Wind power plant characteristic analysis method and system based on climate difference

Abstract

The invention discloses a wind power plant characteristic analysis method and system based on climate difference. The method comprises the following steps: acquiring fan point position parameters in a wind power plant, establishing a three-dimensional terrain model of the wind power plant, and determining the atmospheric junction stability of each fan point position; carrying out numerical simulation and feature extraction on the surface layer wind field according to the atmospheric strata stability, and obtaining a microclimate parameter time sequence and a microclimate fluctuation feature of each fan point location; based on the microclimate fluctuation characteristics, establishing a microclimate parameter short-term prediction model and predicting a microclimate fluctuation scene; and according to the structure parameters of the fan, constructing a fan aeroelastic model, obtaining a fatigue load spectrum of a fan part, and according to the fatigue load spectrum and the fan aeroelastic model, optimizing the operation of the wind power plant. According to the method, fine simulation and prediction of the microclimate of the wind power plant are realized, the influence of microclimate fluctuation on the wind power stability is effectively dealt with, the operation efficiency of the wind power plant is remarkably improved, the service life of equipment is prolonged, and safe and stable operation of a power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm analysis and optimization, and in particular to a method and system for analyzing wind farm characteristics based on climate differences. Background Art

[0002] In the planning and construction of wind farms, the optimization strategy of micro-site selection is directly subject to the microclimate fluctuation characteristics of the wind turbine layout point, which constitutes a key bottleneck in the implementation of the technology. The diversity of the natural environment inside the wind farm leads to significant differences in local microclimates. Among them, factors such as terrain undulation, vegetation coverage, and water distribution work together to cause rapid fluctuations in parameters such as wind speed, wind direction, temperature and humidity, posing a severe challenge to the performance stability and energy output efficiency of wind turbines.

[0003] The operation of wind turbines is highly dependent on immediate adaptation to wind conditions, that is, by dynamically adjusting the blade pitch and rotation rate to maximize wind energy capture. Frequent microclimate changes force frequent adjustments to the equipment, which not only increases the wear and fatigue of mechanical components and shortens their service life, but also causes instability in power generation, posing a threat to the reliable operation of the power grid and the quality of power. What's more, when the wind speed changes suddenly, high-frequency microclimate fluctuations interfere with the control mechanism of the wind turbine, reducing its dynamic response speed and further reducing the efficient use of wind energy. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for analyzing wind farm characteristics based on climate differences to solve the problems of equipment wear, unstable power generation efficiency and increased operation and maintenance costs caused by high-frequency microclimate fluctuations in wind farm micro-site selection.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for analyzing wind farm characteristics based on climate differences, comprising:

[0008] Obtain relevant parameters and structural parameters of wind turbine locations in the wind farm, establish a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location;

[0009] According to the atmospheric stratification stability, numerical simulation is performed on the near-ground wind field to obtain the time series of microclimate parameters of each wind turbine point, and the time series of microclimate parameters is decomposed to obtain the microclimate fluctuation characteristics;

[0010] Based on the microclimate fluctuation characteristics, a short-term prediction model for microclimate parameters is established, and the microclimate fluctuation scenario is predicted using the short-term prediction model for microclimate parameters;

[0011] According to the structural parameters of the wind turbine, an aeroelastic model of the wind turbine is constructed, based on the aeroelastic model of the wind turbine, a fatigue load spectrum of the wind turbine components is obtained, and according to the fatigue load spectrum and the aeroelastic model of the wind turbine, the operation of the wind farm is optimized.

[0012] As a preferred solution of the method for analyzing wind farm characteristics based on climate differences described in the present invention, the steps of establishing a three-dimensional terrain model of the wind farm and determining the atmospheric stratification stability of each wind turbine point include:

[0013] Obtaining the geographic coordinates and elevation data of each wind turbine point in the wind farm, establishing a three-dimensional terrain model of the wind farm, performing numerical simulation on the three-dimensional terrain model, dividing the terrain roughness according to the results of the simulation analysis and in combination with terrain parameters, obtaining the terrain roughness parameters of each wind turbine point, combining the terrain roughness parameters with the geographic coordinates of the wind turbine point, and generating a terrain roughness distribution map of each point in the wind farm;

[0014] Obtain meteorological observation data of the wind farm, obtain vertical distribution characteristics of meteorological elements in the area where each wind turbine point is located based on the meteorological observation data of the wind farm, and calculate the atmospheric stratification stability of different height layers in the area where each wind turbine point is located based on the terrain roughness parameters and vertical distribution characteristics of meteorological elements of each wind turbine point.

