[0025] The following description and drawings fully illustrate specific embodiments of the present invention to enable those skilled in the art to practice them. Other implementations may include structural, logical, electrical, process, and other changes. The examples only represent possible changes. Unless explicitly required, individual components and functions are optional, and the order of operations can be changed. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims. In this context, these embodiments of the present invention may be individually or collectively denoted by the term "invention", this is only for convenience, and if more than one invention is actually disclosed, it is not intended to automatically limit the application The scope is any single invention or inventive concept.
[0026] The term "extreme weather" as used herein refers to weather conditions in which the temperature continues to drop significantly and accompanied by snowfall.
[0027] Now it will be explained in conjunction with the drawings. figure 1 Shown are flowcharts of methods for predicting wind power in some optional embodiments.
[0028] Such as figure 1 As shown, in some optional embodiments, a method for wind power prediction is disclosed, and the method includes:
[0029] S11. Predict extreme weather based on the received meteorological data, and when the forecast result is extreme weather, record extreme weather-related impact data;
[0030] In some optional embodiments, the meteorological data in the method is calculated by a meteorological prediction model, such as a WRF (Weather Research and Forecasting) meteorological prediction model, and the meteorological data includes: the wind speed of the measured wind field , Wind direction, temperature, humidity, pressure, air density, snowfall, etc.
[0031] In some optional embodiments, the extreme weather-related impact data in the method includes: the duration t of the temperature drop of the measured wind field; the temperature drop intensity w of the measured wind field; the snowfall of the measured wind field Intensity s. Among them, t refers to the time period during which the temperature shows a continuous downward trend under extreme weather, w refers to the temperature difference between the beginning and the end of the time period t, and s refers to the time period measured during the time period t. The average snowfall amount obtained by dividing the sum of snowfall by the time period t.
[0032] S12. Determine the correction coefficient of the measured wind field based on the historical operating data of the measured wind field corresponding to all extreme weather before this prediction;
[0033] In some optional embodiments, the method of determining the correction coefficient of the measured wind field based on the historical operating data of the measured wind field corresponding to all extreme weather before this prediction includes: The historical operating data of the measured wind field corresponding to the extreme weather is input into the computer program; the computer program is used to calculate the correction coefficient K1 of the influence factor of the extreme weather of the measured wind field on the power generation power of the wind turbine and the wind turbine of the measured wind field The correction coefficient K2 of the power generation, the correction coefficients K1 and K2 make the prediction result of the power generation power of the wind turbine closest to the real power generation state with the smallest error. In the specific implementation process, the historical operating data of the measured wind field corresponding to the extreme weather before this prediction can be input into the Matlab program to calculate the correction coefficients K1 and K2. Among them, the history of the measured wind field Operating data includes: meteorological data, real power generation data of wind turbines, and predicted power generation data of wind turbines. The real power generated by the wind turbine here refers to the power generated by the wind turbine under extreme weather conditions, and the predicted power generated by the wind turbine refers to the power generated by the wind turbine predicted by the existing wind power prediction system in the tested wind farm.
[0034] S13: Determine the influence factor of the extreme weather on the power generation power of the wind turbine according to the influence data and the correction coefficient;
[0035] In some optional embodiments, the process of determining the influence factor of extreme weather on wind turbine power generation according to the influence data and the correction coefficient in the method includes: calculating the influence factor of wind turbine power generation according to the formula α=K1×t×w×s α. For example: when t=0.5, w=15, s=10, K1=0.2, α=0.2×0.5×15×10=15.
[0036] S14. Determine the wind turbine power generation affected parameters related to the extreme weather according to the impact factor;
[0037] In some optional embodiments, the process of determining the wind turbine power generation affected parameters related to extreme weather according to the influence factor in the method includes: determining the influence intensity m on the power generation according to the linear relationship between the influence factor and the wind power generation influence degree.
[0038] S15. According to the correction coefficient and the affected parameters of the wind turbine, adjust the power generation power of the wind turbine predicted by the existing wind power prediction system in the measured wind farm, and use the adjusted result as the wind turbine in the measured wind farm Power generation.
[0039] In this article, the existing wind power prediction system refers to the existing wind power prediction system that has not been corrected according to extreme weather factors in the measured wind farm.
[0040] In some optional embodiments, the method adjusts the generated power of the wind turbine predicted by the existing wind power prediction system in the measured wind farm according to the correction coefficient and the affected parameters of the wind turbine, and uses the adjusted result as the The process of measuring the generating power of the wind turbines in the wind farm includes: using the existing wind power prediction system in the measured wind farm to calculate the generating power P1 of the wind turbines in the measured wind farm; calculating the measured power according to the formula P=P1×K2/m The adjusted power generation P of the wind turbine of the wind farm; the power generation P of the adjusted wind turbine is used as the power generation of the wind turbine in the tested wind farm.
[0041] In some optional embodiments, the method further includes: performing statistical analysis and error calculation on the generating power of the wind turbine; and reporting the generating power of the wind turbine to the dispatching system.
[0042] This method can be used to predict wind power generation under extreme weather conditions, improve the accuracy of wind power prediction under extreme weather conditions, and greatly reduce the error between predicted power and actual power. The invention effectively utilizes this part of the data of the wind turbine generating power under extreme weather, discovers the power generation performance of the wind turbine under extreme weather, and accurately quantifies the power generation of the wind turbine.
[0043] Reference figure 2 , figure 2 Shown are structural diagrams of a system for wind power prediction in some optional embodiments.
