Offshore wind power operation simulation method and system

Abstract

The application provides a kind of offshore wind power operation simulation method and system, it is related to offshore wind power generation technical field, the application obtains the surge displacement time history of floating wind power platform under the action of wave, and obtains surge velocity time history by derivation, constructs surge additional energy peak after extracting the frequency spectrum characteristics of surge velocity time history, establishes equivalent inflow turbulence spectrum after surge modulation according to additional energy peak, synthesizes equivalent inflow wind speed time history as the inflow boundary condition of floating wind turbine coupling simulation, the surge modulation effect is reflected in the frequency spectrum level by embedding additional energy peak in the form of atmospheric turbulence spectrum to the frequency spectrum characteristics of surge, solves the load evaluation deviation problem.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power generation technology, specifically to an offshore wind power operation simulation method and system. Background Technology

[0002] Offshore floating wind power generation is an important technological direction for deep-sea wind power development. Floating wind turbines generate six degrees of freedom motion under the action of waves. Among them, the sway motion is consistent with the inflow direction, which directly changes the relative inflow wind speed on the wind turbine plane. In the design certification and performance evaluation of floating wind turbines, load simulation calculation is one of the key links, and the accuracy of the inflow wind speed boundary conditions directly affects the reliability of the simulation results.

[0003] Currently, standard atmospheric turbulence spectrum models are widely used in wind turbine load simulation to generate inflow wind speed time histories. These standard models are based on measured data from stationary wind turbines and assume that the rotor plane is fixed relative to the incoming flow. However, floating wind turbines experience continuous platform motion with waves during operation. This swaying motion adds a low-frequency periodic velocity component related to the wave frequency in the inflow direction, which alters the frequency distribution of inflow turbulent energy. Existing standard turbulence spectrum models cannot reflect this modulation effect, resulting in the absence of platform motion characteristics in the inflow wind speed time histories generated based on them. Consequently, the inflow boundary conditions in floating wind turbine load simulations deviate from the actual situation at the spectral level, affecting the accuracy of dynamic response indicators such as blade load, tower bending moment, and fatigue damage assessment.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for simulating offshore wind power operation, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method and system for simulating offshore wind power operation, comprising the following steps: The sway velocity time history of the floating wind power platform is obtained, and the sway velocity power spectral density is obtained by frequency domain transformation of the sway velocity time history. The peak frequency, amplitude, and bandwidth are extracted from the power spectral density of the oscillation velocity. The peak frequency is the frequency corresponding to the maximum energy value in the power spectral density of the oscillation velocity. The amplitude of the oscillation energy is the energy integral value within a preset frequency band near the peak frequency. The bandwidth is half of the frequency range corresponding to the point where the energy on both sides of the peak frequency drops to half of the peak value. Construct additional energy peaks for the oscillation based on the peak frequency, oscillation energy amplitude, and bandwidth. By introducing a coupling modulation coefficient, multiplying the oscillation-added energy peak with the coupling modulation coefficient, and then superimposing it with the standard atmospheric turbulence spectrum, an equivalent inflow turbulence spectrum is obtained. Using the equivalent inflow turbulence spectrum as the target spectrum, the equivalent inflow wind speed time history is synthesized.

[0007] In one embodiment, the steps of performing frequency domain transformation on the oscillation velocity time history to obtain the oscillation velocity power spectral density, and extracting the oscillation peak frequency, oscillation energy amplitude, and bandwidth from the oscillation velocity power spectral density include: A fast Fourier transform is performed on the oscillation velocity time history to convert the oscillation velocity signal in the time domain into a Fourier spectrum in the frequency domain, which contains the amplitude and phase information corresponding to each frequency component. Based on the amplitude information of each frequency component in the Fourier spectrum, the power spectral density of the oscillation velocity is calculated, which describes the distribution of the energy of the oscillation velocity at different frequencies. The maximum energy value is searched across the entire frequency range of the sway velocity power spectral density. The frequency corresponding to the maximum energy value is determined as the sway peak frequency. This frequency reflects the dominant frequency position of the wave energy and is affected by the platform's sway natural frequency. A preset frequency band is selected with the peak frequency of the oscillation as the center. The oscillation velocity power spectral density within the preset frequency band is integrated. The integration result is used as the oscillation energy amplitude, which reflects the total energy intensity of the oscillation. In the oscillating velocity power spectral density, the frequency positions corresponding to the energy drop to half of the peak value are searched from the peak frequency to both sides to obtain two frequency boundaries. The frequency range between the two frequency boundaries is calculated, and half of the frequency range is taken as the bandwidth. The bandwidth reflects the frequency distribution range of the oscillating motion energy.