[0015] As a preferred solution of the wind farm characteristic analysis method based on climate difference described in the present invention, wherein: according to the atmospheric stratification stability, the near-ground layer wind field is numerically simulated to obtain the microclimate parameter time series of each wind turbine point, including:

[0016] If the atmospheric stratification stability is within the preset range, it is judged as a neutral state; if the atmospheric stratification stability is less than the preset range, it is judged as an unstable state; if the atmospheric stratification stability is greater than the preset range, it is judged as a stable state;

[0017] When the atmospheric stratification stability is neutral or unstable, the large eddy simulation method is used to perform numerical simulation on the simulation range of the near-ground wind field. The three-dimensional wind speed component data of each wind turbine location at different times are obtained by the large eddy simulation method. The three-dimensional wind speed component data are synthesized to obtain the horizontal wind speed and wind direction data at different times. Through time series analysis, the time series of microclimate parameters of each wind turbine location is obtained.

[0018] As a preferred solution of the method for analyzing wind farm characteristics based on climate differences described in the present invention, decomposing the microclimate parameter time series to obtain microclimate fluctuation characteristics includes:

[0019] The microclimate parameter time series is subjected to continuous wavelet transform, and the wavelet transform coefficients are rapidly decomposed using the Mallat algorithm to obtain the fluctuation characteristics of the microclimate parameter time series on different time scales.

[0020] As a preferred solution of the method for analyzing wind farm characteristics based on climate differences described in the present invention, wherein: establishing a short-term prediction model for microclimate parameters, and using the short-term prediction model for microclimate parameters to predict microclimate fluctuation scenarios includes:

[0021] The historical event sequence of microclimate parameters and the microclimate fluctuation characteristics are input into the short-term prediction model of microclimate parameters. The short-term prediction model of microclimate parameters adopts a rolling prediction method. According to the prediction time scale and data time resolution, the length and step size of the rolling window are determined to predict the microclimate fluctuations within a preset time period in the future.

[0022] As a preferred solution of the method for analyzing wind farm characteristics based on climate differences described in the present invention, the fatigue load spectrum of wind turbine components is obtained based on the wind turbine aeroelastic model, including:

[0023] The microclimate fluctuation scenario is used as the input load condition of the wind turbine aeroelastic model, and the wind turbine aeroelastic model is solved by time domain integration to simulate the dynamic response of the wind turbine under the microclimate fluctuation scenario;

[0024] The dynamic response results are statistically analyzed to obtain fatigue load spectra of wind turbine components.

[0025] As a preferred solution of the method for analyzing wind farm characteristics based on climate differences described in the present invention, the optimization of wind farm operation according to the fatigue load spectrum and the wind turbine aeroelastic model includes:

[0026] According to the wind turbine aeroelastic model and fatigue load spectrum, a model predictive control algorithm is used to adjust the wind turbine single-machine variable pitch and speed change strategy in real time;

[0027] Based on the microclimate fluctuation scenario, the future output power of the wind farm is obtained, and a charging and discharging plan for the energy storage system is formulated according to the output power.

[0028] In a second aspect, the present invention provides a system for analyzing characteristics of a wind farm based on climate differences, comprising:

[0029] The data acquisition and three-dimensional model building module is used to obtain the relevant parameters of each wind turbine location in the wind farm, build a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location;

[0030] A microclimate characteristic acquisition module is used to perform numerical simulation on the near-ground wind field according to the atmospheric stratification stability, obtain the microclimate parameter time series of each wind turbine point, and decompose the microclimate parameter time series to obtain the microclimate fluctuation characteristics;

[0031] A microclimate prediction module, used to establish a short-term prediction model for microclimate parameters according to the microclimate fluctuation characteristics, and use the short-term prediction model for microclimate parameters to predict microclimate fluctuation scenarios;

[0032] The optimization module is used to construct an aeroelastic model of the wind turbine based on the structural parameters of each wind turbine, obtain the fatigue load spectrum of the wind turbine components based on the aeroelastic model of the wind turbine, and optimize the operation of the wind farm according to the fatigue load spectrum and the aeroelastic model of the wind turbine.

[0033] In a third aspect, the present invention provides a computing device, comprising:

[0034] Memory and processor;

[0035] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the wind farm characteristic analysis method based on climate differences are implemented.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the wind farm characteristic analysis method based on climate differences.

[0037] Compared with the prior art, the present invention has the following beneficial effects: the present invention comprehensively considers the geographical characteristics of wind farms, local microclimate fluctuations and dynamic responses of wind turbines, and adopts advanced technologies such as large eddy simulation, wavelet transform and model predictive control to achieve refined simulation and prediction of wind farm microclimate, effectively respond to the impact of microclimate fluctuations on wind power stability, significantly improves the operating efficiency of wind farms, extends equipment life, and ensures the safe and stable operation of power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0039] Figure 1 The figure is a schematic diagram of the overall process of a method for analyzing wind farm characteristics based on climate differences according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0044] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Example 1

[0047] Reference Figure 1 , which is an embodiment of the present invention, provides a method for analyzing wind farm characteristics based on climate differences, comprising:

