Weak current inversion method and system based on unmanned aerial sensor

Abstract

This invention discloses a method and system for weak current inversion based on a UAV-borne sensor. The method includes: controlling a UAV carrying a sensor to hover at any position within a preset range from a current-carrying conductor to be measured; rotating the UAV a preset number of times around its fuselage center, and acquiring magnetic field data measured by the sensor; and calculating the current on the current-carrying conductor and the distance between the UAV and the conductor based on the magnetic field data. This method uses a single sensor mounted on a UAV to measure the magnetic field around the current-carrying conductor, and accurately inverts the weak current to obtain the current on the conductor and the distance between the UAV and the conductor. It solves the problems of difficult layout, shortened UAV battery life, and increased cost caused by the large number of sensors carried by UAVs in non-contact measurements. It achieves the beneficial effects of flexible measurement location, rapid response, and cost reduction.

Description

Technical Field

[0001] This invention belongs to the field of intelligent algorithm technology, and more specifically, relates to a method and system for weak current inversion based on UAV-borne sensors. Background Technology

[0002] A common method for fault inspection in power distribution networks is to inject current into the lines and locate the fault point by detecting the amount of current injected. Currently, this is mostly done manually, requiring inspectors to personally go to the site to determine the fault location through close-range measurements. This is time-consuming and labor-intensive, and the complex geographical environment along the lines further increases the difficulty of the work. Using drones for power distribution network fault inspection allows for rapid approach to the lines, enabling the drone to quickly identify and assess fault information using onboard sensors. Inspectors can then analyze the transmitted data in real time from the ground to quickly determine the fault location. This inspection method allows for long-distance measurement and fault diagnosis, greatly improving inspection efficiency while also ensuring the safety of inspection personnel.

[0003] In applications of the signal injection method, the signal current is in the range of amperes or even milliamperes, while the magnetic field measured by the UAV is only 10. 1 ~10 2 nT, while the geomagnetic field in the background magnetic field is at 10 5 The magnetic field strength is on the order of nT, which is hundreds or even thousands of times stronger than the signal magnetic field. At the same time, UAVs also introduce electromagnetic interference and mechanical vibration interference during the measurement. Therefore, it is necessary to study the measurement and extraction of signal magnetic fields under UAV-borne sensor conditions.

[0004] Currently, most magnetic field sensors used in non-contact measurements are fixed to poles or towers, and the relative positions of the measurement point and the conductor are fixed, making the relative positional relationship unknown when using drones for measurement. Most research addressing this issue relies on arrays of multiple sensors to invert line current; however, increasing the number of sensors leads to difficulties in installation and layout, reduced drone flight time, and increased costs. Summary of the Invention

[0005] To address the shortcomings of related technologies, the present invention aims to provide a method and system for weak current inversion based on UAV-borne sensors. Based on the positioning principle of signal injection method, the method uses a UAV equipped with a single sensor to measure the magnetic field around the current-carrying conductor under test, and achieves rapid and accurate location of single-phase grounding fault points in the power distribution network through accurate inversion of weak current, thereby improving the reliability of power supply. This invention aims to solve the problems of difficult layout, shortened UAV endurance, and increased cost caused by the large number of sensors carried by UAVs in non-contact measurement.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for weak current inversion based on an unmanned aerial vehicle (UAV) onboard sensor, comprising:

[0007] Control the drone equipped with sensors to hover at any position within a preset range from the live wire to be tested;

[0008] Using the center of the drone's fuselage as the rotation center, the drone is controlled to rotate a preset number of times at a fixed point, and the magnetic field data measured by the sensor is acquired;

[0009] The current on the conductor under test and the distance between the UAV and the conductor under test are calculated by reverse calculation based on the magnetic field data.

[0010] Optionally, the step of calculating the current on the conductor under test and the distance between the UAV and the conductor under test by reverse calculation based on the magnetic field data includes:

[0011] The curve formed by connecting the peaks of the signal waveform of the magnetic field data is fitted, and the current I on the current-carrying conductor under test is obtained by back-calculation based on the fitting parameters.

