Radio Environmental Assessment Methodology for Offshore Wind Farms

Abstract

This invention relates to the field of radio environment monitoring and electromagnetic compatibility technology, and discloses a method for assessing the radio environment of offshore wind farms. The method includes: synchronously acquiring mechanical operating parameters of wind turbine generators and radio signals; establishing a nonlinear mapping rule between the instantaneous spatial topology of the blades and the multipath scattering phase; performing dynamic phase decoupling on the signals to eliminate dynamic multipath noise caused by rotating blades, generating clean wavefront data; and using a holographic reconstruction algorithm to invert the three-dimensional electromagnetic intensity distribution and output interference assessment indicators for the power system control loop. This invention, by constructing a deterministic mapping mechanism between the physical state of dynamic scatterers and electromagnetic phase distortion, removes the modulation interference of equipment operation on the background electromagnetic environment from a physical mechanism perspective, thus realizing the reconstruction and assessment of the electromagnetic environment of wind farms under complex dynamic operating conditions.

Description

Technical Field

[0001] This invention relates to a method for assessing the radio environment of offshore wind farms, belonging to the field of radio environment monitoring and electromagnetic compatibility technology. Background Technology

[0002] Currently, ensuring electromagnetic compatibility (EMC) between wind farm facilities and surrounding radio service systems is a prerequisite for guaranteeing power production safety and uninterrupted regional communication during the construction and grid connection of offshore wind power systems. To this end, the industry typically adopts the assessment methods used for terrestrial base stations, employing a scalar propagation loss model based on empirical formulas, coupled with the deployment of discrete spectrum monitoring points around the predetermined site. By acquiring signal frequency and power amplitude data, the overall distribution of the electromagnetic environment is estimated. This assessment method, based on static statistical characteristics, can, in terrestrial or simple terrain environments, fit a field strength coverage map that meets engineering accuracy with a limited number of sampling points at a relatively low computational cost, thus providing a basis for wind power system construction. The site selection and layout of wind farms provide fundamental data support. However, when this general approach is applied to offshore wind farms consisting of dozens or even hundreds of megawatt-level units, the specular reflection effect and atmospheric waveguide phenomenon in the high humidity and high salinity environment of the sea surface, combined with the physical properties of wind turbine units as special power facilities, make the propagation behavior of electromagnetic waves extremely complex. In particular, the giant metal blades, which are the core components for energy capture, undergo continuous periodic rotation during operation, which actually constitutes a huge time-varying scatterer. This causes the electromagnetic field inside and around the wind farm to no longer be a stable static distribution, but rather to be accompanied by severe multipath interference and wavefront distortion.

[0003] In complex operating conditions characterized by high dynamics and strong scattering, simply increasing the density of discrete test points or optimizing the parameters of the static propagation model is insufficient to eliminate the systematic deviations introduced by the mechanical motion of power equipment. Existing monitoring architectures have limitations, and the signal processing and evaluation methods have not addressed the problem of nonlinear interference caused by dynamic scattering. For example, Chinese invention patent application CN120468516A discloses a method for assessing the electromagnetic environment impact of offshore wind farms. Although the scheme compares test data from existing and planned wind farms to sort out electromagnetic interference signals and uses a marine electromagnetic wave propagation loss model to extrapolate the received signal strength, the core logic remains at the level of statistical analysis of signal frequency and power scalar data. Existing technologies treat wind farms as static environments or simple signal sources that only produce amplitude attenuation, ignoring the periodic modulation effect of the giant metal blade rotation on the electromagnetic wave phase. Although such methods can quantify the amplitude and intensity of interference signals, they cannot separate the Doppler frequency shift and dynamic multipath noise generated by blade rotation. As a result, when evaluating precision systems such as control loops that are sensitive to phase, the evaluation conclusions deviate from the actual physical conditions due to the loss of phase dimension information.