[0048] S100: Obtain relevant parameters and structural parameters of wind turbine locations in the wind farm, establish a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location;

[0049] In the embodiment of the present application, the relevant parameters of the wind turbine point include the precise geographic coordinates of each wind turbine point and the corresponding elevation data, and the structural parameters of the wind turbine include the aerodynamic shape parameters of the wind turbine blades, such as airfoil, blade length, airfoil distribution and geometric parameters and material property parameters of the wind turbine tower, such as tower height, diameter, wall thickness, material density, elastic modulus, etc.;

[0050] Preferably, the geographic coordinates and elevation data of each wind turbine point in the wind farm are obtained, a three-dimensional terrain model of the wind farm is established, a numerical simulation is performed on the three-dimensional terrain model, and according to the results of the simulation analysis, the terrain roughness is divided in combination with the terrain parameters, and the terrain roughness parameters of each wind turbine point are obtained, and the terrain roughness parameters are combined with the geographic coordinates of the wind turbine point to generate a terrain roughness distribution map of each point in the wind farm;

[0051] In the embodiment of the present application, a three-dimensional terrain model is established to simulate the influence of local terrain undulation on the wind field near the ground layer, and the specific process of obtaining the terrain roughness distribution map is as follows:

[0052] First, the precise geographic coordinates and corresponding elevation data of each wind turbine location in the wind farm are obtained, and the acquired data is preprocessed by noise filtering, outlier removal, data format conversion and other preprocessing operations. The processed geographic coordinates and elevation data are used to construct a three-dimensional terrain model of the wind farm. The model contains the spatial location information of the wind turbine location and the undulating characteristics of the surrounding terrain.

[0053] Then, the computational fluid dynamics method was used in combination with the k-epsilon turbulence model to perform numerical simulation analysis on the established three-dimensional terrain model, and the distribution characteristics of the wind field near the ground layer, such as velocity, pressure, and turbulence intensity, were obtained, so as to obtain the influence of local terrain undulation on the wind field near the ground layer. According to the results of the simulation analysis, combined with parameters such as terrain undulation, slope, and slope direction, the terrain roughness was divided into different levels with reference to the terrain roughness classification method in the IEC61400-1 standard, and the terrain roughness parameters of each wind turbine point were calculated to determine the degree of influence of the terrain characteristics around each wind turbine point on the wind speed and direction.

[0054] The calculated terrain roughness parameters are associated with the geographic coordinates of the wind turbine locations to generate a terrain roughness distribution map of each point in the wind farm. Through the terrain roughness distribution map, the impact of terrain characteristics in different areas of the wind farm on the distribution of wind energy resources is analyzed to identify advantageous areas with rich wind energy resources. The wind turbine layout plan of the wind farm is optimized by comprehensively considering the geographic coordinates, elevation data and terrain roughness parameters of the wind turbine locations to maximize the utilization of wind energy resources in the wind farm.

[0055] Preferably, meteorological observation data of the wind farm is obtained, and vertical distribution characteristics of meteorological elements in the area where each wind turbine point is located are obtained based on the meteorological observation data of the wind farm, and the atmospheric stratification stability of different height layers in the area where each wind turbine point is located is calculated based on the terrain roughness parameters of each wind turbine point and the vertical distribution characteristics of meteorological elements;

[0056] In the embodiment of the present application, the specific process of determining the atmospheric stratification stability at different altitudes in the area where each wind turbine point is located is as follows: according to the scope of the wind farm coverage area and the geographical coordinates of each wind turbine point, combined with the terrain roughness distribution map, determine the area where refined meteorological observations are required and the spatial location distribution of the observation points; comprehensively consider factors such as the size of the wind farm, the complexity of the terrain, and the spatial variability of meteorological conditions, determine the number of observation sites that need to be deployed, and select typical locations that can represent different terrains and meteorological conditions, deploy multiple meteorological observation sites and sounding systems in the determined observation area, and obtain vertical profile observation data of meteorological elements such as temperature, air pressure, humidity and radiation intensity at different altitudes;

[0057] The obtained meteorological observation data are identified, eliminated and interpolated by using methods such as the 3σ rule and Grubbs rule, and the consistency test of time series and space series is carried out; by analyzing the meteorological element profile data of different altitude layers of each observation station, the vertical distribution characteristics of the meteorological elements of temperature, air pressure, humidity and radiation intensity in the area where each wind turbine point is located are obtained;

[0058] According to the terrain roughness parameters of the wind turbine locations and the vertical distribution characteristics of meteorological elements, the Richardson number method is used to calculate the stability parameters of the atmospheric stratification at different altitudes in the area where each wind turbine location is located. The calculation formula of the Richardson number is expressed as:

[0059]

[0060] Among them, g is the acceleration of gravity, T is the average temperature, ΔT / Δz is the temperature gradient, Δu / Δz is the wind speed gradient, and it is necessary to select a suitable height layer spacing Δz for calculation;

[0061] It should be noted that the present invention performs spatial interpolation on the calculated atmospheric stratification stability parameters, generates an atmospheric stratification stability distribution map of the wind farm coverage area, and performs overlay analysis with the terrain roughness distribution map to identify areas with complex and changeable atmospheric environmental conditions, comprehensively considers the terrain roughness parameters and atmospheric stratification stability parameters of the wind turbine locations, optimizes the wind farm wind turbine selection and operation control strategy, and improves the power generation efficiency and operation reliability of the wind turbine units.

