60 results about "Wind resource assessment" patented technology

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention discloses a wind resource assessment report generation method based on a large model technology. The method comprises the following steps: processing multi-source data, and constructing a domain knowledge base and a fine tuning database; field adaptive RAG enhancement is carried out, professional literatures, historical cases and industry specifications in a field knowledge base are retrieved by adopting a retrieval enhancement generation technology, and wind resource assessment contents meeting technical standards are generated through a multi-modal fusion word vector technology; performing large model fine tuning, and optimizing the generative large model through a gradient-free optimization algorithm Wind-DFO on the basis of a fine tuning database by adopting a field self-adaptive fine tuning strategy; and automatically generating a report, arranging and matching multiple templates through an AI workflow, embedding a data visualization chart, executing verification of a grammar layer, a logic layer and a compliance layer, and outputting a standardized wind resource assessment report. The accuracy, specialty and efficiency of report generation are remarkably improved, and the problems that a traditional method depends on artificial experience, the data utilization efficiency is low, and standardization is insufficient are solved.

The invention relates to the technical field of wind energy resource evaluation, in particular to a method and a system for calculating a hydrodynamics wind field numerical value based on high-precision landform, and the method comprises the steps: generating a three-dimensional unstructured grid for CFD wind field simulation based on high-precision landform data; analyzing an atmospheric boundary layer structure, and establishing a turbulence calculation model; according to the turbulence calculation model, the influence of atmospheric stability on the mountain flowing wind acceleration factor is researched, and a mountain flowing model is developed; performing CFD wind field simulation according to the three-dimensional unstructured grid and the mountain flow model, and correcting parameters of the mountain flow model; a refined grid is made through terrain elevation data; and according to the refined grid, driving the corrected mountain flow model to carry out numerical simulation, and carrying out wind power plant wind resource evaluation. Through the method, the complex characteristics of the wind field can be reflected more comprehensively, the precision and the calculation efficiency of wind resource evaluation are remarkably improved, and the efficiency and the accuracy of numerical simulation of the complex terrain wind field are improved.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention discloses a CFD (computational fluid dynamics) analogue simulation-based wind power plant wind resource evaluation method and a CFD analogue simulation-based wind power plant wind resource evaluation device. The evaluation method comprises the following steps: collecting and processing landform and meteorological data of a wind power plant site; establishing a three-dimensional CFD simulation model of the wind power plant according to the processed landform and meteorological data of the wind power plant site, performing grid division on a wind power plant area, and setting simulation boundary conditions; performing numerical simulation on the three-dimensional CFD simulation model of the wind power plant, and analyzing wind speed field distribution information of each position in the wind power plant; and according to the wind speed distribution information, calculating and evaluating the wind resource condition of the wind power plant to obtain a wind resource evaluation result. According to the technical scheme, through the three-dimensional simulation-evaluation-optimization closed loop with the CFD as the core, the wind speed and turbulence can be accurately quantified in the design stage, so that unit model selection, tower height, blade length and machine position layout are matched with a real wind field, key parameters can be determined at a time, the design period can be shortened, and the operation and maintenance and grid connection cost can be reduced.

According to the complex mountain fan arrangement optimization method, the positions of fans in an array are adjusted one by one towards one side, the purpose of increasing the angle with the prevailing wind direction is achieved, the wake flow influence is reduced, the generating capacity is calculated and compared, and therefore the optimal fan position is determined. Through the optimization method, the position of the fan can be dynamically adjusted according to the actual situation, the wake flow influence and the shielding effect are reduced, and the generating capacity is improved. According to the method, the wind resource evaluation software micro-siting result and the wake flow adjustment strategy are combined, fan arrangement can be effectively optimized in the complex mountain environment, and the wind energy utilization efficiency is improved.

