Wind power plant annual average wind speed prediction method and system based on cycle stacking extrapolation

Abstract

The invention discloses a wind power plant annual average wind speed prediction method based on cycle stacking extrapolation. The method mainly comprises the steps of data extraction, climate correction, linear trend analysis, linear trend influence removal, wind speed main cycle extraction, cycle stacking extrapolation, cycle component and trend component combination and the like. According to the method, wavelet transformation calculation is performed on average wind speeds over the years of a wind power plant mainly through wavelet analysis, main cycles are extracted, then the main cycles are utilized for stacking extrapolation, and therefore a result with good correlativity and a low average deviation value is obtained. The invention furthermore discloses a wind power plant annual average wind speed prediction system based on cycle stacking extrapolation. The prediction method and system are more scientific and accurate, and the prediction result can provide a more scientific and effective basis for investment decision making and operation management of the wind power plant.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows us to predict how fast an offshore wind turbine operates compared with other types of generators by analyzing its cycles over time. By doing this we aimed at providing better understanding about future weather patterns that affect it.

Problems solved by technology

This patents discusses how wind turbines work differently from other sources due to their irregularly changing output levels over time or varying weather conditions that affects its performance. It suggests developing methods to accurately estimate this variation through measurements at different times along the entire lifecycle of each machine's component parts. These estimates help identify areas where there may be potential future shortages of use compared to previous periods when they were expected beforehand. Additionally, these techniques could also aid in optimizing operational planning and managing costs associated with wind generations.

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