Autonomous wireless charging control method for electric power inspection unmanned aerial vehicle

Abstract

The invention provides an autonomous wireless charging control method for an electric power inspection unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles. According to the technical scheme, the energy continuing requirement of the unmanned aerial vehicle for executing a long-time electric power inspection task is met, the wireless charging mode combining the magnetic coupling induction technology and the loose coupling transformer is researched, and the energy transmission efficiency of the unmanned aerial vehicle under the non-tight coupling condition is improved. On the basis, a multi-objective optimization problem of considering a lightweight parameter design scheme and efficient transmission performance of the coupling mechanism is ensured; the continuity of the unmanned aerial vehicle inspection task is considered, charging base station deployment and unmanned aerial vehicle charging task scheduling are achieved, and it is guaranteed that the unmanned aerial vehicle can be rapidly and efficiently matched with a target base station. The unmanned aerial vehicle and power grid base station interaction technology is taken as a carrier, software and hardware integration of the wireless charging system is realized, accurate positioning and autonomous landing of the unmanned aerial vehicle are realized, and efficient interaction between the unmanned aerial vehicle and the charging base station is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to an autonomous wireless charging control method for an electric power inspection unmanned aerial vehicle. Background Art

[0002] In recent years, my country's power transmission network has been constantly trying new inspection modes for power transmission lines. Compared with technologies such as helicopter live inspection and drone hanging traction rope to assist manual live work, drone power inspection has the advantages of low cost, flexibility, safety and efficiency, and easy to find faults above the tower flat surface. It does not require any modification or change to the line, and will not cause damage to the line. Therefore, drone inspection is easier to combine with manual inspection and become a regular inspection method, which is an important development trend of smart grids.

[0003] One of the main constraints on the efficiency of drone inspections is insufficient endurance, so the research on wireless charging of power inspection drones is of great significance. Wireless charging of drones has become a focus of domestic and foreign social attention, and will inevitably lead to extensive changes in multiple fields such as wireless charging and power inspection in the future, thus having a profound impact on the power industry. Therefore, it is urgent to carry out more in-depth and extensive research on wireless charging of drones in the field of power inspection.

[0004] At present, the wireless charging industry has a relatively short history of development in my country. How to improve the efficiency of wireless power transmission, reduce the impact of weather on the charging system, extend the inspection time of drones, and build an integrated wireless charging base station are technical issues to be solved in this field. In addition, in addition to the charging technology itself, to extend the endurance of drones and improve their stability and reliability, it is necessary to match the design of control strategies such as flight control, path planning, and task scheduling, so as to improve the charging and power efficiency of drones more comprehensively and efficiently. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to construct an autonomous wireless charging control solution suitable for drones to improve the charging and power efficiency of drones and their endurance.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] An autonomous wireless charging control method for electric power inspection UAVs is proposed. In the method, magnetic coupling inductive wireless charging is adopted. The charger adopts a static planar loosely coupled transformer and is provided with a rectifier filter circuit. The weight of the receiving coil and the transmission efficiency of the system are used as objective functions, and a multi-objective optimization algorithm is used to reduce the weight of the receiving coil. A compensation network is provided for the coupling coil for reactive power compensation. An electromagnetic shielding structure is provided for the coupling coil. The multi-objective NSGA-II algorithm is used to perform optimal multi-objective optimization on the wireless charging system.

[0008] Preferably, the electromagnetic shielding structure is realized by using a metal material having an eddy current effect.

[0009] Preferably, the rectification and filtering circuit comprises a rectifier and a filter, and the alternating current is converted into unidirectional pulsating direct current by the rectifier, and then the pulsation is removed by the filter.

[0010] Preferably, in the method, a coil with a frequency and current higher than a certain frequency is used to reduce the skin effect of the coil.

[0011] As a preferred method, the coupling coil and the magnetic core in the loosely coupled transformer are optimized as follows: a three-coil coupling mechanism is designed, the frequency, the number of turns, the outer diameter and the material of the secondary coil are determined, the self-inductance value of the secondary coil is obtained by simulation, the outer diameter of the primary rectangular coil is determined, the self-inductance value and the mutual inductance value of the two primary coils are obtained by simulation, and when the self-inductance value of the primary coil minus the mutual inductance value = the self-inductance value of the secondary coil, the coupling coefficient K is obtained. max .

[0012] Preferably, in a magnetically coupled inductive wireless power transmission system, a DC power supply passes through a full-bridge inverter circuit and a power supply circuit respectively. The full-bridge inverter circuit converts the DC power into the high-frequency AC power required by the system, and the power supply circuit converts it into the DC power required by the STM32 single-chip microcomputer and its peripheral circuits and drive circuits; then, the high-frequency AC power passes through the primary side LCC compensation network, and then transmits the energy to the secondary side receiving coil through two reversely connected transmitting coils in series with an alternating magnetic field as a carrier; then, the electric energy received by the secondary coil first passes through the secondary side LCC compensation network, and then is converted into a DC voltage through a rectifier and filter circuit, and the charging of the lithium battery is managed through the battery management system.