[0008] In one embodiment, the step of constructing the additional oscillation energy peak based on the oscillation peak frequency, oscillation energy amplitude, and bandwidth includes: The Gaussian peak function is selected as the functional form of the oscillation additional energy peak. This functional form can simulate the characteristic that the energy is continuously distributed near the oscillation peak frequency and decays smoothly to both sides. The oscillation peak frequency is set as the center position parameter of the Gaussian peak function, so that the Gaussian peak function reaches its maximum value at the oscillation peak frequency. This center position parameter determines the position of the additional energy peak on the frequency axis. The amplitude of the oscillation energy is set as the total energy parameter of the Gaussian peak function, so that the amplitude of the Gaussian peak function increases with the increase of the amplitude of the oscillation energy. This total energy parameter determines the energy magnitude of the additional energy peak. The bandwidth is converted into the width parameter of the Gaussian peak function, so that the flatness of the Gaussian peak function increases with the increase of the bandwidth. This width parameter determines the frequency range covered by the additional energy peak. Based on the center position parameter, total energy parameter, and width parameter, a power spectral density function for the oscillation additional energy peak is generated. This function reaches its maximum value at the oscillation peak frequency and decays smoothly as the frequency deviates from the oscillation peak frequency.

[0009] In one embodiment, the step of introducing a coupling modulation coefficient, multiplying the oscillation-added energy peak by the coupling modulation coefficient, and then superimposing it with the standard atmospheric turbulence spectrum includes: Obtain a preset coupling modulation coefficient, which characterizes the efficiency ratio of oscillating motion energy to additional energy of inflow turbulence; The power spectral density function of the oscillating additional energy peak is multiplied by the coupling modulation coefficient to obtain the modulated oscillating additional energy peak. The amplitude of the modulated oscillating additional energy peak is scaled according to the coupling modulation coefficient. Obtain the power spectral density function of the standard atmospheric turbulence spectrum, which describes the atmospheric turbulence energy distribution characteristics experienced by the stationary wind turbine; The modulated oscillation additional energy peak is added point by point in the frequency domain to the power spectral density function of the standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum. This equivalent inflow turbulence spectrum contains both the high-frequency random characteristics of atmospheric turbulence and the low-frequency modulation characteristics of oscillation motion.

[0010] In one embodiment, the value of the coupling modulation coefficient is determined by both aerodynamic damping and the wind turbine rotation effect: When the aerodynamic damping is small and the attenuation effect of the wind turbine rotation on the sway motion is weak, more of the sway motion energy is converted into additional energy of the inflow turbulence, and the value of the coupling modulation coefficient increases accordingly. When the aerodynamic damping is large and the attenuation effect of the wind turbine rotation on the sway motion is strong, less of the sway motion energy is converted into additional energy of the inflow turbulence, and the value of the coupling modulation coefficient is reduced accordingly. The coupling modulation coefficients were calibrated and determined by comparing measured turbulence data with simulation results under oscillation modulation conditions and without oscillation modulation conditions.

[0011] In one embodiment, the step of synthesizing the equivalent inflow wind speed time history, using the equivalent inflow turbulence spectrum as the target spectrum, includes: Discrete sampling is performed on the equivalent inflow turbulence spectrum in the frequency domain to obtain the power spectral density value at each discrete frequency point. The square root operation is performed on the power spectral density value at each discrete frequency point to obtain the amplitude spectral value at each discrete frequency point. Random phase values ​​are generated independently at each discrete frequency point, and the random phases between each discrete frequency point are independent of each other. The random phase values ​​follow a uniform distribution over a preset interval. The amplitude spectrum value at each discrete frequency point is combined with the corresponding random phase value to form a frequency domain complex sequence, which contains both amplitude and phase information. An inverse Fourier transform is performed on the frequency domain complex sequence to convert the frequency domain data to the time domain, resulting in the equivalent inflow wind speed time history. The spectral energy distribution of this equivalent inflow wind speed time history is consistent with the equivalent inflow turbulence spectrum.

[0012] In one embodiment, when discretely sampling the equivalent inflow turbulence spectrum in the frequency domain, the parameter settings satisfy the following conditions: The frequency discrete range covers from zero frequency to the energy cutoff frequency, which is determined by the energy distribution of the equivalent inflow turbulence spectrum to ensure that high-frequency energy components are not missed in discrete sampling. The number of discrete points meets the frequency resolution requirement to ensure that the spectral characteristics of the synthesized equivalent inflow wind speed time history are consistent with the equivalent inflow turbulence spectrum; The time-domain sampling interval simultaneously satisfies the requirements of the Nyquist sampling theorem and the requirement of capturing low-frequency oscillation modulation characteristics, so as to ensure that the synthesized time history can simultaneously reflect high-frequency turbulence characteristics and low-frequency oscillation modulation characteristics.

[0013] In one embodiment, the step of obtaining the sway velocity time history of the floating wind power platform includes: The time history of the sway displacement of the floating wind power platform under wave action is obtained by hydrodynamic frequency domain analysis, time domain coupled simulation or water tank model test. This sway displacement time history describes the change of the horizontal reciprocating displacement of the platform along the wave propagation direction with time. The first time derivative of the sway displacement time history is obtained to obtain the sway velocity time history, which describes the change of the instantaneous velocity of the platform's sway motion with time. The standard atmospheric turbulence spectrum includes the Kaimal spectrum or the Von Karman spectrum. This standard atmospheric turbulence spectrum is established based on measured data from stationary wind turbines and describes the distribution characteristics of atmospheric turbulence energy with frequency.

