Method and system for optimizing site selection of offshore wind plant based on GIS (Geographic Information System)

Abstract

The invention relates to a GIS-based offshore wind plant site selection optimization method and system, and the method comprises the following steps: monitoring the wind measurement data of a plurality of preselected regions, and uploading the wind measurement data to a wind resource database in real time; the wind resource database arranges the wind measurement data downloaded according to the longitude and latitude according to spatial attributes, and processes the wind measurement data by using ERA5 reanalysis data to generate wind resource map data of the pre-selected area according to the processed wind measurement data; relevant data of the terrain are collected and correspond to the actual longitude and latitude, and the scattered data are spliced to obtain terrain data and upload the terrain data to the ocean terrain database; the ocean terrain database obtains seabed obstacle data through model segmentation and recognition; the wind resource map data and the seabed obstacle data of the pre-selected area are integrated, then uploaded to a cloud server and transmitted to an AI model for analysis; and the AI model is used for integrating the input data and providing a corresponding offshore wind power plant site selection scheme.

Description

Technical Field

[0001] The invention relates to a GIS-based offshore wind farm site selection optimization method and system, belonging to the technical field of offshore wind farm site selection. Background Art

[0002] With increasingly stringent environmental protection requirements and the gradual depletion of coal resources, the demand for clean and renewable energy is growing, so offshore wind farms have begun to develop vigorously. Offshore wind farms refer to clusters of facilities built in the ocean that use wind power to generate electricity. They usually consist of multiple large wind turbines, submarine cables, substations and related auxiliary facilities.

[0003] In order to fully utilize offshore wind energy, it is necessary to measure the wind resources in the offshore area as a basis for the site selection of wind farms. However, the existing wind resource measurement relies on meteorological observation data, and the meteorological observation stations are relatively sparsely distributed at sea, and the observation time is limited, which cannot reflect the wind energy resource situation of the entire site selection area in real time, resulting in miscalculation of wind energy resources and affecting the accuracy of site selection. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a GIS-based offshore wind farm site selection optimization method and system.

[0005] The technical solution of the present invention is as follows:

[0006] In one aspect, the present invention provides a method for optimizing the site selection of an offshore wind farm based on GIS, comprising the following steps:

[0007] Monitor wind measurement data in multiple pre-selected areas and upload the wind measurement data to the wind resource database in real time;

[0008] The wind resource database will arrange the wind measurement data downloaded according to longitude and latitude according to spatial attributes, and use the ERA5 reanalysis data of the same range to verify, interpolate and revise the wind measurement data, and automatically map the processed wind measurement data to automatically generate wind resource map data for the pre-selected area;

[0009] The relevant data of terrain is collected by scanner and matched with the actual longitude and latitude, and the scattered data is pieced together to obtain terrain data and uploaded to the marine terrain database;

[0010] The ocean terrain database uses segmentation and recognition models to identify submarine obstacles in the submarine terrain map and obtain submarine obstacle data;

[0011] Integrate the wind resource map data and seabed obstacle data of the pre-selected area, upload them to the cloud server and transmit them to the AI ​​model for analysis;

[0012] The AI ​​model integrates the input data and proposes corresponding offshore wind farm site selection plans.

[0013] As a preferred embodiment, the wind measurement includes wind speed data and wind direction data, and the wind measurement data of monitoring multiple pre-selected areas includes:

[0014] The wind speed and wind direction data of the pre-selected area are directly or indirectly measured by anemometers and wind vanes, and data collection, processing and transmission are started according to preset programs or user instructions, and the status of anemometers and wind vanes in the pre-selected area are monitored and managed.

[0015] As a preferred embodiment, the step of processing the ERA5 reanalysis data comprises:

[0016] The real-time wind measurement data of ERA5 reanalysis data is downloaded through the data interface of ERA5 reanalysis data in combination with longitude and latitude points, and the wind measurement data downloaded according to longitude and latitude are arranged according to spatial attributes. The processing, storage and display of ERA5 reanalysis data are completed through data verification, interpolation and revision.

[0017] As a preferred implementation, the wind resource database runs in a distributed cloud database. After any cloud database automatically generates wind resource map data for a preselected area, the wind resource map data generated by each cloud database are connected in series through a network.