[0013] Preferably, in the magnetically coupled inductive wireless power transmission system, the transmitting circuit includes a full-bridge inverter circuit, a drive circuit, an STM32 single-chip microcomputer PWM generation circuit, a power supply circuit and a primary side LCC compensation network; the DC power supply inputs a DC voltage to the transmitting end, the STM32 single-chip microcomputer transmits a drive signal to the drive circuit, the drive circuit amplifies the drive signal to drive the power switch tube in the full-bridge inverter circuit, converts the DC power input by the DC power supply into AC power, and couples the transmitting and receiving coils for energy transmission.

[0014] Preferably, in this method, a full-bridge inverter circuit is selected as a high-frequency inverter to complete the process of converting direct current to alternating current, MOSFET is selected as the switching tube of the inverter circuit, and an RC resistor-capacitor absorption network is adopted; the hardware circuit of the receiving end includes a rectifier filter circuit, a charge and discharge circuit, a sampling circuit, a control circuit and a DC-DC circuit.

[0015] Preferably, in this method, the primary side of the UAV wireless power transmission system adopts an LCC compensation topology, and the secondary side compensation topology of the system adopts a series compensation topology.

[0016] Preferably, in this method, the coupling coil is an asymmetric circular three-coil magnetic coupling mechanism, whose magnetic core is a Mn-Zn power magnetic core made of PC95 material, and a cylindrical hollow coaxial with the cylindrical magnetic core is provided at the center of the cylindrical magnetic core of the receiving coil.

[0017] The present invention provides an autonomous wireless charging control method for a power inspection UAV. The innovations of this technical solution are mainly reflected in the following aspects:

[0018] (1) Three-coil transmission structure

[0019] This project adopts a wireless charging method that combines magnetically coupled inductive wireless power transmission technology with loosely coupled transformers. In view of the problem that traditional loosely coupled transformers have insufficient anti-offset ability, the geometric layout and key parameters of the coils are optimized, and simulation software is used to obtain reasonable coil self-inductance and mutual inductance values, thereby improving the physical proximity between the coils and enhancing the coil coupling coefficient, effectively improving the robustness of the power transmission effect when the primary and secondary coils are difficult to align.

[0020] (2) Lightweighting of the receiving end to coordinate transmission efficiency

[0021] In order to solve the problem of additional load on the UAV caused by the weight of the receiving end, multi-objective constraints are set based on the coil weight and power transmission efficiency. The stability and adaptability of the system are optimized using the SS compensation structure and LCC-S compensation topology to cope with complex operating environments and changing conditions. The performance parameters of the receiving coil, such as the coil shape and coil core, are solved by optimization methods. A lightweight coupling mechanism is designed to reduce the impact of the wireless charging module on the UAV load, effectively improving the wireless charging transmission efficiency of the UAV from a hardware perspective.

[0022] (3) Charging Base Station Deployment Strategy

[0023] In view of issues such as the number and spacing of base stations to be deployed, with the goal of reducing deployment costs, the actual range of the UAV and the requirements of the inspection mission are comprehensively considered to ensure that the UAV maintains a safe power level during the inspection process. Deep reinforcement learning is used to solve the above optimization problems, effectively reducing the cost of deploying power grid base stations and subsequent maintenance.

[0024] (4) Multi-CUAV charging task scheduling for long-distance inspection drones

[0025] Based on an in-depth analysis of the application scenarios and needs of long-distance inspections, the charging strategy for inspection drones is optimized, and CUAV is used to charge drones that perform long-distance inspection tasks. In scenarios with multiple CUAVs, the multi-agent deep reinforcement learning optimization charging scheduling problem is clarified, and the scheduling goal of minimizing the average task completion time of inspection drones is achieved, which reduces the task interruption time of drone inspections.

[0026] (5) UAV precise positioning

[0027] Aiming at the problem of inaccurate positioning of UAV during self-landing, a scanning positioning method based on laser distance sensing is proposed. A step-by-step scanning capture strategy is adopted to dynamically read the return information of the laser ranging sensor. The stepper motor and laser ranging module are used to scan and capture the outrigger position, so that the UAV can land at a more accurate charging point.

[0028] Aiming at the energy demand of UAVs performing long-term power inspection tasks, the present invention studies the wireless charging method combining magnetic coupling induction technology and loosely coupled transformers, thereby improving the energy transmission efficiency of UAVs under non-tight coupling conditions. On this basis, it is ensured that the multi-objective optimization problem that takes into account the lightweight parameter design scheme of the coupling mechanism and the efficient transmission performance is taken into account; considering the continuity of the UAV inspection tasks, the deployment of charging base stations and the scheduling of charging tasks for inspection UAVs are realized, ensuring that the UAVs can quickly and efficiently match the target base station. The present invention uses the interaction technology between UAVs and power grid base stations as a carrier to realize the integration of wireless charging system hardware and software, realize the precise positioning and autonomous landing of UAVs, and ensure the efficient interaction between UAVs and charging base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is the technical roadmap of the magnetic coupling induction wireless charging system of the power inspection drone in the present invention.

[0030] Figure 2 This is a structural block diagram of the ICPT wireless charging system in the present invention.

[0031] Figure 3 This is a three-coil design flow chart in the present invention.

[0032] Figure 4It is an overall scheme diagram of the magnetically coupled inductive wireless power transmission system in the present invention.

