A fan positioning interference suppression method and system based on adaptive filtering

Abstract

The application provides a fan positioning interference suppression method and system based on adaptive filtering, and belongs to the technical field of fan control. By combining the real-time running state of the fan with the satellite line-of-sight vector, a rotating plane geometric model is established, which can accurately determine which satellite signals are subjected to periodic shielding or reflection interference by the blades, realize targeted identification of the interference source, avoid blind processing of all satellite data, and improve the judgment accuracy and efficiency of the system. Based on the rotational speed of the impeller, the periodic interference frequency is calculated, and an adaptive notch filter is constructed, which can dynamically track and filter out the interference components related to the blade rotation frequency, effectively suppress the periodic multipath and shielding effect, and improve the signal-to-noise ratio and stability of the observation data. After filtering, a dynamic weight reduction fusion mechanism is introduced to reasonably reduce the weight of the observation values of the disturbed satellites, reduce the influence of abnormal observations on positioning calculation, and enhance the robustness of the system in a complex interference environment.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine control technology, specifically relating to a method and system for suppressing wind turbine positioning interference based on adaptive filtering. Background Technology

[0002] As a crucial component of clean energy, wind power generation is experiencing continuous expansion in scale, with increasing single-unit capacity and tower height. This presents higher technical requirements and safety challenges for the intelligent inspection and long-term deformation monitoring of wind power assets. Currently, the industry generally relies on universal high-precision positioning solutions based on RTK (Real-Time Kinematic) technology to drive automated equipment such as drones and robots to perform external inspections or provide reference coordinates for structural monitoring. However, in the unique and complex dynamic industrial scenario of wind power, existing technologies are gradually revealing their inherent limitations, mainly in the following three aspects: 1. Severe Dynamic Multipath Interference and Insufficient Positioning Reliability: Traditional RTK and its anti-interference algorithms are mainly designed for environments such as static base stations or low-speed vehicle movement. Their multipath suppression models cannot effectively cope with the strong dynamic interference unique to wind farms. When wind turbines are running, the composite material blades, which are tens of meters long, rotate periodically at high speeds, continuously and regularly blocking and reflecting GNSS (Global Navigation Satellite System) signals, forming a dynamic stroboscopic multipath effect. The intensity, delay, and direction of arrival of this interference change in real time with the blade pitch angle and rotational speed, exhibiting strong time-varying and non-stationary characteristics. Existing algorithms struggle to model, predict, and eliminate this interference, leading to cycle slips or loss of lock in the receiver carrier phase observations, and sudden jumps in positioning results ranging from centimeters to decimeters. In applications such as autonomous drone inspection, such jumps can easily be misinterpreted by the flight control system as real attitude changes, thus triggering incorrect correction actions. This can lead to deviations from the inspection track and failure to collect data, or even collisions between the drone and the rotating blades or loss of control near the tower, posing serious safety hazards and property damage.

[0003] 2. Indoor and outdoor positioning space is fragmented, making seamless coverage of the entire operation and maintenance process difficult: Wind power operation and maintenance is an integrated process covering both the exterior and interior of the tower. When maintenance personnel or inspection equipment enter the tower from the outside, the fully enclosed metal walls completely block GNSS signals, causing a momentary interruption of positioning services. To meet the safety management needs of personnel and material positioning and electronic fence operation inside the tower, current solutions typically require the deployment of an additional independent indoor positioning system inside the tower. This brings new problems: First, it requires the construction and maintenance of another hardware base station network, significantly increasing deployment costs and engineering complexity; second, the indoor coordinate system and the outdoor GNSS / RTK coordinate system are independent of each other, and the coordinate transformation and smooth switching technology between the two is not yet mature, resulting in positioning blind spots or jumps in the critical transition area of ​​"indoor-door-outdoor". This makes it impossible to achieve continuous, high-availability positioning and tracking for maintenance personnel from the site to the turbine position, and from the tower base to the nacelle, creating an artificial spatial fragmentation.

