Energy Consumption Optimization Method and System for Power System Inspection Unmanned Aerial Vehicles

Abstract

This invention discloses a method for optimizing the energy consumption of a power system inspection drone, comprising: acquiring data information of the target power system inspection drone system; constructing an inspection model of the target power system drone; constructing an energy consumption optimization model of the drone with the objectives of maximizing the drone's energy efficiency and maximizing the energy obtained by the sensors; and solving the constructed model to complete the energy consumption optimization for the power system inspection drone. This invention also discloses a system for implementing the energy consumption optimization method for the power system inspection drone. By acquiring data from the target inspection system and constructing and solving a corresponding energy consumption optimization model based on the acquired data, this invention not only achieves energy consumption optimization for power system inspection drones but also offers higher reliability and better accuracy.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a method and system for optimizing energy consumption of unmanned aerial vehicles (UAVs) used for power system inspection. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Power system inspection is a prerequisite for ensuring the stable and reliable operation of the power system. Traditional power system inspection requires manual inspection by power system personnel, which is time-consuming, labor-intensive, and has poor reliability and safety. With the rapid development of drone technology, power systems have begun to use drones on a large scale for power system inspection.

[0004] However, battery energy is a significant concern during drone inspections of power systems. Furthermore, due to weather, environmental, and communication factors, drone battery energy management remains a key research focus. Currently, energy management solutions for drones used in power system inspections often employ relatively simple energy models; while simple, these solutions lack reliability and accuracy, severely impacting the drone's inspection performance. Summary of the Invention

[0005] One of the objectives of this invention is to provide a highly reliable and accurate method for optimizing energy consumption of unmanned aerial vehicles (UAVs) used for power system inspection.

[0006] The second objective of this invention is to provide a system for implementing the energy consumption optimization method for power system inspection drones.

[0007] The energy consumption optimization method for power system inspection drones provided by this invention includes the following steps:

[0008] S1. Acquire data information from the target power system inspection drone system;

[0009] S2. Based on the data obtained in step S1, construct a UAV inspection model for the target power system;

[0010] S3. Based on the inspection model constructed in step S2, with the goal of maximizing the energy consumption efficiency of the UAV and maximizing the energy obtained by the sensors, construct an energy consumption optimization model for the UAV.

[0011] S4. Based on the game theory iterative algorithm, solve the model constructed in step S3 to complete the energy consumption optimization for the power system inspection drone.

[0012] Step S2, which involves constructing a UAV inspection model for the target power system, specifically includes the following steps:

[0013] The constructed target power system drone inspection model includes a nested server, drones, and several sensors; among them, the drones are equipped with N U The source-transmitting drone with a root antenna is used to send information to the nest server and sensors; the nest server is used to receive data information sent by the source-transmitting drone; the sensors are used to receive energy harvesting signals sent by the drone; the nest server, drone and sensors communicate with each other using a B / S communication method.

[0014] Step S3, which aims to maximize the energy efficiency of the UAV and the energy obtained by the sensors, involves constructing an energy consumption optimization model for the UAV. This specifically includes the following steps:

[0015] The transmitted signal vector x is represented as x = ws, where w is the beamforming vector and s is the secure signal. For transposing secure signals;

[0016] The received signal of the nest server is represented as y N =h N x+n N , where y N For the received signal of the nest server, h N To represent the channel vector between the UAV and the nest, n N Additive white Gaussian noise for legitimate users and CN() follows a complex Gaussian distribution, σ N Noise power;

[0017] The energy signal received by the sensor is represented as y I =h I x+n I , where y I The energy signal received by the sensor; h I To represent the channel vector between the UAV and the sensor and A L A is the path loss factor for the Loss of Path (LoS) link. N For non-LoS links, d I α is the distance between the drone and the sensor. L α is the path loss exponent for Loss of Path (LoS) links. N K is the path loss exponent for non-LoS links. Ih is the Rice factor of the channel between the drone and the sensor. I,L Indicates the composition of Loss, h I,N Indicates non-LoS composition; n I Let be the noise vector at the sensor and σ I Noise power;

[0018] The energy obtained by the sensor is expressed as Where E I The energy obtained by the sensor, The energy absorption coefficient, The channel vector estimate, Δh I This represents the channel vector error value.

[0019] The following formula is used as the objective function for the UAV:

[0020]

[0021] stTr(Q w )≤P U

[0022] R N ≥R th

[0023] Q w ≥0

[0024] Rank(Q w ) = 1

[0025] In the formula Q w Let R be the covariance matrix of w; N The information transmission rate between the drone and the nest; Tr(Q) w ) represents the UAV's transmit power; P U R is the maximum transmit power of the drone; th The minimum data transmission rate between the drone and the nest;

[0026] The following formula is used as the objective function of the sensor:

[0027]

[0028] stTr(Q w )≤P

[0029] E I ≥E th

[0030] In the formula, P represents the maximum transmit power of the UAV; E I For the sensor to receive energy; E th This represents the minimum energy reception requirement for the sensor.

