uses this as the amount of electricity required to compensate for the fault. The compensation cost after the fault occurs is calculated based on the power outage scope and power outage time. Specifically, by inputting the grid parameter information after the line fault based on the fault-based load shedding calculation model, the maximum load that the system can supply after the line fails can be calculated; the minimum load shedding under different fault conditions of the system can be known from the load demand when the line does not fail and the maximum supplyable load after the failure; data support is provided for the next step of calculating the effectiveness of deploying drones for inspections on transmission lines.

[0035] The fault load shedding calculation model in step 3 is specifically as follows: considering the impact of manual inspection and drone digital inspection on the failure probability of transmission lines, in order to measure the impact of different failure probabilities on the power grid revenue, the present invention selects the power outage caused by the line fault, and the compensation that the power grid company needs to pay to the power user as the cost of the line fault. After the fault occurs, the amount of load cut should be reduced as much as possible, the scope and impact of the power outage should be reduced, the inconvenience and economic losses caused to the user should be reduced, and the compensation cost of the power grid company to the power outage user should also be reduced. Therefore, the fault load shedding amount should be as small as possible, which can be equivalent to the maximum remaining supplyable load after the fault. The objective function of the fault load shedding calculation model of the present invention is to maximize the remaining supplyable load of the system: In the formula, Dnum represents the number of loads connected to the system; d i represents the size of the i-th load after the fault load shedding; Const1 represents the power balance constraint of the power system; Gnum is the number of generator sets; g j represents the output of the jth unit; Const2 represents the power flow transmission constraint; PL maxis the maximum transmission power matrix of the line; LF represents the power transmitted on the line; SF is the transformation factor matrix; KP is the node association matrix of the generator set; KD is the node association matrix of the load; Const3 represents the maximum and minimum output constraints of the generator set; G j,min represents the minimum output of the jth generator set; G j,max represents the maximum output of the jth generator set; Const4 represents the maximum and minimum load demand constraints. The maximum value of the remaining available load after a fault occurs is the load value when no load needs to be cut off, and the minimum value is the load value when all loads need to be cut off; d i,max is the i-th load demand value when there is no fault in the line.

[0036] The present invention is a method for analyzing the investment effectiveness of digital inspection of transmission lines based on drones, and proposes an investment decision-making model for digital inspection of transmission lines based on drones, aiming to provide decision support for power grid investment through input-output analysis. In use, first, a fault compensation cost calculation module is designed, and the difference in line failure probability between manual inspection and drone-based inspection is considered. By analyzing the historical data of transmission line failures, the line failure probability under the manual inspection mode is obtained; the failure conditions before and after drone inspections are adopted in existing areas are used to predict the failure probability after drone inspections are adopted; based on the fault load shedding calculation model, the load that needs to be shelved after the fault tends to be stable is calculated, and this is used as the amount of electricity required to compensate for the fault, and the compensation cost after the fault occurs is calculated according to the scope and time of the power outage; by comparing the inspection efficiency of manual inspection and drone-based digital inspection, the human resource input of the two is calculated, and the difference in labor costs is obtained; considering the inspection The difference in efficiency is analyzed, the number of automobile transportations and the number of round trips required for line inspection are analyzed, and the costs incurred by vehicle transportation are compared and analyzed; at the same time, the additional costs incurred by digital inspections are calculated, including the power costs and maintenance costs of drones; based on the benefit analysis model, the additional benefits brought by digital inspections are calculated, and they are used as input to calculate the input-output of the introduction of drone inspections by power grid companies through the input-output analysis model; finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment; through these steps, the present invention realizes a comprehensive, dynamic, and multi-dimensional cost-benefit evaluation of drone digital inspections and manual inspection modes, helping power grid companies to optimize the selection of inspection modes, improve inspection efficiency, reduce operating costs, and enhance overall economic benefits and operational reliability; the present invention has the advantages of comprehensive analysis of direct and indirect costs, more comprehensive cost-benefit evaluation, and benefit analysis for complex environment inspections to ensure the best decision.