[0004] 3. Information silos are prominent, limiting the level of system intelligence: Existing positioning and monitoring systems are typically independent of the wind turbine's main control system (such as SCADA data acquisition and monitoring systems). The positioning system acts only as a passive signal receiving and processing unit, unable to utilize the real-time status information of the wind turbine itself. For example, it cannot obtain key attitude and operating parameters such as the turbine's current real-time yaw angle, the pitch and azimuth angles of the three blades, and the generator speed. These parameters are precisely the core context for predicting and characterizing dynamic multipath effects and judging the impact of tower sway on fixed monitoring points. This system fragmentation means the positioning engine can only process contaminated signals in isolation, unable to utilize prior information about the wind turbine's status for intelligent joint anti-interference and error compensation, thus failing to deeply explore the value of the data. Furthermore, the positioning results cannot be fed back to the wind turbine's main control system to support higher-level applications, such as precise location-based preventative maintenance and dynamic safety exclusion zone settings, limiting the overall improvement of the site's intelligent operation and maintenance level.

[0005] In summary, existing general-purpose RTK-based technical solutions face three core challenges in the specific scenario of wind farms: insufficient dynamic anti-interference capability, poor spatial continuity, and low information fusion. These challenges make it difficult to meet the growing demands of the wind power industry for highly reliable, comprehensive, and intelligent inspection and monitoring. Therefore, there is an urgent need for an integrated and innovative solution that can deeply integrate scene perception, intelligent anti-interference, and achieve seamless indoor and outdoor positioning. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and system for suppressing wind turbine positioning interference based on adaptive filtering.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for suppressing wind turbine positioning interference based on adaptive filtering, comprising the following steps: Real-time acquisition of satellite observation data and operational status data of the target wind turbine; Based on the satellite observation data and operating status data of the target wind turbine, the rotation plane of the wind turbine blades is determined and the line of sight of each satellite is constructed; according to the geometric relationship between the rotation plane of the wind turbine blades and the line of sight of each satellite, the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades are identified. Based on the impeller speed, the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite is calculated; An adaptive notch filter is constructed based on the periodic interference frequency. The observation data of the interfered satellite is filtered by the adaptive notch filter to obtain the filtered satellite observation data. Dynamic weighted fusion processing is performed on the filtered satellite observation data to construct the observation noise variance matrix and then weighted correction is applied to obtain the actual noise variance. The position information of the target wind turbine is corrected based on the actual noise variance and the state update equation of the Kalman filter.

[0008] In the step of real-time acquisition of satellite observation data and operational status data of the target wind turbine, the satellite observation data includes carrier phase observation values, signal-to-noise ratio (SNR), elevation angle, and azimuth angle of each visible satellite, and the operational status data includes the impeller speed and nacelle yaw angle of the wind turbine.

[0009] The method for determining the rotation plane of the wind turbine blades and constructing the lines of sight of each satellite based on satellite observation data and operational status data of the target wind turbine; and for identifying the interfered satellites whose signals are affected by periodic blocking or reflection from the wind turbine blades based on the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite, is as follows: Establish a local spatial rectangular coordinate system with the center of the target wind turbine hub as the origin; The normal vector of the wind turbine blade rotation plane is determined based on the nacelle yaw angle, and the spatial plane equation of the rotation plane is constructed using the normal vector of the wind turbine blade rotation plane. Based on the satellite's elevation and azimuth angles, a satellite line-of-sight ray vector is constructed, originating from the phase center of the terminal antenna. Calculate the intersection point of the spatial plane equation and the satellite line-of-sight ray vector, and calculate the distance from the intersection point to the origin; If the distance is less than or equal to the length of the wind turbine blade, it is determined that the corresponding satellite's line of sight passes through the blade's scanning area, and the satellite is an interfered satellite.

[0010] The formula for calculating the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite, based on the impeller speed, is as follows:

[0011] Where N is the number of fan blades and ω is the impeller speed, in revolutions per minute.

[0012] In the step of constructing an adaptive notch filter based on the periodic interference frequency, and filtering the observation data of the interfered satellite using the adaptive notch filter to obtain filtered satellite observation data, the center stopband frequency of the adaptive notch filter is: Because the impeller speed changes in real time with the wind speed, The center stopband frequency of the adaptive notch filter also changes accordingly. The changes are updated in real time.