[0033] Figure 5 It is the technical roadmap for autonomous wireless charging and transmission efficiency of power inspection UAV in the present invention.

[0034] Figure 6 It is a schematic diagram of the hollowing out of the magnetic core of the receiving coil in the present invention.

[0035] Figure 7 This is the technical roadmap for the deployment and scheduling of wireless charging base stations for drones based on deep reinforcement learning in the present invention.

[0036] Figure 8 This is the integrated technical roadmap of wireless charging for power inspection drones based on wireless charging base stations in the present invention. DETAILED DESCRIPTION

[0037] The specific embodiments of the present invention will be described in detail below. In order to avoid too much unnecessary details, the well-known structures or functions will not be described in detail in the following examples. The approximate language used in the following examples can be used for quantitative expression, indicating that the quantity can be allowed to have a certain change without changing the basic function. Unless otherwise defined, the technical and scientific terms used in the following examples have the same meaning as those generally understood by those skilled in the art to which the present invention belongs.

[0038] 1. Magnetic coupling induction wireless charging system technology for power inspection drones

[0039] The present invention first proposes a small UAV wireless charging system based on the magnetic coupling inductive principle, and designs a loosely coupled transformer for the power inspection UAV charging system. In view of the problem that the traditional loosely coupled transformer has insufficient anti-offset ability, a three-coil transmission structure is designed, and the structure is simulated and analyzed by software. Finally, the magnetic coupling inductive wireless charging system is designed, which mainly includes the hardware circuit design of the transmitting end and the hardware circuit design of the receiving end, as well as the battery management system design, and then the components suitable for the design requirements are selected according to the parameter calculation and actual needs. According to the results of the design analysis and actual needs, the implementation scheme structure of the magnetic coupling inductive wireless power transmission system designed by the present invention is as follows Figure 1 shown.

[0040] (1) Magnetic coupling induction charging method suitable for power inspection drone operations

[0041] The magnetically coupled inductive wireless charging system is mainly divided into two parts: primary side transmitting and secondary side receiving; the transmitting part includes power supply, inverter, primary side transmitting coil and primary side compensation network, etc.; the receiving part includes receiving coil, secondary side compensation network, rectifier, load, etc.

[0042] In the primary transmitting part, the power supply converts electrical energy into wireless electromagnetic energy through the inverter, and then transmits it to the receiving part through the primary transmitting coil. In the secondary receiving part, the receiving coil accurately captures the wireless electromagnetic energy, the secondary compensation network further improves the receiving efficiency, and the rectifier converts the received energy into current to supply the load. A resonant compensation network is set to ensure efficient energy transmission during the transmission process and enhance the system's tolerance to position deviation. The structural block diagram of the ICPT wireless charging system proposed in the present invention is as follows: Figure 2 shown.

[0043] First of all, the DC regulated power supply plays a vital role in the system. It provides the required energy for the full-bridge inverter circuit by providing a stable DC current. Under the action of the inverter circuit, the DC current is efficiently converted into AC with the same frequency as the switching device. This process ensures the efficiency and stability of energy transmission.

[0044] Secondly, after being processed by the primary compensation network, the high-frequency alternating current is accurately converted into an alternating magnetic field, generating strong magnetic induction lines around the transmitting coil. This changing magnetic field becomes the carrier of energy transmission, transferring energy from the primary side to the secondary side. This efficient wireless energy transmission method makes energy transmission more convenient and intelligent. At the receiving end, the receiving coil receives the transmitted alternating current and converts it into stable direct current with the help of a rectifier and filter circuit, ensuring the efficiency and stability of energy transmission, allowing the receiving end to make full use of the received energy.

[0045] Finally, after precise control by DC-DC circuit and battery management system, direct current is used to charge and manage lithium battery. This intelligent charging method not only ensures the safety and stability of lithium battery charging, but also improves charging efficiency and speed.

[0046] (2) Design of loosely coupled transformer for charging system of power inspection drone

[0047] The loosely coupled transformer is a crucial component in the magnetically coupled inductive UAV wireless charging system. The transmission efficiency and transmission power of the wireless charging system are related to the design of the loosely coupled transformer. The present invention analyzes and designs the number of turns, spatial structure and magnetic core of the coil of the loosely coupled transformer.

[0048] First, the present invention improves the key parts such as the number of turns, spatial structure and magnetic core of the coil in the loosely coupled transformer. In the wireless charging system of the drone, the coupling coil can be divided into two types: planar coil and spatial spiral coil. Among them, the magnetic field of the planar coupling coil is mostly distributed between the two coupling coils, and is evenly distributed. The magnetic field strength will weaken as the distance between the primary and secondary coupling coils increases, and the space occupied is relatively small. When the area is the same, the primary and secondary coils of the planar coil have a greater mutual inductance. Therefore, the present invention uses a planar circular coupling coil, and the distance between the landing gear of the drone and the charging platform is closer. Therefore, the coupling coil can be placed on the landing gear of the drone.

[0049] Next, the spatial structure of the loosely coupled transformer is designed. According to different structural characteristics, loosely coupled transformers are divided into static, sliding and rotating types. The present invention adopts a static planar loosely coupled transformer. In the charging system of a small drone, the primary and secondary sides of the static transformer may be misaligned, but in actual work, the positions of the two are relatively fixed, which meets the environmental requirements when charging the power inspection drone.