The invention relates to the technical field of wind energy resource assessment, and discloses a wind resource assessment method based on a millimeter wave wind finding radar. The method comprises the steps of collecting wind field data of a target area through a millimeter wave wind measurement radar and performing preprocessing to generate a standardized data set; inputting the data into a prediction model to obtain a wind energy distribution characteristic graph; generating a parameter set by adopting threshold segmentation based on the feature map, and dynamically constructing an evaluation function for simulation evaluation; synchronously collecting actual wind energy data, and calculating a deviation matrix between the actual wind energy data and a predicted value to quantify an evaluation error; performing parameter correction on the prediction model by using the error to obtain an optimized model; and finally, regenerating a feature map by adopting the optimization model and completing resource evaluation. According to the method, through online model correction and dynamic evaluation function construction, closed-loop optimization and data driving in the evaluation process are realized, and the accuracy of an evaluation result and the adaptability to a specific site are improved.

The invention discloses a method for quickly speculating high-altitude wind speed based on earth surface observation, which belongs to the technical field of wind resource assessment and comprises the following steps: acquiring earth surface high-frequency ultrasonic data and iron tower wind speed data, extracting earth surface wind speed, friction speed and atmospheric stability, and determining earth surface roughness, boundary layer height and earth turning wind; a wind profile calculation model is selected according to different change conditions of the high-altitude wind speed under the atmospheric stability condition, and wind speed vertical distribution data is calculated by combining the surface roughness, the boundary layer height and the geostrophic wind. According to the method, the problem of insufficient precision in high-altitude wind speed calculation of a traditional method is solved, the change of the wind speed along with the height under different atmospheric stability conditions is accurately reflected, the applicability and reliability of the wind profile model are remarkably improved, and efficient and low-cost technical support is provided for wind power plant site selection, wind turbine design and wind power prediction.

The invention relates to the technical field of wind resource assessment, and provides a mesoscale mode optimization method and device for wind resource assessment in a complex terrain area, a medium and a product, and the method comprises the steps: obtaining high-precision terrain data and high-precision land utilization data, and putting the data into a WRF for calling; selecting a corresponding parameterization scheme in the WRF according to the resolution of the simulation area, generating an initial condition by the WRF, solving an atmospheric physical change process, and obtaining a simulation result of the WRF; and evaluating a simulation effect based on a simulation result of the WRF, and selecting an optimal parameterization scheme. According to the invention, the simulation capability of a near-ground wind field in a complex terrain can be obviously improved.

The present invention discloses an energy management platform and method for wind turbines in wind farms, and relates to the field of wind power generation technology, including data acquisition and monitoring systems, wind resource assessment and prediction, energy optimization and fault diagnosis, economic analysis and user interface, safety protection and system integration. The present invention, through the implementation of the data acquisition and monitoring system, makes the collection and monitoring of real-time data more efficient and accurate, enhances the quality of data and management efficiency, and improves the overall operation level of the wind farm. In terms of wind resource assessment and optimization, accurate wind speed and wind direction prediction can be achieved by combining historical data and real-time data. These prediction results provide energy optimization and fault diagnosis functions. The operating status of the wind turbine can be optimized according to the prediction results and grid demand, thereby maximizing the power generation and minimizing the cost. By reducing the downtime of the wind turbine, the economic benefits and operational efficiency of the wind farm are further improved.

The invention relates to the technical field of wind resource assessment, and discloses an offshore wind power plant site selection method based on development scene feature representation classification, which comprises the following steps: acquiring data of multiple dimensions such as a wind resource development environment, a construction environment and an operation and maintenance environment, and classifying initial development scenes of an offshore wind power plant in combination with a feature representation method; the defect that a traditional method depends on a single index is overcome; a multi-layer perceptron model is utilized and combined with multi-dimensional data, comprehensive prediction of the wind power plant development environment is achieved, and prediction deviation caused by traditional subjective experience is avoided. Meanwhile, by predicting the productivity and the cost in advance, the development risk and the cost caused by incomplete consideration of environmental factors are avoided. Furthermore, a multi-objective decision-making method is adopted for scoring based on a prediction result, so that multi-objective conflicts such as productivity, cost and risk are comprehensively balanced, site selection one-sidedness caused by a single index in a traditional method is avoided, scientificity and objectivity of decision making are improved, and development risk and cost are reduced.