[0013] The method for dynamically reducing and fusing the filtered satellite observation data to construct the observation noise variance matrix and then reducing and correcting it to obtain the actual noise variance is as follows: Construct the observation noise covariance matrix; For each satellite, its corresponding noise variance inflation factor is dynamically determined based on whether it is marked as the interfered satellite and the signal-to-noise ratio after notch filtering. The constructed observation noise covariance matrix is ​​corrected using the noise variance inflation factor to obtain the actual noise variance of the final input observation noise covariance matrix.

[0014] The formula for correcting the target wind turbine's position information based on the actual noise variance and combined with the state update equation of Kalman filtering is expressed as follows:

[0015] in, This is the corrected location information for the target wind turbine. The wind turbine position was calculated at the previous moment. For Kamer gain.

[0016] Secondly, the present invention provides a wind turbine positioning interference suppression system based on adaptive filtering, comprising: The data acquisition module is used to collect satellite observation data and operational status data of the target wind turbine in real time; The satellite identification module is used to determine the rotation plane of the wind turbine blades and construct the lines of sight of each satellite based on the satellite observation data and operating status data of the target wind turbine; and to identify the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades according to the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite. The calculation module is used to calculate the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite based on the impeller speed; The filtering module is used to construct an adaptive notch filter based on the periodic interference frequency, and to filter the observation data of the interfered satellite through the adaptive notch filter to obtain filtered satellite observation data. The weight reduction correction module is used to perform dynamic weight reduction fusion processing on the filtered satellite observation data, construct the observation noise variance matrix and perform weight reduction correction on it to obtain the actual noise variance. The position calculation module is used to correct the position information of the target wind turbine based on the actual noise variance and the state update equation of Kalman filtering.

[0017] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a wind turbine positioning interference suppression method based on adaptive filtering.

[0018] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a wind turbine positioning interference suppression method based on adaptive filtering.

[0019] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a wind turbine positioning interference suppression method and system based on adaptive filtering. By combining the real-time operating status of the wind turbine with the satellite line-of-sight vector, a rotating plane geometric model is established. This model can accurately determine which satellite signals are affected by periodic blade obstruction or reflection interference, enabling targeted identification of interference sources and avoiding blind processing of all satellite data, thus improving the system's judgment accuracy and efficiency. The periodic interference frequency is calculated based on the impeller rotation speed, and an adaptive notch filter is constructed to dynamically track and filter out interference components related to the blade rotation frequency, effectively suppressing periodic multipath and obstruction effects and improving the signal-to-noise ratio and stability of the observation data. A dynamic weighting fusion mechanism is further introduced after filtering. By constructing and correcting the observation noise variance matrix, the observation values ​​of the interfered satellites are reasonably weighted, reducing the impact of abnormal observations on positioning calculations and enhancing the system's robustness in complex interference environments. Finally, Kalman filtering is combined with the corrected actual noise variance for state updates, effectively improving the position estimation accuracy of the target wind turbine. Especially in scenarios where continuous wind turbine operation causes periodic signal fluctuations, stable and reliable high-precision positioning can be achieved. This method only requires acquiring routine wind turbine operation data and satellite observation information, without adding any additional hardware. The algorithm can be embedded into existing wind turbine monitoring or positioning systems, making it easy to implement and promote. It is suitable for positioning assurance and safety monitoring of large-scale wind farms. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the interference geometry modeling and line-of-sight occlusion principle of the present invention; Figure 3 This is a schematic diagram of the single-axis constraint positioning inside the tower in this invention; Figure 4 This is a system diagram of Embodiment 3 of the present invention. Detailed Implementation