[0012] The horizontal distance L and vertical distance H between the UAV and the current-carrying conductor under test are calculated based on the magnetic field data and the current I.

[0013] The magnetic field data measured by the sensor is the magnetic field data around the current-carrying conductor to be measured, and its expression is:

[0014]

[0015] μ0 is the free permeability, L and H are the horizontal and vertical distances from the center of rotation to the current-carrying conductor being measured, respectively, d is the diameter of rotation, and f is the magnetic permeability of free space. r f0 is the rotation frequency of the drone and f0 is the signal current frequency.

[0016] Optionally, fitting the curve formed by connecting the peaks of the signal waveform of the magnetic field data, and inverting the current I on the current-carrying conductor under test based on the fitting parameters, includes:

[0017] Using the unconstrained optimization algorithm Nelder-Mead, we introduce the objective function F, which is expressed as follows:

[0018] F = ||B cal -B sam ||

[0019] Among them, B cal B represents the magnetic field strength at the sampling point calculated based on the UAV rotation model. sam This represents the magnetic flux density measured by the actual sensor.

[0020] By minimizing the value of the objective function F, the error between the measured value and the theoretical value is minimized, thereby inverting the current value on the current-carrying conductor to be measured.

[0021] Optionally, before calculating the current on the conductor under test and the distance between the UAV and the conductor under test based on the magnetic field data, the method further includes:

[0022] The background magnetic field is filtered out from the measured magnetic field data, while the target magnetic field signal is retained;

[0023] In this system, a low-insertion-loss IIR filter is used for bandpass filtering, and the IIR system transfer function is:

[0024]

[0025] a i and b i These are the coefficients in the numerator and denominator of the filter, respectively;

[0026] Through auxiliary calculations, it is transformed into difference form, expressed as:

[0027]

[0028] Where y(n) is the current filter output value, y(ni) is the i-th filter output value in the past, and x(ni) is the i-th input value in the past.

[0029] Optionally, before controlling the drone equipped with the sensor to hover at any position within a preset range from the current-carrying wire to be tested, the method further includes:

[0030] A current of a different frequency than that of the conductor under test is injected into the conductor under test to form a current-carrying conductor under test.

[0031] Optionally, the sensor is a triaxial magnetic field sensor.

[0032] In a second aspect, the present invention also provides a weak current inversion system based on UAV-borne sensors, comprising a UAV carrying sensors and a back-end processor connected to the UAV; the back-end processor is used to execute a weak current inversion method based on UAV-borne sensors as described in any one of the first aspects.

[0033] In summary, compared with the prior art, the above-described technical solutions conceived by this invention have the following advantages:

[0034] Beneficial effects:

[0035] (1) In the case of unknown distance between the conductor to be measured and the measurement point, a method for current inversion with a single sensor is proposed in combination with the measurement characteristics of UAV-borne sensors. This solves the problem of difficult sensor array installation and layout. Furthermore, the hovering position of the UAV is not limited by space, the measurement position is flexible, and the response is rapid. On the other hand, the reduction in the number of sensors also greatly reduces the cost, which is conducive to its widespread use in complex power distribution networks.

[0036] (2) Using a high-precision, low-noise magnetic field sensor, it can realize non-contact measurement and inversion of weak currents at the milliampere level; even when a high-resistance grounding fault occurs in the distribution network and the injected current is extremely small, it can accurately distinguish the current difference between the upstream and downstream of the fault point, ensuring the accuracy of the location.

[0037] (3) While performing current inversion, this method can also obtain the positional relationship between the UAV and the current-carrying conductor under test, which can be used to guide the UAV's subsequent flight along the line and realize the intelligent UAV inspection and fault finding. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a weak current inversion method based on an unmanned aerial vehicle (UAV) onboard sensor provided in Embodiment 1 of the present invention.

