A wind power short-term power prediction method, system and device affected by icing

Abstract

The application provides a wind power short-term power prediction method, system and device affected by icing. First, weather data and icing data on the surface of a fan blade are acquired, the icing data is analyzed to obtain an icing type, then changes in the weather data are monitored and analyzed to obtain weather prediction data, and finally, the wind power short-term power is predicted in real time according to the icing type and the weather prediction data. The application can observe the icing structure characteristics from multiple directions, subdivide the icing structure, facilitate more detailed understanding of the type of the icing, better control the change trend of the icing, and make more reasonable prediction on the short-term power.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, and in particular to a method, system and equipment for short-term wind power prediction affected by icing. Background Technology

[0002] Icing on wind turbine blades alters their shape, changing the flow field distribution on their surface and affecting their wind energy utilization efficiency, thus impacting the turbine's output power. The extent of blade icing makes it difficult to predict the power impact caused by icing, and a power prediction system for icing-related issues is lacking. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, and device for short-term wind power prediction under the influence of icing. It enables multi-faceted observation of icing structure characteristics, detailed analysis of icing structures, and a more comprehensive understanding of icing types. This allows for better control of icing trends and more reasonable predictions of short-term power output.

[0004] To achieve the above objectives, it is necessary to provide a method, system, and equipment for short-term wind power prediction under the influence of icing, addressing the aforementioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a method for short-term wind power forecasting affected by icing, the method comprising the following steps:

[0006] Acquire weather data and icing data on the surface of wind turbine blades, analyze the icing data, and determine the icing type;

[0007] Based on the weather data, monitor and analyze the changes in the weather data to obtain weather forecast data;

[0008] Based on the icing type and weather forecast data, the short-term wind power output caused by the icing type is predicted in real time, and the prediction results are obtained.

[0009] Furthermore, the analysis of the icing data to obtain the icing type includes:

[0010] Based on the characteristics of ice, the ice types are classified as: rime, wet snow, mixed rime, granular rime, and crystalline rime;

[0011] According to the ice formation mechanism, the ice types are divided into: sublimation ice, cloud ice, and precipitation ice;

[0012] According to the growth process of ice on the surface of the conductor, the icing type is divided into: dry growth process and wet growth process;

[0013] According to the cross-sectional shape of the ice on the conductor, the icing type is divided into: airfoil streamline, crescent, circular or elliptical icing.

[0014] Furthermore, the analysis of icing data to determine the icing type also includes:

[0015] Obtain wind turbine blade data, cut the wind turbine blade data into several layers of modules with equal cross-sectional size, longitudinally cut the transversely cut modules, remove the wind turbine blade data, and obtain transverse and longitudinal slice data;

[0016] Based on the weather data, the horizontal and vertical slice data are analyzed. If the ratio of the data of equal areas of two compared slices is within a predetermined range, the two slices are classified into the same type of icing; otherwise, they are classified into different types of icing.

[0017] Furthermore, based on the topographic data and weather data of the location of the wind turbine blades, weather changes are predicted to obtain weather forecast data.

[0018] Furthermore, based on the weather data, the surrounding weather conditions are identified; the surrounding weather conditions include strong winds, dense fog, and rain / snow.

[0019] Acquire the terrain and landform data of the wind turbine blades; the terrain and landform data of the wind turbine blades includes: whether it is in the mountains, whether it is surrounded by mountains, whether it is in an open area, and whether it is on a mountaintop;

[0020] Based on the weather data, surrounding weather conditions, and topographical data of the wind turbine blades, weather changes are predicted to obtain weather forecast data.

[0021] Furthermore, the step of making real-time predictions of the short-term wind power output caused by the icing type based on the icing type and weather forecast data, and obtaining prediction results, includes:

[0022] Based on the aforementioned weather forecast data, the changes in icing are predicted, resulting in icing change prediction data.

[0023] Based on the predicted data of icing changes, the utilization rate of wind energy by blades under icing conditions was analyzed.

[0024] Calculate the short-term power output of wind power based on the utilization rate;

[0025] The formula for calculating short-term wind power is as follows:

[0026] P = W * a / t

[0027] Where P represents short-term wind power, W represents wind energy, a represents wind energy utilization rate, and t represents time.

[0028] Furthermore, the predetermined range is 0.8-1.2.

[0029] Secondly, a short-term wind power forecasting system affected by icing, characterized in that it includes:

[0030] The acquisition module is used to acquire weather data and icing data on the surface of wind turbine blades, analyze the icing data, and obtain the icing type.

[0031] The analysis module is used to monitor and analyze changes in the weather data to obtain weather forecast data.