The invention discloses a wind resource assessment method based on multi-source data fusion, a medium and a system. The method comprises the following steps: S1, acquiring project basic information and multi-source data of a wind power project; the project basic information comprises a project name, a geographic position and topographic information; the multi-source data comprises anemometer tower data, mesoscale data and CFD data; s2, preprocessing the anemometer tower data to generate corrected anemometer data; s3, performing multi-source data fusion on the corrected wind measurement data, mesoscale data and CFD data, and performing wind resource parameter analysis to obtain a machine site wind resource evaluation result; s4, performing reduction and uncertainty analysis based on the wind resource evaluation result of the machine site, and calculating the generating capacity; and S5, generating a wind resource assessment report according to the power generation amount calculation result and the project basic information. According to the method, the wind resource state of each machine site in the wind field can be evaluated more accurately.

The invention discloses an atmospheric turbulence intensity and gust factor evaluation method, system and device based on a numerical weather forecast mode, and belongs to the technical field of wind resource evaluation. The method aims at solving the problems that the deviation between a WRF simulation result and actually measured data is large, and WRF output variables cannot be directly used for estimating turbulence intensity and gust factors. A machine learning method is combined to carry out multi-meteorological-element correction on wind speed and turbulence energy simulated by WRF, and then a prediction model for estimating turbulence intensity and gust factors according to parameters such as the wind speed and the turbulence energy is established. The turbulence intensity and the gust factor are accurately estimated. The method provides a more accurate and effective way for wind resource assessment, and has important practical significance for development and utilization of wind energy resources.

The present application discloses a method, device, electronic device, and storage medium for determining turbulence intensity. The method includes: generating a turbulence matrix based on wind speed data, wind direction data, and measured turbulence intensity; eliminating the measured turbulence intensity corresponding to wind speed segments that do not meet the measurement target in the turbulence matrix, and determining the wind speed segment after eliminating the measured turbulence intensity as the target wind speed segment; for each target wind speed segment, fitting multiple wind speed segments in a target sector corresponding to the target wind speed segment with the measured turbulence intensity corresponding to each wind speed segment to obtain a target fitting function, and determining a target correlation coefficient between the target fitting function and the measured turbulence intensity; and determining the target turbulence intensity based on the target correlation coefficient. That is, the solution of the present application determines the turbulence intensity of the wind speed segment where the measurement anomaly occurs, thereby improving the accuracy of turbulence intensity determination and thereby enhancing the accuracy of wind resource assessment in wind farms.

The invention discloses a quantitative evaluation method for generating capacity of a wind turbine generator in a wind-light same field, and relates to the technical field of wind energy resource evaluation and wind-light collaborative optimization. The method comprises the following steps: collecting multi-dimensional meteorological, terrain and photovoltaic array geometric parameter data of a wind-light same-field area, and constructing a three-dimensional digital model; establishing a photovoltaic array-surface roughness dynamic response model based on a weighted fusion algorithm and a turbulent kinetic energy redistribution coefficient; a data interface module is developed, and real-time butt joint between the dynamic roughness model and an on-site monitoring system and between CFD numerical simulation and wind resource evaluation software is achieved; a wind-solar coupling characteristic model is established by combining a fluid mechanics theory and a machine learning algorithm, and the influence of photovoltaic array arrangement on wind speed distribution, turbulence intensity and wind turbine generator power output is quantified; and determining fan arrangement and photovoltaic array parameters through model verification and multi-objective optimization. The method can accurately reflect the dynamic evolution of the surface roughness, and is suitable for the resource evaluation and collaborative design of the same-field project of wind and light.

The invention discloses a wind measurement data correction method based on flow field simulation, and the method comprises the following steps: obtaining the current topographic data and historical target data of a target mountainous area; identifying key weather factors according to historical target data; constructing a quantitative relation model of the key weather factors and the surface form change; obtaining a topographic data prediction value of the target mountainous area; constructing a prediction digital elevation model according to the topographic data prediction value; obtaining corrected wind speed prediction data; comparing the corrected wind speed prediction data with the actually measured wind speed data of the field anemometer tower, and calculating the deviation; and constructing a space correction function based on the deviation, and applying the correction function to subsequent wind resource evaluation or fan layout optimization to realize dynamic correction of the wind measurement data. The fan flow field prediction method solves the problems that fan flow field prediction precision has a large error and the prediction result is not high in accuracy.