[0050] In addition, in response to the problems of traditional loosely coupled transformers and their single structure and low anti-offset ability, the present invention analyzes and designs the coupling coil and magnetic core in the loosely coupled transformer to meet the requirements of miniaturization, long operation time and harsh environment of power inspection drones.

[0051] When designing a coil, we must first determine the operating frequency of the system. When the system is in a resonant state, the reactive power can be reduced. Considering the limitations on the size of the coil that can be placed on the landing gear of the drone, the secondary coil must ensure high transmission efficiency as much as possible while not overloading the drone. After determining the frequency, number of turns, outer diameter and material of the secondary coil, establish a secondary coil model based on actual physical parameters. After the self-inductance of the secondary coil is obtained through modeling and calculation, the primary coil is designed. The primary coil of the designed coil consists of a large rectangular coil P and a small circular planar coil T, and the secondary coil consists of a rectangular coil S. The design flow chart is shown below. Figure 3 shown.

[0052] During actual charging, the magnetic field is mainly distributed between the primary and secondary coils, but the problem of magnetic field leakage outside the coil is also difficult to avoid. Magnetic field leakage may cause damage to other devices or be affected by other devices. In response to this problem, the present invention proposes two parallel solutions. As the magnetic induction intensity increases, the transmission efficiency of the coil will also increase. Therefore, the purpose of increasing the magnetic induction intensity is achieved by adding a magnetic core to the primary and secondary coils. Solution one is to add an aluminum plate to each of the primary and secondary coils, and the other solution is to add a magnetic core. The two solutions are combined to constrain the magnetic field distribution to the maximum extent.

[0053] (3) Magnetic coupling inductive wireless charging system circuit design

[0054] In order to realize the charging system of the present invention, it is necessary to complete the circuit design of each part. The design of the transformer part has been completed, and the design of the circuit of other parts of the wireless charging system will be carried out below.

[0055] The wireless charging system design intends to use 4S lithium batteries for charging, and selects the constant voltage charging method. The overall scheme of the magnetic coupling inductive wireless power transmission system designed by the present invention is as follows Figure 4 shown.

[0056] The system consists of two parts: the transmitter and the receiver. Its basic working principle is as follows: First, the DC power supply passes through the full-bridge inverter circuit and the power supply circuit respectively. The full-bridge inverter circuit converts the DC power into the high-frequency AC power required by the system, and the power supply circuit converts it into the DC power required by the STM32 microcontroller and its peripheral circuits and drive circuits. Then, after the high-frequency AC power passes through the primary side LCC compensation network, it transmits the energy to the secondary side receiving coil through two reverse series-connected transmitting coils with the alternating magnetic field as the carrier. Then, the electric energy received by the secondary coil first passes through the secondary side LCC compensation network, and then is converted into DC voltage through the rectifier and filter circuit, and the lithium battery is charged and managed through the battery management system. The hardware design methods of each link in the system will be introduced in detail below.

[0057] First, the transmitting end circuit mainly includes a full-bridge inverter circuit, a driving circuit, an STM32 single-chip microcomputer PWM generation circuit, a power supply circuit and a primary side LCC compensation network. The DC power supply inputs a DC voltage to the transmitting end, and the STM32 single-chip microcomputer transmits a driving signal to the driving circuit. The driving circuit amplifies the driving signal to realize the power switch tube in the driving full-bridge inverter circuit, and converts the 24V DC power input by the DC power supply into a high-frequency AC power. The transmitting and receiving coils are coupled for energy transmission. The present invention selects a full-bridge inverter circuit as a high-frequency inverter to complete the process of converting DC power to AC power. The transmission efficiency of the magnetic coupling inductive wireless charging system is related to the conversion efficiency of the full-bridge inverter circuit. It is necessary to calculate and analyze the reasons for the loss of the full-bridge inverter circuit, so as to facilitate the subsequent selection of suitable control methods and components. When designing a full-bridge inverter circuit, the device loss in the circuit can be reduced by optimizing the selection of the switch tube. In addition to the high operating frequency, the MOSFET power switch tube also has the characteristics of simple driving circuit and good thermal stability, so the present invention selects MOSFET as the switch tube of the inverter circuit. Since the switch tube needs to be turned on and off quickly at high frequency, this will cause drastic changes in the voltage and current at both ends of the switch tube. There must be parasitic inductance in the actual application circuit, so it is easy to generate voltage spikes and oscillations on the switch tube under high frequency conditions, further increasing its loss when it is turned off. In order to avoid and reduce the loss, the present invention adopts an RC resistor-capacitor absorption network.

[0058] Next, the hardware circuit of the receiving end includes a rectifier and filter circuit, a charge and discharge circuit, a sampling circuit, a control circuit and a DC-DC circuit. Among them, the rectifier and filter circuit converts the alternating current transmitted from the primary coil to the secondary coil into direct current, and then filters it through the subsequent capacitor. The purpose is to supply the direct current without pulsating components to the load. In the design of the present invention, a bridge rectifier circuit is selected. The working process of the bridge rectifier circuit is divided into two stages, namely the positive half cycle and the negative half cycle. The control circuit design mainly includes the following parts: UART serial port, SWD download port, crystal oscillator circuit, reset circuit, power supply circuit. In addition, by selecting other devices, the circuit design analysis of the wireless charging system is completed.