[0037] Example 2

[0038] like Figure 1As shown, a method for analyzing the investment effectiveness of digital inspection of power transmission lines based on drones comprises the following steps:

[0039] Step 1: First, build a fault compensation cost calculation module, consider the difference between the line failure probability of manual inspection and drone-based inspection, and obtain the line failure probability under the manual inspection mode by analyzing the historical data of transmission line failures;

[0040] Step 2: Using the failure conditions before and after drone inspections in existing areas, predict the failure probability after drone inspections;

[0041] Step 3: Based on the fault load shedding calculation model, calculate the load that needs to be shedding after the fault is stabilized, and use this as the amount of electricity required for compensation for the fault. Calculate the compensation cost after the fault occurs based on the scope and duration of the power outage.

[0042] Step 4: Compare the inspection efficiency of manual inspection and drone-based digital inspection, calculate the human resource input of the two, and find the difference in labor costs;

[0043] Step 5: Consider the difference in inspection efficiency, analyze the number of vehicles and round trips required for line inspection, and compare and analyze the costs incurred by vehicle transportation; at the same time, calculate the additional costs incurred by digital inspection, including the power and maintenance costs of drones;

[0044] Step 6: Based on the benefit analysis model, calculate the additional benefits brought by digital inspection, use it as input, and calculate the input-output of introducing drone inspection by power grid enterprises through the input-output analysis model;

[0045] Step 7: Finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment.

[0046] The relevant costs in step 4 and step 5 are implemented using an effectiveness analysis model. Specifically, based on the effectiveness analysis model, the two different inspection modes of manual inspection and drone digital inspection are analyzed for effectiveness. Compared with manual inspection, the benefits of drone digital inspection mostly come from the reduced inspection costs. Therefore, the effectiveness analysis model includes: line fault compensation cost, line failure probability and labor cost.

[0047] In order to quantify the benefits of manual inspection and UAV digital inspection on the probability of line failure, the present invention uses the compensation that the power grid enterprise needs to pay to the power user due to line failure as the line failure compensation cost. The line failure compensation cost includes: ① Line failure compensation cost of manual inspection: In the formula, is the line failure compensation cost in manual inspection mode; Lnum is the number of lines; represents the probability of failure of the ath line in the manual inspection mode; Δd a It indicates the load that needs to be removed by the system when the ath line fails, that is, the scope of power outage of the accident; ② The line failure compensation cost based on drone inspection: in, Compensation costs for line failures in drone digital inspection mode; represents the probability of failure of the ath line in the UAV digital inspection mode; K fault It is the load shedding compensation coefficient per megawatt.

[0048] The line failure probability is specifically as follows: the required load removal amount during a fault is given by the fault load shedding calculation model; the line failure probability under the manual inspection mode is obtained from the historical data given by the power grid; the line failure probability of the UAV digital inspection is obtained from the line failure historical data of the demonstration project that has implemented the UAV digital inspection. It can be used to estimate the lines that have not been put into UAV inspection and predict the line failure probability after the UAV inspection is put into use.

[0049] Compared with manual inspection, a major advantage of drone digital inspection is the efficiency of drone inspection, so drone inspection may save more manpower. The labor cost includes: ① Labor cost under manual inspection mode: in, is the labor cost in manual inspection mode; L i is the length of the i-th line; E m K is the patrol efficiency of manual inspection, which means the patrol distance per person per day; labor ②Labor cost of UAV digital inspection: in, E is the labor cost in the UAV inspection mode; UAV is the patrol efficiency of drone inspection, which means the patrol distance that each drone can cover every day; N is the number of staff equipped with each drone.

