High slope wind field evaluation system and method based on big data

Abstract

The invention relates to the technical field of wind power plant site selection, in particular to a high slope wind field evaluation system and method based on big data. The system comprises a surveying and mapping module, a terrain modeling module, a dynamic monitoring module, a flow field simulation module, a risk assessment module and a visual decision platform. The surveying and mapping module comprises a landform surveying and mapping unit and a surface roughness measuring unit; the terrain modeling module constructs a three-dimensional high slope terrain model; the dynamic monitoring module monitors wind field data in real time; the flow field simulation module simulates turbulent wind field characteristics of the terrain; the risk assessment module is used for wind field risk assessment; and the visual decision-making platform superposes a risk assessment result to a geographic information system in a three-dimensional thermodynamic diagram form. According to the method, the risk level map is generated through the three-dimensional high slope terrain model and the multi-body dynamics joint simulation model, so that the risk assessment process is visualized, actually measured data and a simulation predicted value are compared through a learning algorithm, and the risk assessment model parameters are dynamically corrected.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm site selection, and in particular to a high-slope wind farm assessment system and method based on big data. Background Art

[0002] A large number of areas with rich wind resources are located in hilly and mountainous areas. With the in-depth development of wind power projects in plain areas, new wind power projects are more often selected in areas with complex terrain, which also brings certain difficulties to the design and development of wind farms.

[0003] The Chinese patent with the announcement number CN113283122B discloses a method and system for evaluating the risk of wind turbine site selection caused by high-slope terrain, which belongs to the field of wind farm site selection technology. First, the preselected machine site that needs to undergo a supplementary site selection risk assessment is determined; then, the micro-topography within the range surrounding the point where the high slope phenomenon is caused by construction in the preselected machine site is mapped to obtain micro-topography elevation data; the roughness distribution data within the micro-topography range is measured; the important wind parameters in the complete annual wind measurement data are refined and analyzed to obtain important wind resource parameters; the wind resource distribution data of the preselected machine site is refined and modeled; finally, based on the calculation results, the impact of the high-slope terrain on the power generation of the wind turbine at the preselected machine site is evaluated, and the economic benefits are recalculated based on the analysis results. The present invention can systematically evaluate the risks brought by high-slope terrain to wind turbine site selection, reduce the possibility of investment errors, and ensure the economic benefits of project investment.

[0004] However, the above technical solution has the following shortcomings: the risk assessment report ultimately serves investors, and the parameters required for the wind farm assessment report are generated through monitoring data. The wind farm is evaluated only based on the generated final data report, which requires a high level of professionalism from investors, resulting in a lack of persuasiveness in the assessment report. Summary of the Invention

[0005] The purpose of the present invention is to address the problems existing in the background technology and propose a high slope wind field evaluation system and method based on big data.

[0006] The technical solution of the present invention is a high slope wind field assessment system based on big data, comprising:

[0007] A mapping module includes a topography mapping unit and a surface roughness measurement unit. The topography mapping unit is used to map microtopography and obtain microtopography elevation data. The surface roughness measurement unit is used to measure roughness distribution data within the microtopography range.

[0008] The terrain modeling module builds a three-dimensional high-slope terrain model based on the measurement data of the surveying and mapping module. The terrain modeling module discretizes the terrain using unstructured grid division technology;

[0009] Dynamic monitoring module, which deploys a sensor network to monitor wind speed, wind direction, temperature and humidity changes in real time;

[0010] Flow field simulation module, which uses computational fluid dynamics technology to simulate the turbulent wind field characteristics of high-slope terrain;

[0011] Risk assessment module, which includes direct risk calculation, indirect risk analysis and comprehensive risk assessment for wind farm risk assessment;

[0012] The visual decision-making platform overlays risk assessment results onto the geographic information system in the form of a three-dimensional heat map, supports interactive parameter adjustment and multi-scheme comparison, and has a built-in historical case library to provide intelligent recommendation functions for risk mitigation measures.

[0013] Preferably, the topographic mapping unit includes a flatbed instrument, a total station, an RTK measuring instrument and an aerial photogrammetry device. The measurement data of the topographic mapping unit is combined with satellite maps and aircraft site coordinates to provide a data basis for the terrain modeling module.