[0004] Therefore, the technical problem to be solved by this invention is to overcome the blind spot of traditional scalar measurement in sensing dynamic phase information and to construct an electromagnetic environment inversion mechanism that can deeply integrate the mechanical motion characteristics of wind power equipment, so as to realize the holographic reconstruction and refined evaluation of the complex electromagnetic wavefront of offshore wind farms. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A method for assessing the radio environment of offshore wind farms, comprising the following steps: Step 101: Real-time synchronous acquisition of real-time mechanical operating parameters of wind turbine generators in offshore wind farms and raw radio signal data of the predetermined monitoring area. Real-time mechanical operating parameters include blade speed, yaw angle, pitch angle and generator operating load. Step 102: Based on real-time mechanical operating parameters, calculate the instantaneous spatial topology of the giant metal blades in the wind turbine generator on the radio propagation path, and establish a nonlinear mapping rule between the instantaneous spatial topology and the electromagnetic wave multipath scattering phase. Step 103: Using nonlinear mapping rules, the original radio signal data is subjected to dynamic phase decoupling processing to remove the dynamic multipath noise and random phase jitter caused by the periodic rotation of the giant metal blade, thereby generating a pure wavefront data stream that characterizes the original radiation features of the space electromagnetic field. Step 104: The pure wavefront data stream is processed using a holographic reconstruction algorithm to invert and obtain a three-dimensional electromagnetic intensity distribution map of the predetermined monitoring area. Based on the three-dimensional electromagnetic intensity distribution map, an environmental assessment index reflecting the interference intensity of the offshore wind farm on the control loop of the power system is output to ensure the safe operation of the power supply system.

[0006] Preferably, the specific process of establishing the nonlinear mapping rule in step 102 includes: step 201, dividing the predetermined monitoring area into multiple holographic sampling grid points, and determining the geometric topological relationship of each holographic sampling grid point relative to the wind turbine generator; step 202, calculating the instantaneous projection geometry of the giant metal blade in the direction of the holographic sampling grid points based on the geometric topological relationship and the yaw angle; step 203, calculating the frequency offset caused by the giant metal blade to the original radio signal data based on the instantaneous projection geometry and the blade rotation speed, and mapping the frequency offset to the phase distortion component in the mapping rule.

[0007] Preferably, the specific process of synchronous acquisition in step 101 includes: using high-precision timestamps to align and label real-time mechanical operating parameters and raw radio signal data; when real-time mechanical operating parameters undergo a step change, automatically increasing the sampling frequency of raw radio signal data to capture sudden electromagnetic transient characteristics caused by the instantaneous attitude change of the giant metal blade.

[0008] Preferably, the method further includes: real-time monitoring of sea surface wave height and wave surging period; establishing a sea surface specular reflection correction factor based on sea surface wave height and wave surging period, and using the sea surface specular reflection correction factor to perform multipath gain compensation on the three-dimensional electromagnetic intensity distribution map.

[0009] Preferably, the calculation rule followed by the dynamic phase decoupling process in step 103 is as follows: ,in, For the phase of the pure wavefront data stream, Γ represents the initial phase of the original radio signal data, Γ is the phase disturbance function determined by the blade speed ω, yaw angle θ, and pitch angle α, and T is the time span of signal processing.

[0010] Preferably, the specific process of outputting environmental assessment indicators in step 104 includes: extracting the peak power density and electromagnetic gradient change rate from the three-dimensional electromagnetic intensity distribution map; comparing the peak power density with the preset electromagnetic tolerance threshold of the power system control terminal; and generating an interference avoidance control command to adjust the operating attitude of the wind turbine generator when the peak power density exceeds the preset electromagnetic tolerance threshold.

[0011] Preferably, the method further includes: acquiring atmospheric refractive index profile data of the area where the offshore wind farm is located; using the atmospheric refractive index profile data to identify whether atmospheric waveguide effect exists; if atmospheric waveguide effect exists, then correcting the environmental assessment indicators for propagation loss based on the changes in the atmospheric refractive index gradient.

[0012] Preferably, the execution logic of the interference avoidance control command includes: calculating the electromagnetic scattering envelope of the giant metal blade at different yaw angles; selecting the target yaw angle with the lowest interference intensity in the electromagnetic scattering envelope; and controlling the yaw system of the wind turbine generator to drive the nacelle to turn to the target yaw angle, so as to reduce electromagnetic interference to communication services while maintaining power generation.

[0013] Preferably, environmental assessment indicators are also used to: assess the electromagnetic shielding effectiveness at different tower locations within an offshore wind farm; and optimize the deployment locations of wireless communication nodes within the power system based on the distribution patterns of electromagnetic shielding effectiveness.