[0044] Such as figure 2 As shown, in some optional embodiments, a system (for example, the system 200) for wind power prediction is disclosed, including:
[0045] Weather forecasting module (for example, weather forecasting module 201), for forecasting extreme weather based on received weather data; and, when the forecast result is extreme weather, recording extreme weather-related impact data;
[0046] The first calculation module (for example, the first calculation module 202) is used to determine the correction coefficient of the measured wind field according to the historical operating data of the measured wind field corresponding to all extreme weather before this prediction;
[0047] The second calculation module (for example, the second calculation module 203) is configured to determine the influence factor of the extreme weather on the power generation power of the wind turbine according to the influence data and the correction coefficient;
[0048] A third calculation module (for example, the third calculation module 204), configured to determine the wind turbine power generation affected parameters related to extreme weather according to the influence factor;
[0049] The power prediction module (for example, the power prediction module 205) is used to adjust the generating power of the wind turbine predicted by the existing wind power prediction system in the measured wind farm according to the correction coefficient and the wind turbine affected parameters, and adjust The latter result is used as the power generation of the wind turbines in the tested wind farm, and the existing wind power prediction system is a wind power prediction system that is not corrected according to extreme weather factors in the tested wind farm.
[0050] In some optional embodiments, the weather prediction module (such as the weather prediction module 201) in the system (such as the system 200) receives calculations from a weather prediction model, such as a WRF (Weather Research and Forecasting) weather prediction model The meteorological data includes: wind speed, wind direction, temperature, humidity, pressure, air density, snowfall, etc. of the measured wind field.
[0051] In some optional embodiments, the first calculation module (for example, the first calculation module 202) in the system (for example, the system 200) includes: a receiving module (for example, the receiving module 2021) for receiving all extremes before this prediction Weather-corresponding historical operation data of the measured wind field; a sub-calculation module (for example, sub-calculation module 2022), used to calculate the influence factor of extreme weather of the measured wind field on the power generation power of the wind turbine according to the received historical operation data The correction coefficient K1 of the measured wind farm and the correction coefficient K2 of the generator power of the wind turbine in the measured wind field, the correction coefficients K1 and K2 make the predicted result of the generator power of the wind turbine closest to the real power generation state with the smallest error. In the specific implementation process, the receiving module (such as the receiving module 2021) receives human input or other modules sent by other modules to predict all the historical operating data of the measured wind field corresponding to the extreme weather before this prediction; the sub-computing module (such as the sub-computing module) 2022) Calculate the received historical operating data using the Matlab program, and obtain the correction coefficients K1 and K2 after calculation. Among them, the historical operating data of the measured wind farm includes: meteorological data, real power generation data of wind turbines, and predicted wind turbines The actual power generation data of the wind turbine here refers to the power generation power of the wind turbine under extreme weather conditions, and the predicted power generation power of the wind turbine refers to the prediction obtained by the existing wind power prediction system in the tested wind farm The generating power of the wind turbine.
[0052] In some optional embodiments, the extreme weather-related impact data recorded by the weather prediction module (such as the weather prediction module 201) in the system (such as the system 200) includes: the duration t of the temperature drop of the measured wind field; The temperature drop intensity w of the measured wind field; the snowfall intensity s of the measured wind field. Among them, t refers to the time period during which the temperature continues to decline under extreme weather, w refers to the temperature difference between the beginning and the end of the time period t, and s refers to the measured value of each moment in the time period t. The sum of snowfall is divided by the average snowfall of time period t.
[0053] In some optional embodiments, the second calculation module (for example, the second calculation module 203) in the system (for example, the system 200) is used to calculate the influence factor of the wind power generation power according to the formula α=K1×t×w×s α.
[0054] In some optional embodiments, the third calculation module (for example, the third calculation module 204) in the system (for example, the system 200) is used to determine the intensity of the influence on the power generation according to the linear relationship between the influence factor and the degree of influence on the power generation of the wind turbine. m.
[0055] In some optional embodiments, the power prediction module (such as the power prediction module 205) in the system (such as the system 200) includes: a first prediction module (such as the first prediction module 2051), which is used to utilize the measured wind field The existing wind power prediction system calculates the power generation P1 of the wind turbines in the measured wind field; the second prediction module (for example, the second prediction module 2052) is used to calculate the measured wind field power according to the formula P=P1×K2/m The adjusted power generation P of the wind turbine; use the adjusted power generation P of the wind turbine as the power generation power of the wind turbine in the tested wind farm.
[0056] In some optional embodiments, the system (for example, the system 200) further includes:
[0057] The transmission module (for example, the transmission module 206) is used to transmit the generated power of the wind turbine in the tested wind farm to other related systems.
[0058] In some optional embodiments, the transmission module (such as the transmission module 206) in the system (such as the system 200) is used to transmit the power generated by the wind turbines in the tested wind farm to the following system: human-machine interface system; prediction Data reporting system.
[0059] The system can be used to predict wind power generation under extreme weather conditions, improve the accuracy of wind power prediction under extreme weather conditions, and greatly reduce the error between predicted power and actual power. The invention effectively utilizes this part of the data of the wind turbine generating power under extreme weather, discovers the power generation performance of the wind turbine under extreme weather, and accurately quantifies the power generation of the wind turbine.
[0060] Those skilled in the art should also understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are described above generally around their functions. As for whether this function is implemented as hardware or as software, it depends on the specific application and the design constraints imposed on the entire system. Skilled technicians can implement the described functions in a flexible manner for each specific application, but this implementation decision should not be interpreted as a departure from the protection scope of the present disclosure.
