Wind power short-term prediction method

Abstract

The invention relates to the technical field of wind power prediction, and discloses a wind power short-term prediction method. The method uses wind speed as an input, adopts a regression model of a least square support vector machine to predict output power of a wind power plant, and parameters of the regression model of the least square support vector machine are optimized by adoption of a chaotic particle swarm algorithm. The wind power short-term prediction method provided by the invention introduces chaotic motion characteristics into an iterative process, uses ergodicity of chaotic motion to improve a global searching capability of the algorithm in a searching process, overcomes the defects that the particle swarm algorithm is easy to fall into a local extreme point and is slow in convergence and low in precision in a later period of evolution, effectively solves the problem of prematurity of the particle swarm algorithm, can ensure global optimum, and achieves a better prediction effect; the method uses the least square support vector machine to predict, avoids the problem of solving quadratic programming, converts the prediction problem to a process of solving a linear equation set, and the solving process is greatly simplified; and the method adopts single wind speed as input data, and thus a prediction model is simpler.

Description

technical field [0001] The present invention relates to a wind power prediction method, in particular to a short-term wind power prediction method. Background technique [0002] With the improvement of wind power unit capacity and the development of automation technology, the wind power generation system has also developed from the original user distributed energy to centralized large-scale wind farms. The proportion of wind power in the power grid continues to increase, and a large number of grid-connected wind power has brought severe challenges to the dispatching operation and safety and stability of the power system. Effective wind power forecasting can reduce the reserve capacity of the power system, reduce system operating costs, reduce the adverse impact of wind power on the grid, and increase the proportion of wind power in the power system. Therefore, it is of great significance to predict wind power. [0003] At present, there are physical methods and statistical ...

AI Technical Summary

Problems solved by technology

With the deepening of wind power technology, these methods have exposed defects that are difficult to overcome, such as poor prediction accuracy, slow convergence speed, and limitations.

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