[0014] In one embodiment, the equivalent inflow wind speed time history and the sway displacement time history of the floating wind turbine platform under wave action are input together into the floating wind turbine coupled simulation system as inflow boundary conditions: The equivalent inflow wind speed time history is applied to the rotor rotation plane, driving the aerodynamic model to calculate the aerodynamic loads on each section of the blade; The time history of the sway displacement is applied to the platform foundation, driving the hydrodynamic model to calculate the interaction force between the waves and the platform; The dynamic response indices of blade load, tower bending moment, mooring tension, and generator output power are calculated using aerodynamic-hydraulic-servo-elastic coupling solution.

[0015] The present invention also provides an offshore wind power operation simulation system, the simulation system being used to execute the above-described offshore wind power operation simulation method, comprising: The motion calculation module is used to obtain the sway velocity time history of the floating wind power platform, and to perform frequency domain transformation on the sway velocity time history to obtain the sway velocity power spectral density. The spectrum extraction module is used to extract the oscillation peak frequency, oscillation energy amplitude, and bandwidth from the oscillation velocity power spectral density. The oscillation peak frequency is the frequency corresponding to the maximum energy value in the oscillation velocity power spectral density. The oscillation energy amplitude is the energy integral value within a preset frequency band near the oscillation peak frequency. The bandwidth is half of the frequency range corresponding to the point where the energy on both sides of the oscillation peak frequency drops to half of the peak value. Turbulence modulation module, used to construct additional oscillation energy peaks based on oscillation peak frequency, oscillation energy amplitude and bandwidth; The turbulence spectrum equivalent module is used to introduce the coupling modulation coefficient. After multiplying the oscillation additional energy peak with the coupling modulation coefficient, it is superimposed with the standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum. The wind speed synthesis module is used to synthesize the equivalent inflow wind speed time history using the equivalent inflow turbulence spectrum as the target spectrum. Compared with the prior art, the beneficial effects of this invention are: This invention extracts the velocity spectrum characteristics of the sway motion of a floating wind power platform and maps the spectral information of the sway motion to an additional energy peak on the turbulence spectrum. This solves the problem that existing methods only focus on the average wind speed correction and ignore the influence of platform motion on the energy distribution structure of the turbulence spectrum, so that the inflow wind speed model reflects the modulation effect of platform sway at the spectral level. This invention constructs an equivalent inflow turbulence spectrum modulated by oscillation and synthesizes an equivalent inflow wind speed time history containing oscillation modulation features. This makes the inflow boundary conditions of the coupled simulation of floating wind turbines more consistent with the actual operating conditions in terms of spectral energy distribution. It solves the problem of load assessment deviation caused by the lack of oscillation modulation features in the existing simulation input, which leads to the low simulation results of blade load, tower bending moment and fatigue damage. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 A graph showing the variation trend of heave motion parameters under different sea state conditions; Figure 3 Comparison of wave modulation effects under different sea conditions; Figure 4 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: Please see Figure 1 The present invention provides a technical solution: A method for simulating offshore wind power operation, comprising the following steps: Step 1: Obtain the time history of the sway displacement of the floating wind turbine platform under wave action. Calculate the time derivative of the sway displacement time history to obtain the sway velocity time history. A floating wind turbine platform refers to an offshore wind turbine platform that uses a floating foundation structure to support wind turbine generators. It includes semi-submersible, single-column, and tension leg types. Under wave action, the floating platform will generate six degrees of freedom of motion, including three linear displacement degrees of freedom, namely sway, roll, and heave, and three angular displacement degrees of freedom, namely roll, pitch, and yaw. Among them, sway refers to the horizontal reciprocating motion of the platform along the wave propagation direction. Because this motion is consistent with the inflow direction, it directly changes the relative motion relationship between the wind turbine plane and the incoming airflow.

[0020] The wave displacement time history refers to the sequence of horizontal reciprocating displacements of a floating wind turbine platform along the wave propagation direction under wave action, which varies with time. This time history can be obtained using hydrodynamic frequency domain methods. By performing frequency domain hydrodynamic analysis on the floating platform using hydrodynamic analysis software, the response amplitude operator of the platform's wave degree of freedom is obtained. This operator describes the relationship between the ratio of the platform's wave displacement response amplitude to the incident wave amplitude and the wave frequency. The platform's wave response amplitude operator is convolved with the wave spectrum of the target sea area in the frequency domain to obtain the platform's wave displacement response spectrum. Then, the response spectrum is transformed to the time domain using inverse Fourier transform to obtain the wave displacement time history. The wave displacement time history can also be directly extracted through time-domain coupled simulation or directly measured by displacement sensors arranged on the platform deck in a water tank model test.

[0021] Under wave action, the sway displacement of a floating platform typically exhibits an approximately periodic oscillation characteristic in the time domain. Its dominant frequency component corresponds to the concentrated frequency of wave energy, i.e., the peak frequency of the wave spectrum, and also includes the free decay component corresponding to the platform's natural sway frequency. For ease of mathematical processing, the sway displacement time history can be expressed as a superposition of multiple harmonic components: in, For a moment The oscillation displacement value at that location, , and The first The amplitude, frequency, and initial phase of each simple harmonic component. This represents the total number of simple harmonic components.