[0018] As a preferred embodiment, the collecting of terrain data by a scanner and matching it with the actual longitude and latitude includes:

[0019] Collect the topography and altitude data of the seabed terrain through corresponding terrain data collection equipment, including total station, laser rangefinder, and photogrammetry;

[0020] The GPS positioning system is used to determine the location of the corresponding terrain data collection equipment, and the collected seabed terrain morphology and altitude data are matched with the actual location.

[0021] As a preferred implementation, the step of identifying seabed obstacles in the seabed topographic map and obtaining seabed obstacle data includes:

[0022] By eliminating abnormal and invalid data and importing the terrain single-band raster data collected by the multi-environment scanning module into the segmentation and recognition model, obstacles in the seabed terrain are automatically mapped;

[0023] Then, by calling the manual marking mode and using the box selection operation, the seabed obstacles can be manually marked.

[0024] As a preferred implementation, the step of integrating the wind resource map data and the seabed obstacle data of the pre-selected area, uploading them to the cloud server and transmitting them to the AI ​​model for analysis includes:

[0025] The generated wind resource map data and seabed obstacle data are transmitted to a large-capacity hard disk through a network channel for integration;

[0026] The cloud server receives the integrated data through a large-capacity hard drive and selects an AI model to analyze and process the integrated data based on specific analysis tasks and needs.

[0027] On the other hand, the present invention further provides a GIS-based offshore wind farm site selection optimization system, which is used in the GIS-based offshore wind farm site selection optimization method described in any embodiment of the present invention, comprising:

[0028] ERA5 wind measurement module, including anemometer and wind vane for testing wind resources in the target area, collects and transmits real-time wind measurement data through anemometer and wind vane;

[0029] The wind resource processing and analysis module automatically generates statistical analysis results based on the wind resource data transmitted by the ERA5 wind measurement module, automatically generates wind resource report data for a given time and place, and supports saving to files and downloading;

[0030] 5G communication module, used for data transmission between modules;

[0031] A multi-environment terrain scanning module includes a scanner for scanning various terrains, and the scanner monitors and records terrain data in real time;

[0032] The ocean terrain statistical analysis module generates grid point data by combining the seabed terrain data transmitted by the multi-environment terrain scanning module with longitude and latitude;

[0033] The cloud storage module synchronizes data among multiple devices by uploading real-time wind measurement data, wind resource report data, terrain data, and grid point data to the cloud server;

[0034] The AI ​​analysis module selects offshore wind farm sites by integrating real-time wind measurement data, wind resource report data, terrain data, and grid point data.

[0035] On the other hand, the present invention further proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the GIS-based offshore wind farm site optimization method as described in any embodiment of the present invention is implemented.

[0036] On the other hand, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the offshore wind farm site selection optimization method based on GIS as described in any embodiment of the present invention.

[0037] The present invention has the following beneficial effects:

[0038] 1. The present invention obtains the wind measurement data in all longitude and latitude grid points within the range of interest according to the period of grid generation required in the wind resource database, places them in the grid points according to the longitude and latitude grid points, generates a single-period grid of ERA5 wind measurement data, and stores it in the cloud server. Compared with manual statistical data, the method of the present invention can update regional wind resource information in real time, accurately understand the wind speed and wind direction information in different sea areas, and provide a quantitative basis for the site selection of offshore wind farms.

[0039] 2. The present invention collects single-band raster data of different seabed topography, calls the model, automatically maps the obstacles in the seabed topography, and reasonably adjusts the position and spacing of wind turbines according to the distribution of obstacles to maximize the efficiency of wind energy capture, while effectively avoiding dangerous obstacles such as reefs and shipwrecks to ensure the safe construction and operation of offshore wind farms.

[0040] 3. In the present invention, the 5G communication module can achieve high-speed, low-latency data transmission, and collect data from various devices and sensors at the offshore wind farm site in real time. At the same time, the wind speed, wind direction, and terrain data of the site selection area are transmitted to the cloud server and these data are used for data analysis and modeling to optimize the site selection of the wind farm.