[0014] Preferably, the method further includes: recording historical trend data of environmental assessment indicators over time; performing spectral feature analysis on the historical trend data to identify whether there is irregular aeroelastic flutter in the giant metal blades due to structural aging; and outputting fatigue warning information for the mechanical structure of the offshore wind farm when irregular aeroelastic flutter is identified.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the radio environment assessment of offshore wind farms, based on the dynamic phase decoupling and nonlinear distortion elimination of wind turbine mechanical operating conditions, this invention introduces the real-time mechanical operating parameters of the wind turbine generator into the electromagnetic wavefront reconstruction logic, constructing a deterministic mapping mechanism between the physical state of dynamic metal scatterers and spatial electromagnetic phase distortion. Addressing the complex multipath effects and wavefront phase jitter caused by the periodic rotation of giant metal blades in offshore wind farms, this invention utilizes the real-time known data of wind turbine speed, yaw angle, and other operating data to calculate and subtract the nonlinear dynamic interference components generated by rotating components at the signal processing level. This process restores the wind turbine generator from a static obstacle in traditional assessment models to a time-varying scattering source, physically eliminating the modulation influence of equipment dynamic operation on the background electromagnetic environment, and ensuring the authenticity and stability of the electromagnetic environment assessment of wind farms under complex dynamic scattering conditions.

[0016] 2. Adaptive Radial Holographic Array and Wavefront Vector Reconstruction for Collector Topology: This invention employs a radial holographic sampling array centered on the booster station, combined with a holographic operator based on the inverse propagation principle, to achieve spatial synthesis from discrete observation points to continuous electromagnetic wavefront functions. The array topology conforms to the physical layout characteristics of offshore wind farm power collection facilities and the energy attenuation law of electromagnetic waves propagating along the line of sight along the sea surface. Through limited sparse sampling data, the complete complex electromagnetic field distribution at the wind farm outlet plane is inverted. This method overcomes the limitation of traditional discrete scalar measurement in obtaining spatial phase information, and can capture and reconstruct the vector propagation characteristics of electromagnetic waves in high-humidity environments at sea, providing a holographic data foundation for analyzing the interference and diffraction behavior of electromagnetic waves in non-measurement areas. Attached Figure Description

[0017] Figure 1 This is a flowchart of the evaluation method for dynamic phase decoupling and holographic reconstruction of the present invention; Figure 2 This is a system architecture diagram of the present invention that integrates a physical monitoring domain and an evaluation computing platform.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] A method for assessing the radio environment of offshore wind farms includes the following steps: Step 101: Real-time synchronous acquisition of real-time mechanical operating parameters of wind turbine generators in offshore wind farms and raw radio signal data of the predetermined monitoring area. Real-time mechanical operating parameters include blade speed, yaw angle, pitch angle and generator operating load. Step 102: Based on real-time mechanical operating parameters, calculate the instantaneous spatial topology of the giant metal blades in the wind turbine generator on the radio propagation path, and establish a nonlinear mapping rule between the instantaneous spatial topology and the electromagnetic wave multipath scattering phase. Step 103: Using nonlinear mapping rules, the original radio signal data is subjected to dynamic phase decoupling processing to remove the dynamic multipath noise and random phase jitter caused by the periodic rotation of the giant metal blade, thereby generating a pure wavefront data stream that characterizes the original radiation features of the space electromagnetic field. Step 104: The pure wavefront data stream is processed using a holographic reconstruction algorithm to invert and obtain a three-dimensional electromagnetic intensity distribution map of the predetermined monitoring area. Based on the three-dimensional electromagnetic intensity distribution map, an environmental assessment index reflecting the interference intensity of the offshore wind farm on the control loop of the power system is output to ensure the safe operation of the power supply system.

[0021] Preferably, the specific process of establishing the nonlinear mapping rule in step 102 includes: step 201, dividing the predetermined monitoring area into multiple holographic sampling grid points, and determining the geometric topological relationship of each holographic sampling grid point relative to the wind turbine generator; step 202, calculating the instantaneous projection geometry of the giant metal blade in the direction of the holographic sampling grid points based on the geometric topological relationship and the yaw angle; step 203, calculating the frequency offset caused by the giant metal blade to the original radio signal data based on the instantaneous projection geometry and the blade rotation speed, and mapping the frequency offset to the phase distortion component in the mapping rule.

[0022] Preferably, the specific process of synchronous acquisition in step 101 includes: using high-precision timestamps to align and label real-time mechanical operating parameters and raw radio signal data; when real-time mechanical operating parameters undergo a step change, automatically increasing the sampling frequency of raw radio signal data to capture sudden electromagnetic transient characteristics caused by the instantaneous attitude change of the giant metal blade.

[0023] Preferably, the method further includes: real-time monitoring of sea surface wave height and wave surging period; establishing a sea surface specular reflection correction factor based on sea surface wave height and wave surging period, and using the sea surface specular reflection correction factor to perform multipath gain compensation on the three-dimensional electromagnetic intensity distribution map.

[0024] Preferably, the calculation rule followed by the dynamic phase decoupling process in step 103 is as follows: ,in, For the phase of the pure wavefront data stream, Γ represents the initial phase of the original radio signal data, Γ is the phase disturbance function determined by the blade speed ω, yaw angle θ, and pitch angle α, and T is the time span of signal processing.