[0022] time history of oscillation displacement Find the first-order time derivative to obtain the oscillation velocity time history. : The sway velocity time history describes the change of the instantaneous velocity of the platform's sway motion over time. The magnitude of the sway velocity time history depends on wave conditions and the platform's hydrodynamic characteristics. Under typical sea states, the sway velocity amplitude of a semi-submersible platform is usually in the range of 0.2~1.0 m / s. Since the direction of the sway motion is the same as the incoming wind direction, the sway velocity, as an additional velocity component, is directly superimposed on the relative inflow wind speed at the rotor plane. That is, the equivalent inflow wind speed equals the natural inflow wind speed minus the sway velocity. When the platform moves in the windward direction, the rotor moves in the same direction as the incoming air, so the relative inflow wind speed decreases. When the platform moves in the leeward direction, the rotor moves in the opposite direction to the incoming air, so the relative inflow wind speed increases. This velocity superposition effect is the fundamental reason why the sway motion changes the characteristics of the inflow wind speed. The low-frequency periodicity of the sway velocity is transmitted to the equivalent inflow wind speed through the superposition effect, resulting in a periodic component in the inflow wind speed that is related to the wave frequency.

[0023] Step 2: Extract the spectral characteristics of the oscillating motion from the oscillation velocity time history. Spectral characteristics refer to the energy distribution characteristics of the oscillation motion in the frequency domain. These are obtained by processing the oscillation velocity time history using Fourier transform. Specifically, the oscillation velocity time history... Perform a fast Fourier transform to obtain its Fourier spectrum, and then calculate the oscillation velocity power spectral density. This function describes the distribution of energy at different frequencies of the oscillation velocity.

[0024] From the power spectral density of the oscillation velocity Extract three spectral feature parameters: Peak oscillation frequency : refers to the power spectral density of the oscillation velocity The frequency corresponding to the maximum energy in the middle is expressed mathematically as follows: The peak frequency of the oscillation reflects the dominant frequency of wave energy in physics, and is also affected by the natural frequency of the platform's oscillation. It is usually located in the range of 0.05 to 0.2 Hz. The oscillation motion has the largest oscillation speed at this frequency, which means that its modulation effect on the inflow wind speed is most significant at this frequency.

[0025] Surge energy amplitude : refers to the power spectral density of the oscillation velocity The mathematical expression for the energy integral value in the frequency band near the peak frequency of the oscillation is: The amplitude of oscillation energy physically reflects the total energy intensity of oscillations: the larger the wave and the more violent the platform shaking. The larger the value, the stronger the periodic component of the oscillation motion superimposed on the inflow wind speed.

[0026] bandwidth : refers to the power spectral density of the oscillation velocity The half-width of the energy concentration band at the peak frequency of the oscillation is half the frequency range where the energy drops to halfway down the peak on both sides. This bandwidth reflects the frequency distribution range of the oscillation energy: when wave energy is concentrated in a narrower frequency range... When the wave energy is relatively small, the oscillation modulation effect is concentrated around a single frequency; when the wave energy is distributed over a wider frequency range... The modulation effect is relatively large and spreads over a wide frequency band.

[0027] This embodiment focuses on a semi-submersible floating wind turbine, selecting several typical sea states for sway motion calculation. The calculation results of the platform sway motion parameters under each sea state condition are shown in Table 1: Table 1: Platform heave motion parameters under different sea states Please see Figure 2 And Table 1, Figure 2 The variation trends of significant wave height, oscillation velocity amplitude, and oscillation energy amplitude under various operating conditions are presented in the form of stacked line graphs. Table 1 lists the quantified values ​​for each operating condition. From Table 1 and... Figure 2 It can be seen that as the significant wave height increases, both the amplitude of the sway displacement and the amplitude of the sway velocity show an increasing trend. The peak frequency of the sway decreases with the increase of the peak period of the wave spectrum. The amplitude of the sway energy increases significantly with the enhancement of wave energy, indicating that the potential modulation ability of the sway motion on the inflow turbulence increases significantly when the wave energy is enhanced.

[0028] Step 3: Construct an additional energy peak at the peak frequency of the standard atmospheric turbulence spectrum. The standard atmospheric turbulence spectrum refers to the turbulence power spectral density function used in the normal turbulence model specified in the International Electrotechnical Commission (IEC) wind power design standards, including two standard forms: the Kaimal spectrum and the Von Karman spectrum. The power spectral density function of the Kaimal spectrum is: in, The standard deviation of turbulent velocity is determined by the turbulence grade and the average wind speed. The integral scale for turbulence reflects the average size of the turbulent vortex. The average wind speed at the hub height. The frequency is represented by the Von Karman spectrum. The Von Karman spectrum is expressed in a slightly different form, but it also describes the distribution characteristics of atmospheric turbulent energy with frequency. The standard turbulence spectrum model is based on measured data from a stationary wind turbine and assumes that the rotor plane is stationary relative to the incoming flow, thus not considering the influence of platform motion on the inflow turbulence.

[0029] The oscillation-added energy peak refers to a spectral correction factor applied to the standard atmospheric turbulence spectrum by adding an additional energy bulge centered at the oscillation peak frequency to simulate the modulation effect of oscillation motion on inflow turbulence. This added energy peak is constructed using a Gaussian peak function, and its mathematical expression is as follows: in, The power spectral density function for the additional energy peak of the oscillation. The peak frequency of the oscillation is the parameter that determines the position of the additional energy peak on the frequency axis. The amplitude of the oscillation energy determines the total energy of the additional energy peak. The larger the value, the higher the amplitude of the additional energy peak. The bandwidth parameter is determined by the bandwidth. The calculation yielded: This parameter controls the width of the additional energy peak. The larger the value, the flatter the additional energy peak and the wider the frequency range it covers.