[0062] S102: According to the atmospheric stratification stability, numerical simulation is performed on the near-ground wind field to obtain the time series of microclimate parameters of each wind turbine point, and the time series of microclimate parameters is decomposed to obtain the microclimate fluctuation characteristics;

[0063] Preferably, if the atmospheric stratification stability is within a preset range, it is determined to be a neutral state; if the atmospheric stratification stability is less than the preset range, it is determined to be an unstable state; if the atmospheric stratification stability is greater than the preset range, it is determined to be a stable state;

[0064] Preferably, when the atmospheric stratification stability is in a neutral or unstable state, a large eddy simulation method is used to perform numerical simulation on the simulation range of the near-surface wind field, and the three-dimensional wind speed component data of each wind turbine position at different times are obtained by the large eddy simulation method, and the three-dimensional wind speed component data are synthesized to obtain the horizontal wind speed and wind direction data at different times, and the microclimate parameter time series of each wind turbine position is obtained by time series analysis;

[0065] In the embodiment of the present application, a large eddy simulation method is used to perform numerical simulation on a determined near-ground wind field simulation range. During the simulation process, the DynamicSmagorinsky model is selected as the turbulence model, and the NS equation is discretized using the WENO format. The grid cells are integrated using the finite volume method, and the flux terms on the cell boundaries are reconstructed using the WENO format to improve the numerical accuracy and stability, and effectively capture the discontinuities and sharp gradients in the flow field.

[0066] Preferably, the microclimate parameter time series is subjected to continuous wavelet transform, and the wavelet transform coefficients are rapidly decomposed using the Mallat algorithm to obtain the fluctuation characteristics of the microclimate parameter time series at different time scales;

[0067] Specifically, according to the time series data of microclimate parameters such as turbulence intensity, wind shear and wind direction at the wind turbine location, the appropriate wavelet basis function is selected to perform continuous wavelet transform on the microclimate parameter sequence. Through the time-frequency distribution diagram of the wavelet transform coefficient, the energy distribution characteristics of the microclimate parameters on different time scales are identified, and the fluctuation strength of different frequency bands is judged according to the size of the wavelet coefficient modulus.

[0068] The Mallat algorithm is used to quickly decompose the wavelet coefficients. The detailed calculation steps of the Mallat algorithm are: firstly, the signal is low-pass and high-pass filtered, and then the filtering results are down-sampled to obtain approximate coefficients and detail coefficients of different scales, and then the coefficients are reconstructed to obtain signal components of different frequency bands, thereby obtaining the fluctuation characteristics of microclimate parameters on different time scales;

[0069] Calculate the variance of wavelet coefficients in different frequency bands to obtain the fluctuation intensity of microclimate parameters at different time scales. By comparing the variance of each frequency band, determine the dominant time scale of microclimate fluctuations. Use the wavelet variance analysis method to perform variance analysis on the wavelet coefficient sequences of different frequency bands. The calculation formula of the wavelet variance is to calculate the mean square value of the wavelet coefficients at each scale to obtain the wavelet variance value. The wavelet variance reflects the energy distribution of the signal at different scales. The scale corresponding to the peak value is the main period. The main period of microclimate parameter fluctuations can be determined by the peak position of the wavelet variance versus time scale curve.

[0070] According to the dominant time scale and main period, the time scale of microclimate parameters at wind turbine locations is classified to obtain the characteristics of microclimate parameters at different time scales.

[0071] It should be noted that the wind speed component data of each point obtained by large eddy simulation is synthesized to obtain the horizontal wind speed and wind direction data at different times, and the wind direction microclimate parameters of each point are obtained through time series analysis. The turbulence intensity, wind shear and wind direction microclimate parameters of each wind turbine point are combined to form a time series data set of the wind field near the ground layer of the wind farm, which is used for the subsequent optimization selection of wind turbines and the formulation of operation control strategies;

[0072] It should also be noted that, by comprehensively considering factors such as the geographical location, terrain conditions and climate zone of the wind turbine location, multivariate statistical analysis methods are used to conduct correlation analysis on microclimate parameters and environmental factors, and the influencing factors and laws of the spatiotemporal distribution of microclimate parameters are obtained. A microclimate distribution map of the wind farm is generated, which intuitively presents the spatiotemporal variation characteristics of the microclimate and forms the spatiotemporal distribution laws of the microclimate characteristics of the wind farm.

