Gaussian distribution-based wind turbine generator set wake flow analyzing and modeling method

Abstract

The invention belongs to the technical field of wind power generation micro-sitting selection and particularly relates to a Gaussian distribution-based wind turbine generator set wake flow analyzing and modeling method. The Gaussian distribution-based wind turbine generator set wake flow analyzing and modeling method, under the premise of appropriate assumptions, by combining the law of conservation of mass and the law of one-dimensional momentum conservation and according to the laws that wind speed loss radially follows Gaussian distribution and wake flow radius presents linear expansion, deducing a calculation model of wind turbine generator set wake zone wind speed distribution; according to analysis on wake flow speeds of different downstream positions, determining the value range ofdownstream wake boundary coefficients; by combining with the expansion law of wind turbine wake flow, determining the value range of wind turbine wake flow boundary coefficients. A simplified wake flow model acquired through the Gaussian distribution-based wind turbine generator set wake flow analyzing and modeling method can help rapidly, easily, conveniently and accurately calculate out wind speed distribution of wind turbine generator set wake zones and provide reference for taking into consideration wake flow effects during wind power generation micro-sitting selection.

Description

technical field [0001] The invention belongs to the technical field of wind power generation microcosmic site selection, and in particular relates to a Gaussian distribution-based analytical modeling method for wind turbine wakes. Background technique [0002] Among the many factors that affect the power generation efficiency of wind turbines, the loss of power generation caused by the wake effect of upstream wind turbines is huge. Therefore, accurately predicting the velocity distribution in the wake region and quantifying the power loss caused by it is of great significance for the micro-site selection of wind farms, power prediction, and improving the economic benefits of wind farms. Analytical wake model has become a mathematical method widely used in engineering to study wake due to its advantages of strong theory, simple structure, short calculation time and high calculation accuracy. At present, the most classic analytical wake model is the Jensen model, but this mod...

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Problems solved by technology

At present, the most classic analytical wake model is the Jensen model, but this model assumes that the wind speed in the wake area is uniformly distributed along the radial direction (that is, the top hat distribution) and only applies mass conservation, so it overestimates the wind speed in the wake area; the Katic model Although the Jensen model and the Frandsen model have been improved from different aspects, they still assume that the wind speed in the wake area obeys the top hat distribution, which is quite different from the actual situation; although the Ishihara model assumes a Gaussian distribution of wind speed along the radial direction, it is generally high The velocity loss in the wake region is estimated, especially in the near wake region; the Bastankha and Porté-Agel model (hereinafter referred to as the BP model) uses mass conservation, momentum conservation and Gaussian distribution to solve the wake region velocity, and the accuracy is very high. However, due to the difficulty in determining the values ​​of the model parameters, the calculation and application of the BP model are not convenient enough

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