[0030] Gaussian peak function at oscillation peak frequency It reaches its maximum value at a certain point, and deviates as the frequency changes. , The function decreases smoothly due to the decay characteristics of the Gaussian function. The shape of this function can better simulate the energy peak characteristics added to the turbulent spectrum by the actual oscillation motion. That is, the energy is concentrated near the oscillation frequency, but is continuously distributed within a certain frequency range, rather than being concentrated on a discrete spectral line at a single frequency.

[0031] Step 4: Construct the equivalent inflow turbulence spectrum modulated by oscillation based on the oscillation-added energy peak. The equivalent inflow turbulence spectrum modulated by oscillation refers to the equivalent inflow wind speed power spectral density function that comprehensively considers the inherent characteristics of atmospheric turbulence and the modulation effect of platform oscillation motion. It is constructed by multiplying the oscillation-added energy peak by the coupling modulation coefficient and then superimposing it with the obtained standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum modulated by oscillation. Taking the standard Kaimal spectrum as an example, the equivalent inflow turbulence spectrum modulated by oscillation... The mathematical expression is: in, The power spectral density function of the standard Kaimal atmospheric turbulence spectrum. Add an energy peak to the oscillation. The coupling modulation coefficient is a preset dimensionless parameter. Its physical meaning is the efficiency ratio of the actual energy of the oscillation motion converted into the additional energy of the inflow turbulence. The value of the coupling modulation coefficient ranges from 0.3 to 1.0, and its value is jointly determined by aerodynamic damping and the wind turbine rotation effect. When the aerodynamic damping is small and the attenuation effect of the wind turbine rotation on the oscillation motion is weak, If the value is too large, it indicates that the aerodynamic damping is high and the rotation of the wind turbine has a significant attenuation effect on the oscillation motion. The value is too small. For a typical floating wind turbine, Values ​​are typically taken within the range of 0.5 to 0.8. In practical engineering applications, The calibration can be determined by comparing the measured turbulence data with and without oscillation modulation conditions.

[0032] When the Von Karman spectrum is used instead of the Kaimal spectrum, the equivalent inflow turbulence spectrum after oscillation modulation is obtained. The expression becomes: in, The power spectral density function of the standard Von Karman atmospheric turbulence spectrum. The equivalent inflow turbulence spectrum modulated by oscillation. It reflects the energy distribution characteristics of the inflow wind speed experienced by the floating wind turbine under real operating conditions. While retaining the inherent high-frequency turbulent energy distribution of the standard atmospheric turbulence spectrum, it also reflects the peak frequency of the oscillation. An additional energy peak is added nearby, which corresponds to the periodic velocity component added to the platform's oscillation motion in the inflow direction. As the target spectrum for subsequent synthesis of equivalent inflow wind speed time histories, it ensures that the synthesized wind speed data can simultaneously contain the high-frequency random characteristics of atmospheric turbulence and the low-frequency modulation characteristics of oscillation motion.

[0033] To verify the impact of the equivalent inflow turbulence spectrum modulated by oscillation on the load simulation results, the inflow wind speed time history was generated using both the standard turbulence spectrum and the equivalent inflow turbulence spectrum modulated by oscillation for a certain working condition. This time history was then input into the floating wind turbine coupled simulation system for load comparison calculations. The results are shown in Table 2. Table 2: Comparison of turbulent energy and load before and after oscillation modulation As can be seen from Table 2, the turbulent energy in the concentrated frequency band of the sway energy is significantly increased after sway modulation. The standard deviation of the blade flapping moment and the standard deviation of the tower bottom bending moment are both increased. The equivalent fatigue damage of the tower and blades is significantly higher than that in the unmodulated case. The above results indicate that using the standard turbulence spectrum will underestimate the fatigue load of the floating wind turbine. This method can obtain more realistic load assessment results by introducing the sway modulation effect.

[0034] Step 5: Using the modulated equivalent inflow turbulence spectrum as the target spectrum, synthesize the equivalent inflow wind speed time history. Use this equivalent inflow wind speed time history as the inflow boundary condition for the coupled simulation of the floating wind turbine. The equivalent inflow wind speed time history refers to the spectrum with the modulated equivalent inflow turbulence spectrum. Wind speed time series data with consistent spectral energy distribution characteristics, To obtain the target spectrum, the equivalent inflow wind speed time history is synthesized using the inverse Fourier transform method. The basic principle of the inverse Fourier transform method is that the power spectral density function in the frequency domain contains the amplitude information of the signal but loses the phase information. Therefore, the time domain signal can be reconstructed by applying a random phase in the frequency domain, so that the power spectral density of the reconstructed signal is consistent with the target spectrum.

