Combined method for predicting short-term wind speed in wind power plant

Abstract

The invention relates to a combined method for predicting short-term wind speed. The method comprises the following steps: 1, extracting historical wind speed data from a related data acquisition and monitoring control system; 2, performing sequence analysis on the extracted wind speed data by adopting clustering empirical mode decomposition; 3, respectively establishing a least squares support vector machine model for each subsequence obtained through the clustering empirical mode decomposition, and comprehensively selecting three parameters which influence the prediction effect of the least squares support vector machine by adopting an adaptive disturbance particle swarm algorithm and learning effect feedback; 4, predicting by selecting the optimal parameters according to the learning effect of the least squares support vector machine; 5, superposing the prediction result of each subsequence, and obtaining a wind speed prediction result; and 6, performing error analysis on the wind speed prediction result. The modeling process is simple and practical, and the wind speed can be rapidly and effectively predicted, so that the wind power is effectively predicted, and the method has significance on safety, stability and scheduled operation of the power system and has wide popularization and application values.

Description

technical field [0001] The invention relates to a short-term wind speed combination forecasting method, in particular to a wind farm short-term wind speed combination forecasting method. Background technique [0002] As a kind of green energy, wind energy has been paid more and more attention to by countries all over the world and has been developed rapidly. However, the inherent intermittency and volatility of wind energy have brought many challenges to the power system. If the wind speed of the wind farm can be effectively predicted, it will help the dispatching department to adjust the dispatching plan in time, reduce the spinning reserve and operating costs of the power system, Alleviating the impact of wind power on the power grid and laying the foundation for wind farms to participate in power generation bidding has great economic and engineering application value. [0003] At present, a lot of research has been done on wind speed prediction at home and abroad, and th...

AI Technical Summary

Problems solved by technology

The advantage of these two methods is that they can provide high-quality learning samples for the model. The disadvantage is that although high-quality learning samples are provided for the prediction model, the model does not know the learning effect of the samples, which will increase to a certain extent. big predicted risk

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