[0031] Step S4, which uses a game theory-based iterative algorithm to solve the model constructed in step S3, specifically includes the following steps:

[0032] The Dinkelbach method is used to handle the fractional programming problem, and then a semidefinite programming method is used to transform the UAV objective function constructed in step S3 into a convex problem of the following form:

[0033]

[0034] stTr(Q w )≤P U

[0035] R N ≥R th

[0036] Q w ≥0

[0037] Rank(Q w ) = 1

[0038] In the formula μ * For maximum energy efficiency;

[0039] If and only if At that time, μ * Take the optimal value; where, To optimize the UAV's transmission beam;

[0040] During the solution process, the objective function of the UAV is first solved, and the final transmitted beam of the UAV is then determined. The optimal transmitted beam result of the UAV is then substituted into the objective function of the sensor. The optimal solution of the sensor objective function is then obtained. Finally, when the transmitted beam of the UAV no longer changes, the iteration ends, and the final transmitted beam of the UAV is obtained, thus completing the energy consumption optimization for the UAV used for power system inspection.

[0041] This invention also provides a system for implementing the energy consumption optimization method for power system inspection drones, comprising a system data acquisition module, an inspection model construction module, an optimization model construction module, and an energy consumption optimization module; the system data acquisition module, the inspection model construction module, the optimization model construction module, and the energy consumption optimization module are connected in series; the system data acquisition module is used to acquire data information of the target power system inspection drone system and upload the data information to the inspection model construction module; the inspection model construction module is used to construct an inspection model of the target power system drone based on the received data information and the acquired data information, and upload the data information to the optimization model construction module; the optimization model construction module is used to construct an energy consumption optimization model of the drone based on the received data information and the constructed inspection model, with the objectives of maximizing the drone's energy consumption efficiency and maximizing the energy obtained by the sensors, and upload the data information to the energy consumption optimization module; the energy consumption optimization module is used to solve the constructed model based on a game theory iterative algorithm based on the received data information to complete the energy consumption optimization for power system inspection drones.

[0042] The energy consumption optimization method and system for power system inspection drones provided by this invention acquires data from the target inspection system, constructs and solves a corresponding energy consumption optimization model based on the acquired data, and ultimately not only achieves energy consumption optimization for power system inspection drones, but also has higher reliability and better accuracy. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0044] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0045] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The energy consumption optimization method for power system inspection drones provided by the present invention includes the following steps:

[0046] S1. Acquire data information from the target power system inspection drone system;

[0047] S2. Based on the data obtained in step S1, construct a drone inspection model for the target power system; specifically, this includes the following steps:

[0048] The constructed target power system drone inspection model includes a nested server, drones, and several sensors; among them, the drones are equipped with N UThe source-transmitting drone with a root antenna is used to send information to the nest server and sensors; the nest server is used to receive data information sent by the source-transmitting drone; the sensors are used to receive energy harvesting signals sent by the drone; the nest server, drone and sensors communicate with each other using a B / S communication method.

[0049] S3. Based on the inspection model constructed in step S2, and with the objectives of maximizing the energy consumption efficiency of the UAV and maximizing the energy obtained by the sensors, construct an energy consumption optimization model for the UAV; specifically including the following steps:

[0050] Game theory is employed to solve for the optimal beam optimization strategy for UAV inspection, maximizing UAV energy efficiency and achieving a balance between energy transmission. A game theory model of UAV information and energy transmission in power grid inspection can be established and analyzed from both the UAV and sensor perspectives. A single-leader-single-follower Stackelberg game model is constructed to describe the interaction process between the UAV and the sensor. Since the sensor needs to obtain as much energy as possible, while the UAV needs to minimize energy consumption while ensuring information transmission, there is a conflicting need between the UAV and the sensor.

[0051] The transmitted signal vector x is represented as x = ws, where w is the beamforming vector and s is the secure signal. For transposing secure signals;

[0052] The received signal of the nest server is represented as y N =h N x+n N , where y N For the received signal of the nest server, h N To represent the channel vector between the UAV and the nest, n N Additive white Gaussian noise for legitimate users and CN() follows a complex Gaussian distribution, σ N Noise power;

[0053] The energy signal received by the sensor is represented as y I =h I x+n I , where y I The energy signal received by the sensor; h I To represent the channel vector between the UAV and the sensor and A L A is the path loss factor for the Loss of Path (LoS) link. N For non-LoS links, d I α is the distance between the drone and the sensor. L α is the path loss exponent for Loss of Path (LoS) links.N K is the path loss exponent for non-LoS links. I h is the Rice factor of the channel between the drone and the sensor. I,L Indicates the composition of Loss, h I,N Indicates non-LoS composition; n I Let be the noise vector at the sensor and σ I Noise power;

[0054] The energy obtained by the sensor is expressed as Where E I The energy obtained by the sensor, The energy absorption coefficient, The channel vector estimate, Δh I This represents the channel vector error value.