[0035] The specific steps for synthesizing the equivalent inflow wind speed time history using the inverse Fourier transform method are as follows: In the frequency domain, the equivalent inflow turbulence spectrum after oscillation modulation is analyzed... Perform discrete sampling at each discrete frequency point to The square root yields the amplitude spectrum value at that frequency point. At each frequency point Generate an independent object that obeys Uniformly distributed random phase values ​​over the interval The random phases at each frequency point are independent of each other. Combining the amplitude spectrum values ​​at each frequency point with the random phase values ​​forms a complex sequence in the frequency domain. ,in The imaginary unit is defined as... Finally, an inverse Fourier transform is performed on the frequency domain complex sequence. The frequency domain data is transformed to the time domain to obtain the equivalent inflow wind speed time history. : When generating the equivalent inflow wind speed time history, it is necessary to set appropriate frequency domain discretization parameters. The frequency discretization range should cover 0 Hz to the upper limit frequency that satisfies the Nyquist sampling theorem, and the number of discretization points should be [not specified]. In other words, the frequency resolution should be sufficiently fine to ensure that the spectral characteristics of the synthesized wind speed time history are consistent with the target spectrum. Time-domain sampling interval Should meet ,in The upper limit of the frequency discrete range is determined by the energy cutoff frequency of the target spectrum. Simultaneously, to ensure sufficient capture of the modulation characteristics of the oscillation motion in the low-frequency band, It should also meet That is, the synthesis should be performed at a sampling rate of at least 20 times the peak frequency of the oscillation. If necessary, the harmonic superposition method (WAWS) can be used to replace the inverse Fourier transform method to achieve the same function. After discretizing the target spectrum by frequency, the amplitude of the corresponding sine wave at each frequency point is calculated based on the energy value. The sine waves at each frequency point are superimposed and a random phase is applied to synthesize the wind speed time history that is consistent with the energy distribution of the target spectrum.

[0036] The above-synthesized equivalent inflow velocity time history With platform oscillation displacement time history The aerodynamic-hydraulic-servo-elastic coupling simulation system of the floating wind turbine is input together as the inflow boundary condition in the simulation calculation. In this simulation system, the equivalent inflow wind velocity time history is used. Acting on the plane of rotation of the wind turbine, the aerodynamic loads on each section of the blades are calculated using an aerodynamic model, and the platform sway displacement time history is also calculated. Acting on the platform foundation, the hydrodynamic model calculates the interaction forces between the waves and the platform. Through aerodynamic-hydraulic-servo-elastic coupling, the simulation system comprehensively calculates dynamic response indicators such as blade load, tower bending moment, mooring tension, and generator output power at each time step. Compared to traditional inflow wind speed time histories generated solely based on standard turbulence spectra, this inflow boundary condition additionally includes the spectral modulation characteristics of the platform's sway motion on the inflow turbulent structure. This allows the simulation system to obtain load assessment results that are more consistent with the actual operating conditions of the floating wind turbine when calculating the aforementioned dynamic response indicators. By comparing simulation results with and without sway modulation under the same wave and atmospheric turbulence conditions, the impact of sway modulation on the fatigue damage of key structural components of the floating wind turbine can be quantitatively assessed.

[0037] To verify the modulation effect of this method under different sea states, equivalent inflow turbulence spectra after oscillation modulation were constructed and equivalent inflow wind speed time histories were synthesized under each operating condition. The coupling modulation coefficient α was uniformly set to 0.6. The turbulence energy change rate and tower fatigue damage change rate under each operating condition were calculated. The results are shown in Table 3. Table 3: Summary of modulation effects under different operating conditions Please see Figure 3 And Table 3, Figure 3 The comparison of the rate of change of turbulent energy and the rate of change of tower fatigue damage under various operating conditions is presented in the form of a biaxial bar chart. Table 3 lists the quantitative values ​​for each operating condition. Figure 3It can be seen that the turbulent energy under all operating conditions increases significantly after sway modulation, and the corresponding tower fatigue damage also increases synchronously. The two are highly consistent in trend. The modulation effect is most significant under medium sea state conditions, while the effect is relatively low under low and high sea state conditions. The above results show that the sway modulation effect is most prominent under moderate sea state conditions. This method can effectively reflect the modulation characteristics of sway motion on inflow turbulence in the entire sea state range.

[0038] Please see Figure 4 The present invention also provides an offshore wind power operation simulation system, the simulation system being used to execute the above-described offshore wind power operation simulation method, comprising: The motion calculation module is used to obtain the sway velocity time history of the floating wind power platform, and to perform frequency domain transformation on the sway velocity time history to obtain the sway velocity power spectral density. The spectrum extraction module is used to extract the oscillation peak frequency, oscillation energy amplitude, and bandwidth from the oscillation velocity power spectral density. The oscillation peak frequency is the frequency corresponding to the maximum energy value in the oscillation velocity power spectral density. The oscillation energy amplitude is the energy integral value within a preset frequency band near the oscillation peak frequency. The bandwidth is half of the frequency range corresponding to the point where the energy on both sides of the oscillation peak frequency drops to half of the peak value. Turbulence modulation module, used to construct additional oscillation energy peaks based on oscillation peak frequency, oscillation energy amplitude and bandwidth; The turbulence spectrum equivalent module is used to introduce the coupling modulation coefficient. After multiplying the oscillation additional energy peak with the coupling modulation coefficient, it is superimposed with the standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum. The wind speed synthesis module is used to synthesize the equivalent inflow wind speed time history with the equivalent inflow turbulence spectrum as the target spectrum.