[0014] Preferably, the surface roughness measurement unit mainly measures the roughness element height, windward cross-sectional area, distribution spacing and area ratio; the roughness element height represents the vertical scale of the surface obstacle; the windward cross-sectional area measures the area of the roughness element on the projection surface of the dominant wind direction, which is used to quantify the blocking effect of the obstacle on the airflow; the distribution spacing and area ratio reflect the density of the surface obstacle distribution through the number of roughness elements per unit area and their average spacing.

[0015] Preferably, the dynamic monitoring module includes an anemometer, a wind vane and a temperature and humidity sensor; the anemometer measures real-time wind speed, usually using a three-cup or ultrasonic principle, to provide basic data for wind energy density calculation; the wind vane detects wind direction changes, supports wind field layout optimization and yaw control calibration; the temperature and humidity sensor monitors ambient temperature and humidity, and is used to correct the impact of atmospheric density on wind energy assessment.

[0016] Preferably, direct risk analysis combines parameters such as soil cohesion, internal friction angle, slope height, and wind turbine foundation depth, and uses the grey correlation analysis method to calculate the slope stability coefficient and assess the wind turbine foundation bearing risk.

[0017] Preferably, indirect risk analysis integrates data such as the types of high-risk items, community population density, and building distribution to establish a hazard transfer function model and quantify the impact of wind turbine operation on the surrounding environment.

[0018] Preferably, the comprehensive risk assessment uses the analytic hierarchy process to weight and fuse direct and indirect risk indicators to generate a risk level map.

[0019] On the other hand, the present invention proposes a high slope wind field assessment method based on big data, which uses the above-mentioned high slope wind field assessment system based on big data, and specifically includes the following steps:

[0020] S1, the surveying and mapping module collects various spatial data of high slopes through flatbed instruments, total stations, RTK measuring instruments and aerial photography;

[0021] S2, the terrain modeling module builds a three-dimensional high slope terrain model based on the surveying and mapping module, and combines the projection system parameters to achieve standardized mapping of geographic spatial data, providing a basic model for subsequent flow field simulation;

[0022] S3, the dynamic monitoring module monitors the real-time wind speed, wind direction changes and temperature and humidity change data and synchronizes them to the flow field simulation module and risk assessment module;

[0023] S4, the flow field simulation module performs numerical calculations of the flow field by setting boundary conditions, outputs key parameters such as wind speed distribution on the wind wheel surface and turbulence intensity distribution, and generates a dynamic wind seed file to drive the multi-body dynamics joint simulation model;

[0024] S5. The risk assessment module generates a risk level map by combining the three-dimensional high slope terrain model and the multi-body dynamics joint simulation model;

[0025] S6. The system compares measured data with simulation predictions through machine learning algorithms, dynamically modifies risk assessment model parameters, and continuously optimizes assessment results.

[0026] S7. The visual decision-making platform generates a high-slope wind field assessment report.

[0027] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0028] The present invention generates a risk level map through a three-dimensional high-slope terrain model and a multi-body dynamics joint simulation model, visualizing the risk assessment process. It also compares measured data with simulation prediction values through a learning algorithm, dynamically corrects risk assessment model parameters, and achieves continuous optimization of assessment results. It realizes full-process automated assessment and deeply couples traditional geological stability analysis with wind farm aerodynamic simulation, solving the problem of the synergistic effect of turbulent interference and slope instability in wind turbine site selection under high-slope terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of an embodiment of the present invention;

[0030] Figure 2 The present invention is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Example 1, as Figure 1 As shown, the present invention proposes a high slope wind field assessment system based on big data, including a surveying and mapping module, a terrain modeling module, a dynamic monitoring module, a flow field simulation module, a risk assessment module and a visual decision-making platform;

[0032] The surveying and mapping module includes a topography and geomorphology surveying unit and a surface roughness measurement unit. The topography and geomorphology surveying unit is used to survey microtopography and obtain microtopography elevation data. The surface roughness measurement unit is used to measure the roughness distribution data within the microtopography range.