[0073] S104: Based on the microclimate fluctuation characteristics, a short-term prediction model for microclimate parameters is established, and the microclimate fluctuation scenario is predicted using the short-term prediction model for microclimate parameters;

[0074] Preferably, the historical event sequence of microclimate parameters and the microclimate fluctuation characteristics are input into the short-term prediction model of microclimate parameters. The short-term prediction model of microclimate parameters adopts a rolling prediction method, determines the length and step size of the rolling window according to the prediction time scale and data time resolution, and predicts the microclimate fluctuations within a preset time period in the future;

[0075] In an embodiment of the present application, the prediction accuracy of the short-term prediction model for microclimate parameters is evaluated using root mean square error, mean absolute error, and determination coefficient indicators, and the optimal machine learning algorithm and model parameters are selected based on the evaluation results; the prediction results of the short-term prediction model for microclimate parameters are wind speed and wind direction, and the prediction results of wind speed and wind direction are fused with the prediction results of other meteorological elements such as temperature, humidity, air pressure, and precipitation. These elements can be included in the short-term prediction model for microclimate parameters as additional input features, or an association model with wind speed and direction can be established through physical mechanisms. When fusing the prediction results, weighted averaging, Bayesian model averaging, and other methods are used to comprehensively consider the impact of various meteorological elements on the operation of wind turbines to form a complete microclimate prediction for wind farms.

[0076] S106: constructing an aeroelastic model of the wind turbine according to the structural parameters of the wind turbine, obtaining a fatigue load spectrum of the wind turbine components based on the aeroelastic model of the wind turbine, and optimizing the operation of the wind farm according to the fatigue load spectrum and the aeroelastic model of the wind turbine;

[0077] Preferably, the microclimate fluctuation scenario is used as the input load condition of the wind turbine aeroelastic model, and the wind turbine aeroelastic model is solved by time domain integration to simulate the dynamic response of the wind turbine under the microclimate fluctuation scenario;

[0078] Preferably, statistical analysis is performed on the dynamic response results to obtain fatigue load spectra of the fan components;

[0079] In the embodiment of the present application, according to the aerodynamic shape parameters of the wind turbine blade, such as airfoil, blade length, airfoil distribution, etc., the computational fluid dynamics method is used to numerically simulate the aerodynamic characteristics of the blade, obtain the aerodynamic load coefficient curves such as lift, drag, pitch moment, etc. under different wind speeds, and establish the blade aerodynamic model;

[0080] According to the geometric parameters and material property parameters of the wind turbine tower, such as tower height, diameter, wall thickness, material density, elastic modulus, etc., a finite element model of the tower structure is established. Through modal analysis, the natural frequency and vibration mode characteristics of the tower are obtained, and the tower structure model is established;

[0081] The blade aerodynamic model is coupled with the tower structure model to construct the aeroelastic model of the wind turbine.

[0082] In an embodiment of the present application, a statistical analysis is performed on the dynamic response results of the wind turbine aeroelastic model, and the load-time history of key components of the wind turbine, such as the bending moment at the root of the blade and the displacement of the tower top, is extracted. The load-time history is processed using the rain flow counting method, and the distribution of the number of load cycles and the amplitude is obtained through the steps of peak and valley point identification, cycle extraction, amplitude counting, etc., to form a fatigue load spectrum including statistical parameters such as the number of cycles, mean amplitude, and root mean square value.

[0083] Preferably, according to the aeroelastic model and fatigue load spectrum of the wind turbine, a model predictive control algorithm is used to adjust the variable pitch and speed strategy of the wind turbine in real time;

[0084] In an embodiment of the present application, based on the aeroelastic model of the wind turbine and fatigue load estimation, a dynamic optimization control model is constructed with the goal of minimizing the fatigue load of key components. At the same time, considering the safe operation of the wind turbine and the requirements for grid connection, a model predictive control algorithm is adopted, combined with the aeroelastic model of the wind turbine to predict the future state, and a discrete controller is designed to adjust the pitch angle and speed; the optimization problem is solved by the sequential quadratic programming method, and the future multi-step optimal control sequence is obtained, which is applied to the wind turbine control in real time to achieve dynamic optimization; in the control process, the feedback mechanism is used to correct the model prediction deviation and enhance adaptability. For fluctuations in wind speed and direction, a robustness optimization strategy is introduced to improve the anti-interference ability of the control system; through integration with the wind turbine monitoring system, real-time and accurate pitch angle and speed control is achieved.