The invention relates to the cross technical field of wind resource assessment and meteorological disaster risk management, in particular to a wind resource assessment method and system considering disaster risks based on a reanalysis database, and the method comprises the steps: constructing a congelation intensity and frequency index through the fusion of an anemometer tower and reanalysis data through an information entropy weight method; obtaining a congelation risk index to divide risk grades; correcting a long-term wind speed sequence, selecting a representative year, calculating theoretical generating capacity, and correcting congelation loss to obtain actual generating capacity; finally, protection suggestions and evaluation results are output in combination with the risk level and the actual power generation amount, and comprehensive quantification of disaster risks and power generation benefits is achieved. According to the invention, the accuracy of disaster risk assessment and wind resource assessment is improved.

1. Name of the product in this design: Graphical User Interface for Wind Farm Wind Resource Assessment Toolchain for Electronic Equipment. 2. Purpose of this design product: an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. Designate Design 1 as the base design. 6. Purpose of the graphical user interface: Designs 1 to 3 show the graphical user interface for wind farm planning and design. In the main views of Design 1 and Design 2, the left side of the interface displays specific project information, the middle area displays the location of each project on the map, and the user can select any project on the left. The selected project will display more information in the information box on the left, and the corresponding location of the project on the map will be enlarged. The circle is the user location display style. Design 3 serves the same purpose as Design 1 and Design 2, except that Design 3 additionally displays layer information when the layer button on the right is selected. 7. Other situations requiring explanation: Design 1 and Design 3 request color protection.

The invention discloses a wind resource assessment method and system based on an optimized energy mode factor. According to the method, data preprocessing, parameter estimation, model verification and final evaluation are integrated into a standardized and repeatable process, so that the consistency and reliability of evaluation results are ensured; an energy mode factor directly related to wind energy is introduced to serve as a core target, a trustworthy region broken line algorithm is adopted to optimize parameter solution for solving, and it is ensured that the finally obtained Weibull parameter can reflect the energy characteristics of wind most truly instead of just the statistical distribution form of the wind speed; due to the improvement of the Weibull parameter estimation precision, the average wind energy power density calculated on the basis of the distribution model is closer to a true value, scientific support can be provided for wind power plant site selection, fan type selection and wind energy potential evaluation, and fine development of wind power projects is promoted.

The invention provides a multi-source satellite offshore area wind resource atlas analysis method, device and equipment, and the method comprises the steps: carrying out the preprocessing of a multi-source original sea surface wind field, so as to determine a multi-source target sea surface wind field corresponding to a first grid; constructing an average sea surface wind field set based on the wind measurement station data, the multi-source target sea surface wind field corresponding to the first grid and the reanalysis data; training a machine learning model by using the average sea surface wind field set and the sea surface feature data so as to predict a satellite average sea surface wind field of the second grid based on the reanalysis data corresponding to the second grid through the trained machine learning model; and generating a wind resource map corresponding to the research sea area based on the satellite average sea surface wind fields corresponding to the first grid and the second grid. According to the method, the integrity of regional data, the consistency with actual observation and the data accuracy can be effectively ensured, and the difficulty of collecting data in wind resource evaluation and prediction can be reduced.

The invention provides a wind power plant wind speed time sequence prediction method based on SCADA data correction and CFD modeling. The wind power plant wind speed time sequence prediction method comprises the steps that S1, historical SCADA data and synchronous reference wind speed data of a built wind power plant are collected and preprocessed; s2, establishing an SCADA wind speed correction model based on the preprocessed data, performing primary correction and secondary correction on the SCADA wind speed, and generating a reduced hub height wind speed time sequence; s3, constructing a CFD computational domain covering the constructed wind power plant and the planned wind power plant area, taking the reduced wind speed time sequence as a time-varying boundary condition, and obtaining a space wind speed transfer function through CFD simulation; and S4, extrapolating the reduced wind speed time sequence to each location point of the planned wind power plant by using a space wind speed transfer function, and generating high-precision wind speed time sequence prediction data. The method is especially suitable for wind resource evaluation scenes of complex fields, and overcomes the limitation that a traditional method is difficult to represent wind resource distribution of the complex fields.

