Wind energy forecasting method based on trend detector and mathematical morphology operator

Abstract

The invention discloses a wind energy forecasting method based on a trend detector and a mathematical morphology operator. The concepts of oscillation element and weight barycenter are introduced in the method, on this basis, an average trend detector is designed in combination with a conventional mathematical morphology operator, and unstable historical wind energy data of a wind power plant are decomposed into two dependent components, namely a low-frequency average trend component and a high-frequency random component, the average trend component reflects the total variation trend of wind energy, and the random component is specific embodiment of mutability and randomness of wind energy; the average trend component is forecast by adopting a sine forecasting operator, and the random component is forecast by adopting a local mathematical morphology operator; the forecasting results of the average trend component and the random component are added to obtain the forecasting result of the wind electricity output power of the wind power plant. The wind energy forecasting method disclosed by the invention makes full use of the advantages of mathematical morphology such as simple operation and high speed, so that the forecasting efficiency is improved, and the method is stable in forecasting result and high in forecasting precision.

Description

technical field [0001] The invention relates to a wind energy forecasting method, in particular to a wind energy forecasting method based on a trend detector and a mathematical morphology operator, and belongs to the technical field of wind energy power forecasting. Background technique [0002] In recent years, affected by the global energy crisis and the environmental problems caused by the combustion of fossil fuels, renewable energy represented by wind power has developed rapidly. Unlike thermal power plants, the output of wind farms is uncontrollable. In order to cope with frequent fluctuations in wind farm output and ensure the safety and stability of the power grid, it is necessary to make corresponding adjustments to the output of other conventional power plants and the system reserve capacity. With the large-scale integration of wind power into the grid, these problems seriously affect the safety, reliability and power quality of the power system and other indicator...

AI Technical Summary

Problems solved by technology

[0004] 1) Most of the existing forecasting models are only effective for specific wind farms, which require high prior knowledge of users, and the limitations of forecasting are relatively large

Due to the different conditions of each wind farm, the prediction model of one wind farm may not be able to achieve accurate forecasts in other wind farms;

[0005] 2) Without considering the inherent characteristics of wind energy historical data, the physical meaning or characteristics reflected in the mining data;

[0006] 3) The prediction accuracy is not high, and the stability needs to be strengthened

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