Wind farm annual average wind speed prediction method and system based on periodical superposition and 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

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method and system for predicting the annual average wind speed of a wind farm based on periodic superposition and extrapolation. Background technique [0002] Wind energy is a clean and renewable energy, and the development of wind power generation has become an effective measure to improve the energy structure and protect the ecological environment. However, wind energy is a process energy, which has the characteristics of randomness, intermittent and instability. Therefore, wind power cannot control the number of hours of utilization, and the annual power generation is completely adjusted by changes in wind speed, which makes the early investment decision analysis of wind farms and the flow of operating funds in the later stage have greater uncertainty. [0003] Prediction and analysis of wind resources during the operation period with the idea of ​​wind resour...

AI Technical Summary

Problems solved by technology

[0004] In order to improve the scientificity and accuracy of the early stage investment decision analysis and post-operation of wind farms, it is generally necessary to predict the annual average wind speed during the next 20 years of operation. The investment decisions and operation management of existing wind farms are calculated based on fixed wind measurement years. The obtained power generation does not consider the impact of dynamic wind speed on the economic benefits of the project. Therefore, it is urgent to develop a new method for predicting the annual average wind speed of wind farms to make it more scientific and accurate. The prediction results can be used for wind farm investment decisions and Operation management provides a more scientific and effective basis

Images