The invention discloses a mountain wind power global wind resource assessment method. The method comprises the following steps: acquiring topographic data and constructing a mountain wind speed vertical gradient reasonable interval; troubleshooting and eliminating an unsuitable area, and marking a risk early warning area; deploying a high-frequency unmanned aerial vehicle cluster and an encrypted micro sensor; long-term dynamic monitoring is carried out, monitoring data are verified, and seasonal special monitoring is synchronously implemented; performing preliminary screening on the wind resource candidate point locations in the monitoring area; performing secondary screening on the wind resource primary screening point locations through wake flow interference checking and construction cost evaluation to form a plurality of candidate point locations; constructing a terrain and weather coupling interpolation model, and inputting monitoring data for interpolation to generate a high-resolution global wind resource map; turbulence influence quantification and high-altitude environment correction are carried out based on a global wind resource map, and candidate point locations are graded through a comprehensive scoring system; and determining the number of installation point locations and the optimal installation point location. According to the evaluation method, the arrangement of an anemometer tower can be canceled, and the evaluation of wind resources in each region of the project is realized.

The invention discloses a wind resource evaluation system for changing a wind power plant into a small wind power plant, and the system comprises an original data obtaining module which is used for obtaining the information of an original wind power plant, the historical operation data of the original wind power plant, and the actual measurement wind data, and generating complete wind resource data; the wind power plant information simulation module is used for constructing a multi-dimensional wind power plant model M and an expected wind power plant model M3 based on the original wind power plant information; the evaluation index determination module is used for calculating an index value of a wind resource index according to factors which need to be considered for large-to-small transformation, wherein the factors comprise positive correlation factors and negative correlation factors; the transformation evaluation module is used for carrying out priority calculation according to the index weight value; and the risk assessment module is used for performing risk assessment on the specified single fan and providing a decision scheme according to the management and maintenance difficulty, the management level and the technology. According to the technical scheme, the operation environment of the transformed wind power plant can be simulated, the fan transformation priority level is compared and selected, and the power generation amount after transformation is predicted; and predicting various risk factors in operation and use during and after the transformation of the wind power plant.

The invention provides an automatic ridge line identification method and system based on hydrological analysis and density clustering, and the method comprises the steps: fusing the hydrological analysis with spatial clustering, elevation screening, morphological processing and other multi-step optimization, carrying out the automatic primary selection of ridge points through positive topography and zero convergence accumulation, and effectively removing noise and short branches through the combination of DBSCAN clustering and elevation quantile purification. A set of complete technical scheme of'preprocessing-positive terrain recognition-confluence analysis-clustering denoising-elevation screening-continuous optimization 'is constructed by utilizing a priority flood algorithm to efficiently fill pits and utilizing a skeletonization algorithm to optimize continuity, so that the adaptivity, geometric accuracy and processing efficiency of ridge line extraction are remarkably improved; a more reliable terrain skeleton data basis is provided for wind resource evaluation and fan site selection and layout, and finally the scientificity of wind power generation project planning and power generation capacity estimation is enhanced.

The invention relates to the technical field of offshore wind resource assessment, and discloses a fan missing wind speed restoration method, which comprises the following steps: acquiring adjacent wind speed characteristics of missing wind speed of a target fan and historical wind speed characteristics of adjacent fans; predicting a wind speed prediction value of the missing wind speed based on the adjacent wind speed features and the historical wind speed features; estimating a wind speed correction value of the missing wind speed according to the historical working power and historical wind speed characteristics of the adjacent fans; and performing weighted fusion calculation on the wind speed prediction value and the wind speed correction value to obtain a missing wind speed, and repairing the target fan according to the missing wind speed. According to the method, multi-dimensional prediction of the missing wind speed is achieved by fusing the time sequence wind speed characteristics of the target wind turbine and the space correlation characteristics of the adjacent wind turbines, a physical correction mechanism based on the relation between historical power and the wind speed is introduced, the predicted value is corrected by using the strong physical correlation parameter of the working power of the wind turbines, and the prediction accuracy of the wind turbines is improved. And the precision and the reliability in repairing the missing wind speed are improved.