[0041] Additional aspects and advantages of the present invention will be set forth in the following description, and some of them will be apparent from the description, or may be understood by practicing the present invention. In addition, the various aspects and advantages of the present invention may be realized and obtained by the method steps and combinations particularly pointed out in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of a method flow chart of Embodiment 1 of the present invention;

[0043] Figure 2 Schematic diagram of the system architecture of the second embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0046] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0047] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0048] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0049] Embodiment 1:

[0050] Reference Figure 1 This embodiment provides a GIS-based offshore wind farm site selection optimization method, comprising the following steps:

[0051] S1: By dividing the grid area according to longitude and latitude, a sufficient number of monitoring points should be set up in each area, and anemometers and wind vanes should be deployed at each monitoring point to ensure that the wind resource conditions in the area can be accurately reflected and the data can be uploaded to the wind resource database in real time;

[0052] S2: By processing the ERA5 wind measurement data downloaded according to longitude and latitude in the wind resource database with self-developed algorithms, including data verification, interpolation, revision, as well as data storage, display, automatic mapping and report generation, the value of ERA5 data is fully explored, providing efficient and accurate support for wind resource research and application, improving the work efficiency of wind resource analysis, reducing the workload and errors of manual data processing, and providing a scientific basis for the planning, construction and operation of offshore wind farms.

[0053] S3: By equipping anemometers and wind vanes with sensors that support 5G communication, the collected wind resource data can be directly converted into a 5G network transmission format and sent out. At the same time, the 5G network provides a high-speed channel for data integration and sharing between various modules. Wind resource data can be seamlessly integrated with other related data, such as equipment operation data, power dispatch data, and meteorological data, to achieve comprehensive data analysis and decision support. Through data integration and sharing, information silos are broken and the overall operational efficiency and management level of wind farms are improved.

[0054] S4: Develop a detailed measurement plan based on the scope of the site selection area and the measurement range of a single scanner. At the same time, deploy enough scanners to ensure that the scanning range can evenly cover the entire site selection area to avoid data omissions and scanning blind spots. The scanner and the positioning system both record time information. The terrain data and the corresponding longitude and latitude coordinates are associated through timestamps. During the data collection process, ensure that the time synchronization accuracy of the scanner and the positioning system reaches the millisecond level. Then, according to the same time point, the collected terrain data is matched one by one with the longitude and latitude coordinates at that moment. Finally, the scattered data is pieced together to obtain the terrain data and uploaded to the marine terrain database;

[0055] S5: The marine terrain database analyzes the terrain single-band raster data collected by the scanning instrument through segmentation and recognition algorithms and eliminates obviously unreasonable data values ​​caused by sensor failure, data transmission errors or human factors. Then, the terrain single-band raster data after preliminary screening is imported into the model, and a height threshold is set. Terrain undulations above the threshold will be identified as obstacles. The location and shape of the obstacles are identified according to the algorithm, and the corresponding mapping results are generated and stored in the terrain database. At the same time, for obstacles with irregular shapes that cannot be identified by the model, an entry for users to call the manual mapping mode is provided to supplement the depiction of terrain data in the site selection area;

[0056] S6: The data collected by anemometers and wind vanes at different locations in the site selection area are integrated through the 5G network to calculate statistical information such as average wind speed and wind direction frequency. At the same time, relevant data such as terrain data and marine meteorological data are integrated to form a complete data set. The data collected at different times are reasonably sorted and integrated according to the time sequence. For sensor data at different locations, they are accurately matched and integrated according to their geographical locations. After that, the 5G network transmits the real-time collected data to the cloud server, and the AI ​​model can obtain and analyze it in time.

[0057] S7: The AI ​​model spatially integrates wind resource data and terrain data to analyze the impact of terrain on wind resources. Through geographic information system (GIS) technology, wind resource data such as wind speed, wind direction, and wind power density are superimposed and analyzed with digital elevation models, terrain and landform type data, etc. On the GIS platform, the wind power density in different areas is corrected according to the terrain elevation and slope information, and the acceleration or deceleration effect of the terrain on the wind is considered to obtain more accurate wind resource assessment results. At the same time, the relationship between wind resource data, terrain data and meteorological data, environmental data and socio-economic data in the cloud server is established, and the wind farm site selection area is associated with land use type data and population distribution data to evaluate the impact of wind power station construction on land use and society.