[0039] Figure 2 This is a schematic diagram of the UAV-borne sensor rotating to measure the magnetic field near the current-carrying conductor in Embodiment 1 of the present invention;

[0040] Figure 3 This is a schematic diagram of the signal magnetic field waveform measured in Embodiment 1 of the present invention;

[0041] Figure 4 Based on Figure 3 A schematic diagram of the current inversion result from the schematic diagram of the magnetic field waveform of the signal. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0043] Example 1

[0044] The following description, in conjunction with a preferred embodiment, illustrates the content involved in the above embodiments.

[0045] like Figure 1 As shown, a weak current inversion method based on an unmanned aerial vehicle (UAV) onboard sensor includes:

[0046] S1. Control the drone equipped with sensors to hover at any position within a preset range from the energized wire to be tested.

[0047] S2. Using the center of the drone's fuselage as the rotation center, control the drone to rotate a preset number of times at a fixed point, and acquire magnetic field data measured by sensors.

[0048] S3. Based on the magnetic field data, calculate the current on the conductor under test and the distance between the UAV and the conductor under test.

[0049] Existing methods for inspecting power distribution network faults often require fixed-point detection, resulting in inaccurate fault location and high costs due to the use of numerous sensors. This embodiment employs a weak current inversion method based on unmanned aerial vehicle (UAV) onboard sensors to detect the current in the current-carrying conductor under test and determine the fault location based on the current abrupt change point.

[0050] By maneuvering a drone equipped with a magnetic field sensor to hover at any position within a preset distance of 5 meters from the current-carrying conductor under test, the sensor on the drone can accurately acquire the magnetic field data generated by the conductor. Figure 2 As shown, after hovering, the drone rotates around its fuselage center a preset number of times. Since the sensor is located at one end of the drone, the rotation diameter is twice the distance between the sensor and the rotation center as the drone rotates. The distance between the drone and the conductor being tested changes with rotation, allowing the sensor to measure multiple sets of magnetic field data. The preset number of rotations is greater than two; during the multiple rotations, more than two magnetic field data points are obtained at each position. Acquiring multiple sets of magnetic field data with a single sensor reduces the number of sensors required. Suitable magnetic field data from these sets can be fitted, and the current I on the conductor being tested can be derived from the fitted parameters.

[0051] The drone includes a signal conditioning circuit, an A / D conversion circuit, a microprocessor, and a signal transmission unit. The sensor is electrically connected to the signal conditioning circuit, which in turn is electrically connected to the A / D conversion circuit. The A / D conversion circuit is electrically connected to the microprocessor, and the microprocessor is electrically connected to the signal transmission unit. Magnetic field strength data collected by the magnetic field sensor is filtered, amplified, and converted via A / D conversion before being transmitted back to the back-end processor for processing through the signal transmission unit.

[0052] The error in the magnetic field data measured by the sensor comes from the position change during the rotation of the UAV. Taking the center of the UAV fuselage as the rotation center, the random offset of the rotation center in the horizontal or vertical direction will compensate for each other and improve the accuracy of the inversion.

[0053] After acquiring the magnetic field data obtained by the transmission measurement, the background magnetic field is filtered out from the measured magnetic field data, and the target magnetic field signal is retained.

[0054] The magnetic field data measured by the sensor is the magnetic field data around the current-carrying conductor under test, including the magnetic field data generated by the current-carrying conductor under test, the background magnetic field (geomagnetic field) and the magnetic field generated by the coil current of the UAV itself. The magnetic field data generated by the current-carrying conductor under test is the target magnetic field data.

[0055] Specifically, a low-insertion-loss IIR filter is used for bandpass filtering. The IIR system transfer function is:

[0056]

[0057] a i and b i These are the coefficients in the numerator and denominator of the filter, respectively.

[0058] Then, using MATLAB's filter design tools for auxiliary calculations, it is transformed into a difference form, expressed as:

[0059]

[0060] Where y(n) is the current filter output value, y(ni) is the i-th filter output value in the past, and x(ni) is the i-th input value in the past.