[0021] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0022] Example 1 like Figure 1 As shown, a method for suppressing wind turbine positioning interference based on adaptive filtering includes the following steps: S1: Real-time acquisition of satellite observation data and operational status data of the target wind turbine; S2: Determine the rotation plane of the wind turbine blades and construct the lines of sight of each satellite based on the satellite observation data and operating status data of the target wind turbine; Based on the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite, identify the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades. S3: Based on the impeller speed, calculate the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite; S4: Construct an adaptive notch filter based on the periodic interference frequency, and filter the observation data of the interfered satellite using the adaptive notch filter to obtain filtered satellite observation data. S5: Perform dynamic weight reduction and fusion processing on the filtered satellite observation data, construct the observation noise variance matrix and perform weight reduction correction on it to obtain the actual noise variance; S6: Based on the actual noise variance, the position information of the target wind turbine is corrected using the state update equation of the Kalman filter. Specifically, in S1, real-time acquisition of satellite observation data and wind turbine status data is performed; wherein, the satellite observation data includes carrier phase observation values, signal-to-noise ratio (SNR), satellite elevation angle, and azimuth angle of each visible satellite; the wind turbine status data includes the wind turbine impeller speed ω and nacelle yaw angle α.

[0023] Specifically, in S2, the rotation plane of the wind turbine blades is determined based on the satellite observation data and operating status data of the target wind turbine, and the lines of sight of each satellite are constructed; according to the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite, the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades are identified.

[0024] S21: Establish a local spatial rectangular coordinate system with the center of the target wind turbine hub as the origin; S22: Determine the normal vector of the wind turbine blade rotation plane based on the nacelle yaw angle, and construct the spatial plane equation of the rotation plane using the normal vector of the wind turbine blade rotation plane; The wind turbine blades rotate in a plane approximately perpendicular to the main shaft. The nacelle yaw angle α defines the orientation of the nacelle (i.e., the main shaft) in the horizontal plane. Based on the nacelle yaw angle, the normal vector n of the rotation plane can be determined. For example, when α = 0° (pointing due north), the normal vector n of the rotation plane points due east. Using the point normal form, the spatial plane equation of the rotation plane can be obtained: Ax + By + Cz = 0, where A, B, and C are undetermined coefficients.

[0025] S23: Based on the satellite's elevation and azimuth angles, construct a satellite line-of-sight ray vector originating from the phase center of the terminal antenna; For the i-th visible satellite, starting from its GNSS antenna phase center P0(x0, y0, z0) in the local coordinate system, construct a unit line-of-sight vector pointing towards the satellite based on its elevation angle θ and azimuth angle φ. .

[0026] S24: Calculate the intersection point of the spatial plane equation and the satellite line-of-sight ray vector, and calculate the distance from the intersection point to the origin; ray Substituting the equation of x into the equation of the plane, we can find the intersection point P(x) of the two in space. p ,y p ,z p Calculate the straight-line distance d from the intersection point P to the center of the wheel hub (origin).

[0027] S25: If the distance is less than or equal to the length of the wind turbine blade, it is determined that the corresponding satellite line of sight passes through the blade scanning area, and the satellite is an interfered satellite.

[0028] The distance d is compared with the physical length (sweep radius) R of the wind turbine blade. If d ≤ R, it is determined that the satellite's line of sight passes through the solid disk area formed by the blade sweep. Since the blade is continuously rotating, the satellite signal will inevitably suffer from periodic physical blockage and complex multipath reflections, so it is marked as an interfered satellite and its satellite ID is recorded.

[0029] Specifically, in step S3, based on the impeller rotation speed, the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite is calculated. The specific formula is as follows:

[0030] Where N is the number of fan blades and ω is the impeller speed, in revolutions per minute.

[0031] In a preferred embodiment of this invention, for a three-bladed wind turbine, each rotation of the blades causes three obstructions / reflections to the line of sight of the same satellite. Therefore, the fundamental frequency calculation formula for the periodic interference caused by blade rotation is:

[0032] Specifically, in S4, an adaptive notch filter is constructed based on the periodic interference frequency, and the observation data of the interfered satellite is filtered by the adaptive notch filter to obtain the filtered satellite observation data.

[0033] A notch filter is a special type of band-stop filter characterized by its extremely narrow stopband, specifically designed to filter signals at a particular frequency. The mechanism for achieving periodic error filtering is as follows: In the digital signal processing domain, a center stopband frequency of [frequency value missing] is designed. A second-order IIR digital notch filter. Because ω changes in real time with wind speed. This also changes accordingly, therefore the notch filter is adaptive, and its coefficients are based on... Updated in real time.