[0025] Preferably, the specific process of outputting environmental assessment indicators in step 104 includes: extracting the peak power density and electromagnetic gradient change rate from the three-dimensional electromagnetic intensity distribution map; comparing the peak power density with the preset electromagnetic tolerance threshold of the power system control terminal; and generating an interference avoidance control command to adjust the operating attitude of the wind turbine generator when the peak power density exceeds the preset electromagnetic tolerance threshold.

[0026] Preferably, the method further includes: acquiring atmospheric refractive index profile data of the area where the offshore wind farm is located; using the atmospheric refractive index profile data to identify whether atmospheric waveguide effect exists; if atmospheric waveguide effect exists, then correcting the environmental assessment indicators for propagation loss based on the changes in the atmospheric refractive index gradient.

[0027] Preferably, the execution logic of the interference avoidance control command includes: calculating the electromagnetic scattering envelope of the giant metal blade at different yaw angles; selecting the target yaw angle with the lowest interference intensity in the electromagnetic scattering envelope; and controlling the yaw system of the wind turbine generator to drive the nacelle to turn to the target yaw angle, so as to reduce electromagnetic interference to communication services while maintaining power generation.

[0028] Preferably, environmental assessment indicators are also used to: assess the electromagnetic shielding effectiveness at different tower locations within an offshore wind farm; and optimize the deployment locations of wireless communication nodes within the power system based on the distribution patterns of electromagnetic shielding effectiveness.

[0029] Preferably, the method further includes: recording historical trend data of environmental assessment indicators over time; performing spectral feature analysis on the historical trend data to identify whether there is irregular aeroelastic flutter in the giant metal blades due to structural aging; and outputting fatigue warning information for the mechanical structure of the offshore wind farm when irregular aeroelastic flutter is identified.

[0030] Example 1: This example is applied to an electromagnetic environment monitoring scenario of an offshore wind farm containing multiple megawatt-class turbines. In this scenario, the giant metal blades of the wind turbines are in a state of continuous rotation. The Doppler frequency shift and multipath reflection effect generated by the sweep radius of the blades cause the radio background noise in the predetermined monitoring area to exhibit non-stationary fluctuation characteristics. To solve this dynamic scattering problem, the offshore wind farm radio environment assessment method of this invention establishes a cross-domain synchronous data acquisition mechanism. The system uses high-precision timestamps to align and annotate the real-time mechanical operating parameters of the wind turbines in the wind farm and the original radio signal data of the predetermined monitoring area. The real-time mechanical operating parameters include blade speed ω, yaw angle θ, pitch angle α, and turbine operating load. When the real-time mechanical operating parameters undergo a step change, the system automatically increases the sampling frequency of the original radio signal data to capture the sudden electromagnetic transient characteristics caused by the instantaneous attitude change of the giant metal blades.

[0031] Based on the acquired synchronous data, the processor calculates the instantaneous spatial topology of the giant metal blade in the wind turbine along the radio propagation path and establishes a nonlinear mapping rule between this topology and the phase of electromagnetic wave multipath scattering. The processor discretizes the 3D CAD model of the giant metal blade into a cloud of metal scattering points distributed at equal intervals, containing 500 to 1000 equally spaced points. The instantaneous spatial topology is the real-time coordinate set of the aforementioned scattering point cloud in a 3D Cartesian coordinate system. The steps for establishing the nonlinear mapping rule are as follows: the processor calculates the straight-line propagation distance from each scattering point to the monitoring antenna in real time with a step time of 1 millisecond; the phase hysteresis at a single point is calculated according to the formula that the phase offset equals the straight-line propagation distance divided by the radio wavelength and then multiplied by 360 degrees; finally, the reflected signals of all scattering points are synthesized on the complex plane using the vector superposition method, and the total phase deviation after synthesis is calculated. The value is the nonlinear mapping output value at that moment. The establishment process of this rule is as follows: The predetermined monitoring area is divided into multiple holographic sampling grids, and the geometric topology relationship of each holographic sampling grid relative to the wind turbine generator is determined; based on the geometric topology relationship and the yaw angle θ, the instantaneous projection geometry of the giant metal blade in the direction of the holographic sampling grid is calculated; according to the instantaneous projection geometry and the blade rotation speed ω, the frequency offset caused by the giant metal blade to the original radio signal data is calculated, and it is mapped to the phase distortion component in the mapping rule. Using the nonlinear mapping rule, the processor performs dynamic phase decoupling processing on the original radio signal data, deducting the dynamic multipath noise and phase random jitter caused by the periodic rotation of the giant metal blade, thereby generating a pure wavefront data stream characterizing the original radiation characteristics of the space electromagnetic field. The calculation rule followed by the dynamic phase decoupling processing is: ,in, For the phase of the pure wavefront data stream, Γ represents the initial phase of the original radio signal data, Γ is the phase disturbance function determined by the blade speed ω, yaw angle θ, and pitch angle α, and T is the time span of signal processing.