[0059] 2. Optimization of autonomous wireless charging transmission efficiency of power inspection drones

[0060] First, by comparing the four basic compensation topologies, the SS type structure and the LCC-S type structure are analyzed, and a high-order compensation network is selected as the compensation circuit of the UAV WPT system. Secondly, the characteristics of coils of different shapes are analyzed to complete the design of an asymmetric magnetic coupling mechanism suitable for the UAV wireless power transmission system; at the same time, the configuration necessity, material and thickness of the receiving coil core in the magnetic coupling mechanism are analyzed to optimize the lightweight design of the receiving coil. Finally, the appropriate number of turns, turn spacing and other parameters of the receiving coil are selected through analysis and comparison. Taking the quality and transmission efficiency of the receiving coil as the objective function and the transmission power as the constraint condition, the multi-objective NSGA-Ⅱ algorithm is used to optimize the UAV WPT system. While meeting the system output power, the quality of the receiving coil is reduced to meet the flexible charging requirements of the UAV, thereby improving the unmanned autonomous capability of the UAV. Specifically, Figure 5 shown.

[0061] (1) Topological structure design of UAV wireless power transmission system

[0062] First, determine the most suitable compensation topology. Different compensation methods have the same calculation formula for the secondary compensation capacitor, which is only related to the circuit inductance and operating frequency, but the primary compensation capacitor is different. In the SS compensation network, the transmitter load is only related to its own self-inductance and operating frequency; in the SP compensation network, the transmitter compensation capacitor is not related to the equivalent load, but when the system mutual inductance changes, the primary compensation capacitor will change, so the compensation structure is difficult to maintain resonance when the mutual inductance changes; in the PS and PP compensation structures, the transmitter compensation capacitor is only affected by the equivalent load, so these three compensation structures are suitable for WPT systems with constant loads. In summary, due to the deviation of the landing position of the drone and the change of the lithium battery load characteristics with the charging state, when the SP, PS and PP compensation structures are applied to the drone WPT, it is difficult to keep the system in a resonant state when the mutual inductance changes, which leads to a decrease in the transmission performance of the system, greatly limiting its applicable scenarios. The results show that in the SS compensation structure, the transmitter compensation capacitor only depends on the system's resonant frequency and the coil's self-inductance. The system's resonant state is not affected by the load and mutual inductance. When the system changes, it will not cause the primary current phase angle to change, leading to system detuning. Therefore, the SS compensation topology is analyzed next.

[0063] Then, the LCC-S type compensation topology under lithium battery load is analyzed. Although the SS compensation structure can achieve constant current output, it cannot cope with the problem of unstable mutual inductance. Compared with the simple first-order compensation topology, the high-order resonant compensation topology has higher degrees of freedom and adaptability. By combining any capacitors and inductors, a variety of high-order composite compensation structures such as LCC-S, LCL-P, LCC-LCC, and LCL-LCC can be obtained. In the application of actual wireless power transmission systems, it is usually necessary to comprehensively consider the characteristics of different compensation structures and the needs of different usage scenarios, and select the most appropriate compensation topology. The results show that under the LCC-S compensation network, the output power of the WPT system is mainly determined by the system operating frequency, equivalent load, mutual inductance and transmitter compensation inductance, which directly determine the performance of the WPT system. During the landing process of the drone, there may be a certain deviation when landing on the wireless charging platform, which may cause the system coupling coefficient and secondary side component parameters to change. The transmitting current of the WPT system based on the LCC-S compensation topology is independent of the load. Under the condition of fixed operating frequency and fixed resonant inductance, the inverter output voltage is constant, which can ensure the constant transmission current.

[0064] In summary, the primary side of the UAV wireless power transmission system in the present invention adopts the LCC compensation topology. For the secondary side compensation topology of the system, considering that the secondary side coupling coil and the compensation circuit are installed on the UAV, combined with the structure and quality considerations of the system, a series compensation topology is adopted. The LCC-S compensation network has the characteristics of constant current output on the primary side and constant voltage output on the secondary side, and is not affected by load changes. Combined with the working conditions of this system, the LCC-S compensation circuit is more suitable for the application background of the UAV WPT system.

[0065] (2) Lightweight design of coupling mechanism suitable for UAV wireless charging system

[0066] First, determine the shape of the coupling coil. Different shapes of transceiver coils will show different characteristics. The magnetic field distribution, impedance and mutual inductance will vary with the change of the coil shape. This paper selects a planar spiral coil with a small size, easy to wind and assemble. Planar structures mainly include: circular, square and DD types. Like circular coils, the mutual inductance between the two coils increases with the increase of the number of turns of the coil. The difference is that the slope of the curve of the mutual inductance of the circular coil changes with the number of turns, while the slope of the mutual inductance of the square coil remains relatively unchanged with the change of the number of turns. The mutual inductance of the DD-shaped coil also gradually increases with the increase of the number of turns of the coil, but under the same number of turns, the mutual inductance of the DD-shaped coil is smaller than that of the circular and square coils. Due to the small anti-offset range, small mutual inductance value, and limited space of the drone, its anti-offset ability is poor and cannot meet the transmission requirements of the drone. In addition, when designing the coupling mechanism of the wireless power transmission of the drone, the volume and mass of the receiving end should be minimized to reduce the load of the drone. If a symmetrical magnetic coupling mechanism is used, it is difficult to achieve precise coupling between the coupling coils when the drone lands. Therefore, in order to expand the charging area, the present invention adopts an asymmetric circular three-coil magnetic coupling mechanism. Compared with the traditional symmetrical coil, the asymmetric coupling coil has better magnetic field strength and uniformity, as well as a certain anti-drift ability. When the landing accuracy of the drone is inaccurate, it can continue to charge the drone, ensure the stability and reliability of the drone charging system, and improve the charging capacity of the drone.

