Energy consumption optimization method and system for power system inspection unmanned aerial vehicle

Abstract

The invention discloses an energy consumption optimization method for an electric power system inspection unmanned aerial vehicle. The method comprises the following steps: acquiring data information of a target electric power system inspection unmanned aerial vehicle system; constructing a target power system unmanned aerial vehicle inspection model; constructing an energy consumption optimization model of the unmanned aerial vehicle by taking the maximization of the energy consumption efficiency of the unmanned aerial vehicle and the maximization of the energy obtained by the sensor as targets; and solving the constructed model to complete energy consumption optimization for the power system inspection unmanned aerial vehicle. The invention also discloses a system for realizing the energy consumption optimization method for the power system inspection unmanned aerial vehicle. According to the method, the data of the target inspection system is acquired, and the corresponding energy consumption optimization model is constructed according to the acquired data and is solved, so that the energy consumption optimization for the inspection unmanned aerial vehicle of the power system is finally realized, and the method is higher in reliability and better in accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to an energy consumption optimization method and system for an unmanned aerial vehicle for power system inspection. Background Art

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

[0003] Power system inspection is the premise to ensure the stable and reliable operation of the power system. Traditional power system inspection requires power system staff to conduct manual inspection, which is time-consuming and labor-intensive, and has poor reliability and safety. With the rapid development of drone technology, at this stage, power systems have begun to use drones on a large scale to realize power system inspection.

[0004] However, during the drone inspection of power systems, the battery energy of drones has always been an issue that cannot be ignored. In addition, due to other reasons such as weather, environment, and communication, drone battery energy management has always been one of the research focuses of researchers. At present, the energy management solutions for drones used for power system inspections often use relatively simple energy models for energy management; although such solutions are simple, their reliability and accuracy are not high, which seriously affects the inspection work of drones. Summary of the invention

[0005] One of the purposes of the present invention is to provide an energy consumption optimization method for a power system inspection drone with high reliability and good accuracy.

[0006] A second object of the present 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 a power system inspection drone provided by the present invention comprises the following steps:

[0008] S1. Obtain data information of the target power system inspection drone system;

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

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

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

[0012] The construction of the target power system UAV inspection model described in step S2 specifically includes the following steps:

[0013] The target power system UAV inspection model constructed includes a nest server, a UAV and several sensors; among them, the UAV is equipped with N U The source transmitting drone with the root antenna is used to send information to the nest server and the sensor; the nest server is used to receive the data information sent by the source transmitting drone; the sensor is used to receive the energy collection signal sent by the drone; the nest server, drone and sensor communicate based on the BS communication method.

[0014] The energy consumption optimization model of the UAV is constructed with the goal of maximizing the energy efficiency of the UAV and maximizing the energy obtained by the sensor, which specifically includes the following steps:

[0015] The transmitted signal vector x is expressed as x=ws, where w is the beamforming vector, s is the confidentiality signal and Transpose for confidentiality signal;

[0016] The received signal of the machine nest server is represented by y N =h N x+n N , where y N is the receiving signal of the machine nest server, h N is the channel vector between the UAV and the nest, n N is the additive white Gaussian noise at the legitimate user and CN() is a complex Gaussian distribution, σ N is the noise power;

[0017] The energy signal received by the sensor is represented by y I =h I x+n I , where y I is the energy signal received by the sensor; h I is the channel vector between the UAV and the sensor and A L is the path loss factor of the LoS link, A N is the path loss factor of the non-LoS link, d I is the distance between the drone and the sensor, α L is the path loss exponent of the LoS link, α N is the path loss index of the non-LoS link, K Iis the Rice factor of the channel between the UAV and the sensor, h I,L Indicates the LoS composition, h I,N Indicates non-LoS components; n I is the noise vector at the sensor and σ I is the noise power;

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

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

[0020]

[0021] stTr(Q w )≤P U

[0022] R N ≥R th

[0023] Q w ≥0

[0024] Rank(Q w )=1

[0025] Where Q w is the covariance matrix of w; R N is the information transmission rate between the UAV and the machine nest; Tr(Q w ) is the UAV transmission power; P U is the maximum transmission power of the UAV; R th is 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] Where P is the maximum power sent by the drone; E I Receive energy for the sensor; E th The minimum energy receiving requirement for the sensor.

