Combined wind power prediction method suitable for distributed wind power plant

Abstract

The invention provides a combined wind power prediction method suitable for a distributed wind power plant. The method comprises the following steps: step 1, acquiring data and pre-processing; step 2, utilizing a training sample set and a prediction sample set which are normalized to build a wind speed prediction model based on a radial basis function neural network and predict the wind speed and variation trend of distribution fans at the next moment; step 3, building a distributed wind power plant area CFD (computational fluid dynamics) model and externally deducing the prediction wind speed of each fan in the plant area according to factors such as the terrain, coarseness and wake current influence of a distributed wind field; step 4, acquiring the power data of an SCADA (supervisory control and data acquisition) system fan of the distributed wind field; and step 5, adopting correlation coefficients. The invention firstly provides a double-layer combined neural network to respectively predict the wind speed and power. Models are respectively built through adopting appropriate efficient neural network types, and improved particle swarm optimization with ideas of 'improvement', 'variation' and 'elimination' is additionally added to optimize the neural network, so that the speed and precision of modeling can be effectively improved, and the decoupling between wind speed and power is realized.

Description

technical field [0001] The present invention relates to a data modeling and forecasting method based on artificial intelligence technology, in particular to a wind speed forecasting method based on radial basis neural network suitable for decentralized wind farms and the power of BP neural network based on improved particle swarm optimization. method of prediction. Background technique [0002] Wind power is a variable power source, and the development of large-scale wind power is bound to be limited by the ability of the grid to absorb random power. Due to the short construction period of wind farms, the relatively complicated construction of power grids, which is difficult to complete at the same time as the construction of wind farms, and the increased technical requirements for wind power equipment in power grids, wind power grid integration has begun to transform from physical "difficult grid integration" to technical "difficult grid integration". . At the same time, ...

AI Technical Summary

Problems solved by technology

[0012] The present invention overcomes the defects of the prior art and proposes a combined wind power forecasting method suitable for decentralized access, the purpose of which is to solve the problem of unsatisfactory forecasting accuracy existing in previous methods

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