[0050] The effectiveness analysis model also considers the vehicle transportation costs required during the line inspection process, mainly the vehicle power costs. Considering that the inspection path planning in different transmission line scenarios is different, the vehicle transportation distance is also different, including: ① Vehicle transportation cost for manual inspection: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol work point to the base camp in the manual patrol mode; mThe number of days required for a manual inspection cycle; K tr The cost per kilometer of vehicle travel; 1. The number of vehicles required for line inspection in manual inspection mode; 2. The vehicle transportation cost in the UAV digital inspection mode: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol point to the base camp in the UAV inspection mode; UAV The number of days it takes for a drone to complete an inspection cycle; The number of vehicles required for line inspection in manual inspection mode.

[0051] Compared with manual inspection, the vehicle transportation cost in the UAV digital inspection mode also needs to consider the cost of using the UAV, including the power cost of the UAV: ​​C UAV =L UAV ×K UAV +K re ×N UAV (8), where C UAV is the cost of using the drone; L UAV K is the total distance that the drone needs to fly during the drone inspection; UAV K represents the cost of drone inspection per kilometer, that is, the power cost per kilometer; re The cost of repairing the drone equipment; N UAV is the number of drones equipped; In summary, the total inspection cost under the manual inspection mode is: The total inspection cost of the drone digital inspection mode is: Manual inspections do not require consideration of drone usage fees.

[0052] The input-output analysis model in step 6 is specifically as follows: the present invention uses two methods, an annualized rate of return without considering the interest rate and an annualized rate of return with considering the interest rate, to analyze the input-output, wherein ① the annualized rate of return without considering the interest rate is: In the formula, Y represents the annualized rate of return without considering the interest rate; P is the income brought by the investment one year later; CI is the total investment cost; n is the full life cycle of the investment equipment, which is also the investment year; ② Annualized rate of return considering the interest rate: Where Y r represents the annualized rate of return considering the interest rate; α is the annual interest rate that can be obtained by using the investment cost for other financial management

[0053] The present invention is a method for analyzing the investment effectiveness of digital inspection of transmission lines based on drones, and proposes an investment decision-making model for digital inspection of transmission lines based on drones, aiming to provide decision support for power grid investment through input-output analysis. In use, first, a fault compensation cost calculation module is designed, and the difference in line failure probability between manual inspection and drone-based inspection is considered. By analyzing the historical data of transmission line failures, the line failure probability under the manual inspection mode is obtained; the failure conditions before and after drone inspections are adopted in existing areas are used to predict the failure probability after drone inspections are adopted; based on the fault load shedding calculation model, the load that needs to be shelved after the fault tends to be stable is calculated, and this is used as the amount of electricity required to compensate for the fault, and the compensation cost after the fault occurs is calculated according to the scope and time of the power outage; by comparing the inspection efficiency of manual inspection and drone-based digital inspection, the human resource input of the two is calculated, and the difference in labor costs is obtained; considering the inspection The difference in efficiency is analyzed, the number of automobile transportations and the number of round trips required for line inspection are analyzed, and the costs incurred by vehicle transportation are compared and analyzed; at the same time, the additional costs incurred by digital inspections are calculated, including the power costs and maintenance costs of drones; based on the benefit analysis model, the additional benefits brought by digital inspections are calculated, and they are used as input to calculate the input-output of the introduction of drone inspections by power grid companies through the input-output analysis model; finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment; through these steps, the present invention realizes a comprehensive, dynamic, and multi-dimensional cost-benefit evaluation of drone digital inspections and manual inspection modes, helping power grid companies to optimize the selection of inspection modes, improve inspection efficiency, reduce operating costs, and enhance overall economic benefits and operational reliability; the present invention has the advantages of comprehensive analysis of direct and indirect costs, more comprehensive cost-benefit evaluation, and benefit analysis for complex environment inspections to ensure the best decision.