The present application relates to the field of wind power generation technology, and discloses a cross-scale wind farm wind resource evaluation method based on a numerical model, comprising the following steps: step 1, obtaining multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme suitable for the terrain of the target area; step 3, generating annual-scale wind resource data as initial wind resource data through a WRF dynamic downscaling method; step 4, carrying out partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling a WRF model and a wind farm parameterization model; step 6, optimizing the lower boundary condition of the WRF; step 7, simulating the atmospheric flow of the target area and quantifying the wake evolution characteristics of the wind farm; step 8, constructing a wake analysis model; and step 9, formulating a coordinated capacity planning of the wind farm group. The present application can accurately predict the power generation capacity of a to-be-developed wind farm, and provide a reliable basis for scientific decision-making of the resource distribution development and planning of a million-kilowatt wind farm.

The invention discloses a wind power generation capacity prediction method, device and equipment and a storage medium, which are applied to the field of wind power generation evaluation, and the method comprises the steps: determining the gradient and curvature of a basic grid, and dividing a target region into a flat region, a transition region and an abrupt change region based on the gradient and curvature; based on a preset grid size, carrying out encryption processing on the basic grid in the mutation area, and carrying out gradient encryption processing on the basic grid in the transition area; setting an optimal performance turbulence model of each region, and performing simulation based on the optimal performance turbulence model to obtain a wind resource evaluation result of the target region; and determining a power curve of the fan, and determining a wind power generation evaluation result of the target area based on the power curve and the wind resource evaluation result. According to the method, the grids of the terrain abrupt change region are identified and encrypted, and the turbulence model with the optimal performance is set for each type of region, so that more accurate wind resource evaluation is realized, and a more accurate wind power generation capacity prediction result is obtained.

The invention relates to a machine position wind speed correction calculation method based on multi-source data of a wind power plant, and the method carries out the wind resource evaluation based on the multi-source data, such as a laser radar, an operation period wind power prediction system, and a unit SCADA, and carries out the correction through an analytic hierarchy process, thereby effectively reducing the wind resource evaluation deviation. A three-dimensional scale algorithm (a * b * c) based on wind measurement duration, data integrity and spatial distance is provided, and the reliability of different data sources is quantified; a three-dimensional scale algorithm (a * b * c) based on wind measurement duration, data integrity and spatial distance is provided, and the reliability of different data sources is quantified; compared with a traditional method, the corrected machine position wind speed evaluation deviation is reduced by more than 50%, and the evaluation deviation is remarkably reduced; reliable technical support is provided for application scenarios such as performance improvement of wind power plant units and transformation of replacing small by large, generating capacity prediction errors are reduced, and engineering applicability is high.

The present invention provides a multi-source satellite offshore regional wind resource map analysis method, device, and equipment, comprising: preprocessing multi-source original sea surface wind fields to determine a multi-source target sea surface wind field corresponding to a first grid; constructing an average sea surface wind field set based on wind station data, the multi-source target sea surface wind field corresponding to the first grid, and reanalysis data; training a machine learning model using the average sea surface wind field set and sea surface feature data, so that the trained machine learning model can predict the satellite average sea surface wind field of a second grid based on the reanalysis data corresponding to the second grid; and generating a wind resource map corresponding to the study sea area based on the satellite average sea surface wind fields corresponding to the first and second grids, respectively. The present invention can effectively ensure the integrity of regional data, consistency with actual observations, and data accuracy, and can also reduce the difficulty of collecting data for wind resource assessment and prediction.

The application discloses a wind resource assessment method, device, equipment, medium and product, and belongs to the technical field of wind power generation. Before wind resource assessment modeling is performed, the wind condition parameters of the location of the wind measurement tower are estimated according to different parameter value combinations, combined with the characteristics of the power field, the wind condition parameter estimation results corresponding to the different parameter value combinations are obtained, the optimal parameter value combination matched with the wind field is determined according to the estimation results and the true results, and then the wind resource assessment is performed according to the optimal parameter value combination, that is, the parameter verification and screening steps are added before the wind resource assessment is performed, the parameters required for the wind resource assessment can be optimized through the steps, and then the assessment accuracy of the wind resource can be improved.