[0034] In the Kalman filter preprocessing stage of BeiDou RTK positioning calculation, satellites marked as being interfered with will generate carrier phase or pseudorange observation residuals with severe high-frequency oscillations. Their carrier phase double-difference observation residual sequences are extracted. This residual sequence is then filtered using the aforementioned adaptive notch filter. The notch filter will filter the frequencies in the frequency domain to exactly [value missing]. The amplitude of the signal components (and their harmonics) is greatly attenuated to near zero.

[0035] The notch filter will have a frequency of 1 in its transfer function. The signal components within and near the extremely narrow frequency band (i.e., errors introduced by periodic blade blocking / reflection) are deeply attenuated and eliminated. Due to its extremely narrow stopband, the notch filter only removes interference at the same frequency as the rotational speed, thus preserving the true low-frequency displacement signal of the tower and resolving the problem of frequent jumps in positioning data. Meanwhile, the signal components representing the true low-frequency swaying or deformation of the tower (typically below 0.5Hz) are retained. This fundamentally eliminates the periodic jumps in the positioning results.

[0036] Specifically, in S5, the filtered satellite observation data undergoes dynamic weight reduction and fusion processing to participate in high-precision positioning calculations.

[0037] If the signal-to-noise ratio of a satellite's signal remains below the threshold after filtering, it indicates poor observation quality. Directly removing it would result in an insufficient number of usable satellites. A dynamic weighting strategy is employed to dynamically reduce the weight of that satellite's observations. In the observation noise covariance matrix of the Kalman filter, the noise variance corresponding to that satellite is significantly increased, thus reducing its contribution to the final positioning result.

[0038] S51: Construct the observation noise covariance matrix R; in Kalman filtering, the observation noise covariance matrix R determines the system's level of trust in different satellite data. The matrix is ​​a diagonal matrix:

[0039] in, Let be the observation noise variance of the i-th satellite.

[0040] S52: For each satellite, based on whether it is marked as an interfered satellite and the signal-to-noise ratio after notch filtering, its corresponding noise variance inflation factor is dynamically determined. In the specific scenario of this invention, a dynamic weighted inflation factor is introduced. For satellites that were not determined to have passed through the blade sweep area, or whose signal-to-noise ratio is higher than the safety threshold after notch filtering, =1; For satellites marked as interfered with and whose signal-to-noise ratio is still below the threshold after filtering, set... >1.

[0041] S53: The constructed observation noise covariance matrix is ​​corrected using the noise variance inflation factor to obtain the actual noise variance of the final input observation noise covariance matrix. The correction / weighting formula is as follows:

[0042] in, Basic noise variance: The basic variance calculated in conventional RTK based on the satellite's elevation angle and the satellite signal-to-noise ratio model (the lower the elevation angle, the larger the variance). The variance of the final input, when >1, the satellite corresponds to Significantly increased.

[0043] In the covariance matrix R, If the value increases, the Kalman filter will automatically consider the satellite's observation data to be of extremely poor quality and unreliable. In subsequent calculations of the Kalman gain, it will significantly reduce the satellite's contribution to the final positioning result, thereby avoiding the problem of insufficient available satellites and inability to locate the satellite due to direct satellite removal.

[0044] Specifically, in S6, the position information of the target wind turbine is corrected based on the actual noise variance and the state update equation of the Kalman filter. The state update equation of the Kalman filter is as follows:

[0045] Gain∝1 /

[0046] in, This is the corrected location information for the target wind turbine. The wind turbine position was calculated at the previous moment. For Kamer gain.

[0047] Based on the wind turbine position calculated in the previous epoch, the system uses the state of the previous epoch to predict and estimate the wind turbine state in the current epoch. Combined with the dynamic model describing the wind turbine's motion, it predicts the possible position of the wind turbine in the current epoch. Alternatively, it can quickly calculate a rough, approximate coordinate using ordinary pseudorange single-point positioning.