[0067] Then, the magnetic coupling structure is designed based on the bilateral LCC compensation network, and the size of the receiving coil core is optimized. Since the Mn-Zn power core made of PC95 material has better medium and low temperature characteristics and higher magnetic permeability than other materials, and the magnetic permeability changes little with temperature, the system using the PC95 power core can obtain higher mutual inductance M and coupling coefficient k and smaller loss. A small cylindrical core is hollowed out in the center of the cylindrical core of the receiving coil to reduce the weight of the receiving coil core, such as Figure 6 shown.

[0068] Hollowing out the coil core will cause the magnetic flux density of the remaining receiving coil cylindrical core to increase significantly, even reaching magnetic saturation, and the magnetic permeability of the core will drop significantly, resulting in a significant drop in the transmission efficiency of the coupling mechanism, affecting the normal operation of the wireless charging system. In addition, the magnetic saturation of the core will also produce large hysteresis losses and eddy current losses, increase the energy loss of the magnetic coupling mechanism, and reduce the charging efficiency of the system. Therefore, when designing the hollowed-out small cylindrical volume, it is necessary to consider whether the maximum magnetic flux density βmax of the receiving coil core has reached the saturation magnetic flux density βs of the Mn-Zn ferrite power core made of PC95 material used.

[0069] Finally, in the magnetic coupling WPT system, since the secondary side pickup voltage U is related to the system frequency f, and the mutual inductance M of the magnetic coupling mechanism is independent of the system frequency f, the purpose of lightweighting the receiving side of the drone wireless charging system can be achieved by increasing the system frequency. When the secondary side induced voltage remains unchanged, the mutual inductance M required by the main circuit of the drone WPT system is inversely proportional to the system frequency f and the transmitting coil current I. Therefore, when the secondary side induced voltage remains unchanged, the system's demand for the mutual inductance of the coupling mechanism can be reduced by increasing the system frequency, thereby reducing the number of turns of the receiving coil of the magnetic coupling mechanism to reduce the total weight G of the receiving coil.

[0070] (3) Optimization design of wireless power transmission system for UAVs

[0071] First, the coupling coil is optimized and designed, and the UAV wireless power transmission system is optimized. The design and optimization of the coil is the key to the wireless power transmission system. The appropriate coil parameters such as the number of coil turns and the turn spacing are selected through analysis and comparison; on this basis, in order to ensure that the UAV wireless power transmission system can reduce the mass of the receiving coil while also being able to efficiently and stably transmit energy,

[0072] Then, in order to ensure that the wireless power transmission system of the UAV can transmit energy efficiently and stably while reducing the mass of the receiving coil, the wireless charging system is optimized based on the multi-objective NSGA-II algorithm.

[0073] The NSGA-II algorithm makes the following improvements on the NSGA algorithm:

[0074] 1) The non-dominated sorting method was improved, and a fast non-dominated sorting method was proposed to reduce the complexity of the solution.

[0075] 2) Introduce crowding degree and crowding operator to replace the existing fitness strategy, and use it as the criterion for the superiority and inferiority among the same kind, so that the individuals in the quasi-Pareto domain can be spread in the entire Pareto domain and distributed evenly, ensuring the diversity of the population.

[0076] 3) Introduce an elite strategy, combine the parent generation and its offspring sub-population to expand the sample space, compete to obtain new populations to participate in the evolution of the next generation, and thus obtain better offspring.

[0077] Finally, by optimizing the wireless power transmission system of the UAV through the NSGA-II algorithm, a set of non-dominated solutions can be obtained, which are the optimal solutions under the two objective functions. Even when there is a trade-off between quality and power, multiple optimal solutions can be selected. Finally, according to the actual application and design requirements, the most suitable solution is selected as the optimization result.

[0078] 3. Deployment and scheduling of wireless charging base stations for drones based on deep reinforcement learning

[0079] The present invention optimizes the deployment and maintenance costs of charging base stations by taking into account drone parameters, environmental information and optimization goals through deep reinforcement learning. A multi-level hybrid method is used to wirelessly charge inspection drones. For drones performing short-distance and long-distance inspection tasks, fixed base station charging and CUAV charging are used respectively to reduce the occurrence of drone inspection interruptions caused by charging, improve the stability of the wireless charging system, and reduce the time consumption of power line inspections. The specific scheme is as follows: Figure 7 shown.

[0080] (1) Deployment strategy of wireless charging equipment in transmission line drone inspection

[0081] First, the coordinates of the power towers of each power line are determined according to the drone inspection area, and the collected data are converted into a standard format.