[0032] After obtaining the clean wavefront data stream, the system employs a holographic reconstruction algorithm for its inversion processing. Specifically, the holographic reconstruction algorithm utilizes inverse operation logic based on angular spectrum propagation theory. The processor performs a two-dimensional fast Fourier transform on the clean wavefront data stream in the two-dimensional plane, converting it to the spatial frequency domain. Next, the transform result is multiplied by a free-space transfer function, the phase factor of which is determined by the product of the wavenumber and the back-reamed distance. Finally, a two-dimensional inverse fast Fourier transform is performed on the processed data, and the square of its magnitude is taken to obtain a three-dimensional electromagnetic intensity distribution map within the predetermined monitoring area. Based on this map, the system outputs environmental assessment indicators reflecting the interference intensity of offshore wind farms on the control loops of their respective power systems. The system extracts the peak power density and electromagnetic gradient rate of change from the three-dimensional electromagnetic intensity distribution map and compares the peak power density with the preset electromagnetic tolerance threshold of the power system control terminal. The preset electromagnetic tolerance threshold is based on the International Electrotechnical Commission (IEC). The IEC 61000 standard sets the electromagnetic scattering intensity to 12 volts per meter to 24 volts per meter. When the detected peak power density exceeds this threshold, the system queries a pre-stored yaw angle-scattering intensity mapping table. This table records the total electromagnetic scattering intensity of the nacelle within the range of 0 to 360 degrees, with an index step of 2 degrees. The system retrieves the index angle of the minimum scattering intensity from the table and encapsulates it into a hexadecimal yaw drive control message. When the peak power density exceeds the preset electromagnetic tolerance threshold, the system generates an interference avoidance control command to adjust the operating attitude of the wind turbine generator. The execution logic of this command includes: calculating the electromagnetic scattering envelope of the giant metal blade at different yaw angles, selecting the target yaw angle with the lowest interference intensity in the electromagnetic scattering envelope, and controlling the yaw system of the wind turbine generator to drive the nacelle to turn to the target yaw angle, thereby reducing electromagnetic interference to communication services while maintaining power generation and ensuring the safe operation of the power supply system.

[0033] Example 2: This example constructs a semi-physical simulation verification platform for the electromagnetic environment, including a dynamically rotating scatterer. The aim is to quantitatively verify the signal recovery accuracy of the nonlinear mapping rule and dynamic phase decoupling mechanism under strong multipath interference. The test object is a set of wind turbine metal blade models scaled down to a 1:30 ratio, placed in a microwave anechoic chamber equipped with a vector signal generator and a multi-channel vector signal analyzer. The physical objective of the experiment is to verify whether the method proposed in this invention can accurately invert the electromagnetic field intensity distribution within the monitoring area under conditions of Doppler frequency shift and multipath reflection caused by high-speed blade rotation. The acquisition of experimental data strictly follows the principle of data traceability. The radio frequency signal source uses a continuous wave signal with a center frequency of 2.4 GHz to simulate a wind farm. The surrounding radio communication background signal was analyzed using a vector signal analyzer with a 200MHz instantaneous analysis bandwidth, which was used to acquire the IQ components of the raw radio signal data containing amplitude and phase information. Mechanical operating parameters were obtained by real-time reading of the blade model's rotational speed ω and yaw angle θ using a high-precision photoelectric encoder, with time synchronization accuracy controlled within 10 microseconds. To simulate a real engineering noise environment, background noise with a signal-to-noise ratio of 15dB was injected into the raw signal using an additive white Gaussian noise channel model, and a Rayleigh fading channel model simulating sea surface reflection was superimposed. The setting of the key experimental parameter, the sampling period, followed a signal processing decision logic chain, identifying the core factor affecting the sampling period as the maximum Doppler frequency shift introduced by the linear velocity of the rotating blade tip. Secondly, a technical trade-off needs to be established, namely, to strike a balance between capturing rapidly changing electromagnetic transient characteristics and the throughput of control data processing. Based on the Nyquist sampling theorem and the engineering redundancy criterion, a sampling frequency was established. Decision-making rules: ,in Let λ be the blade tip linear velocity, λ be the signal wavelength, and k be the engineering safety factor, which is not less than 5. Under the typical working conditions of this experiment, the blade model rotation speed is set to 120 revolutions per minute, and the corresponding maximum Doppler frequency shift is calculated to be about 400 Hz. Based on this, the synchronous sampling frequency of the radio signal and mechanical parameters is locked at 5 kHz to ensure complete capture of dynamic phase distortion characteristics. The experiment designed a comparative verification system including gradient variables to analyze the synergistic effect and performance boundary between technical features.

