Long-distance wind power generation power prediction method based on bidirectional timeline

Abstract

The invention discloses a long-distance wind power generation power prediction method based on a bidirectional timeline. The long-distance wind power generation power prediction method specifically comprises the following steps: firstly, carrying out data preprocessing operations such as data normalization, data cleaning, data supplementation and data screening on wind power plant meteorological data, equipment monitoring data and wind turbine generator basic data; and sorting the processed data according to time, and sending the sorted data into a calculation model. And then a bidirectional Transform model is constructed, weight matching and feature extraction are performed on input features through high-dimensional feature calculation and a self-attention mechanism, and part of data is abstracted into important features for output power prediction by means of the model. According to the method, the one-way influence of meteorological changes on the wind driven generator can be simulated, the output power of wind power generation can be predicted more accurately finally through the determined target output power of the future time point and the reverse reduction error, and the long-distance wind power generation power prediction accuracy is improved under the condition that the prediction efficiency is not reduced.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to a long-distance wind power generation power prediction method based on a bidirectional time line. Background technique [0002] With the continuous development of the new energy industry, the total installed capacity and installed capacity of wind power in my country have increased sharply. The intelligentization of wind turbines will be an important development trend in the wind power industry. On the one hand, the intelligent development of wind turbines needs to pass the traditional manufacturing process as the basis, and on the other hand, it needs to fully integrate Internet technology innovation, and use the new generation of big data and sequence forecasting. Information technology can finally realize the background monitoring and full life cycle management of remote wind turbines to ensure the operation status of smart wind farms. The intelligen...

AI Technical Summary

Problems solved by technology

However, the current model training and use process adopts the timeline-dependent modeling method. Although this one-way sequence modeling algorithm can simulate the impact of weather changes on the power generation of wind farms, the error at each step will be large. Accumulated along the time line, especially in the model prediction process, the error will accumulate to the maximum at the last prediction moment, which seriously affects the subsequent grid-connected scheduling of wind farms

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