Electric power inspection annular paddle unmanned aerial vehicle based on acoustic positioning

Abstract

According to the electric power inspection annular paddle unmanned aerial vehicle based on acoustic positioning, a high-precision distributed acoustic positioning system is carried, a sound array adopts a spiral layout, the directional resolution and the available bandwidth of the array are improved, the good sidelobe suppression characteristic is achieved, and weak signals can be extracted under the background of strong noise and multiple interferences; meanwhile, the sound production position can be accurately monitored aiming at the hidden trouble of the circuit equipment, and the defects of the traditional electric power inspection unmanned aerial vehicle in the aspects of aerodynamic noise control, sound source positioning accuracy under the strong noise background and complex environment adaptability can be effectively overcome. Meanwhile, the unmanned aerial vehicle is further matched with a novel annular propeller structure, the aerodynamic lift force is large, the propeller tip vortex intensity is small, the working noise is small, the background noise generated when the inspection unmanned aerial vehicle works is effectively reduced, the acoustic positioning accuracy is further improved, meanwhile, the application range of the acoustic inspection unmanned aerial vehicle in the low-noise requirement environment is expanded, and the practicability is high. The field blank is filled.

Description

Technical Field

[0001] This invention belongs to the field of power line inspection drone technology, and particularly relates to a power line inspection ring propeller drone based on acoustic positioning. Background Technology

[0002] With the global energy structure transformation and the large-scale expansion of power systems, high-voltage power grids, as the core infrastructure for energy transmission, are increasingly extending their coverage to remote mountainous areas and complex terrains across river basins, significantly increasing the spatial span and environmental complexity of inspection tasks. Traditional manual power grid inspections face multiple limitations due to terrain, weather, and lighting conditions, resulting in high inspection costs, low efficiency, and difficulties in overcoming technological bottlenecks. The "Opinions on Accelerating the Digital and Intelligent Development of Energy" issued by the National Energy Administration emphasizes the need to vigorously promote the deep integration of digital and intelligent technologies in the energy industry, utilizing technologies such as drones and intelligent sensors to improve the intelligence level of transmission line inspections, in order to cope with the increasingly complex operation and maintenance challenges of high-voltage power grids, reduce the risks and costs of manual inspections, and improve the efficiency of fault early warning and handling. Drones, with their advantages of flexibility, wide coverage, and ability to reach sparsely populated areas, are gradually replacing manual labor as an important tool for outdoor inspections; while breakthroughs in sensing technology are further promoting the upgrade of inspection modes from "manual visual judgment" to "intelligent equipment perception." The global power industry generally regards "drone + multi-sensor fusion" as the innovative development direction of high-voltage power grid inspection, hoping to overcome the shortcomings of traditional power inspection. This trend has anchored the core direction of technology research and development as "overcoming the pain points of traditional inspection and integrating the advantages of cutting-edge technologies".

[0003] Focusing on the specific technical pain points in the field of high-voltage power grid inspection, in traditional power inspection work, manual inspection is difficult to carry out in complex scenarios and the cost of a single inspection is high. Potential hazards in power equipment, such as internal insulator breakdown and loose conductor joints, cannot be directly identified through traditional video monitoring or infrared detection, requiring acoustic signal capture for accurate fault location. Existing drone inspection solutions generally suffer from high noise levels and low positioning accuracy in power inspection applications, making them unsuitable for increasingly complex power inspection scenarios.

[0004] Currently, traditional inspection drones have poor environmental adaptability. In complex environments such as mountainous and forested areas, trees and mountains easily obstruct the camera's field of view, making it impossible to photograph critical parts such as the top of power poles and sections of power lines crossing mountains. In urban power grids, tall buildings can restrict the drone's flight path, preventing close-up photography of electrical components on both sides of the street, creating blind spots, rendering visual data acquisition ineffective, and limiting the inspection range. This invention integrates a high-precision distributed acoustic positioning system and a real-time display device, significantly improving scene adaptability and enabling the direct generation of acoustic images of fault areas in power equipment.

[0005] Traditional inspection drones generate strong vortices and airflow separation at the propeller tips during rotation, resulting in significant noise. When inspecting electrical facilities such as substations and high-voltage towers, this noise can easily interfere with sensitive components. Furthermore, inspections in sensitive areas such as schools, hospitals, residential areas, and airport perimeters can cause noise pollution, forcing power lines in these areas to rely solely on manual inspections, leading to low efficiency. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a power line inspection ring propeller UAV based on acoustic positioning. By incorporating a novel ring propeller and employing a low-noise design, the overall noise level of the UAV is reduced by more than 2dB compared to traditional propellers, significantly lowering the background noise level during operation and improving positioning accuracy. Furthermore, this invention expands the application scenarios of inspection UAVs and enhances work efficiency.