[0048] The residuals are generated by comparing the actual observations acquired by the GNSS receiver with the theoretical observations for the current time, predicted based on the state at the previous time step. Considering that the rotation of wind turbine blades introduces periodic multipath interference, resulting in significant anomalous noise in the observation residuals, further adaptive filtering is required. Based on the noise variance inflation factor, the system uses an adaptive notch filter to selectively filter out satellite observation data identified as interfered, extracting the net residuals reflecting the true physical displacement; while for satellite data not significantly interfered with, the original residuals are retained.

[0049] As a preferred embodiment of this invention, such as Figure 3 As shown, in the event of Figure 3 When the metal shielding environment shown causes a complete loss of satellite signal lock, the system can automatically activate the TDOA (Time Difference of Arrival) positioning mode based on two (or more) ground base stations. In this mode, the system utilizes the geometric constraint relationship d1 between the base stations. d2 = c × Δt, P ∈ Axis, where c represents the electromagnetic wave, Δt represents the time difference between the arrival of the wireless signal from terminal P to base station A and base station B, P represents the position of the moving target in the current coordinate system, and Axis is the axis of operation movement.

[0050] By reducing complex 3D solutions to 1D line-of-sight positioning, continuous and reliable location services can still be provided even under extreme conditions.

[0051] The purified residuals and the actual noise variance corrected by variance expansion are substituted into the state update equation of the Kalman filter. The filter automatically calculates the optimized Kalman gain based on the actual noise variance, and uses this gain to weight the net residuals, ultimately achieving high-precision correction of the target wind turbine position information.

[0052] Furthermore, the system uses the corrected wind turbine position from the current epoch as the prior state for the next epoch. By combining the dynamic model describing the kinematic laws of the wind turbine, state prediction is performed, thereby entering the solution cycle of the next epoch, and continuously ensuring the high-precision positioning of the wind turbine against interference.

[0053] Example 2 A wind turbine positioning interference suppression system based on adaptive filtering adopts a three-layer collaborative architecture of "cloud-edge-device". A BeiDou ground-based augmentation reference station is deployed in a geologically stable area with a wide field of vision within the wind farm's booster station to generate high-precision real-time differential correction data. Two GNSS monitoring terminals are installed on each wind turbine: one is installed outside the nacelle to monitor blade flapping and turbine nose sway; the other is installed outside the tower base, which, in addition to monitoring tower base displacement, more importantly serves as a differential reference station, forming a baseline with the booster station's reference station to provide localized high-precision differential services for the wind turbine area. All GNSS terminals are connected to the wind farm's production control area (zone three) network.

[0054] An industrial-grade 5G CPE device is deployed inside the tower base and nacelle of each wind turbine to receive and amplify 5G signals from the outdoor macro base station in the site, ensuring wireless coverage in the working area inside the tower. A miniaturized 5G micro base station is deployed on top of the wind turbine nacelle to extend signal coverage in the high-altitude working area. These devices together form a dedicated 5G wireless network for the wind turbine, providing a channel for positioning and data backhaul.

[0055] Within the Zone 1 network of the Supervisory Control and Data Acquisition (SCADA) system, core operating parameters of the wind turbine, including rotor speed, nacelle yaw angle, and pitch angle of each blade, are read in real time via a secure isolation device. This data is then securely synchronized to the Zone 3 network for use by the edge computing unit.

[0056] Inspection personnel are equipped with smart safety helmets or handheld PDAs that integrate 5G modules and Beidou high-precision positioning modules, while drones are equipped with the same type of high-precision positioning terminal.

[0057] Deploy a core converged gateway in the substation equipment room to achieve aggregation and protocol conversion of multi-source data; deploy a lightweight 5G core network user plane function (UPF) to achieve offloading of local data traffic and low-latency transmission; deploy a spatiotemporal information cloud platform server to provide cloud services such as high-precision positioning calculation and digital twin modeling.

[0058] A spatiotemporal edge computing box is deployed within the control cabinet of each wind turbine tower. This computing box possesses powerful local computing capabilities and includes the following modules: The data acquisition module is used to collect satellite observation data and operational status data of the target wind turbine in real time. The edge computing box synchronously collects two sets of time-aligned data at each solution epoch (e.g., 1Hz): raw observation data from the GNSS receiver, including carrier phase observations, pseudorange, signal-to-noise ratio, satellite elevation angle and azimuth angle of all visible satellites; and wind turbine status data from the SCADA system, the core of which is the rotor speed ω and nacelle yaw angle α at the current moment.