[0055] The following formula is used as the objective function for the UAV:

[0056]

[0057] stTr(Q w )≤P U

[0058] R N ≥R th

[0059] Q w ≥0

[0060] Rank(Q w ) = 1

[0061] In the formula Q w Let R be the covariance matrix of w; N The information transmission rate between the drone and the nest; Tr(Q) w ) represents the UAV's transmit power; P U R is the maximum transmit power of the drone; th The minimum data transmission rate between the drone and the nest;

[0062] In the objective function of the above UAV, the first constraint is the maximum transmit power constraint of the UAV, and the second constraint is the data transmission rate constraint.

[0063] The following formula is used as the objective function of the sensor:

[0064]

[0065] stTr(Q w )≤P

[0066] EI ≥E th

[0067] In the formula, P represents the maximum transmit power of the UAV; E I For the sensor to receive energy; E th This represents the sensor's minimum energy reception requirement;

[0068] In the objective function of the above sensor objective function;

[0069] S4. Based on the game theory iterative algorithm, solve the model constructed in step S3 to complete the energy consumption optimization for the power system inspection drone; specifically including the following steps:

[0070] The Dinkelbach method is used to handle the fractional programming problem, and then a semidefinite programming method is used to transform the UAV objective function constructed in step S3 into a convex problem of the following form:

[0071]

[0072] stTr(Q w )≤P U

[0073] R N ≥R th

[0074] Q w ≥0

[0075] Rank(Q w ) = 1

[0076] In the formula μ * For maximum energy efficiency;

[0077] If and only if At that time, μ * Take the optimal value; where, To optimize the UAV's transmission beam;

[0078] During the solution process, the objective function of the UAV is first solved, and the final transmitted beam of the UAV is then determined. The optimal transmitted beam result of the UAV is then substituted into the objective function of the sensor. The optimal solution of the sensor objective function is then obtained. Finally, when the transmitted beam of the UAV no longer changes, the iteration ends, and the final transmitted beam of the UAV is obtained, thus completing the energy consumption optimization for the UAV used for power system inspection.

[0079] This section evaluates the performance of the proposed robust and secure beamforming design scheme through simulation. For lack of generality, the coordinates of the UAV base station, ground pod, and sensor are set to (0,0,80m), (20m,0,0), and [other coordinates not specified]. Rice factor KN and K I Set to respectively definition This represents the ratio of the maximum estimated error of AOD between the drone and its nest and sensors. This represents the maximum AOD estimation error ratio between the UAV and the sensor. A larger AOD estimation error indicates more severe UAV jitter and poorer channel quality. Environmental parameters are set as a = 5 and b = (2 / π)ln3. Other main simulation parameter settings are shown in Table 1.

[0080] Table 1 Simulation Parameter Diagram

[0081] parameter Parameter values Number of antennas for drone base stations 8 Maximum transmission power of drones Nu*30dBm drone altitude 80m carrier frequency 2.4GHz Antenna spacing 6.25cm Noise power -104dBm

[0082] The MRT scheme for transmitting useful signals was selected as an existing comparative scheme. For the MRT scheme, the beam vector... This represents the power allocated to the legitimate user Bob. Compared to existing solutions, the proposed algorithm achieves a significant gain in UAV energy efficiency performance; particularly, the method performs better when the AOD error is large.

[0083] like Figure 2 The diagram shows the functional modules of the system of the present invention: The system disclosed in this invention for implementing the energy consumption optimization method for power system inspection drones includes a system data acquisition module, an inspection model construction module, an optimization model construction module, and an energy consumption optimization module; the system data acquisition module, the inspection model construction module, the optimization model construction module, and the energy consumption optimization module are connected in series; the system data acquisition module is used to acquire data information of the target power system inspection drone system and upload the data information to the inspection model construction module; the inspection model construction module is used to construct an inspection model of the target power system drone based on the received data information and the acquired data information, and upload the data information to the optimization model construction module; the optimization model construction module is used to construct an energy consumption optimization model of the drone based on the received data information and the constructed inspection model, with the objectives of maximizing the drone's energy consumption efficiency and maximizing the energy obtained by the sensors, and upload the data information to the energy consumption optimization module; the energy consumption optimization module is used to solve the constructed model based on a game theory iterative algorithm based on the received data information to complete the energy consumption optimization for power system inspection drones.

