Wind power prediction method based on multi-meteorological source wind speed fusion based on probability statistics and particle swarm optimization

Abstract

The invention discloses a wind power prediction method for multi-meteorological-source wind speed convergence based on probability statistics and particle swarm optimization. The wind power prediction method comprises the steps that step one, the wind power plant data within the preset time range are acquired, and the wind power plant data are preprocessed, wherein the wind power plant data include wind power plant actual wind speed, actual power and multi-meteorological-source NWP wind speed data; step two, the wind power plant historical data are acquired, statistics of annual wind speed distribution is performed, and the multi-meteorological-source NWP wind speed data are corrected according to the wind speed statistical law so that the corrected wind speed data can be obtained; step three, the data are updated by tracking the extremum in iteration of particles by using a PSO algorithm so that the optimal solution of the population can be found; and multiple corrected NWP wind speed convergence coefficients are calculated by utilizing the PSO algorithm so as to perform convergence and obtain the better wind speed data after convergence, and the wind speed data after convergence act as the input of the prediction model; and step four, a regression model of the wind speed and the power is established.

Description

technical field [0001] The invention relates to the field of wind power prediction, in particular to a wind power prediction method based on probability statistics and particle swarm optimization for wind speed fusion of multiple weather sources. Background technique [0002] With the rapid consumption of fossil energy, human beings are facing the dual crises of energy depletion and environmental degradation. Therefore, clean and renewable wind energy has received extensive attention and development worldwide in recent years. The total installed capacity of wind power in China has leapt to the first place in the world. The large-scale development of wind power and the reduction of the use of fossil energy have alleviated the energy crisis to a certain extent. However, due to the strong intermittency and randomness of wind energy, with the increase in the number of wind farms and the increasing installed capacity, the large-scale grid connection of wind power has brought grea...

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Problems solved by technology

[0004] The present invention provides a wind power prediction method based on probability statistics and particle swarm optimization based on multi-weather source wind speed fusion, which solves the problem of NWP wind speed fluctuation and accuracy affecting wind power in the prior art based on NWP wind speed mathematical model modeling method The technical problem of power prediction accuracy has achieved the technical effect of improving the accuracy of NWP wind speed, reducing the random error of NWP wind speed, improving the accuracy of wind speed input in the wind power prediction model, and further improving the accuracy of wind power prediction

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