Wind power prediction method and device based on gathering local multi-point different climatic characteristics

Abstract

The invention provides a wind power prediction method and device based on gathering local multi-point different climatic characteristics. The method comprises steps of establishing an electric field model, obtaining numerical weather forecasting results around an electric field, and obtaining the values of single point forecast wind speeds in a three-dimensional space through a data interpolation method; synthesizing the single point forecast wind speeds into two-point or three-point wind speeds to obtain a synthesized wind speed; and converting the synthesized wind speed into wind power by the neural network method. Based on the original single point forecasting, the single point results are integrated to form multi-point forecasting, the wind speed fluctuation is reduced, and the accuracy of wind power predication is improved. The gathered weather data of the invention adopt horizontal and vertical three-dimensional high resolution lattice points, and the climatic states of different horizontal points and different heights are integrated to improve the accuracy of wind power prediction. The invention adjusts the predicted wind speed and improves the accuracy of the wind power prediction by integrating the multi-point climatic characteristics and reducing the fluctuation of the wind field, and being free of influences of the electric field capacity expansion and current limiting and other factors.

Description

technical field [0001] The invention belongs to the technical field of wind farm power forecasting and forecasting, and in particular relates to a wind power forecasting method and device based on gathering local multi-point different climate characteristics. Background technique [0002] Wind power is a clean and renewable energy source. By the end of 2015, the cumulative installed capacity of wind power in the world reached 432,419MW, of which China accounted for 48.4% of the total capacity. Because wind power generation is affected by changes in wind speed and wind direction, this makes wind power generation have obvious volatility, intermittent and randomness, which has a great impact on the safety and stable operation of the power system, and through accurate wind power prediction , the short-term forecast of the output of wind farms can effectively regulate the operation and maintenance of the farm and reduce the loss of electricity. Therefore, accurate wind power fore...

AI Technical Summary

Problems solved by technology

[0005] The chaotic characteristics of the atmosphere make it difficult to predict the meteorological results of a single point even if the best numerical model in the world is used. The systematic error and wrong forecast of the model itself will indirectly lead to the forecast error of wind speed

[0006] Since the current power modeling technology is based on the average wind speed of the fans in the whole field, which represents the overall climate state of the whole field, it is difficult to describe the average wind speed state of the whole field by only roughly selecting a single point according to the arrangement of the fans in the electric field, and it cannot be accurate captures the climatic state of the farm, therefore, forecasted wind speeds are not appropriate for wind farms

[0007] The spatial span of the electric field is large, and the altitudes of different electric fields are also different. The wind speed in the boundary layer changes significantly with height, which will lead to great differences in the climate characteristics of different electric fields. Only the results of single-layer or hub height in the numerical model are used. will be very inaccurate

[0008] To sum up, according to the layout of wind turbines, artificially select a geographical coordinate, and finally interpolate the grid point data of numerical weather prediction to this point, so that it cannot accurately reflect the overall climate characteristics of the entire wind farm, and ultimately lead to the wind power prediction accuracy not ideal

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