[0032] The prediction module is used to make real-time predictions of the short-term wind power caused by the icing type based on the icing type and weather forecast data, and obtain prediction results.

[0033] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0034] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0035] This invention provides a method, system, and device for predicting short-term wind power output affected by icing. First, weather data and icing data on the wind turbine blade surface are acquired. The icing data is analyzed to determine the icing type. Then, changes in weather data are monitored and analyzed to obtain weather forecast data. Finally, based on the icing type and weather forecast data, the short-term wind power output is predicted in real time. This invention observes the characteristics of the icing structure from multiple perspectives and subdivides the icing structure, facilitating a more detailed understanding of the icing type and better control of icing change trends, enabling more reasonable predictions of short-term power output. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a method for predicting short-term wind power affected by icing according to an embodiment of the present invention.

[0037] Figure 2 This is a system block diagram of a wind power short-term power prediction system affected by icing according to an embodiment of the present invention;

[0038] Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and beneficial effects of this application clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of the present invention and are used to illustrate the present invention, but are not intended to limit the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0040] In one embodiment, such as Figure 1 As shown, a method for short-term wind power forecasting affected by icing is provided, including the following steps:

[0041] S11. Acquire weather data and icing data on the surface of wind turbine blades, analyze the icing data, and obtain the icing type;

[0042] The types of icing include:

[0043] Based on the characteristics of ice, the ice types are classified as: rime, wet snow, mixed rime, granular rime, and crystalline rime;

[0044] According to the ice formation mechanism, the ice types are divided into: sublimation ice, cloud ice, and precipitation ice;

[0045] According to the growth process of ice on the surface of the conductor, the icing type is divided into: dry growth process and wet growth process;

[0046] According to the cross-sectional shape of the ice on the conductor, the icing type is divided into: airfoil streamline, crescent, circular or elliptical icing.

[0047] Analyzing the icing data reveals the icing type, which also includes:

[0048] Obtain wind turbine blade data, cut the wind turbine blade data into several layers of modules with equal cross-sectional size, longitudinally cut the transversely cut modules, remove the wind turbine blade data, and obtain transverse and longitudinal slice data;

[0049] Based on the weather data, the horizontal and vertical slice data are analyzed. If the ratio of the data of equal areas of two compared slices is within a predetermined range, the two slices are classified into the same type of icing; otherwise, they are classified into different types of icing.

[0050] Preferably, the predetermined range is 0.8-1.2.

[0051] As mentioned earlier, icing can be classified into several types based on different characteristics. For example, the white crystals formed when water vapor molecules in the air directly sublimate, adhere, and freeze onto objects at low temperatures are called rime ice, which has a relatively loose ice layer structure. The transparent, smooth, and highly adhesive icing formed when supercooled water droplets in the air freeze onto the surface of wind turbine blades is called rain rime ice. Mixed rime ice is a mixture of fog and rain icing, where supercooled water droplets first collide with the blade surface to form rain rime ice, and then rime ice forms on the outer layer of the rain rime ice. Mixed rime ice grows rapidly and has a greater impact on the output power of wind turbines. Because different types of icing have different effects on power generation, distinguishing between icing types is beneficial for more accurate prediction of wind power output.

[0052] In addition, when analyzing slice data, if the ratio of equal area data is within a predetermined range, they are classified into the same icing type. This setting groups similar slices into one category, which helps to simplify the slice data analysis process to a certain extent. The analysis of horizontal and vertical slices is more three-dimensional and comprehensive than the analysis of only horizontal or vertical slices, avoiding the problem that some slice features may be missed by single-sided slices.

[0053] S12. Based on the weather data, monitor and analyze the changes in the weather data to obtain weather forecast data;

[0054] Based on the topographic data and weather data of the location of the wind turbine blades, weather changes are predicted to obtain weather forecast data.

[0055] Based on the weather data, identify the surrounding weather conditions; the surrounding weather conditions include strong winds, dense fog, and rain / snow.

[0056] Acquire the terrain and landform data of the wind turbine blades; the terrain and landform data of the wind turbine blades includes: whether it is in the mountains, whether it is surrounded by mountains, whether it is in an open area, and whether it is on a mountaintop;

[0057] Based on the weather data, surrounding weather conditions, and topographical data of the wind turbine blades, weather changes are predicted to obtain weather forecast data.