[0061] The curve formed by connecting the peaks of the filtered magnetic field data signal waveform is fitted, and the current I on the current-carrying conductor under test is inferred from the fitted parameters. For example... Figure 3 As shown, when f0 is much larger than f r When the magnetic field signal waveform obtained by rotation measurement is measured, a clear envelope will appear. The curve formed by connecting the peaks of the signal waveform is then fitted. According to the Biot-Savart law, the expression for the magnetic field data around the current-carrying conductor is:

[0062]

[0063] Where μ0 is the free permeability, L and H are the horizontal and vertical distances from the center of rotation to the current-carrying conductor being measured, respectively, d is the diameter of rotation, and f is the magnetic permeability of free space. r f0 is the rotation frequency of the drone and f0 is the signal current frequency.

[0064] During the fitting process, the unconstrained optimization algorithm Nelder-Mead is used, and the objective function F is introduced, as follows:

[0065] F = ||B cal -B sam ||

[0066] Among them, B cal B represents the magnetic field strength at the sampling point calculated based on the UAV rotation model. sam This represents the magnetic flux density measured by the actual sensor.

[0067] By minimizing the objective function F, the error between the measured and theoretical values ​​is minimized, thus selecting the magnetic field data with the smallest error during the measurement process. The current I on the current-carrying conductor under test is then inverted based on the fitted parameters. The inversion result in this embodiment is as follows: Figure 4 As shown, the relative error between the actual current value and the actual current value is less than 5%.

[0068] A drone equipped with sensors measures the current of a energized conductor at different locations. When a sudden change in current occurs, the corresponding location is identified as the fault point. The horizontal distance L and vertical distance H between the drone and the conductor are then calculated based on the magnetic field data, the current I, and the expression B0(t) for the magnetic field data. Since power transmission lines typically consist of multiple lines, the fault location can be accurately pinpointed using the calculated horizontal and vertical distances L and H between the drone and the conductor.

[0069] Furthermore, while performing current inversion, the positional relationship between the UAV and the current-carrying conductor under test is obtained, which can be used to guide the UAV's subsequent flight along the line, realizing intelligent fault finding by UAV line inspection.

[0070] Optionally, in this embodiment, the drone is equipped with only one sensor to complete the inspection of power distribution network faults, which reduces the number of sensors and improves the drone's endurance.

[0071] Optionally, the sensor in the embodiment is a triaxial magnetic field sensor.

[0072] This embodiment employs a high-precision, low-noise triaxial magnetic field sensor with excellent triaxial orthogonality and low measurement error of the total magnetic field during attitude changes. It also boasts high sensitivity, enabling non-contact measurement and accurate inversion of milliampere-level weak currents. Even in the event of a high-resistance grounding fault in the distribution network with extremely small injected current, it can accurately distinguish the current differences between the upstream and downstream of the fault point, ensuring accurate location.

[0073] Before step S1, the method further includes: injecting a current of a different frequency than the current carrying frequency of the conductor under test into the conductor under test, forming a conductor under test. For example, the current frequency in the power supply line cable is 50 Hz. When the power supply line is under maintenance, the cable is de-energized, and a current of a different frequency of 5 Hz-20 Hz is injected to form a conductor under test. The 5 Hz-20 Hz different frequency current can avoid interference from the power frequency magnetic field (50 Hz), i.e., interference from the current of other power transmission lines; and avoid interference from the current of the UAV's own coil. When the UAV is measuring the magnetic field by rotation, vibration will cause position changes, which may cause the coordinate axis direction of the sensor to change, causing the measured value of the geomagnetic field to change in a short time, thereby introducing a low-frequency magnetic field component. The different frequency current can also eliminate the magnetic field changes caused by the mechanical vibration of the UAV itself.

[0074] This invention provides a method for weak current inversion based on UAV-borne sensors. When the distance between the current-carrying conductor to be measured and the measurement point is unknown, the current I on the current-carrying conductor to be measured is obtained by fitting the curve formed by connecting the peaks of the magnetic field data signal waveform and inverting the current based on the fitting parameters. This solves the problem of requiring multiple sensors and the difficulty in installing and arranging sensor arrays. Furthermore, the hovering position of the UAV is not limited by space, the measurement position is flexible, and the response is rapid, which greatly improves the flexibility of current measurement. On the other hand, the reduction in the number of sensors also greatly reduces the cost, which is conducive to its widespread use in complex power distribution networks.