[0007] An acoustic positioning-based power line inspection ring propeller UAV includes a UAV body 1, a signal processing module 4 and a multi-helix acoustic array module 6 mounted on the UAV body 1, and a microphone array 7 and a camera 8 mounted on the multi-helix acoustic array module 6.

[0008] The multi-helix acoustic array module 6 is used to drive the microphone array 7 and the camera 8 to tilt, so that the microphone array 7 and the camera 8 are facing the sound source. The microphone array 7 is used to acquire the sound signal emitted by the sound source in real time; the camera 8 is used to acquire the image signal of the sound source in real time. The signal processing module 4 is used to extract the acoustic characteristic frequency of the sound signal and determine whether the sound source is faulty based on the acoustic characteristic frequency. If so, the image signal of the sound source and the spatial coordinates of the sound source are transmitted down to the ground end.

[0009] Furthermore, the main body 1 of the UAV is a quadcopter configuration, wherein each rotor includes two annular propeller blades, and the two annular propeller blades are coaxially spliced ​​together through the propeller root. Meanwhile, the annular propeller blade is a closed annular structure that is smoothly connected by three parts: the front section of the blade, the tip transition section, and the rear section of the blade. The tip transition section connects the front section and the rear section of the blade by twisting. At the same time, the airfoil angle between the two annular propeller blades at the root is 90 degrees.

[0010] Furthermore, the method by which signal processing module 4 determines whether the sound source is faulty is as follows: The acoustic signal is converted from the time domain to the frequency domain, and the resulting frequency domain spectrum is used as the acoustic characteristic frequency. The acoustic characteristic frequency is compared with the built-in fault sound wave characteristic frequency database. If the similarity between any fault sound wave characteristic frequency and the acoustic characteristic frequency is greater than the set value, the sound source is considered to be faulty.

[0011] Furthermore, the signal processing module 4 is also used to take the fault type corresponding to the fault sound wave characteristic frequency corresponding to the maximum similarity as the fault type of the sound source, and transmit the fault type to the ground end.

[0012] Furthermore, the microphone array 7 is arranged according to a non-uniformly distributed multi-arm spiral topology.

[0013] Furthermore, the multi-helix acoustic array module 6 is assembled from an upper cover plate and a lower cover plate. The upper cover plate and the lower cover plate are connected by threaded fixing holes. The upper cover plate is connected to the main body of the UAV 1 by a servo motor. The lower cover plate has a camera 8 embedded in the center and a microphone array 7 embedded around it.

[0014] Furthermore, each microphone in the microphone array 7 is equipped with an independent spherical windscreen made of porous foam material.

[0015] Furthermore, the method by which the signal processing module 4 obtains the spatial coordinates of the sound source is as follows: The time series of the sound signal arriving at each of the 7 microphones in the microphone array were obtained by using the generalized cross-correlation time delay estimation method. The spatial coordinates of the sound source are calculated from the time series using an adaptive beamforming algorithm.

[0016] Beneficial effects: 1. This invention provides a ring-propeller UAV for power line inspection based on acoustic positioning. It is equipped with a high-precision distributed acoustic positioning system. The acoustic array adopts a helical layout, which improves the directional resolution and available bandwidth of the array and has good sidelobe suppression characteristics, which helps to extract weak signals in the context of strong noise and multiple interferences. At the same time, this invention can accurately monitor the sound source location for potential faults in circuit equipment, and can effectively overcome the shortcomings of traditional power line inspection UAVs in terms of aerodynamic noise control, sound source positioning accuracy in strong noise backgrounds, and adaptability to complex environments.

[0017] 2. This invention provides a power line inspection ring propeller UAV based on acoustic positioning. Equipped with a novel ring propeller structure, it features high aerodynamic lift, low tip vortex intensity, and low operating noise, effectively reducing background noise during operation and further improving acoustic positioning accuracy. It also expands the application range of acoustic inspection UAVs in low-noise environments, filling a gap in the field. Specifically, the ring propeller blade of this invention consists of three smoothly connected sections—a front section, a transition section, and a rear section—forming a closed ring structure. The two blades at the root have a 90-degree airfoil angle, while the tips are twisted and connected as one unit. This continuously twisted blade surface design divides the vortex originally concentrated at the tip into multiple weaker vortices, significantly reducing the overall vortex intensity and weakening the noise amplitude caused by airflow pressure pulsation, thereby significantly reducing the background noise during UAV operation. Attached Figure Description