[0082] Secondly, a system model is established, and the system is modeled based on the Markov decision process. The deployment problem of wireless charging base stations is studied from the perspective of the flight path of the drone. Since the drone flies autonomously and inspects the power lines in the designated area, the power is replenished by the wireless charging equipment deployed on the power poles during the inspection. Therefore, the drone should have a moderate power supply during the flight to ensure that it can fly to the next power pole where wireless charging is deployed. After inspecting one transmission line, the drone moves to another transmission line to continue the inspection. At this time, the available power of the drone still needs to meet its flight to the coverage area of ​​the next wireless charging base station. When the drone inspection is completed, it needs to return to the starting point, and the available power needs to meet its return to the starting point. The deployment optimization problem of wireless charging base stations in transmission line inspection is established based on the coordinates of the power poles, the deployment cost and maintenance cost of the wireless charging base stations, and the relevant parameters of the drone. Finally, deep reinforcement learning is used to solve the above optimization problems, so as to obtain the deployment strategy of the wireless charging base stations and achieve a balance between cost and efficiency.

[0083] (2) Path planning strategy for short-distance inspection drones returning to fixed base stations

[0084] First, the drone's current battery level, location, flight speed, and surrounding environment data, including the location of the charging base station, terrain information, and weather conditions, are collected through various sensors on the drone (such as GPS, lidar, and cameras). These data are then preprocessed, including denoising, filtering, and data fusion, to ensure data accuracy and real-time performance.

[0085] Secondly, based on the above data, the A* algorithm is used to estimate the distance from the current position to the target position using the Euclidean distance, and the heuristic function is designed, focusing on the shortest distance, lowest energy consumption and highest safety of the path, and calculating the optimal path from the current position of the drone to the fixed base station. At the same time, the sensor data is updated in real time, and the flight path is dynamically adjusted to avoid obstacles and bad weather.

[0086] Finally, the drone returns to the fixed charging base station according to the planned route, and monitors the flight status in real time to ensure that it arrives at the fixed charging base station smoothly and completes charging.

[0087] (3) Multi-CUAV charging task scheduling strategy for long-distance inspection drones

[0088] First, determine the objective function, that is, the goal of optimizing the charging scheduling strategy for multiple CUAVs at the same time. Ensure that CUAVs provide sufficient charging capacity for all inspection drones to avoid mission interruptions and delays in mission completion due to insufficient power. The common goal of optimizing all CUAV charging scheduling strategies is to minimize the mission completion time of the inspection drones.

[0089] Secondly, parameter modeling. A multi-agent deep reinforcement learning algorithm is used to solve the decision-making problems encountered by multiple CUAVs in the charging scheduling process, and Markov game is used to represent the decision-making process of multiple CUAVs performing charging tasks at the same time. The established model is solved, and a multi-CUAV charging scheduling optimization algorithm based on a multi-agent deep reinforcement learning algorithm is designed. On the premise of ensuring that CUAV can complete the task of wirelessly charging the inspection drone, a charging strategy with the highest energy utilization and the shortest inspection task execution time is obtained.

[0090] Finally, the strategy is implemented and the system is optimized. The optimal strategy obtained is deployed in the actual system, its status is monitored in real time and the optimized charging strategy is executed. The system operation data is regularly analyzed to evaluate the effectiveness and efficiency of the strategy.

[0091] 4. Key technologies for integrated wireless charging of power inspection drones based on wireless charging base stations

[0092] The present invention firstly aims at the demand of autonomous landing of UAV on the base station platform, and clarifies the autonomous landing technology of UAV based on image navigation; secondly, in order to effectively ensure higher charging efficiency, the precise positioning of UAV based on positioning algorithm and coil self-alignment technology are clarified; finally, on the basis of the present invention, the software and hardware integration technology of base station wireless charging system is clarified. Figure 8 shown.

[0093] (1) UAV landing mark recognition technology and autonomous landing technology

[0094] Firstly, based on the YOLOv5 target detection algorithm, a landing sign recognition method is implemented in the context of visual-assisted UAV autonomous landing. Using the target detection algorithm based on deep learning, the target detection problem is transformed into a regression problem, and image features are effectively extracted in complex scenes with poor lighting, scale, and angle, thus improving the robustness of sign recognition.

[0095] Secondly, based on the already established landing sign recognition method, we further develop a sign recognition model with high generalization capability for different landing signs to improve the accuracy of visually guided UAV autonomous landing.

[0096] Finally, based on the completion of landing mark recognition, a software and hardware solution was developed to control the UAV to autonomously and accurately land on the base station platform. The position information of the UAV relative to the landing mark obtained through image processing assisted the UAV flight control system to complete vertical landing.

[0097] (2) UAV precise positioning technology and coil self-registration technology

[0098] First, in order to detect the position of the drone landing on the charging pile platform, the perspective transformation method is used according to the drone's beacon to solve the equation group to calibrate the position parameters, so as to preliminarily locate the drone position, which is convenient for the subsequent fine-tuning of the base station transmitting coil position.

[0099] Then, based on the preliminary positioning of the UAV by perspective transformation, the scanning positioning technology based on laser distance sensing is introduced. The scanning positioning method can make up for the shortcomings of the positioning method based on perspective transformation and accurately locate the UAV position.