[0075] Example 2

[0076] A weak current inversion system based on UAV-borne sensors includes a UAV equipped with sensors and a back-end processor connected to the UAV; the back-end processor is used to execute a weak current inversion method based on UAV-borne sensors as described in any of the above embodiments.

[0077] The drone, equipped with sensors, hovers at any position within a preset range from the current-carrying conductor under test, controlled by a back-end processor. It then rotates a preset number of times around its fuselage center. The sensors measure magnetic field data, which is transmitted back to the back-end processor for processing. The back-end processor uses the magnetic field data to calculate the current in the conductor and the distance between the drone and the conductor.

[0078] The weak current inversion system based on UAV-borne sensors provided in this embodiment of the invention can execute the weak current inversion method based on UAV-borne sensors provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0079] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A weak current inversion method based on unmanned airborne sensors, characterized in that, include: Control the drone equipped with sensors to hover at any position within a preset range from the live wire to be tested; Using the center of the drone's fuselage as the rotation center, the drone is controlled to rotate a preset number of times at a fixed point, and the magnetic field data measured by the sensor is acquired; The current on the conductor under test and the distance between the UAV and the conductor under test are calculated by reverse calculation based on the magnetic field data. The step of calculating the current on the conductor under test and the distance between the UAV and the conductor under test by reverse calculation based on magnetic field data includes: fitting a curve to the succession of peaks of the signal waveform of the magnetic field data and back-calculating the current on the energized conductor from the fitting parameters I ; According to the magnetic field data and the current I The horizontal distance between the unmanned aerial vehicle and the to-be-tested live wire is calculated L And the vertical distance H ; The magnetic field data measured by the sensor is the magnetic field data around the current-carrying conductor to be measured, and its expression is: μ 0 is the permeability of free space. L and H These represent the horizontal and vertical distances from the center of rotation to the current-carrying conductor being tested, respectively. d For the diameter of rotation, f r The rotation frequency of the drone, f 0 represents the signal current frequency.

2. The method of claim 1, wherein, The curve formed by connecting the peaks of the signal waveform of the magnetic field data is fitted, and the current on the current-carrying conductor under test is obtained by inversion based on the fitting parameters. I ,include: The Nelder-Mead algorithm, an unconstrained optimization algorithm, is used to introduce the objective function. F The expression is as follows: wherein, B cal represents the magnetic induction strength at the sampling point calculated according to the unmanned aerial vehicle rotation model, B sam represents the magnetic induction strength actually measured by the sensor; by minimizing the value of the objective function F which minimizes the error between the measured and theoretical values, thereby inverting the current values on the wires.

3. The method of claim 1, wherein, Before calculating the current on the conductor under test and the distance between the UAV and the conductor under test based on the magnetic field data, the method further includes: The background magnetic field is filtered out from the measured magnetic field data, while the target magnetic field signal is retained; In this system, a low-insertion-loss IIR filter is used for bandpass filtering, and the IIR system transfer function is: a i and b i are the coefficients of the filter numerator and denominator, respectively; Through auxiliary calculations, it is transformed into difference form, expressed as: wherein, y(n) is the current filter output value, y(ni) is the past i-th filter output value, x(ni) is the past i-th filter output value, i is the past i-th filter output value.

4. The method according to claim 1, characterized in that, Before the drone equipped with the sensor hovers at any position within a preset range from the current-carrying wire to be tested, the method further includes: A current of a different frequency than that of the conductor under test is injected into the conductor under test to form a current-carrying conductor under test.

5. The method according to claim 1, characterized in that, The sensor is a triaxial magnetic field sensor.

6. A weak current inversion system based on unmanned airborne sensors, characterized in that, It includes a drone equipped with sensors and a back-end processor connected to the drone; the back-end processor is used to execute a weak current inversion method based on drone-borne sensors as described in any one of claims 1-5.

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