Wind power field power prediction method based on deep neural network

Abstract

The invention discloses a wind field power predication method based on a deep neural network in the technical field of power system prediction and control. The method includes the steps that firstly, data of the wind speed, the wind direction, the temperature, the humidity and the atmospheric pressure which are actually measured are obtained; secondly, the projection pursuit is adopted for extracting main constituents, the neutral-position absolute deviation serves as a projection index in the projection pursuit, the interference of outliers irrelevant or little relevant to data structures and features can be effectively removed, and the main constituents can be extracted stably. A deep neural network model is adopted, and a predication model between five influence factors and the output power of a wind field is established, wherein the five influence factors include the wind speed, the wind direction, the humidity, the temperature and the atmospheric pressure. The power of the wind field is predicated, and the predicated power is obtained. According to the wind field power predication method, the precision of predicated power in future 72 hours of the wind electric field is improved, a basis is provided for reasonable dispatching of power grids, and the grid connection pressure is relieved.

Description

technical field [0001] The invention belongs to the technical field of electric power system prediction and control, and in particular relates to a method for predicting power of a wind farm based on a deep neural network. Background technique [0002] Wind power is a clean and non-polluting renewable energy power generation method, and it has been paid more and more attention by countries all over the world. The installed capacity of wind power has grown rapidly, and its proportion in the power grid has continued to increase. The total amount will account for 12% of the world's total electricity. However, wind power generation also has its disadvantages. Due to the characteristics of wind energy, such as volatility, intermittent, low energy density, and uncontrollability, wind power is also fluctuating and intermittent. For grids with large-scale wind power access, the fluctuation of wind power will have a great impact on the power balance of the entire grid, which brings ...

AI Technical Summary

Problems solved by technology

The forecast errors of these models are generally 15%-20%, and the effect is not satisfactory

And because the uniqueness of my country's wind farms is not fully considered, foreign wind power output forecasting systems cannot be directly applied in China, or the application effect is very poor

Domestic research on this aspect started relatively late, and is still in the preliminary exploration and research stage. The research work is mainly focused on the wind speed prediction of wind farms, and there is less research on power generation in the true sense. Currently, most of the products that have been released are in the stage of trial operation and accumulation of experience. , the prediction accuracy still needs to be tested and improved

Images