[0033] The terrain modeling module constructs a three-dimensional high-slope terrain model based on the measurement data of the surveying and mapping module. The terrain modeling module discretizes the terrain through unstructured grid division technology;

[0034] The dynamic monitoring module deploys a sensor network to monitor wind speed changes, wind direction changes, and temperature and humidity changes in real time;

[0035] The flow field simulation module uses computational fluid dynamics technology to simulate the turbulent wind field characteristics of high slope terrain;

[0036] The risk assessment module includes direct risk calculation, indirect risk analysis and comprehensive risk assessment for wind farm risk assessment;

[0037] The visual decision-making platform overlays risk assessment results onto the geographic information system in the form of a three-dimensional heat map, supports interactive parameter adjustment and multi-scheme comparison, and has a built-in historical case library, providing intelligent recommendation functions for risk mitigation measures.

[0038] The topographic and geomorphic surveying and mapping unit includes a flatbed instrument, a total station, an RTK measuring instrument and an aerial photogrammetry device. The measurement data of the topographic and geomorphic surveying and mapping unit is combined with satellite maps and aircraft site coordinates to provide a data basis for the terrain modeling module.

[0039] The surface roughness measurement unit mainly measures the height of the roughness element, the windward cross-sectional area, and the distribution spacing and area ratio; the roughness element height represents the vertical scale of the surface obstacle; the windward cross-sectional area measures the area of the roughness element on the projection surface of the dominant wind direction, which is used to quantify the blocking effect of the obstacle on the airflow; the distribution spacing and area ratio reflect the density of the surface obstacle distribution through the number of roughness elements per unit area and their average spacing.

[0040] The dynamic monitoring module includes an anemometer, a wind vane, and a temperature and humidity sensor; the anemometer measures real-time wind speed, usually using a three-cup or ultrasonic principle, providing basic data for wind energy density calculation; the wind vane detects changes in wind direction, supporting wind farm layout optimization and yaw control calibration; the temperature and humidity sensor monitors ambient temperature and humidity, and is used to correct the impact of atmospheric density on wind energy assessment.

[0041] Direct risk analysis combines parameters such as soil cohesion, internal friction angle, slope height, and wind turbine foundation depth, and uses the grey correlation analysis method to calculate the slope stability coefficient and assess the bearing risk of the wind turbine foundation.

[0042] Indirect risk analysis integrates data such as the types of high-risk items, community population density, and building distribution to establish a hazard transfer function model and quantify the impact of wind turbine operation on the surrounding environment.

[0043] Comprehensive risk assessment uses the analytic hierarchy process to weight and fuse direct and indirect risk indicators to generate a risk level map.

[0044] Example 2, as Figure 2 As shown, the present invention proposes a high slope wind field assessment method based on big data, which adopts the high slope wind field assessment system based on big data in implementation one, and specifically includes the following steps:

[0045] S1, the surveying and mapping module collects various spatial data of high slopes through flatbed instruments, total stations, RTK measuring instruments and aerial photography;

[0046] S2, the terrain modeling module builds a three-dimensional high slope terrain model based on the surveying and mapping module, and combines the projection system parameters to achieve standardized mapping of geographic spatial data, providing a basic model for subsequent flow field simulation;

[0047] S3, the dynamic monitoring module monitors the real-time wind speed, wind direction changes and temperature and humidity change data and synchronizes them to the flow field simulation module and risk assessment module;

[0048] S4, the flow field simulation module performs numerical calculations of the flow field by setting boundary conditions, outputs key parameters such as wind speed distribution on the wind wheel surface and turbulence intensity distribution, and generates a dynamic wind seed file to drive the multi-body dynamics joint simulation model;

[0049] S5. The risk assessment module generates a risk level map by combining the three-dimensional high slope terrain model and the multi-body dynamics joint simulation model;

[0050] S6. The system compares measured data with simulation predictions through machine learning algorithms, dynamically modifies risk assessment model parameters, and continuously optimizes assessment results.

[0051] S7. The visual decision-making platform generates a high-slope wind field assessment report.