[0039] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0040] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0041] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0042] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for simulating the operation of offshore wind power, characterized in that, The specific steps include: The sway velocity time history of the floating wind power platform is obtained, and the sway velocity power spectral density is obtained by frequency domain transformation of the sway velocity time history. The peak frequency, amplitude, and bandwidth are extracted from the power spectral density of the oscillation velocity. The peak frequency is the frequency corresponding to the maximum energy value in the power spectral density of the oscillation velocity. The amplitude of the oscillation energy is the energy integral value within a preset frequency band near the peak frequency. The bandwidth is half of the frequency range corresponding to the point where the energy on both sides of the peak frequency drops to half of the peak value. Construct additional energy peaks for the oscillation based on the peak frequency, oscillation energy amplitude, and bandwidth. By introducing a coupling modulation coefficient, multiplying the oscillation-added energy peak with the coupling modulation coefficient, and then superimposing it with the standard atmospheric turbulence spectrum, an equivalent inflow turbulence spectrum is obtained. Using the equivalent inflow turbulence spectrum as the target spectrum, the equivalent inflow wind speed time history is synthesized.

2. The offshore wind power operation simulation method according to claim 1, characterized in that: The steps of performing frequency domain transformation on the oscillation velocity time history to obtain the oscillation velocity power spectral density, and extracting the oscillation peak frequency, oscillation energy amplitude, and bandwidth from the oscillation velocity power spectral density include: A fast Fourier transform is performed on the oscillation velocity time history to convert the oscillation velocity signal in the time domain into a Fourier spectrum in the frequency domain, which contains the amplitude and phase information corresponding to each frequency component. Based on the amplitude information of each frequency component in the Fourier spectrum, the power spectral density of the oscillation velocity is calculated, which describes the distribution of the energy of the oscillation velocity at different frequencies. The maximum energy value is searched across the entire frequency range of the sway velocity power spectral density. The frequency corresponding to the maximum energy value is determined as the sway peak frequency. This frequency reflects the dominant frequency position of the wave energy and is affected by the platform's sway natural frequency. A preset frequency band is selected with the peak frequency of the oscillation as the center. The oscillation velocity power spectral density within the preset frequency band is integrated. The integration result is used as the oscillation energy amplitude, which reflects the total energy intensity of the oscillation. In the oscillating velocity power spectral density, the frequency positions corresponding to the energy drop to half of the peak value are searched from the peak frequency to both sides to obtain two frequency boundaries. The frequency range between the two frequency boundaries is calculated, and half of the frequency range is taken as the bandwidth. The bandwidth reflects the frequency distribution range of the oscillating motion energy.

3. The offshore wind power operation simulation method according to claim 2, characterized in that: The steps for constructing the additional energy peak of the oscillation based on the peak frequency, amplitude of the oscillation energy, and bandwidth include: The Gaussian peak function is selected as the functional form of the oscillation additional energy peak. This functional form can simulate the characteristic that the energy is continuously distributed near the oscillation peak frequency and decays smoothly to both sides. The oscillation peak frequency is set as the center position parameter of the Gaussian peak function, so that the Gaussian peak function reaches its maximum value at the oscillation peak frequency. This center position parameter determines the position of the additional energy peak on the frequency axis. The amplitude of the oscillation energy is set as the total energy parameter of the Gaussian peak function, so that the amplitude of the Gaussian peak function increases with the increase of the amplitude of the oscillation energy. This total energy parameter determines the energy magnitude of the additional energy peak. The bandwidth is converted into the width parameter of the Gaussian peak function, so that the flatness of the Gaussian peak function increases with the increase of the bandwidth. This width parameter determines the frequency range covered by the additional energy peak. Based on the center position parameter, total energy parameter, and width parameter, a power spectral density function for the oscillation additional energy peak is generated. This function reaches its maximum value at the oscillation peak frequency and decays smoothly as the frequency deviates from the oscillation peak frequency.

4. The offshore wind power operation simulation method according to claim 1, characterized in that: The steps of introducing a coupling modulation coefficient, multiplying the oscillation-added energy peak by the coupling modulation coefficient, and then superimposing it with the standard atmospheric turbulence spectrum include: Obtain a preset coupling modulation coefficient, which characterizes the efficiency ratio of oscillating motion energy to additional energy of inflow turbulence; The power spectral density function of the oscillating additional energy peak is multiplied by the coupling modulation coefficient to obtain the modulated oscillating additional energy peak. The amplitude of the modulated oscillating additional energy peak is scaled according to the coupling modulation coefficient. Obtain the power spectral density function of the standard atmospheric turbulence spectrum, which describes the atmospheric turbulence energy distribution characteristics experienced by the stationary wind turbine; The modulated oscillation additional energy peak is added point by point in the frequency domain to the power spectral density function of the standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum. This equivalent inflow turbulence spectrum contains both the high-frequency random characteristics of atmospheric turbulence and the low-frequency modulation characteristics of oscillation motion.