[0031] The game theory-based iterative algorithm described in step S4 solves the model constructed in step S3, which specifically includes the following steps:

[0032] The Dinkelbach method is used to deal with the fractional programming problem, and then the semidefinite programming method is used to convert the objective function of the UAV constructed in step S3 into a convex problem in 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] Where μ * For maximum energy efficiency;

[0039] If and only if When * Take the optimal value; among them, Transmit beams for optimal drones;

[0040] When solving the problem, first solve the objective function of the UAV and solve the final UAV transmission beam; then substitute the optimal UAV transmission beam result into the objective function of the sensor; solve the optimal solution of the sensor objective function; finally, when the UAV transmission beam no longer changes, the iteration ends, and the final UAV transmission beam is obtained, completing the energy consumption optimization of the UAV used for power system inspection.

[0041] The present 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 in sequence; 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 a target power system drone inspection model 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 for the drone based on the received data information and the constructed inspection model, with the goal of maximizing the drone energy consumption efficiency and maximizing the energy obtained by the sensor, 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 the received data information and the game theory iterative algorithm to complete the energy consumption optimization of the power system inspection drone.

[0042] The energy consumption optimization method and system for power system inspection drones provided by the present invention acquires data from the target inspection system, and constructs and solves the corresponding energy consumption optimization model based on the acquired data. Ultimately, not only the energy consumption optimization of power system inspection drones is achieved, but also the present invention has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0044] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0045] like Figure 1 The method flow chart of the method of the present invention is shown as follows: The energy consumption optimization method for a power system inspection drone provided by the present invention comprises the following steps:

[0046] S1. Obtain data information of the target power system inspection drone system;

[0047] S2. Based on the data information obtained in step S1, a target power system UAV inspection model is constructed; specifically, the following steps are included:

[0048] The target power system UAV inspection model constructed includes a nest server, a UAV and several sensors; among them, the UAV is equipped with N UThe source transmitting drone of the root antenna is used to send information to the nest server and sensor; the nest server is used to receive data information sent by the source transmitting drone; the sensor is used to receive the energy collection signal sent by the drone; the nest server, drone and sensor communicate based on the BS communication method;

[0049] S3. Based on the inspection model constructed in step S2, an energy consumption optimization model for the UAV is constructed with the goal of maximizing the energy efficiency of the UAV and maximizing the energy obtained by the sensor; specifically, the following steps are included:

[0050] The game theory method is used to solve the optimal UAV inspection beam optimization strategy, so that the UAV energy efficiency is maximized and the energy transmission is balanced; the game theory model of UAV information and energy transmission in power grid inspection can be considered to be established and analyzed from the perspective of UAV and sensor; a single leader-single follower Stackelberg game model is established to describe the interaction process between UAV and sensor; since sensors need to obtain as much energy as possible, UAVs have to reduce energy consumption as much as possible while ensuring information transmission, and there are conflicting requirements between UAVs and sensors;

[0051] The transmitted signal vector x is expressed as x=ws, where w is the beamforming vector, s is the confidentiality signal and Transpose for confidentiality signal;

[0052] The received signal of the machine nest server is represented by y N =h N x+n N , where y N is the receiving signal of the machine nest server, h N is the channel vector between the UAV and the nest, n N is the additive white Gaussian noise at the legitimate user and CN() is a complex Gaussian distribution, σ N is the noise power;

