Support vector machine-based wind electric powder prediction device and method

Abstract

The invention discloses a support vector machine-based wind electric powder prediction device and a support vector machine-based wind electric powder prediction method, and belongs to the technical field of wind electric power prediction. The device comprises a microprocessor, a sensor, an external expanding memory, a data acquisition module, an energy management module (EMS) and a communication interface. The prediction method applying the device comprises the following steps of: 1, normalizing historical data; 2, acquiring meteorological information of a prediction day by using the data acquisition module to form a training sample set; 3, obtaining an optimal clustering centre matrix which meets accuracy requirement; 4, classifying the training sample set according to distances of clustering centers; and 5, obtaining the wind electric power prediction result. The device and the method have the advantages that: the induction principle of structural risk minimization is realized, the training is equivalent to that the quadratic programming problem of linear constraint is solved; and the device and the method have unique solution.

Description

technical field [0001] The invention belongs to the technical field of wind power prediction, and in particular relates to a wind power prediction device and method based on a support vector machine (SVM). Background technique [0002] In recent years, with the continuous expansion of the scale of wind power, the connection of large-capacity wind power to the power grid will bring severe challenges to the safe and stable operation of the power system and to ensure power quality. Therefore, an accurate forecast of the power of the wind farm in the next 24 hours must be made. In this way, the dispatch plan can be adjusted in time, the rotating reserve of the system can be reduced, the operating cost of the system can be reduced, and the foundation for wind farms to participate in power generation bidding is laid. [0003] At present, there are two methods for predicting the output power of wind farms: one is the physical method, that is, first use the numerical weather foreca...

AI Technical Summary

Problems solved by technology

With the deepening of wind power technology, these methods have exposed some difficult defects, such as poor prediction accuracy, slow convergence speed and poor stability.

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