5. The offshore wind power operation simulation method according to claim 4, characterized in that: The value of the coupling modulation coefficient is determined by both aerodynamic damping and the wind turbine rotation effect: When the aerodynamic damping is small and the attenuation effect of the wind turbine rotation on the sway motion is weak, more of the sway motion energy is converted into additional energy of the inflow turbulence, and the value of the coupling modulation coefficient increases accordingly. When the aerodynamic damping is large and the attenuation effect of the wind turbine rotation on the sway motion is strong, less of the sway motion energy is converted into additional energy of the inflow turbulence, and the value of the coupling modulation coefficient is reduced accordingly. The coupling modulation coefficients were calibrated and determined by comparing measured turbulence data with simulation results under oscillation modulation conditions and without oscillation modulation conditions.

6. The offshore wind power operation simulation method according to claim 1, characterized in that: The steps for synthesizing the equivalent inflow wind velocity time history, using the equivalent inflow turbulence spectrum as the target spectrum, include: Discrete sampling is performed on the equivalent inflow turbulence spectrum in the frequency domain to obtain the power spectral density value at each discrete frequency point. The square root operation is performed on the power spectral density value at each discrete frequency point to obtain the amplitude spectral value at each discrete frequency point. Random phase values ​​are generated independently at each discrete frequency point, and the random phases between each discrete frequency point are independent of each other. The random phase values ​​follow a uniform distribution over a preset interval. The amplitude spectrum value at each discrete frequency point is combined with the corresponding random phase value to form a frequency domain complex sequence, which contains both amplitude and phase information. An inverse Fourier transform is performed on the frequency domain complex sequence to convert the frequency domain data to the time domain, resulting in the equivalent inflow wind speed time history. The spectral energy distribution of this equivalent inflow wind speed time history is consistent with the equivalent inflow turbulence spectrum.

7. The offshore wind power operation simulation method according to claim 6, characterized in that: When discretizing the equivalent inflow turbulence spectrum in the frequency domain, the parameter settings must satisfy the following conditions: The frequency discrete range covers from zero frequency to the energy cutoff frequency, which is determined by the energy distribution of the equivalent inflow turbulence spectrum to ensure that high-frequency energy components are not missed in discrete sampling. The number of discrete points meets the frequency resolution requirement to ensure that the spectral characteristics of the synthesized equivalent inflow wind speed time history are consistent with the equivalent inflow turbulence spectrum; The time-domain sampling interval simultaneously satisfies the requirements of the Nyquist sampling theorem and the requirement of capturing low-frequency oscillation modulation characteristics, so as to ensure that the synthesized time history can simultaneously reflect high-frequency turbulence characteristics and low-frequency oscillation modulation characteristics.

8. The offshore wind power operation simulation method according to claim 1, characterized in that: The steps to obtain the sway velocity time history of a floating wind power platform include: The time history of the sway displacement of the floating wind power platform under wave action is obtained by hydrodynamic frequency domain analysis, time domain coupled simulation or water tank model test. This sway displacement time history describes the change of the horizontal reciprocating displacement of the platform along the wave propagation direction with time. The first time derivative of the sway displacement time history is obtained to obtain the sway velocity time history, which describes the change of the instantaneous velocity of the platform's sway motion with time. The standard atmospheric turbulence spectrum includes the Kaimal spectrum or the Von Karman spectrum. This standard atmospheric turbulence spectrum is established based on measured data from stationary wind turbines and describes the distribution characteristics of atmospheric turbulence energy with frequency.

9. The offshore wind power operation simulation method according to claim 1, characterized in that: The equivalent inflow wind velocity time history and the sway displacement time history of the floating wind turbine platform under wave action are input together into the coupled simulation system of the floating wind turbine as inflow boundary conditions: The equivalent inflow wind speed time history is applied to the rotor rotation plane, driving the aerodynamic model to calculate the aerodynamic loads on each section of the blade; The time history of the sway displacement is applied to the platform foundation, driving the hydrodynamic model to calculate the interaction force between the waves and the platform; The dynamic response indices of blade load, tower bending moment, mooring tension, and generator output power are calculated using aerodynamic-hydraulic-servo-elastic coupling solution.

10. An offshore wind power operation simulation system, characterized in that: The system is used to execute the offshore wind power operation simulation method according to any one of claims 1-9, including: The motion calculation module is used to obtain the sway velocity time history of the floating wind power platform, and to perform frequency domain transformation on the sway velocity time history to obtain the sway velocity power spectral density. The spectrum extraction module is used to extract the oscillation peak frequency, oscillation energy amplitude, and bandwidth from the oscillation velocity power spectral density. The oscillation peak frequency is the frequency corresponding to the maximum energy value in the oscillation velocity power spectral density. The oscillation energy amplitude is the energy integral value within a preset frequency band near the oscillation peak frequency. The bandwidth is half of the frequency range corresponding to the point where the energy on both sides of the oscillation peak frequency drops to half of the peak value. Turbulence modulation module, used to construct additional oscillation energy peaks based on oscillation peak frequency, oscillation energy amplitude and bandwidth; The turbulence spectrum equivalent module is used to introduce the coupling modulation coefficient. After multiplying the oscillation additional energy peak with the coupling modulation coefficient, it is superimposed with the standard atmospheric turbulence spectrum to obtain the equivalent inflow turbulence spectrum. The wind speed synthesis module is used to synthesize the equivalent inflow wind speed time history with the equivalent inflow turbulence spectrum as the target spectrum.

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