[0034] Control group A uses the traditional scalar measurement method, only using the amplitude information of the signal for field strength interpolation, without introducing mechanical parameters. Control group B is a partially missing control group, introducing mechanical parameter synchronization, but removing the dynamic phase decoupling processing in step 103, and inputting the disturbed phase data into the holographic reconstruction algorithm. The sample group of this invention fully executes the entire process from mechanical parameter synchronization, nonlinear mapping rule establishment to dynamic phase decoupling. In addition, an over-range control group is set up, increasing the blade speed to 1.5 times the design limit to explore the performance inflection point of the algorithm under extreme Doppler frequency shift. The experimental process and data analysis are as follows: The first scene presents the unprocessed original input state. In the constellation diagram display of the vector signal analyzer, the affected... The multipath effect of the rotating blades causes severe random phase jitter in the received signal, with the root mean square value of the phase noise exceeding 45 degrees. This results in the originally clear signal constellation points spreading into a blurred ring distribution. This phenomenon indicates that, before decoupling processing, the dynamic scatterer causes destructive interference to the coherence of the electromagnetic wavefront. The second act reveals the evolution of key intermediate features. For the sample of this invention, the processor calculates the phase perturbation function Γ in real time based on the real-time acquired rotational speed ω and yaw angle θ using a nonlinear mapping rule. Data shows that the compensated phase value output by this function exhibits a high negative correlation with the rotational period of the blades. Its waveform accurately cancels the periodic phase deflection caused by the Doppler effect in the original signal. (Further details are needed for a complete translation.) After processing, the root mean square value of phase jitter in the signal constellation diagram converged to within 3 degrees, restoring the original phase characteristics of the signal and confirming the ability of mechanical parameters to correct the electromagnetic phase. The third act showed the final field strength inversion accuracy. The inversion results of control group A showed that the average relative error between its reconstructed electromagnetic field strength distribution map and the standard value was as high as 28.5%, and it could not locate the reflection hotspot. Although control group B introduced a holographic algorithm, due to the failure to remove phase noise, obvious artifacts appeared in the reconstructed image, resulting in a judgment deviation of more than 5 meters in the location of the interference source. The test results of the sample group of this invention showed that after dynamic phase decoupling, the average relative error of the three-dimensional electromagnetic strength distribution map was reduced to 4.2%, successfully suppressing more than 90% of false reflection artifacts, and accurately locating the specific blade part that produces strong scattering. The data of the out-of-range control group showed that when the rotation speed exceeded the threshold, due to the aliasing effect caused by the relatively insufficient sampling rate, the inversion error showed a nonlinear exponential increase, confirming the effective working window defined by the aforementioned sampling frequency decision rule.

[0035] Example 3: This example focuses on the core parameter calibration and accuracy verification process in the construction of the disclosed nonlinear mapping rule. In the key step of establishing the nonlinear mapping rule between the instantaneous spatial topology and the electromagnetic wave multipath scattering phase, the accuracy of the nonlinear image directly determines the effect of subsequent dynamic phase decoupling. However, due to the complexity of the marine environment, theoretical model parameters often need to be adaptively corrected based on field measurement data. Therefore, this example elaborates on an adaptive parameter calibration method based on multidimensional data fusion. It clarifies the input conditions of the initial calibration state. The system needs to acquire the geometric dimension data of a single wind turbine generator in the wind farm, including the tower height H, blade length L, and nacelle size. Combined with the electromagnetic wave propagation path loss index n measured on-site, an initial electromagnetic scattering geometric model is constructed. At the same time, a period of stable wind is selected as the calibration window. Within this window, the wind turbine generator is forced to perform specific yaw and pitch actions by the control system, covering the entire angular range, which serves as the calibration excitation source. At this time, the received signal r(t) collected by the vector signal analyzer can be represented as the superposition of the direct path signal and the multipath scattering signal.