[0053] The energy signal received by the sensor is represented by y I =h I x+n I , where y I is the energy signal received by the sensor; h I is the channel vector between the UAV and the sensor and A L is the path loss factor of the LoS link, A N is the path loss factor of the non-LoS link, d I is the distance between the drone and the sensor, α L is the path loss exponent of the LoS link, αN is the path loss index of the non-LoS link, K I is the Rice factor of the channel between the UAV and the sensor, h I,L Indicates the LoS composition, h I,N Indicates non-LoS components; n I is the noise vector at the sensor and σ I is the noise power;

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

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

[0056]

[0057] stTr(Q w )≤P U

[0058] R N ≥R th

[0059] Q w ≥0

[0060] Rank(Q w )=1

[0061] Where Q w is the covariance matrix of w; R N is the information transmission rate between the UAV and the machine nest; Tr(Q w ) is the UAV transmission power; P U is the maximum transmission power of the UAV; R th is 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 transmission 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] Where P is the maximum power sent by the drone; E I Receive energy for the sensor; E th The minimum energy receiving requirement for the sensor;

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

[0069] S4. Based on the game theory iterative algorithm, the model constructed in step S3 is solved to optimize the energy consumption of the power system inspection drone; specifically, the steps include:

[0070] The Dinkelbach method is used to deal with the fractional programming problem, and then the semidefinite programming method is used to convert the objective function of the UAV constructed in step S3 into a convex problem in 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] Where μ * For maximum energy efficiency;

[0077] If and only if When * Take the optimal value; among them, Transmit beams for optimal drones;

[0078] When solving the problem, first solve the objective function of the UAV and solve the final UAV transmission beam; then substitute the optimal UAV transmission beam result into the objective function of the sensor; solve the optimal solution of the sensor objective function; finally, when the UAV transmission beam no longer changes, the iteration ends, and the final UAV transmission beam is obtained, completing the energy consumption optimization of the UAV used for power system inspection.

[0079] In this section, the performance of the proposed robust safety beam design scheme is evaluated through simulation. Without loss of generality, the coordinates of the UAV base station, ground nest and sensor are set to (0,0,80m), (20m,0,0) and Rice factor KN and K I Set to definition represents the maximum AOD estimation error ratio between the drone, the nest and the sensor, Represents the maximum AOD estimation error ratio between the drone and the sensor. The larger the AOD estimation error, the more severe the drone jitter and the worse the channel quality. The environmental parameters are set to a=5, b=(2 / π)ln3. For other main simulation parameter settings, refer to Table 1:

[0080] Table 1 Simulation parameter diagram

[0081] parameter Parameter Value Number of antennas in drone base stations 8 Maximum transmission power of drone Nu*30dBm Drone altitude 80m Carrier frequency 2.4GHz Antenna spacing 6.25cm Noise Power -104dBm

[0082] The MRT scheme for transmitting useful signals is selected as the existing comparison scheme. For the MRT scheme, the beam vector Indicates the power allocated to the legitimate user Bob. Compared with the existing scheme, the proposed algorithm can obtain a larger performance gain of UAV energy efficiency; especially when the AOD error is large, the effect of the method of the present invention is better.

[0083] like Figure 2 The functional module schematic diagram of the system of the present invention is shown as follows: the system disclosed in the present invention for realizing the energy consumption optimization method for the power system inspection drone comprises 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 in sequence; 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 the target power system drone inspection model 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 the energy consumption optimization model of the drone based on the received data information and the constructed inspection model, with the goal of maximizing the energy consumption efficiency of the drone and maximizing the energy obtained by the sensor, 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 the received data information and the game theory iterative algorithm to complete the energy consumption optimization of the power system inspection drone.

Claims

1. A method for optimizing energy consumption of a power system inspection drone, comprising the following steps: S1. Obtain data information of the target power system inspection drone system; S2. Based on the data information obtained in step S1, a target power system UAV inspection model is constructed; S3. Based on the inspection model constructed in step S2, an energy consumption optimization model for the UAV is constructed with the goal of maximizing the energy efficiency of the UAV and maximizing the energy obtained by the sensor; S4. Based on the game theory iterative algorithm, the model constructed in step S3 is solved to complete the energy consumption optimization of the power system inspection drone.