Claims

1. The investment effectiveness analysis method of digital inspection of power transmission lines based on drones is characterized by: The method comprises the following steps: Step 1: First, build a fault compensation cost calculation module, consider the difference between the line failure probability of manual inspection and drone-based inspection, and obtain the line failure probability under the manual inspection mode by analyzing the historical data of transmission line failures; Step 2: Using the failure conditions before and after drone inspections in existing areas, predict the failure probability after drone inspections; Step 3: Based on the fault load shedding calculation model, calculate the load that needs to be shedding after the fault is stabilized, and use this as the amount of electricity required for compensation for the fault. Calculate the compensation cost after the fault occurs based on the scope and duration of the power outage. Step 4: Compare the inspection efficiency of manual inspection and drone-based digital inspection, calculate the human resource input of the two, and find the difference in labor costs; Step 5: Consider the difference in inspection efficiency, analyze the number of vehicles and round trips required for line inspection, and compare and analyze the costs incurred by vehicle transportation; at the same time, calculate the additional costs incurred by digital inspection, including the power and maintenance costs of drones; Step 6: Based on the benefit analysis model, calculate the additional benefits brought by digital inspection, use it as input, and calculate the input-output of the grid company's introduction of drone inspection through the input-output analysis model; Step 7: Finally, the annualized rate of return on investment in drone digital inspection technology is given as an indicator to provide decision support for power grid investment.

2. The method for analyzing investment effectiveness of digital inspection of power transmission lines based on unmanned aerial vehicles according to claim 1, characterized in that: The fault-based load shedding calculation model in step 3 calculates the load that needs to be shelved after the fault becomes stable, and uses this as the amount of electricity required to compensate for the fault. The compensation cost after the fault occurs is calculated based on the power outage scope and power outage time. Specifically, by inputting the grid parameter information after the line fault based on the fault-based load shedding calculation model, the maximum load that the system can supply after the line fails can be calculated; the minimum load shedding under different fault conditions of the system can be known from the load demand when the line does not fail and the maximum supplyable load after the failure; data support is provided for the next step of calculating the effectiveness of deploying drones for inspections on transmission lines.

3. The method for analyzing investment effectiveness of digital inspection of power transmission lines based on unmanned aerial vehicles according to claim 2, characterized in that: The fault load shedding calculation model in step 3 is specifically as follows: the power outage caused by the line fault is selected, and the compensation that the power grid company needs to pay to the power user is used as the cost of the line fault. After the fault occurs, the amount of load cut should be reduced as much as possible, the scope and impact of the power outage should be reduced, the inconvenience and economic losses caused to the user should be reduced, and the compensation cost of the power grid company to the power outage user should also be reduced. Therefore, the fault load shedding amount should be as small as possible, which can be equivalent to the maximum remaining supplyable load after the fault. The objective function of the fault load shedding calculation model of the present invention is to maximize the remaining supplyable load of the system: In the formula, Dnum represents the number of loads connected to the system; d i It indicates the size of the i-th load after the fault load shedding; Const1 represents the power balance constraint of the power system; Gnum is the number of generator sets; g j represents the output of the jth unit; Const2 represents the power flow transmission constraint; PL max is the maximum transmission power matrix of the line; LF represents the power transmitted on the line; SF is the transformation factor matrix; KP is the node association matrix of the generator set; KD is the node association matrix of the load; Const3 represents the maximum and minimum output constraints of the generator set; G j,min represents the minimum output of the jth generator set; G j,max represents the maximum output of the jth generator set; Const4 represents the maximum and minimum load demand constraints. The maximum value of the remaining available load after a fault occurs is the load value when no load needs to be cut off, and the minimum value is the load value when all loads need to be cut off. i,max is the i-th load demand value when there is no fault in the line.

4. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicle according to claim 1, characterized in that: The relevant costs in step 4 and step 5 are implemented using an effectiveness analysis model. Specifically, based on the effectiveness analysis model, the two different inspection modes of manual inspection and drone digital inspection are analyzed for effectiveness. Compared with manual inspection, the benefits of drone digital inspection mostly come from the reduced inspection costs. Therefore, the effectiveness analysis model includes: line fault compensation cost, line failure probability and labor cost.