[0100] Finally, in order to improve charging efficiency and avoid the waste of excess coils, a new and efficient drone charging coil alignment technology is introduced. Based on the precise positioning of the drone position, a two-axis sliding platform is used to drive the transmitting coil to adaptively align with the receiving coil. To ensure the accuracy of the alignment, after completing the alignment, the charging position self-checking technology is introduced to fine-tune the position of the transmitting coil to improve the alignment accuracy.

[0101] (3) Base station wireless charging system hardware and software integration technology

[0102] First, we designed and built a drone-mounted hardware system that includes a charging management module, an integrated navigation module, a visual docking module, and a Bluetooth module, as well as a charging base station hardware system that includes a Bluetooth module and a wireless charging module to achieve the integration of wireless charging hardware.

[0103] Secondly, design and develop the UAV onboard software system including navigation module, load battery monitoring module, visual docking module, Bluetooth module, and scheduling control module, as well as the charging base station software system including Bluetooth module and wireless charging module to realize wireless charging integration.

[0104] Finally, based on the above results, a wireless charging base station with integrated software and hardware was developed to realize the software and hardware integrated service functions of autonomous landing, precise positioning, and wireless charging of inspection drones.

[0105] The embodiments of the present invention are described in detail above, but the contents are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. The autonomous wireless charging control method for power inspection drones is characterized by: In this method, magnetic coupling inductive wireless charging is adopted; the charger adopts a static planar loosely coupled transformer and is provided with a rectifier filter circuit; the weight of the receiving coil and the transmission efficiency of the system are taken as objective functions, and the weight of the receiving coil is lightweighted by using a multi-objective optimization algorithm; a compensation network is provided for the coupling coil for reactive power compensation; an electromagnetic shielding structure is provided for the coupling coil; and the multi-objective NSGA-II algorithm is used to perform optimal multi-objective optimization of the wireless charging system.

2. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: The electromagnetic shielding structure is realized by using a metal material with eddy current effect.

3. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: The rectification and filtering circuit includes a rectifier and a filter. The alternating current is converted into unidirectional pulsating direct current by the rectifier, and then the pulsation is removed by the filter.

4. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In this method, a coil with a frequency and current above a certain level is used to reduce the skin effect of the coil.

5. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In this method, the coupling coil and the magnetic core in the loosely coupled transformer are optimized as follows: a three-coil coupling mechanism is designed, the frequency, the number of turns, the outer diameter and the material of the secondary coil are determined, the self-inductance of the secondary coil is simulated, the outer diameter of the primary rectangular coil is determined, the self-inductance and mutual inductance of the two primary coils are simulated, and when the self-inductance of the primary coil - the mutual inductance = the self-inductance of the secondary coil, the coupling coefficient K is obtained. max .

6. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In the magnetically coupled inductive wireless power transmission system, the DC power supply passes through the full-bridge inverter circuit and the power supply circuit respectively. The full-bridge inverter circuit converts the DC power into the high-frequency AC power required by the system, and the power supply circuit converts it into the DC power required by the STM32 microcontroller and its peripheral circuits and drive circuits; then, the high-frequency AC power passes through the primary side LCC compensation network, and then transmits the energy to the secondary side receiving coil through two reverse series-connected transmitting coils with the alternating magnetic field as the carrier; then, the electric energy received by the secondary coil first passes through the secondary side LCC compensation network, and then is converted into a DC voltage through the rectifier and filter circuit, and the lithium battery is charged and managed through the battery management system.

7. The autonomous wireless charging control method for a power inspection drone according to claim 6 is characterized in that: In the magnetically coupled inductive wireless power transmission system, the transmitting circuit includes a full-bridge inverter circuit, a drive circuit, an STM32 single-chip microcomputer PWM generation circuit, a power supply circuit and a primary-side LCC compensation network; the DC power supply inputs the DC voltage to the transmitting end, the STM32 single-chip microcomputer transmits a drive signal to the drive circuit, the drive circuit amplifies the drive signal to drive the power switch tube in the full-bridge inverter circuit, converts the DC power input by the DC power supply into AC power, and couples the transmitting and receiving coils for energy transmission.

8. The autonomous wireless charging control method for a power inspection drone according to claim 7 is characterized in that: In this method, a full-bridge inverter circuit is selected as a high-frequency inverter to complete the process of converting DC to AC, MOSFET is selected as the switching tube of the inverter circuit, and an RC resistor-capacitor absorption network is adopted; the hardware circuit of the receiving end includes a rectifier filter circuit, a charge and discharge circuit, a sampling circuit, a control circuit and a DC-DC circuit.

9. The autonomous wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In this method, the LCC compensation topology is used for the primary side of the UAV wireless power transmission system, and the series compensation topology is used for the secondary side compensation topology of the system.

10. The autonomous wireless charging control method for a power inspection drone according to claim 1, characterized in that: In this method, the coupling coil is an asymmetric circular three-coil magnetic coupling mechanism, whose magnetic core is a Mn-Zn power magnetic core made of PC95 material, and a cylindrical hollow coaxial with the cylindrical magnetic core is provided at the center of the cylindrical magnetic core of the receiving coil.

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