2. The energy consumption optimization method for power system inspection drone according to claim 1 is characterized in that The construction of the target power system UAV inspection model described in step S2 specifically includes the following steps: The target power system UAV inspection model constructed includes a nest server, a UAV and several sensors; among them, the UAV is equipped with N U The source transmitting drone with the root antenna is used to send information to the nest server and the sensor; the nest server is used to receive the data information sent by the source transmitting drone; the sensor is used to receive the energy collection signal sent by the drone; the nest server, drone and sensor communicate based on the BS communication method.

3. The energy consumption optimization method for power system inspection drone according to claim 2 is characterized in that The energy consumption optimization model of the UAV is constructed with the goal of maximizing the energy efficiency of the UAV and maximizing the energy obtained by the sensor, which specifically includes the following steps: The transmitted signal vector x is expressed as x=ws, where w is the beamforming vector, s is the confidentiality signal and Transpose for confidentiality signal; The received signal of the machine nest server is represented by y N =h N x+n N , where y N is the receiving signal of the machine nest server, h N is the channel vector between the UAV and the nest, n N is the additive white Gaussian noise at the legitimate user and CN() is a complex Gaussian distribution, σ N is the noise power; The energy signal received by the sensor is represented by y I =h I x+n I , where y I is the energy signal received by the sensor; h I is the channel vector between the UAV and the sensor and A L is the path loss factor of the LoS link, A N is the path loss factor of the non-LoS link, d I is the distance between the drone and the sensor, α L is the path loss exponent of the LoS link, α N is the path loss index of the non-LoS link, K I is the Rice factor of the channel between the UAV and the sensor, h I,L Indicates the LoS composition, h I,N Indicates non-LoS components; n I is the noise vector at the sensor and σ I is the noise power; The energy obtained by the sensor is expressed as Where E I is the energy obtained by the sensor, is the energy absorption coefficient, is the channel vector estimate, Δh I is the channel vector error value; The following formula is used as the objective function of the UAV: s.t.Tr(Q w )≤P U R N ≥R th Q w ≥0 Rank(Q w )=1 Where Q w is the covariance matrix of w; R N is the information transmission rate between the UAV and the machine nest; Tr(Q w ) is the UAV transmission power; P U is the maximum transmission power of the UAV; R th is 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 AND I ≥E th Where P is the maximum power sent by the drone; E I Receive energy for the sensor; E th The minimum energy receiving requirement for the sensor.

4. The energy consumption optimization method for a power system inspection drone according to claim 3 is characterized in that The game theory-based iterative algorithm described in step S4 solves the model constructed in step S3, which specifically includes the following steps: The Dinkelbach method is used to deal with the fractional programming problem, and then the semidefinite programming method is used to convert the objective function of the UAV constructed in step S3 into a convex problem in the following form: s.t.Tr(Q w )≤P U R N ≥R th Q w ≥0 Rank(Q w )=1 Where μ * For maximum energy efficiency; If and only if When * Take the optimal value; among them, Transmit beams for optimal drones; When solving the problem, first solve the objective function of the UAV and solve the final UAV transmission beam; then substitute the optimal UAV transmission beam result into the objective function of the sensor; solve the optimal solution of the sensor objective function; finally, when the UAV transmission beam no longer changes, the iteration ends, and the final UAV transmission beam is obtained, completing the energy consumption optimization of the UAV used for power system inspection.

5. A system for implementing the energy consumption optimization method for a power system inspection drone according to any one of claims 1 to 4, characterized in that It 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 in sequence; the system data acquisition module is used to obtain 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 a target power system drone inspection model 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 for the drone based on the received data information and the constructed inspection model, with the goal of maximizing the drone energy consumption efficiency and maximizing the energy obtained by the sensor, and upload the data information to the energy consumption optimization module; The energy consumption optimization module is used to solve the constructed model according to the received data information and based on the game theory iterative algorithm to complete the energy consumption optimization of the power system inspection drone.

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