[0085] Preferably, based on the microclimate fluctuation scenario, the future output power of the wind farm is obtained, and a charging and discharging plan for the energy storage system is formulated according to the output power;

[0086] In an embodiment of the present application, for the wind farm grid-connected point, real-time voltage and frequency fluctuation data of the substation and the collection line are obtained to determine whether the power fluctuation caused by the microclimate fluctuation meets the grid-connected technical requirements; if the power fluctuation caused by the microclimate fluctuation exceeds the grid-connected technical limit, a short-term power prediction and energy storage coordination control strategy is adopted to reduce peak power and fill valley power, thereby ensuring the smooth output of wind farm power and the safe and stable operation of the power grid.

[0087] Specifically, in the scenario where the wind farm power fluctuation caused by microclimate fluctuation exceeds the grid connection technical limit, in order to ensure the smooth output of wind farm power and the safe and stable operation of the power grid, the present invention adopts a series of comprehensive strategies;

[0088] First, relying on short-term power forecasting technology, combined with the actual operation data of wind farms and meteorological parameters such as wind speed, wind direction, temperature, etc., the support vector machine algorithm is used to accurately predict wind power, and the minimum redundancy maximum correlation algorithm is used to screen key meteorological parameters and optimize the prediction model to ensure that the prediction error is controlled within the root mean square value of less than 10%;

[0089] Based on the above prediction results, an optimization scheduling model for the joint operation of wind and storage was established, aiming to minimize the deviation between the actual output power of the wind farm and the planned power. Through rolling optimization, the wind power and load demand for the next 4 hours are predicted every 15 minutes, and the planned output power curve of the wind farm is formulated accordingly. The planned power curve will be processed by moving average filtering to make the output more stable.

[0090] When the deviation between the actual output power and the planned power exceeds the grid-connected specification limit, the energy storage system is immediately started for power compensation. During this process, the model predictive control algorithm is used to predict and optimize the charging and discharging plan of the energy storage system within the next hour based on the predicted wind power value, the planned output power and the current state of the energy storage system. Specifically, when the wind power output is higher than the plan, the energy storage system absorbs excess electricity; otherwise, it releases electricity to make up for the shortfall, ensuring the stability of the wind farm's output power. The charging and discharging process of the energy storage system is subject to strict capacity, power and SOC range restrictions to ensure its efficient and safe operation.

[0091] The above is a schematic scheme of a method for analyzing wind farm characteristics based on climate differences in this embodiment. It should be noted that the technical scheme of the system for analyzing wind farm characteristics based on climate differences and the technical scheme of the method for analyzing wind farm characteristics based on climate differences are of the same concept. For details not described in detail in the technical scheme of the system for analyzing wind farm characteristics based on climate differences in this embodiment, please refer to the description of the technical scheme of the method for analyzing wind farm characteristics based on climate differences.

[0092] The wind farm characteristic analysis system based on climate differences in this embodiment includes:

[0093] The data acquisition and three-dimensional model building module is used to obtain the relevant parameters of each wind turbine location in the wind farm, build a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location;

[0094] The microclimate characteristic acquisition module is used to numerically simulate the near-surface wind field according to the atmospheric stratification stability, obtain the microclimate parameter time series of each wind turbine point, and decompose the microclimate parameter time series to obtain the microclimate fluctuation characteristics;

[0095] The microclimate prediction module is used to establish a short-term prediction model for microclimate parameters according to the microclimate fluctuation characteristics, and use the short-term prediction model for microclimate parameters to predict microclimate fluctuation scenarios;

[0096] The optimization module is used to construct an aeroelastic model of the wind turbine based on the structural parameters of each wind turbine, obtain the fatigue load spectrum of the wind turbine components based on the aeroelastic model of the wind turbine, and optimize the operation of the wind farm according to the fatigue load spectrum and the aeroelastic model of the wind turbine.

[0097] This embodiment further provides a computing device, which is applicable to the case of analyzing wind farm characteristics based on climate differences, and includes:

[0098] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the wind farm characteristic analysis method based on climate differences as proposed in the above embodiment.

[0099] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for analyzing characteristics of a wind farm based on climate differences as proposed in the above embodiment is implemented.

[0100] The storage medium proposed in this embodiment and the method for realizing wind farm characteristic analysis based on climate difference proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0101] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0102] Example 2

[0103] Referring to Table 1, an embodiment of the present invention provides a method for analyzing characteristics of a wind farm based on climate differences. In order to verify its beneficial effects, comparison results of two solutions are provided.

[0104] The Delaunay triangulation method was used to construct a three-dimensional terrain model, and the influence of terrain on wind shear rate was analyzed through CFD simulation. The results show that for every 0.1 increase in wind shear rate, the terrain roughness level increases by 1 level. According to the IEC61400-1 standard, the Kriging interpolation technology was used to generate a 10m resolution terrain roughness distribution map, in which the area ≤2 was identified as the dominant area;

[0105] The Fisher-Jenks algorithm is used to divide the wind farm into multiple areas with different terrain complexity and meteorological conditions, and 1-2 observation stations are deployed in each area. When analyzing the vertical distribution of meteorological elements, the 10-minute profile data is wavelet transformed to extract the 1-hour trend, and the height layer average and standard deviation of each element are calculated.