[0052] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A high slope wind field assessment system and method based on big data, characterized in that: include: A mapping module includes a topography mapping unit and a surface roughness measurement unit. The topography mapping unit is used to map microtopography and obtain microtopography elevation data. The surface roughness measurement unit is used to measure roughness distribution data within the microtopography range. The terrain modeling module builds a three-dimensional high-slope terrain model based on the measurement data of the surveying and mapping module. The terrain modeling module discretizes the terrain using unstructured grid division technology; Dynamic monitoring module, which deploys a sensor network to monitor wind speed, wind direction, temperature and humidity changes in real time; Flow field simulation module, which uses computational fluid dynamics technology to simulate the turbulent wind field characteristics of high-slope terrain; Risk assessment module, which includes direct risk calculation, indirect risk analysis and comprehensive risk assessment for wind farm risk assessment; The visual decision-making platform overlays risk assessment results onto the geographic information system in the form of a three-dimensional heat map, supports interactive parameter adjustment and multi-scheme comparison, and has a built-in historical case library to provide intelligent recommendation functions for risk mitigation measures.

2. The high slope wind field assessment system and method based on big data according to claim 1 is characterized in that: The topographic and geomorphic surveying and mapping unit includes a flatbed instrument, a total station, an RTK measuring instrument and an aerial photogrammetry device. The measurement data of the topographic and geomorphic surveying and mapping unit is combined with satellite maps and aircraft site coordinates to provide a data basis for the terrain modeling module.

3. The high slope wind field assessment system and method based on big data according to claim 2 is characterized in that: The surface roughness measurement unit mainly measures the height of the roughness element, the windward cross-sectional area, and the distribution spacing and area ratio; the height of the roughness element represents the vertical scale of the surface obstacle; the windward cross-sectional area measures the area of the roughness element projected on the dominant wind direction, which is used to quantify the obstruction effect of the obstacle on the airflow; Distribution spacing and area ratio reflect the density of surface obstacle distribution through the number of rough elements per unit area and their average spacing.

4. The high slope wind field assessment system and method based on big data according to claim 3 is characterized in that: The dynamic monitoring module includes an anemometer, a wind vane, and a temperature and humidity sensor; the anemometer measures real-time wind speed, usually using a three-cup or ultrasonic principle, providing basic data for wind energy density calculation; the wind vane detects changes in wind direction, supporting wind farm layout optimization and yaw control calibration; the temperature and humidity sensor monitors ambient temperature and humidity, and is used to correct the impact of atmospheric density on wind energy assessment.

5. The high slope wind field assessment system and method based on big data according to claim 4 is characterized in that: Direct risk analysis combines parameters such as soil cohesion, internal friction angle, slope height, and wind turbine foundation depth, and uses the grey correlation analysis method to calculate the slope stability coefficient and assess the bearing risk of the wind turbine foundation.

6. A high slope wind field assessment system and method based on big data according to claim 5, characterized in that: Indirect risk analysis integrates data such as the types of high-risk items, community population density, and building distribution to establish a hazard transfer function model and quantify the impact of wind turbine operation on the surrounding environment.

7. The high slope wind field assessment system and method based on big data according to claim 6 is characterized in that: Comprehensive risk assessment uses the analytic hierarchy process to weight and fuse direct and indirect risk indicators to generate a risk level map.

8. A high slope wind farm assessment method based on big data, using the high slope wind farm assessment system based on big data according to any one of claims 1 to 7, characterized in that: The specific steps include: S1, the surveying and mapping module collects various spatial data of high slopes through flatbed instruments, total stations, RTK measuring instruments and aerial photography; S2, the terrain modeling module builds a three-dimensional high slope terrain model based on the surveying and mapping module, and combines the projection system parameters to achieve standardized mapping of geographic spatial data, providing a basic model for subsequent flow field simulation; S3, the dynamic monitoring module monitors the real-time wind speed, wind direction changes and temperature and humidity change data and synchronizes them to the flow field simulation module and risk assessment module; S4, the flow field simulation module performs numerical calculations of the flow field by setting boundary conditions, outputs key parameters such as wind speed distribution on the wind wheel surface and turbulence intensity distribution, and generates a dynamic wind seed file to drive the multi-body dynamics joint simulation model; S5. The risk assessment module generates a risk level map by combining the three-dimensional high slope terrain model and the multi-body dynamics joint simulation model; S6. The system compares measured data with simulation predictions through machine learning algorithms, dynamically modifies risk assessment model parameters, and continuously optimizes assessment results. S7. The visual decision-making platform generates a high-slope wind field assessment report.

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