[0058] As mentioned above, weather factors are also a significant influence on blade icing. For example, when the temperature is below freezing, water droplets in the air undergo supercooling, and these supercooled droplets envelop the wind turbine blades, forming ice on their surface. Furthermore, if the wind turbine is located on a mountaintop, the higher the altitude, the greater the thickness and weight of the icing. Using weather data and topographical factors as analytical elements here helps to more accurately analyze the short-term power output of wind power under the influence of icing.

[0059] S13. Based on the icing type and weather forecast data, make a real-time prediction of the short-term wind power caused by the icing type to obtain the prediction result.

[0060] Based on the aforementioned weather forecast data, the changes in icing are predicted, resulting in icing change prediction data.

[0061] Based on the predicted data of icing changes, the utilization rate of wind energy by blades under icing conditions was analyzed.

[0062] Calculate the short-term power output of wind power based on the utilization rate;

[0063] The formula for calculating short-term wind power is as follows:

[0064] P = W * a / t

[0065] Where P represents short-term wind power, W represents wind energy, a represents wind energy utilization rate, and t represents time.

[0066] Generally, wind turbine blades deform when covered by ice. The ice cover is usually irregular in shape, with many tiny protrusions on the blade surface. These protrusions alter the original aerodynamic structure of the blade, increase the surface roughness, and greatly impair the aerodynamic characteristics of the blade. This leads to a decrease in the lift coefficient, an increase in the drag coefficient, and a decrease in the lift-to-drag ratio, resulting in reduced wind energy utilization and consequently a decrease in the wind turbine's output power. This wind energy utilization is often difficult to quantify. This study analyzes icing changes using weather forecast data to obtain a quantified wind energy utilization rate. By predicting the short-term wind power output under icing conditions using this rate, the accuracy of wind power forecasts can be effectively improved, providing strong support for the safety and efficiency of wind power generation and effectively guiding wind farm production and operation.

[0067] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0068] This invention provides a method for predicting short-term wind power output affected by icing. First, weather data and icing data on the wind turbine blade surface are acquired. The icing data is analyzed to determine the icing type. Then, changes in the weather data are monitored and analyzed to obtain weather forecast data. Finally, based on the icing type and weather forecast data, short-term wind power output is predicted in real time. This invention observes the characteristics of the icing structure from multiple perspectives, subdividing the icing structure to facilitate a more detailed understanding of the icing type and better control of icing change trends, enabling more reasonable predictions of short-term power output.

[0069] Based on the above-mentioned method for predicting short-term wind power affected by icing, this embodiment of the invention also provides a system for predicting short-term wind power affected by icing, such as... Figure 2 As shown, the system includes:

[0070] Acquisition module 1 is used to acquire weather data and icing data on the surface of wind turbine blades, analyze the icing data, and obtain the icing type;

[0071] Analysis module 2 is used to monitor and analyze changes in the weather data based on the weather data to obtain weather forecast data;

[0072] The prediction module 3 is used to make real-time predictions of the short-term wind power caused by the icing type based on the icing type and weather forecast data, and obtain prediction results.

[0073] Specific limitations regarding a short-term wind power forecasting system affected by icing can be found in the limitations of a short-term wind power forecasting method affected by icing described above, and will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0074] Figure 3 This diagram illustrates the internal structure of a computer device in one embodiment, which may specifically be a terminal or a server. The computer device includes a processor, memory, a network interface, a display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. The display screen may be a liquid crystal display (LCD) or an e-ink display. The input devices may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0075] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than shown in the diagram, or combine certain components, or have the same component arrangement.

[0076] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0077] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0078] This invention provides a method, system, and device for predicting short-term wind power output affected by icing. First, weather data and icing data on the wind turbine blade surface are acquired. The icing data is analyzed to determine the icing type. Then, changes in the weather data are monitored and analyzed to obtain weather forecast data. Finally, based on the icing type and weather forecast data, the short-term wind power output is predicted in real time. This invention observes the characteristics of the icing structure from multiple perspectives and subdivides the icing structure, facilitating a more detailed understanding of the icing type and better control of icing change trends, enabling more reasonable predictions of short-term power output.