[0018] Figure 1 An isometric view of the main body of the drone; Figure 2 Front view of the connection between the drone and the signal processing module; Figure 3 Bottom view of the multi-helix acoustic array module; Figure 4 This is a ground-side model diagram; Figure 5 This is a model diagram of a ring-shaped propeller. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0020] In the field of power line inspection, the need to operate in complex natural environments and areas inaccessible to human labor has long faced numerous challenges, including high costs, low efficiency, and technological bottlenecks. Firstly, in mountainous canyons, areas spanning rivers and lakes, and high-altitude permafrost regions, manual inspections require the construction of temporary cableways, helicopter rentals, and manpower to search for fault areas, resulting in high costs per inspection and safety risks such as falls and electric shocks. Secondly, while existing power line inspection drones have achieved functions such as visible light aerial photography and infrared thermography, there are still no effective methods for inspecting hidden faults in power equipment (such as internal insulator breakdowns and loose wire joints). Furthermore, the open-type propellers of traditional drones generate significant noise, further limiting their operational capabilities in densely populated urban areas.

[0021] To address the aforementioned issues, this invention discloses a novel power grid inspection drone that combines acoustic detection technology with signal processing algorithms and is equipped with a high-performance, low-noise ring propeller, providing a new technical approach for power grid equipment fault detection. Specifically, this invention uses a motor paired with a novel ring propeller configuration as its power source, and incorporates a high-precision distributed acoustic positioning system module for integrated drone collaborative optimization design. It aims to solve key technical problems inherent in traditional manual inspection methods for high-voltage power grids, such as low safety, high cost, low efficiency, and insufficient fault location accuracy. Simultaneously, it overcomes the shortcomings of traditional power grid inspection drones in aerodynamic noise control, sound source positioning accuracy in strong noise environments, and adaptability to complex environments. This enables a shift in high-voltage power grid inspection from "periodic maintenance" to "precise monitoring," meeting the demands of "low cost, high efficiency, and high precision" in power grid inspection and breaking the limitations of traditional power grid inspection methods.

[0022] Specifically, such as Figure 1 and Figure 2 As shown, a power line inspection ring propeller UAV based on acoustic positioning includes a UAV body 1, a signal processing module 4 and a multi-helix acoustic array module 6 installed on the UAV body 1, and a microphone array 7 and a camera 8 installed on the multi-helix acoustic array module 6. The multi-helix acoustic array module 6 is used to tilt the microphone array 7 and camera 8 so that they face the sound source. Specifically, the multi-helix acoustic array module 6 is assembled from an upper cover plate and a lower cover plate, which are connected by threaded fixing holes. The upper cover plate is connected to the UAV body 1 via a servo motor. The camera 8 is embedded in the center of the lower cover plate, and the microphone array 7 is embedded around it. In other words, the multi-helix acoustic array module 6 can achieve a certain angle of tilting via the servo motor to meet the needs of locating sound sources at different angles.

[0023] like Figure 3 As shown, the microphone array 7 is arranged in a non-uniformly distributed multi-arm spiral topology to acquire sound signals emitted by the sound source in real time; the camera 8 is used to acquire image signals of the sound source in real time and transmit them to the signal processing module through a signal line; in addition, each microphone in the microphone array 7 is equipped with an independent spherical windproof cover made of porous foam material, which can effectively attenuate the wind noise intensity while maintaining the transmission performance of sound waves, thereby ensuring signal quality.

[0024] The signal processing module 4 is used to extract the acoustic characteristic frequencies of the sound signal and determine whether the sound source is faulty based on the acoustic characteristic frequencies. If so, the image signal and spatial coordinates of the sound source are transmitted down to the ground. The method by which the signal processing module 4 determines whether the sound source is faulty is as follows: The acoustic signal is converted from the time domain to the frequency domain by Fourier transform, and the resulting frequency domain spectrum is used as the acoustic characteristic frequency. The acoustic characteristic frequency is compared with the built-in fault acoustic wave characteristic frequency database to identify and eliminate the characteristic noise frequency of the UAV's ring propeller. If the similarity between any fault acoustic wave characteristic frequency and the acoustic characteristic frequency is greater than a set value, the sound source is considered to be faulty, and the fault type can be quickly identified.

[0025] In addition, signal processing module 4 is also used to take the fault type corresponding to the fault sound wave characteristic frequency corresponding to the maximum similarity as the fault type of the sound source, and to transmit the fault type down to, for example, Figure 4 The ground end shown.

[0026] The method by which the signal processing module 4 obtains the spatial coordinates of the sound source is as follows: The time series of the sound signal arriving at each microphone in the microphone array 7 were obtained by using the generalized cross-correlation time delay estimation method; the spatial coordinates of the sound source were calculated from the time series by the adaptive beamforming algorithm.