[0036] During the calibration process, the system uses the least squares method to iteratively optimize the key coefficients in the nonlinear mapping rule, and substitutes the real-time acquired mechanical operating parameters into the initial geometric model to calculate the theoretical multipath scattering phase. The error function is constructed by comparing it with the phase change ϕ(t) extracted from the measured signal. By using the gradient descent algorithm, the scattering coefficient σ and path delay correction τ in the model are adjusted to make the error function E converge to a preset minimum value. This is achieved when the error change rate is less than a certain value over three consecutive iterations. When the calibration is complete, the determined parameter combination is considered the optimal mapping parameter for that environment. To further verify the stability of the calibrated mapping rule, the system introduces a cross-validation mechanism. Data from a different time period than the calibration window is selected as the test set. The calibrated mapping rule is used to predict the electromagnetic wave phase change during that time period. The predicted values ​​are then compared with the measured values. If the correlation coefficient is greater than 0.95, the mapping rule is considered valid. Otherwise, the system will automatically trigger a new round of parameter calibration. This ensures that the nonlinear mapping rule can adaptively follow the dynamic changes of the marine environment, thereby guaranteeing the stability and accuracy of the dynamic phase decoupling processing in long-term operation and eliminating the risk of model failure caused by environmental parameter drift.

[0037] Example 4: For the engineering deployment of an electromagnetic environment assessment system for offshore wind farms, this example discloses a pre-deployment calibration procedure to eliminate systematic errors introduced by differences in equipment and initial environmental conditions. Before the system is officially put into operation, a standardized silent baseline calibration process is performed. During wind farm shutdown maintenance, or in a static state where the wind speed is lower than the cut-in wind speed, a holographic sampling array is activated to monitor background noise for no less than 24 hours. During this period, all wind turbine generators keep their blades locked to eliminate dynamic scattering interference. The system collects radio environment data at this time, constructs a static electromagnetic background fingerprint database, and calculates the noise floor mean and variance of each monitoring frequency point as the zero-point benchmark for subsequent dynamic assessment.

[0038] After completing the silent baseline calibration, dynamic response sensitivity calibration is required. The system uses a single wind turbine as a controlled excitation source, controlling it to rotate at preset stepped speeds (e.g., 30%, 60%, 90% of rated speed) while keeping the other units stationary. The system synchronously records the Doppler frequency shift characteristics and received signal strength changes of the unit at different speeds and compares them with the predicted values ​​of the theoretical model. If the measured Doppler frequency shift deviation exceeds 5% of the theoretical value, the sensitivity compensation algorithm is automatically triggered to adjust the digital filter bandwidth and gain coefficient of the receiver until the measured characteristics match the theoretical values. Through this calibration process that iterates through each unit, the system can establish an independent electromagnetic scattering characteristic profile for each unit, ensuring that even under the complex operating conditions of full-field grid-connected operation, it can still achieve accurate source tracing and quantitative assessment of a single interference source.

[0039] Example 5: To address the adaptability issue of the core nonlinear mapping rule construction process in this invention under different environments and initial equipment conditions, this example proposes a standardized initial calibration and adaptive update procedure for model parameters. It executes the calibration process for static baseline parameters. In the initial stage of wind farm construction or in a static state after major overhaul (blades locked, generator shut down), a holographic sampling array is activated to continuously monitor background noise for at least 24 hours. The system collects radio signals during this period and uses statistical analysis methods to calculate the noise floor mean at each monitoring frequency point. With variance A static electromagnetic background fingerprint database is constructed. This step aims to quantify the level of electromagnetic interference from the environment itself, including thermal noise, cosmic noise, and other non-wind farm sources, as a zero-point benchmark for subsequent dynamic assessment, ensuring the accuracy of the net value of the assessment results.

[0040] In addition to the sensitivity calibration process for executing dynamic response parameters, the system selects a single wind turbine located at the edge of the wind farm with no surrounding obstructions as the controlled excitation source, controls it to rotate at a preset stepped speed sequence, while keeping other turbines in the farm stationary. At each speed step, the system synchronously records the Doppler frequency shift characteristic value generated by the turbine. With received signal strength The measured values ​​are compared with the predicted values ​​calculated by the theoretical model. If the deviation exceeds a preset threshold, the model correction algorithm is automatically triggered to adjust the path loss exponent n and the scattering cross-section coefficient in the electromagnetic wave propagation model. The process continues until the root mean square error between the model's predicted and measured values ​​converges to within the allowable range. Through this process of traversing or sampling each unit for verification, an independent electromagnetic scattering characteristic file is established for each unit. This eliminates systematic errors introduced by differences in unit manufacturing tolerances, installation locations, and local microenvironments, ensuring the stability and accuracy of the overall evaluation model.