[0079] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0080] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A wind power short-term power prediction method affected by icing, characterized by, The method comprises the following steps: acquiring weather data and icing data on the surface of the fan blade, analyzing the icing data to obtain an icing type; monitoring and analyzing changes in the weather data according to the weather data to obtain weather prediction data; real-time predicting wind power in the short term caused by the icing type according to the icing type and the weather prediction data to obtain a prediction result; the step of analyzing the icing data to obtain the icing type comprises the following steps: according to the performance characteristics of ice, the icing type is divided into: glaze, wet snow, mixed rime, granular rime and crystalline rime; according to the formation mechanism of ice, the icing type is divided into: sublimation icing, cloud icing and precipitation icing; according to the growth process of ice on the surface of the conductor, the icing type is divided into: dry growth process, wet growth process; according to the cross-sectional shape of ice on the conductor, the icing type is divided into: wing-shaped streamline, crescent-shaped, circular or elliptical icing; the step of analyzing the icing data to obtain the icing type further comprises the following steps: acquiring fan blade data, cutting the fan blade data into a plurality of modules with equal cross-sectional area, longitudinally cutting the modules after cross-sectioning, removing the fan blade data, and obtaining cross-sectional and longitudinal slice data; according to the weather data, performing slice analysis on the cross-sectional and longitudinal slice data; if the ratio of the equal-area data of two compared slices is within a predetermined range, the two slices are divided into the same type of icing; otherwise, the two slices are divided into different types of icing; the step of real-time predicting wind power in the short term caused by the icing type according to the icing type and the weather prediction data to obtain a prediction result comprises the following steps: predicting the change of icing according to the weather prediction data to obtain icing change prediction data; analyzing the utilization rate of wind energy by the blade under icing according to the icing change prediction data; calculating wind power in the short term according to the utilization rate; the formula for calculating wind power in the short term is: P = W * a / t wherein P represents wind power in the short term, W represents wind energy, a represents the utilization rate of wind energy, and t represents time.

2. The method for short-term power prediction of wind power affected by icing according to claim 1, characterized in that, the step of monitoring and analyzing changes in the weather data according to the weather data to obtain weather prediction data comprises the following steps: predicting weather changes according to the topographic and geomorphic data and weather data of the location of the fan blade to obtain weather prediction data.

3. The method according to claim 2, wherein, the step of predicting weather changes according to the topographic and geomorphic data and weather data of the location of the fan blade to obtain weather prediction data comprises the following steps: identifying the surrounding weather conditions according to the weather data; the surrounding weather conditions include strong wind, heavy fog and snow; acquiring topographic and geomorphic data of the location of the fan blade; the topographic and geomorphic data of the location of the fan blade include: whether in a mountain, whether surrounded by mountains, whether in an open field and whether on a mountain top; predicting weather changes according to the weather data, the surrounding weather conditions and the topographic and geomorphic data of the location of the fan blade to obtain weather prediction data.

4. The method for short-term power prediction of wind power affected by icing according to claim 1, characterized in that, the predetermined range is 0.8-1.

2.

5. A wind power short-term power prediction system affected by icing, characterized by, The method comprises the following steps: acquiring weather data and icing data on the surface of the fan blade, analyzing the icing data to obtain an icing type; an analysis module, configured to monitor and analyze changes in the weather data according to the weather data, to obtain weather prediction data; a prediction module, configured to predict wind power short-term power caused by the icing type in real time according to the icing type and the weather prediction data, to obtain a prediction result; the analysis of the icing data to obtain the icing type comprises: the icing type is classified into glaze, wet snow, mixed rime, granular rime and crystalline rime according to the performance characteristics of ice; the icing type is classified into sublimation icing, cloud icing and precipitation icing according to the formation mechanism of ice; the icing type is classified into dry growth process and wet growth process according to the growth process of ice on the surface of a conductor; the icing type is classified into wing-shaped streamline, crescent, circular or elliptical icing according to the cross-sectional shape of ice on a conductor; the analysis of the icing data to obtain the icing type further comprises: obtaining wind turbine blade data, cutting the wind turbine blade data into a plurality of modules with equal cross-sectional area, longitudinally cutting the modules after cross-sectioning, removing the wind turbine blade data, and obtaining cross-sectional and longitudinal slice data; performing slice analysis on the cross-sectional and longitudinal slice data according to the weather data; if the ratio of equal-area data of two compared slices is within a predetermined range, the two slices are classified into the same icing type; otherwise, the two slices are classified into different icing types; the prediction of wind power short-term power caused by the icing type in real time according to the icing type and the weather prediction data to obtain a prediction result comprises: predicting changes in icing according to the weather prediction data, to obtain icing change prediction data; analyzing the utilization rate of wind energy by the blade under icing according to the icing change prediction data; calculating wind power short-term power according to the utilization rate; the calculation formula of the wind power short-term power is: P = W * a / t wherein, P represents wind power short-term power, W represents wind energy, a represents the utilization rate of wind energy, and t represents time.

6. A computer device, comprising a memory, a processor and a computer program stored in the memory and executable by the processor, A computer program running on a processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 4 when the computer program is executed.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, the computer program implements the steps of the method of any one of claims 1 to 4 when executed by the processor.

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