An indirect prediction method of wind energy suitable for multi-variable input of wind farms

Abstract

The invention discloses a wind energy indirect prediction method suitable for multivariable input of a wind power plant. The method comprises the steps of 1) predicting a wind speed; 2) predicting an average trend component by adopting a polynomial fitting method and forecasting a random component by adopting a frequency reduction prediction method; 3) integrating forecasting results of two independent components, namely, the average trend component and the random component to obtain a wind speed prediction value; and 4) making a historical wind energy and wind speed data distribution graph, performing polynomial fitting, generating a power curve, and calculating a wind energy prediction value according to the wind speed prediction value by utilizing the power curve so as to realize wind energy indirect prediction. The wind power plant with relatively high wind energy fluctuation also can be accurately predicted without depending on the influence on wind power plant modeling. In addition, the prediction method is a data-driven and adaptive method, and a prediction result of the prediction method does not depend on priori knowledge of a user.

Description

technical field [0001] The invention relates to the technical field of wind power generation power forecasting, in particular to an indirect wind power forecasting method suitable for multi-variable input of wind farms. Background technique [0002] With the scarcity of resources and the growing call for environmental friendliness of human beings, the development and utilization of wind energy has been paid more and more attention. However, due to its strong randomness and intermittent nature, it brings great difficulties to wind energy prediction, which directly limits the application of wind energy in large power grids. The randomness, variability and limited predictability of wind energy cause difficulties in grid operation and management, especially in the balance control of power consumption and production. The reliability of wind power generation is not satisfactory because it cannot provide a stable power system. Therefore, when the proportion of wind power in the p...

AI Technical Summary

Problems solved by technology

This method has a large workload, and the accuracy of modeling greatly affects the prediction accuracy of wind energy;

[0006] 3. The utilization of multi-variable data of wind farms is not high, and the relationship between wind energy and other variables such as wind speed and pitch angle has not been fully explored

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