[0059] The satellite identification module is used to determine the rotation plane of the wind turbine blades and construct the lines of sight of each satellite based on the satellite observation data and operating status data of the target wind turbine; and to identify the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades according to the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite. The calculation module is used to calculate the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite based on the impeller speed; The filtering module is used to construct an adaptive notch filter based on the periodic interference frequency, and to filter the observation data of the interfered satellite through the adaptive notch filter to obtain filtered satellite observation data. The weight reduction correction module is used to perform dynamic weight reduction fusion processing on the filtered satellite observation data, construct the observation noise variance matrix and perform weight reduction correction on it to obtain the actual noise variance. The position calculation module is used to correct the position information of the target wind turbine based on the actual noise variance and the state update equation of Kalman filtering.

[0060] Furthermore, this invention also achieves seamless continuous positioning from the outdoor area of ​​the wind farm to the interior of the wind turbine tower. When workers or drones are outside the wind turbine, the terminal uses BeiDou tri-frequency RTK positioning and integrates 5G signal-assisted speed measurement and initial satellite search to achieve centimeter / decimeter-level positioning outdoors.

[0061] A CPE is deployed at the entrance of the wind turbine tower. When a terminal enters the tower from the outside, two events occur instantaneously: the BeiDou signal strength (C / N0) drops sharply below the unlock threshold; simultaneously, a specific 5G reference signal with a sudden increase in strength is detected emitted by the CPE at the entrance. Either of these events can trigger a state machine switch for the positioning mode. The entire switching process is managed by the state machine of the edge computing box or cloud platform, ensuring that the location information stream received by business applications (such as personnel tracking and electronic fences) is continuous and uninterrupted, regardless of whether the terminal is outdoors or indoors.

[0062] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for a wind turbine positioning interference suppression method based on adaptive filtering; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0063] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the wind turbine positioning interference suppression method based on adaptive filtering described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0064] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0065] The memory 101 in the electronic device 100 stores multiple instructions to implement a wind turbine positioning interference suppression method based on adaptive filtering, and the processor 102 can execute the multiple instructions to achieve the following: Real-time acquisition of satellite observation data and operational status data of the target wind turbine; Based on the satellite observation data and operating status data of the target wind turbine, the rotation plane of the wind turbine blades is determined and the line of sight of each satellite is constructed; according to the geometric relationship between the rotation plane of the wind turbine blades and the line of sight of each satellite, the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades are identified. Based on the impeller speed, the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite is calculated; An adaptive notch filter is constructed based on the periodic interference frequency. The observation data of the interfered satellite is filtered by the adaptive notch filter to obtain the filtered satellite observation data. Dynamic weighted fusion processing is performed on the filtered satellite observation data to construct the observation noise variance matrix and then weighted correction is applied to obtain the actual noise variance. The position information of the target wind turbine is corrected based on the actual noise variance and the state update equation of the Kalman filter.

[0066] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for suppressing wind turbine positioning interference based on adaptive filtering, characterized in that, Includes the following steps: Real-time acquisition of satellite observation data and operational status data of the target wind turbine; Based on the satellite observation data and operating status data of the target wind turbine, the rotation plane of the wind turbine blades is determined and the line of sight of each satellite is constructed; according to the geometric relationship between the rotation plane of the wind turbine blades and the line of sight of each satellite, the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades are identified. Based on the impeller speed, the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite is calculated; An adaptive notch filter is constructed based on the periodic interference frequency. The observation data of the interfered satellite is filtered by the adaptive notch filter to obtain the filtered satellite observation data. Dynamic weighted fusion processing is performed on the filtered satellite observation data to construct the observation noise variance matrix and then weighted correction is applied to obtain the actual noise variance. The position information of the target wind turbine is corrected based on the actual noise variance and the state update equation of the Kalman filter.

2. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 1, characterized in that, In the step of real-time acquisition of satellite observation data and operational status data of the target wind turbine, the satellite observation data includes carrier phase observation values, signal-to-noise ratio (SNR), elevation angle, and azimuth angle of each visible satellite, and the operational status data includes the impeller speed and nacelle yaw angle of the wind turbine.

3. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 2, characterized in that, The method for determining the rotation plane of the wind turbine blades and constructing the lines of sight of each satellite based on satellite observation data and operational status data of the target wind turbine; and for identifying the interfered satellites whose signals are affected by periodic blocking or reflection from the wind turbine blades based on the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite, is as follows: Establish a local spatial rectangular coordinate system with the center of the target wind turbine hub as the origin; The normal vector of the wind turbine blade rotation plane is determined based on the nacelle yaw angle, and the spatial plane equation of the rotation plane is constructed using the normal vector of the wind turbine blade rotation plane. Based on the satellite's elevation and azimuth angles, a satellite line-of-sight ray vector is constructed, originating from the phase center of the terminal antenna. Calculate the intersection point of the spatial plane equation and the satellite line-of-sight ray vector, and calculate the distance from the intersection point to the origin; If the distance is less than or equal to the length of the wind turbine blade, it is determined that the corresponding satellite's line of sight passes through the blade's scanning area, and the satellite is an interfered satellite.

4. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 1, characterized in that, The formula for calculating the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite, based on the impeller speed, is as follows: Where N is the number of fan blades and ω is the impeller speed, in revolutions per minute.

5. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 4, characterized in that, In the step of constructing an adaptive notch filter based on the periodic interference frequency, and filtering the observation data of the interfered satellite using the adaptive notch filter to obtain filtered satellite observation data, the center stopband frequency of the adaptive notch filter is: Because the impeller speed changes in real time with the wind speed, The center stopband frequency of the adaptive notch filter also changes accordingly. The changes are updated in real time.

6. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 5, characterized in that, The method for dynamically reducing and fusing the filtered satellite observation data to construct the observation noise variance matrix and then reducing and correcting it to obtain the actual noise variance is as follows: Construct the observation noise covariance matrix; For each satellite, its corresponding noise variance inflation factor is dynamically determined based on whether it is marked as the interfered satellite and the signal-to-noise ratio after notch filtering. The constructed observation noise covariance matrix is ​​corrected using the noise variance inflation factor to obtain the actual noise variance of the final input observation noise covariance matrix.

7. The wind turbine positioning interference suppression method based on adaptive filtering according to claim 5, characterized in that, The formula for correcting the target wind turbine's position information based on the actual noise variance and combined with the state update equation of Kalman filtering is expressed as follows: in, This is the corrected location information for the target wind turbine. The wind turbine position was calculated at the previous moment. For Kamer gain.

8. A wind turbine positioning interference suppression system based on adaptive filtering, characterized in that, include: The data acquisition module is used to collect satellite observation data and operational status data of the target wind turbine in real time; The satellite identification module is used to determine the rotation plane of the wind turbine blades and construct the lines of sight of each satellite based on the satellite observation data and operating status data of the target wind turbine; and to identify the interfered satellites whose signals are affected by the periodic blocking or reflection of the wind turbine blades according to the geometric relationship between the rotation plane of the wind turbine blades and the lines of sight of each satellite. The calculation module is used to calculate the periodic interference frequency generated by the wind turbine blades on the rotation of the interfered satellite based on the impeller speed. The filtering module is used to construct an adaptive notch filter based on the periodic interference frequency, and to filter the observation data of the interfered satellite through the adaptive notch filter to obtain filtered satellite observation data. The weight reduction correction module is used to perform dynamic weight reduction fusion processing on the filtered satellite observation data, construct the observation noise variance matrix and perform weight reduction correction on it to obtain the actual noise variance. The position calculation module is used to correct the position information of the target wind turbine based on the actual noise variance and the state update equation of Kalman filtering.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the wind turbine positioning interference suppression method based on adaptive filtering as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind turbine positioning interference suppression method based on adaptive filtering as described in any one of claims 1 to 7.

Images