Wind power plant ultrashort term wind speed prediction method based on combination kernel function

Abstract

The invention discloses a wind power plant ultrashort term wind speed prediction method based a combination kernel function. The method comprises the following steps: adopting a support vector machine based on the combination kernel function, combining a wavelet kernel function and a polynomial kernel function, combining a global characteristic of the polynomial kernel function with high precision and good learning capacity of the wavelet kernel function, effectively improving predictive capacity of a support vector machine model, and reducing predicting errors. Meanwhile, in building of the support vector machine model, choosing similar data, building models in a classified mode, enabling a training sample and a test sample to have a similar relation, and therefore strengthening nonlinear fitting capacity of a support vector regression model. According to combination of the combination kernel function and the similar data, the wind power plant ultrashort term wind speed prediction method based the combination kernel function improves precision of wind speed prediction.

Description

technical field [0001] The invention belongs to the technical field of ultra-short-term wind speed prediction in wind power generation systems, and more specifically relates to a method for ultra-short-term wind speed prediction of wind farms based on combined kernel functions. Background technique [0002] At present, the research on various topics of wind power generation at home and abroad is getting more and more in-depth. As an intermittent energy source, wind power has great randomness and uncontrollability. The fluctuation range of its output power is usually large and the speed is fast. As a result, it is very difficult to control the peak load, reactive power and voltage of the power grid, and bring new problems to the safety, stability and normal dispatch of the power grid. [0003] Common methods for wind speed prediction based on historical data include continuation method, Kalman filter method, random time series method, neural network method, fuzzy logic method...

AI Technical Summary

Problems solved by technology

However, a single kernel function has its application limitations, which will directly affect the accuracy of wind speed prediction

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