[0058] Embodiment 2:

[0059] See also Figure 2 This embodiment provides a GIS-based offshore wind farm site selection optimization system, which is used to implement the GIS-based offshore wind farm site selection optimization method as described in the first embodiment above. The system includes:

[0060] ERA5 wind measurement module, including anemometer and wind vane for testing wind resources in the target area, collects and transmits real-time data through sensors;

[0061] The wind resource processing and analysis module automatically generates wind resource reports at specific times and locations based on the wind resource data transmitted by the ERA5 wind measurement module, and supports saving to files and downloading;

[0062] 5G communication module, used for data transmission between modules;

[0063] A multi-environment terrain scanning module includes a scanner for scanning various terrains, and the scanner monitors and records terrain data in real time;

[0064] The ocean terrain statistical analysis module generates grid point data by combining the seabed terrain data transmitted by the multi-environment terrain scanning module with longitude and latitude;

[0065] The cloud storage module synchronizes data among multiple devices by uploading all monitoring and analysis data to the cloud server;

[0066] The AI ​​analysis module enables offshore wind farm site selection by integrating wind resources and ocean terrain data.

[0067] Specifically, the ERA5 wind measurement module includes:

[0068] Data acquisition unit, which directly or indirectly measures wind speed and direction through anemometers and wind vanes;

[0069] A control unit that starts data collection, processing and transmission according to a preset program or user's instructions, and monitors and manages the status of the data collection unit;

[0070] Wind resource processing and analysis modules include:

[0071] ERA5 data real-time download unit, based on ERA5 data interface, downloads ERA5 real-time wind measurement data through longitude and latitude points;

[0072] The wind data analysis and calculation unit arranges the wind data downloaded according to longitude and latitude according to spatial attributes, completes the processing, storage and display of ERA5 data, and automatically draws maps and generates reports based on the processed wind data;

[0073] The 5G communication module includes:

[0074] The communication unit realizes data exchange between the wind resource processing and analysis module, the ocean terrain statistical analysis module, and the AI ​​analysis module through 5G wireless communication;

[0075] The multi-environment terrain scanning module includes:

[0076] The terrain data collection unit collects terrain morphology and altitude data through total stations, laser rangefinders, and photogrammetry equipment;

[0077] The positioning and navigation unit determines the location of the terrain data collection unit through the GPS positioning system and is used to match the collected data with the actual terrain;

[0078] The ocean terrain statistical analysis module includes:

[0079] Data screening unit, which avoids errors in mapping by eliminating abnormal and invalid data;

[0080] The marine obstacle identification unit automatically plots obstacles in the seabed terrain by importing the terrain single-band raster data collected by the multi-environment scanning module into the model;

[0081] The marine obstacle plotting unit manually plots the seabed obstacles by calling the manual plotting mode and using the frame selection operation;

[0082] The cloud storage module includes:

[0083] The storage unit, which consists of a large-capacity hard disk, is used to actually store monitoring and analysis data;

[0084] The data transmission unit enables data to be quickly and stably transmitted to the storage unit through the optical fiber channel;

[0085] AI analysis modules include:

[0086] The data integration unit collects data from the wind resource processing and analysis module, the ocean terrain statistical analysis module, and the cloud storage module to provide comprehensive data support for the subsequent offshore wind power station site selection;

[0087] The model management unit selects the appropriate AI model to process the data provided by the data integration unit according to specific analysis tasks and requirements;

[0088] The solution proposal and evaluation unit conducts in-depth analysis of the data processed by the model management unit and displays the analysis results through reports, evaluates the risks that the solution may bring, and analyzes potential risk factors and impacts.

[0089] Embodiment three:

[0090] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the GIS-based offshore wind farm site optimization method as described in any embodiment of the present invention is implemented.

[0091] Embodiment 4:

[0092] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the offshore wind farm site selection optimization method based on GIS as described in any embodiment of the present invention is implemented.

