Wind power plant power predication method

Abstract

The invention provides a wind power plant power predication method. The method comprises the following steps: step A, collecting wind speed historical data of mesoscale numerical weather forecast and actually measured wind speed historical data matching the wind speed historical data in terms of time; B, matching wind speed historical data of mesoscale numerical weather forecast of a prediction day with the wind speed historical data of the mesoscale numerical weather forecast to obtain historical data with greatest similarity; C, determining a wind measurement tower actually measured wind speed matching the historical data with the greatest similarity in terms of time, and replacing the wind speed historical data of the mesoscale numerical weather forecast of the prediction day with the wind measurement tower actually measured wind speed; and D, establishing a fitting wind speed-power feature curve of a wind power plant area, and through combination with the wind measurement tower actually measured wind speed after replacement in the step C, obtaining predicted power of the wind power plant area at the predication day. According to the invention, compared to a conventional statistical scale-reducing method applying a nerve network, the wind power plant power predication method has the following advantages: the logic structure is optimized, and besides, a curve matching model also has the advantage of high execution efficiency.

Description

technical field [0001] The invention relates to the technical field of wind turbine control, in particular to a wind farm power prediction method. Background technique [0002] In recent years, with the adjustment of my country's energy policy, the installed capacity of grid-connected wind power has grown rapidly, and the centralized grid-connection of large-scale wind power has brought impact on the safe operation of the power grid. Improving the predictability of wind farm output can effectively reduce the impact of wind power on the power grid and reduce the pressure of power grid dispatching and peak regulation. This is of positive significance for making full use of wind energy resources and further increasing the proportion of grid-connected wind power installed capacity. [0003] According to the technical specifications of wind farm power forecasting promulgated and implemented in China, wind farms must upload the next day 0-24h output forecast curve to the power dis...

AI Technical Summary

Problems solved by technology

This method has a small computational cost, but the physical model construction and implementation process is more complicated, and the accuracy improvement is limited

[0007] 2. Use dynamic downscaling methods, such as Computational Fluid Dynamics (CFD, Computational Fluid Dynamics) to simulate the evolution process of the internal flow field of wind farms. This method can obtain more accurate wind speed distribution, but it needs to be used when establishing a forecasted wind speed query database or directly predicting wind speed The CFD method solves the Navier-Stokes equation (Navier-Stokes), which is an equation describing viscous Newtonian fluid in fluid mechanics. It is an equation that has not been completely solved so far, and there are only about one hundred special solutions. It is one of the most complicated equations to be solved, and its engineering implementation is complicated and the calculation cost is huge, which requires extremely high hardware requirements

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