Method for evaluating wind power plant short-period power prediction precision

Abstract

The invention provides a method for evaluating wind power plant short-period power prediction precision. The method comprises a first step of collecting actually measured data of a wind power plant, a second step of calculating wind power plant power actual prediction errors and ideal prediction errors according to the actually measured data of the wind power plant, a third step of comparing the actual prediction errors with ideal prediction errors, and evaluating the power plant short-period power prediction precision. Factors influencing the short-period power prediction precision are analyzed by calculating and comparing the wind power plant short-period power prediction precision errors, and the basis is provided for short-period power prediction adjustment so as to guarantee the wind power plant short-period power prediction precision.

Description

Technical field [0001] The invention relates to the technical field of short-term power prediction of wind farms, in particular to a method for evaluating short-term power prediction accuracy of wind farms. Background technique [0002] In the context of the gradual depletion of conventional energy sources such as coal and petroleum and the increasingly serious environmental problems, wind power generation is currently the most mature, large-scale development conditions and commercial development prospects in the current renewable energy generation technology, which has received great attention from the country and Strong support and rapid development in recent years. [0003] With the rapid development of wind power technology, the grid-connected operation of large-scale wind farms has an increasingly significant impact on the power system. In addition, areas with abundant wind energy resources in my country are generally sparsely populated, with small loads, and relatively weak ...

AI Technical Summary

Problems solved by technology

[0004] However, the forecasting strategies and forecasting models used in the existing short-term power forecasting methods for wind farms are easily affected by terrain and meteorological changes, thus affecting the forecasting accuracy

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