[0027] Therefore, the signal processing module can overlay the raw data such as the frequency domain spectrum and coordinates of the abnormal sound waves of the fault with the grayscale photos of the fault point monitored by the camera at the ground end to generate a high-definition acoustic image and label the fault type. The image is then transmitted to the control panel at the ground end in real time through the satellite communication link. Ground personnel can then analyze the status of the power line equipment through the ground station to achieve real-time monitoring and inspection of the power line.

[0028] It should be noted that the signal processing module is embedded in the UAV body and integrates a PXI system. The acoustic signals captured by the microphone and the images captured by the camera are transmitted to the signal processing module in real time and synchronously. The PXI system, combined with generalized cross-correlation time delay estimation and adaptive beamforming algorithms, and the collaborative processing of the acoustic signal data using both LabVIEW and MATLAB platforms, achieves accurate analysis of the sound source signal, calculates the distance information of the fault point relative to the UAV, and, combined with the UAV's own positioning function, obtains the specific coordinates of the fault point. A signal transmission port is provided at the front of the signal processing module, enabling acoustic signal interaction with the multi-helix acoustic array via signal lines.

[0029] The main body of the drone adopts a conventional quadcopter configuration, with an overall streamlined projectile structure. It is made of carbon fiber material and integrates flight control, current regulator, power supply and other systems.

[0030] The drone's arms are bolted to motors, with the motor wiring integrated inside the drone's fuselage. Two ring-shaped propellers, one in each direction, are connected to the motor shafts; the diagonally opposite propellers rotate in the same direction. For example... Figure 5As shown, each rotor includes two annular propeller blades, which are coaxially joined together at the root. The annular propeller blade is a closed annular structure smoothly connected by three parts: the front section, the tip transition section, and the rear section. The tip transition section connects the front and rear sections of the blade by twisting. The airfoil angle between the two annular propeller blades at the root is 90 degrees.

[0031] It should be noted that the novel annular propeller used in this invention features a unique design where the blade tip is twisted and spliced ​​to form a ring. This continuously twisted blade surface design can divide the vortex, which was originally concentrated at the blade tip, into multiple weaker vortices, thereby significantly reducing the overall vortex intensity and weakening the noise amplitude caused by airflow pressure pulsation. Compared with traditional open propellers, the annular propeller can form a more uniform wake field, resulting in a more balanced sound directionality distribution of noise. Experiments show that the overall noise sound pressure level of the annular propeller can be reduced by more than 2 dB compared with traditional propellers, demonstrating excellent aerodynamic performance and low-noise operation characteristics.

[0032] In summary, compared with the prior art, the present invention has the following advantages: 1. While traditional inspection drones have achieved functions such as visible light aerial photography and infrared temperature measurement, they still lack effective methods for inspecting hidden faults in power equipment. This invention incorporates a high-precision distributed acoustic positioning system and adopts a platform architecture combining professional acoustic sensors and a PXI system. The acoustic sensors are selected from professional equipment with high sensitivity and wide frequency response characteristics, and data transmission uses shielded data signal cables to ensure high-fidelity, low-interference acquisition of fault sound signals.

[0033] The system constructs a software architecture with the PXI system as the high-performance hardware platform and LabVIEW and MATLAB as the dual platforms, which significantly improves the accuracy and robustness of sound source localization: the PXI system, with its high-precision data acquisition capability, synchronizes and records multiple acoustic signals captured by the microphone array through the data acquisition program written in LabVIEW, providing reliable raw data for subsequent analysis.

[0034] In the noise localization algorithm architecture, the system employs a generalized cross-correlation time delay estimation method, which effectively suppresses the effects of noise and reverberation and accurately calculates the minute time difference between the arrival of the fault sound wave at different microphones in the array. Then, based on an adaptive beamforming algorithm, an independent MATLAB processing program is built to dynamically adjust parameters and form a directional receiving beam in space. By calculating the point where the energy converges most strongly, the specific spatial coordinates of the fault sound source are determined.

[0035] This invention can accurately monitor the location of sound sources for potential faults in circuit equipment, effectively overcoming the shortcomings of traditional power inspection drones in terms of aerodynamic noise control, sound source localization accuracy in strong noise backgrounds, and adaptability to complex environments.

[0036] 2. Traditional inspection drones have noisy open-type propellers, and even when equipped with an acoustic positioning system, the background noise is still high, resulting in inaccurate acoustic positioning and hindering practical applications. Furthermore, the use of traditional inspection drones in sound-sensitive environments such as airports, hospitals, and power plants is limited, reducing their operational capabilities.