[0106] The atmospheric stratification stability was evaluated by the Richardson number (Δz = 50 m), which was defined as unstable (Ri < 0.25), stable (Ri > 1.0), and neutral (0.25 ≤ Ri ≤ 1.0). Then, ordinary Kriging interpolation was used to generate a 100 m resolution stratification stability map.

[0107] When judging the stability of atmospheric stratification, the layered lookup table method is used to divide the Ri value into four intervals, and different simulation strategies are adopted for different stability areas. For unstable or neutral areas, the nested grid method is used to simulate the near-surface wind field, in which high-resolution (1-10m) encrypted grids are used around the wind turbines, and coarser grids (50-100m) are used in distant areas.

[0108] The time series of microclimate parameters at wind turbine locations was transformed by wavelet transform, and Morlet wavelet was selected as the mother function to generate a power spectrum density diagram, revealing significant energy peaks such as 10 days and 30 days, indicating periodic fluctuations. The wavelet components with the top three energy proportions and statistical characteristics constituted a feature vector, and support vector regression, i.e., Gaussian kernel, C = 1.0, γ = 0.05, was used to establish a wind speed and direction prediction model. In order to verify the effectiveness of the present invention, the wind speed and direction prediction model of the present invention was compared with the traditional method, and the test results are shown in Table 1;

[0109] Table 1 Comparison of wind speed and direction prediction accuracy

[0110] Experimental indicators Traditional forecasting methods Method of the present invention RMSE (root mean square error, m / s) 1.20 0.80 MAE (mean absolute error, m / s) 0.95 0.60 R2 value (coefficient of determination) 0.85 0.92

[0111] As shown in Table 1, compared with the traditional prediction method, the wind speed and direction prediction model of the present invention reduces the RMSE from 1.20m / s to 0.80m / s, the MAE from 0.95m / s to 0.60m / s, and the R2 value from 0.85 to 0.92. The present invention not only greatly reduces the prediction error, but also significantly improves the accuracy of the prediction, providing more reliable technical support for the wind energy resource assessment of wind farms and the optimized operation of wind turbines.

[0112] The numerical simulation of the aerodynamics of the wind turbine blades was performed using CFX software, combined with the SSTk-ω turbulence model; the tower finite element model was constructed using ANSYS, using Shell181 shell elements, with an element size of 0.1m and a total number of about 100,000 elements. The BlockLanczos method was used to solve the first 10 natural frequencies and vibration modes. ADAMS software was used for the aeroelastic coupling analysis, and the fatigue life was estimated taking into account the blade GRP material. Based on the SN curve, the Miner criterion estimated the cumulative fatigue damage value of 20 years to be 0.32.

[0113] Construct a wind turbine operation optimization control model with the goal of minimizing the weighted sum of the blade root bending moment and the tower top vibration acceleration, with weights of 0.6 and 0.4 respectively, while considering the constraints of pitch angle, speed and generator power;

[0114] The state space model parameters are identified by the least square method using SCADA data and discretized using the backward Euler method with Ts = 0.1s. In the design of the model predictive controller, quadratic penalty terms of Δu and Δv are introduced with λ = 0.2, and the Hildreth-d'Esopo algorithm is used to solve the optimal pitch angle and speed sequence for the next 10 cycles.

[0115] According to the above settings and simulations, when the grid voltage drops to 75% of the rated value for 100ms, the wind turbine can provide 30% of the rated power within 2 seconds until the grid voltage recovers to above 90%. The power and weather forecast results are updated to the monitoring system every 15 minutes through the IEC 61850 protocol.

[0116] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing wind farm characteristics based on climate differences, characterized in that: include: Obtain relevant parameters and structural parameters of wind turbine locations in the wind farm, establish a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location; According to the atmospheric stratification stability, numerical simulation is performed on the near-ground wind field to obtain the time series of microclimate parameters of each wind turbine point, and the time series of microclimate parameters is decomposed to obtain the microclimate fluctuation characteristics; Based on the microclimate fluctuation characteristics, a short-term prediction model for microclimate parameters is established, and the microclimate fluctuation scenario is predicted using the short-term prediction model for microclimate parameters; According to the structural parameters of the wind turbine, an aeroelastic model of the wind turbine is constructed, based on the aeroelastic model of the wind turbine, a fatigue load spectrum of the wind turbine components is obtained, and according to the fatigue load spectrum and the aeroelastic model of the wind turbine, the operation of the wind farm is optimized.