Claims

1. A method for optimizing energy consumption of unmanned aerial vehicles (UAVs) used for power system inspection, comprising the following steps: S1. Acquire data information from the target power system inspection drone system; S2. Based on the data obtained in step S1, construct a UAV inspection model for the target power system; S3. Based on the inspection model constructed in step S2, and with the objectives of maximizing the energy consumption efficiency of the UAV and maximizing the energy obtained by the sensors, construct an energy consumption optimization model for the UAV; specifically including the following steps: The transmitted signal vector x is represented as x = ws, where w is the beamforming vector and s is the secure signal. For transposing secure signals; The received signal of the nest server is represented as y N =h N x+n N , where y N For the received signal of the nest server, h N To represent the channel vector between the UAV and the nest, n N Additive white Gaussian noise for legitimate users and CN() follows a complex Gaussian distribution, σ N Noise power; The energy signal received by the sensor is represented as y I =h I x+n I , where y I The energy signal received by the sensor; h I To represent the channel vector between the UAV and the sensor and A L A is the path loss factor for the Loss of Path (LoS) link. N For non-LoS links, d I α is the distance between the drone and the sensor. L α is the path loss exponent for Loss of Path (LoS) links. N K is the path loss exponent for non-LoS links. I h is the Rice factor of the channel between the drone and the sensor. I,L Indicates the composition of Loss, h I,N Indicates non-LoS composition; n I Let be the noise vector at the sensor and σ I Noise power; The energy obtained by the sensor is expressed as Where E I For the sensor to receive energy, The energy absorption coefficient, The channel vector estimate, Δh I This represents the channel vector error value. The following formula is used as the objective function for the UAV: s.t.Tr(Q w )≤P U R N ≥R th Q w ≥0 Rank(Q w )=1 In the formula Q w Let R be the covariance matrix of w; N The information transmission rate between the drone and the nest; Tr(Q) w ) represents the UAV's transmit power; P U R is the maximum transmit power of the drone; th The minimum data transmission rate between the drone and the nest; The following formula is used as the objective function of the sensor: s.t.Tr(Q w )≤P U AND I ≥E th In the formula P U E represents the maximum transmit power of the drone. I For the sensor to receive energy; E th This represents the minimum energy reception requirement for the sensor. S4. Based on the game theory iterative algorithm, solve the model constructed in step S3 to complete the energy consumption optimization for the power system inspection drone.

2. The energy consumption optimization method for power system inspection drones according to claim 1, characterized in that... Step S2, which involves constructing a UAV inspection model for the target power system, specifically includes the following steps: The constructed target power system drone inspection model includes a nested server, drones, and several sensors; among them, the drones are equipped with N U The source-transmitting drone with a root antenna is used to send information to the nest server and sensors; the nest server is used to receive data information sent by the source-transmitting drone; the sensors are used to receive energy harvesting signals sent by the drone; the nest server, drone and sensors communicate with each other using a B / S communication method.

3. The energy consumption optimization method for power system inspection drones according to claim 2, characterized in that... Step S4, which uses a game theory-based iterative algorithm to solve the model constructed in step S3, specifically includes the following steps: The Dinkelbach method is used to handle the fractional programming problem, and then a semidefinite programming method is used to transform the UAV objective function constructed in step S3 into a convex problem of the following form: s.t.Tr(Q w )≤P U R N ≥R th Q w ≥0 Rank(Q w )=1 In the formula μ * For maximum energy efficiency; If and only if At that time, μ * Take the optimal value; where, To optimize the UAV's transmission beam; During the solution process, the objective function of the UAV is first solved, and the final transmitted beam of the UAV is then determined. The optimal transmitted beam result of the UAV is then substituted into the objective function of the sensor. The optimal solution of the sensor objective function is then obtained. Finally, when the transmitted beam of the UAV no longer changes, the iteration ends, and the final transmitted beam of the UAV is obtained, thus completing the energy consumption optimization for the UAV used for power system inspection.

4. A system for implementing the energy consumption optimization method for power system inspection drones as described in any one of claims 1 to 3, characterized in that... The system includes a system data acquisition module, an inspection model construction module, an optimization model construction module, and an energy consumption optimization module. These modules are connected in series. The system data acquisition module acquires data information from the target power system inspection UAV system and uploads it to the inspection model construction module. The inspection model construction module constructs an inspection model of the target power system UAV based on the received and acquired data information and uploads the data to the optimization model construction module. The optimization model construction module constructs an energy consumption optimization model of the UAV based on the received data and the constructed inspection model, aiming to maximize the UAV's energy efficiency and the energy obtained by the sensors, and uploads the data to the energy consumption optimization module. The energy consumption optimization module is used to solve the constructed model based on the received data information and a game theory iterative algorithm to complete the energy consumption optimization for power system inspection drones.

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