[0037] The novel annular propeller used in this invention features a unique design where the blade tip is twisted and spliced ​​to form a ring. This continuously twisted blade surface design divides the vortex, which would otherwise be concentrated at the blade tip, into multiple weaker vortices, thereby significantly reducing the overall vortex intensity and weakening the noise amplitude caused by airflow pressure pulsations. Compared to traditional open propellers, the annular propeller can generate a more uniform wake field, resulting in a more balanced acoustic directivity distribution of noise. Experiments show that the overall noise sound pressure level of the annular propeller is more than 2 dB lower than that of traditional propellers, demonstrating excellent aerodynamic performance and low-noise operation characteristics.

[0038] Compared to traditional inspection drones, this invention significantly reduces operating noise during power line inspections, making it applicable to densely populated urban areas and effectively expanding the scope of power line inspection drone applications. Simultaneously, the lower overall operating noise facilitates the acquisition and extraction of target signals by a high-precision acoustic positioning system, further improving the accuracy of acoustic signal acquisition.

[0039] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A ring-propeller unmanned aerial vehicle (UAV) for power line inspection based on acoustic positioning, characterized in that, It includes the main body of the drone (1), the signal processing module (4) and the multi-helix acoustic array module (6) installed on the main body of the drone (1), and the microphone array (7) and camera (8) installed on the multi-helix acoustic array module (6); The multi-helix acoustic array module (6) is used to drive the microphone array (7) and the camera (8) to tilt so that the microphone array (7) and the camera (8) face the sound source. The microphone array (7) is used to acquire the sound signal emitted by the sound source in real time; the camera (8) is used to acquire the image signal of the sound source in real time. The signal processing module (4) is used to extract the acoustic characteristic frequency of the sound signal and determine whether the sound source is faulty based on the acoustic characteristic frequency. If so, the image signal of the sound source and the spatial coordinates of the sound source are transmitted down to the ground end.

2. The power line inspection ring-propeller UAV based on acoustic positioning as described in claim 1, characterized in that, The main body (1) of the UAV is a quadcopter configuration, wherein each rotor includes two annular propeller blades, and the two annular propeller blades are coaxially spliced ​​together through the blade root. Meanwhile, the annular propeller blade is a closed annular structure that is smoothly connected by three parts: the front section of the blade, the tip transition section, and the rear section of the blade. The tip transition section connects the front section and the rear section of the blade by twisting. At the same time, the airfoil angle between the two annular propeller blades at the root is 90 degrees.

3. The power line inspection ring-propeller UAV based on acoustic positioning as described in claim 1, characterized in that, The signal processing module (4) determines whether the sound source is faulty using the following method: The acoustic signal is converted from the time domain to the frequency domain, and the resulting frequency domain spectrum is used as the acoustic characteristic frequency. The acoustic characteristic frequency is compared with the built-in fault sound wave characteristic frequency database. If the similarity between any fault sound wave characteristic frequency and the acoustic characteristic frequency is greater than the set value, the sound source is considered to be faulty.

4. The power line inspection ring-propeller UAV based on acoustic positioning as described in claim 3, characterized in that, The signal processing module (4) is also used to take the fault type corresponding to the fault sound wave characteristic frequency corresponding to the maximum similarity as the fault type of the sound source, and transmit the fault type to the ground end.

5. A ring-propeller UAV for power line inspection based on acoustic positioning as described in claim 1, characterized in that, The microphone array (7) is arranged in a non-uniformly distributed multi-arm spiral topology.

6. The power line inspection ring-propeller UAV based on acoustic positioning as described in claim 1, characterized in that, The multi-helix acoustic array module (6) is assembled from an upper cover plate and a lower cover plate. The upper cover plate and the lower cover plate are connected by threaded fixing holes. The upper cover plate is connected to the main body of the UAV (1) by a servo motor. The lower cover plate has a camera (8) embedded in the middle and a microphone array (7) embedded around it.

7. A ring-propeller UAV for power line inspection based on acoustic positioning as described in claim 1, characterized in that, Each microphone in the microphone array (7) is equipped with an independent spherical windproof cover made of porous foam material.

8. A ring-propeller UAV for power line inspection based on acoustic positioning as described in claim 1, characterized in that, The method by which the signal processing module (4) obtains the spatial coordinates of the sound source is as follows: The time series of the sound signal arriving at each microphone of the microphone array (7) were obtained by using the generalized cross-correlation time delay estimation method. The spatial coordinates of the sound source are calculated from the time series using an adaptive beamforming algorithm.

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