2. The method for analyzing wind farm characteristics based on climate differences according to claim 1, characterized in that: Establishing a three-dimensional terrain model of the wind farm and determining the atmospheric stratification stability at each wind turbine location includes: Obtaining the geographic coordinates and elevation data of each wind turbine point in the wind farm, establishing a three-dimensional terrain model of the wind farm, performing numerical simulation on the three-dimensional terrain model, dividing the terrain roughness according to the results of the simulation analysis and in combination with terrain parameters, obtaining the terrain roughness parameters of each wind turbine point, combining the terrain roughness parameters with the geographic coordinates of the wind turbine point, and generating a terrain roughness distribution map of each point in the wind farm; Obtain meteorological observation data of the wind farm, obtain vertical distribution characteristics of meteorological elements in the area where each wind turbine point is located based on the meteorological observation data of the wind farm, and calculate the atmospheric stratification stability of different height layers in the area where each wind turbine point is located based on the terrain roughness parameters and vertical distribution characteristics of meteorological elements of each wind turbine point.

3. The method for analyzing wind farm characteristics based on climate differences according to claim 1 or 2, characterized in that: According to the atmospheric stratification stability, the near-ground wind field is numerically simulated to obtain the time series of microclimate parameters at each wind turbine location, including: If the atmospheric stratification stability is within the preset range, it is judged as a neutral state; if the atmospheric stratification stability is less than the preset range, it is judged as an unstable state; if the atmospheric stratification stability is greater than the preset range, it is judged as a stable state; When the atmospheric stratification stability is neutral or unstable, the large eddy simulation method is used to perform numerical simulation on the simulation range of the near-ground wind field. The three-dimensional wind speed component data of each wind turbine location at different times are obtained by the large eddy simulation method. The three-dimensional wind speed component data are synthesized to obtain the horizontal wind speed and wind direction data at different times. Through time series analysis, the time series of microclimate parameters of each wind turbine location is obtained.

4. The method for analyzing wind farm characteristics based on climate differences according to claim 3, characterized in that: Decomposing the microclimate parameter time series to obtain microclimate fluctuation characteristics includes: The microclimate parameter time series is subjected to continuous wavelet transform, and the wavelet transform coefficients are rapidly decomposed using the Mallat algorithm to obtain the fluctuation characteristics of the microclimate parameter time series on different time scales.

5. The method for analyzing wind farm characteristics based on climate differences according to claim 4, characterized in that: Establishing a short-term prediction model for microclimate parameters, and using the short-term prediction model for microclimate parameters to predict microclimate fluctuation scenarios include: The historical event sequence of microclimate parameters and the microclimate fluctuation characteristics are input into the short-term prediction model of microclimate parameters. The short-term prediction model of microclimate parameters adopts a rolling prediction method. According to the prediction time scale and data time resolution, the length and step size of the rolling window are determined to predict the microclimate fluctuations within a preset time period in the future.

6. The method for analyzing wind farm characteristics based on climate differences according to claim 5, characterized in that: Based on the fan aeroelastic model, the fatigue load spectrum of the fan components is obtained. include, The microclimate fluctuation scenario is used as the input load condition of the wind turbine aeroelastic model, and the wind turbine aeroelastic model is solved by time domain integration to simulate the dynamic response of the wind turbine under the microclimate fluctuation scenario; The dynamic response results are statistically analyzed to obtain fatigue load spectra of wind turbine components.

7. The method for analyzing wind farm characteristics based on climate differences according to claim 6, characterized in that: According to the fatigue load spectrum and wind turbine aeroelastic model, the optimization of wind farm operation includes: According to the wind turbine aeroelastic model and fatigue load spectrum, a model predictive control algorithm is used to adjust the wind turbine single-machine variable pitch and speed change strategy in real time; Based on the microclimate fluctuation scenario, the future output power of the wind farm is obtained, and a charging and discharging plan for the energy storage system is formulated according to the output power.

8. A system for analyzing wind farm characteristics based on climate differences, characterized in that: include, The data acquisition and three-dimensional model building module is used to obtain the relevant parameters of each wind turbine location in the wind farm, build a three-dimensional terrain model of the wind farm, and determine the atmospheric stratification stability of each wind turbine location; A microclimate characteristic acquisition module is used to perform numerical simulation on the near-ground wind field according to the atmospheric stratification stability, obtain the microclimate parameter time series of each wind turbine point, and decompose the microclimate parameter time series to obtain the microclimate fluctuation characteristics; A microclimate prediction module, used to establish a short-term prediction model for microclimate parameters according to the microclimate fluctuation characteristics, and use the short-term prediction model for microclimate parameters to predict microclimate fluctuation scenarios; The optimization module is used to construct an aeroelastic model of the wind turbine based on the structural parameters of each wind turbine, obtain the fatigue load spectrum of the wind turbine components based on the aeroelastic model of the wind turbine, and optimize the operation of the wind farm according to the fatigue load spectrum and the aeroelastic model of the wind turbine.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for analyzing wind farm characteristics based on climate differences according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method for analyzing wind farm characteristics based on climate differences as claimed in any one of claims 1 to 7.

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