[0041] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing the radio environment of offshore wind farms, characterized in that, Includes the following steps: Step 101: Real-time synchronous acquisition of real-time mechanical operating parameters of wind turbine generators in offshore wind farms and raw radio signal data of the predetermined monitoring area. Real-time mechanical operating parameters include blade speed, yaw angle, pitch angle and generator operating load. Step 102: Based on real-time mechanical operating parameters, calculate the instantaneous spatial topology of the giant metal blades in the wind turbine generator on the radio propagation path, and establish a nonlinear mapping rule between the instantaneous spatial topology and the electromagnetic wave multipath scattering phase. Step 103: Using nonlinear mapping rules, perform dynamic phase decoupling processing on the original radio signal data to deduct dynamic multipath noise and random phase jitter caused by the periodic rotation of the giant metal blade. Step 104: The pure wavefront data stream is processed using a holographic reconstruction algorithm to invert and obtain a three-dimensional electromagnetic intensity distribution map of the predetermined monitoring area. Based on the three-dimensional electromagnetic intensity distribution map, an environmental assessment index reflecting the interference intensity of the offshore wind farm on the control loop of the power system is output to ensure the safe operation of the power supply system.

2. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The specific process of establishing the nonlinear mapping rule in step 102 includes: step 201, dividing the predetermined monitoring area into multiple holographic sampling grid points and determining the geometric topological relationship of each holographic sampling grid point relative to the wind turbine generator; step 202, calculating the instantaneous projection geometry of the giant metal blade in the direction of the holographic sampling grid point based on the geometric topological relationship and the yaw angle; step 203, calculating the frequency offset caused by the giant metal blade to the original radio signal data based on the instantaneous projection geometry and the blade rotation speed, and mapping the frequency offset to the phase distortion component in the mapping rule.

3. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The specific process of synchronous acquisition in step 101 includes: using high-precision timestamps to align and label real-time mechanical operating parameters and raw radio signal data; when real-time mechanical operating parameters undergo a step change, automatically increasing the sampling frequency of raw radio signal data to capture sudden electromagnetic transient characteristics caused by the instantaneous attitude change of the giant metal blade.

4. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The method also includes: real-time monitoring of sea surface wave height and wave surging period; establishing a sea surface specular reflection correction factor based on sea surface wave height and wave surging period, and using the sea surface specular reflection correction factor to perform multipath gain compensation on the three-dimensional electromagnetic intensity distribution map.

5. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The calculation rules followed in step 103 for dynamic phase decoupling are as follows: ,in, For the phase of the pure wavefront data stream, Γ represents the initial phase of the original radio signal data, Γ is the phase disturbance function determined by the blade speed ω, yaw angle θ, and pitch angle α, and T is the time span of signal processing.

6. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The specific process of outputting environmental assessment indicators in step 104 includes: extracting the peak power density and electromagnetic gradient change rate from the three-dimensional electromagnetic intensity distribution map; comparing the peak power density with the preset electromagnetic tolerance threshold of the power system control terminal; and generating interference avoidance control commands to adjust the operating attitude of the wind turbine generator when the peak power density exceeds the preset electromagnetic tolerance threshold.

7. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The method also includes: acquiring atmospheric refractive index profile data of the area where the offshore wind farm is located; using the atmospheric refractive index profile data to identify whether atmospheric waveguide effect exists; if atmospheric waveguide effect exists, then correcting the environmental assessment indicators for propagation loss based on the changes in atmospheric refractive index gradient.

8. The method for assessing the radio environment of an offshore wind farm according to claim 6, characterized in that, The execution logic of the interference avoidance control command includes: calculating the electromagnetic scattering envelope of the giant metal blade at different yaw angles; selecting the target yaw angle with the lowest interference intensity in the electromagnetic scattering envelope; and controlling the yaw system of the wind turbine to drive the nacelle to turn to the target yaw angle, so as to reduce electromagnetic interference to communication services while maintaining power generation.

9. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, Environmental assessment indicators are also used to: assess the electromagnetic shielding effectiveness at different tower locations within offshore wind farms; and optimize the deployment locations of wireless communication nodes within the power system based on the distribution patterns of electromagnetic shielding effectiveness.

10. The method for assessing the radio environment of an offshore wind farm according to claim 1, characterized in that, The method also includes: recording historical trend data of environmental assessment indicators over time; performing spectral feature analysis on the historical trend data to identify whether there is irregular aeroelastic flutter in the giant metal blades due to structural aging; and outputting fatigue warning information for the mechanical structure of offshore wind farms when irregular aeroelastic flutter is identified.

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