Real-time error assessment method of wind power prediction based on dictionary learning algorithm

Abstract

The present invention discloses a real-time error assessment method of wind power prediction based on the dictionary learning algorithm. The method comprises: starting from analyzing the error generation mechanism of a wind power prediction system, obtaining factors that affect the prediction error; according to wind power data, using the wavelet transform to extract high-frequency and low-frequency components of the power signal, and forming error assessment parameters by adding the wind power prediction power, the actual wind power, the wind power forecast error variance, the actual wind power variance and other components; by using the dictionary learning, obtaining a sparse matrix and dictionary of the assessment parameters; and finally taking the sparse matrix as input, and establishing a real-time error assessment model of wind power prediction. According to the method disclosed by the present invention, sensitive parameters related to wind power prediction errors are obtained only by analyzing historical data of the wind power at the period before the current time, and real-time values of prediction errors are obtained, and the method is especially applied to the occasions with large wind power fluctuations and strong uncertainties.

Description

technical field [0001] The invention relates to the field of renewable energy utilization, in particular to a method for evaluating wind power prediction errors based on dictionary learning algorithms. Background technique [0002] The prediction of wind power has always been a technical problem, and the short-term prediction error can even reach 40%. Underestimating the power of wind power will result in wind curtailment. The problem of wind curtailment is serious in my country, with an average curtailment rate of 15% in 2015. Overestimation will cause an imbalance between the supply and demand of the power grid and seriously threaten the security of the power grid. For this reason, the power grid needs to be equipped with various forms of energy as backup. It is estimated that a grid containing wind energy needs to be equipped with a reserve of twice the wind power capacity to reduce the risk brought by wind power. This will greatly increase the cost of wind power, furt...

AI Technical Summary

Problems solved by technology

If we abandon the real-time information at the current moment and a period of time before, and only obtain the current error from the statistical results of long-term historical data, the error evaluation will be redundant and inaccurate

Moreover, the prediction error is related to various parameters such as the prediction method, prediction step size, and wind turbine type. It is difficult to use a specific probability density distribution function to fit the distribution of wind power prediction error. big error

Images