[0093] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0094] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0095] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0096] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0097] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A GIS-based offshore wind farm site selection optimization method, characterized in that: The following steps are involved: Monitor wind measurement data in multiple pre-selected areas and upload the wind measurement data to the wind resource database in real time; The wind resource database will arrange the wind measurement data downloaded according to longitude and latitude according to spatial attributes, and use the ERA5 reanalysis data of the same range to verify, interpolate and revise the wind measurement data, and automatically map the processed wind measurement data to automatically generate wind resource map data for the pre-selected area; The relevant data of terrain is collected by scanner and matched with the actual longitude and latitude, and the scattered data is pieced together to obtain terrain data and uploaded to the marine terrain database; The ocean terrain database uses segmentation and recognition models to identify submarine obstacles in the submarine terrain map and obtain submarine obstacle data; Integrate the wind resource map data and seabed obstacle data of the pre-selected area, upload them to the cloud server and transmit them to the AI ​​model for analysis; The AI ​​model integrates the input data and proposes corresponding offshore wind farm site selection plans.

2. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The wind measurement includes wind speed data and wind direction data, and the wind measurement data of monitoring multiple pre-selected areas includes: The wind speed and wind direction data of the pre-selected area are directly or indirectly measured by anemometers and wind vanes, and data collection, processing and transmission are started according to preset programs or user instructions, and the status of anemometers and wind vanes in the pre-selected area are monitored and managed.

3. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The steps of processing the ERA5 reanalysis data include: The real-time wind measurement data of ERA5 reanalysis data is downloaded through the data interface of ERA5 reanalysis data in combination with longitude and latitude points, and the wind measurement data downloaded according to longitude and latitude are arranged according to spatial attributes. The processing, storage and display of ERA5 reanalysis data are completed through data verification, interpolation and revision.

4. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The wind resource database runs in a distributed cloud database. After any cloud database automatically generates wind resource map data of a preselected area, the wind resource map data generated by each cloud database are connected in series through a network.

5. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The collecting of terrain data by means of a scanner and matching it with the actual longitude and latitude includes: Collect the topography and altitude data of the seabed terrain through corresponding terrain data collection equipment, including total station, laser rangefinder, and photogrammetry; The GPS positioning system is used to determine the location of the corresponding terrain data collection equipment, and the collected seabed terrain morphology and altitude data are matched with the actual location.

6. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The step of identifying seabed obstacles in the seabed topographic map and obtaining seabed obstacle data comprises: By eliminating abnormal and invalid data and importing the terrain single-band raster data collected by the multi-environment scanning module into the segmentation and recognition model, obstacles in the seabed terrain are automatically mapped; Then, by calling the manual marking mode and using the box selection operation, the seabed obstacles can be manually marked.

7. The GIS-based offshore wind farm site selection optimization method according to claim 1, characterized in that: The steps of integrating the wind resource map data and the seabed obstacle data of the pre-selected area, uploading them to the cloud server and transmitting them to the AI ​​model for analysis include: The generated wind resource map data and seabed obstacle data are transmitted to a large-capacity hard disk through a network channel for integration; The cloud server receives the integrated data through a large-capacity hard drive and selects an AI model to analyze and process the integrated data based on specific analysis tasks and needs.

8. The GIS-based offshore wind farm site selection optimization system is characterized by: The GIS-based offshore wind farm site selection optimization method for any one of claims 1 to 7 comprises: ERA5 wind measurement module, including anemometer and wind vane for testing wind resources in the target area, collects and transmits real-time wind measurement data through anemometer and wind vane; The wind resource processing and analysis module automatically generates statistical analysis results based on the wind resource data transmitted by the ERA5 wind measurement module, automatically generates wind resource report data for a given time and place, and supports saving to files and downloading; 5G communication module, used for data transmission between modules; A multi-environment terrain scanning module includes a scanner for scanning various terrains, and the scanner monitors and records terrain data in real time; The ocean terrain statistical analysis module generates grid point data by combining the seabed terrain data transmitted by the multi-environment terrain scanning module with longitude and latitude; The cloud storage module synchronizes data among multiple devices by uploading real-time wind measurement data, wind resource report data, terrain data, and grid point data to the cloud server; The AI ​​analysis module selects offshore wind farm sites by integrating real-time wind measurement data, wind resource report data, terrain data, and grid point data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the GIS-based offshore wind farm site selection optimization method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the GIS-based offshore wind farm site selection optimization method as described in any one of claims 1 to 7 is implemented.

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