5. The method for analyzing investment effectiveness of digital inspection of power transmission lines based on unmanned aerial vehicles according to claim 4, characterized in that: The line fault compensation costs include: ① Line fault compensation costs for manual inspection: In the formula, is the line failure compensation cost in manual inspection mode; Lnum is the number of lines; represents the probability of failure of the ath line in the manual inspection mode; Δd a It indicates the load that needs to be removed by the system when the ath line fails, that is, the scope of power outage of the accident; ② The line failure compensation cost based on drone inspection: in, Compensation costs for line failures in drone digital inspection mode; represents the probability of failure of the ath line in the UAV digital inspection mode; K fault It is the load shedding compensation coefficient per megawatt.

6. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicles according to claim 5, characterized in that: The line failure probability is specifically as follows: the required load removal amount during a fault is given by the fault load shedding calculation model; the line failure probability under the manual inspection mode is obtained from the historical data given by the power grid; the line failure probability of the UAV digital inspection is obtained from the line failure historical data of the demonstration project that has implemented the UAV digital inspection. It can be used to estimate the lines that have not been put into UAV inspection and predict the line failure probability after the UAV inspection is put into use.

7. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicles according to claim 6, characterized in that: The labor cost includes: ① Labor cost under manual inspection mode: in, is the labor cost in manual inspection mode; L i is the length of the i-th line; E m K is the patrol efficiency of manual inspection, which means the patrol distance per person per day; labor ②Labor cost of UAV digital inspection: in, E is the labor cost in the UAV inspection mode; UAV is the patrol efficiency of drone inspection, which means the patrol distance that each drone can cover every day; N is the number of staff equipped with each drone.

8. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicle according to claim 4, characterized in that: The effectiveness analysis model also considers the vehicle transportation costs required during the line inspection process, mainly the vehicle power costs. Considering that the inspection path planning in different transmission line scenarios is different, the vehicle transportation distance is also different, including: ① Vehicle transportation cost for manual inspection: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol work point to the base camp in the manual patrol mode; m The number of days required for a manual inspection cycle; K tr The cost per kilometer of vehicle travel; 1. The number of vehicles required for line inspection in manual inspection mode; 2. The vehicle transportation cost in the UAV digital inspection mode: in, It is the vehicle transportation cost under the manual inspection mode; T is the average round trip distance from the patrol point to the base camp in the UAV inspection mode; UAV The number of days it takes for a drone to complete an inspection cycle; The number of vehicles required for line inspection in manual inspection mode.

9. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicles according to claim 8, characterized in that: Compared with manual inspection, the vehicle transportation cost in the UAV digital inspection mode also needs to consider the cost of using the UAV, including the power cost of the UAV: ​​C UAV =L UAV ×K UAV +K re ×N UAV (8), where C UAV is the cost of using the drone; L UAV K is the total distance that the drone needs to fly during the drone inspection; UAV K represents the cost of drone inspection per kilometer, that is, the power cost per kilometer; re The cost of repairing the drone equipment; N UAV is the number of drones equipped; In summary, the total inspection cost under the manual inspection mode is: The total inspection cost of the drone digital inspection mode is:

10. The method for analyzing investment effectiveness of transmission line digital inspection based on unmanned aerial vehicles according to claim 1, characterized in that: The input-output analysis model in step 6 is specifically as follows: the present invention uses two methods, an annualized rate of return without considering the interest rate and an annualized rate of return with considering the interest rate, to analyze the input-output, wherein ① the annualized rate of return without considering the interest rate is: In the formula, Y represents the annualized rate of return without considering the interest rate; P is the income brought by the investment one year later; CI is the total investment cost; n is the full life cycle of the investment equipment, which is also the investment year; ② Annualized rate of return considering the interest rate: Where Y r represents the annualized rate of return taking into account the interest rate; α is the annual interest rate that can be obtained by using the investment cost for other financial management.

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