Wind-power fault early-warning prediction method

Abstract

The application relates to a wind-power fault early-warning prediction method, and belongs to the technical field of the wind-power fault early-warning prediction. The method comprises the following steps: performing characteristic construction on wind-power data from multiple aspects, and performing parallel quick computation extraction of the characteristic; processing the wind-power data basedon the extracted characteristic to obtain early-warning prediction data with high information richness and interpretability; and performing fault early-warning prediction on the wind-power running according to the early-warning prediction data. Through the implementation of the method disclosed by the scheme, the characteristics of a large amount of wind-power data can be quickly realized, the interpretability of the early-warning prediction data is effectively improved, and the accuracy of the wind-power fault early-warning prediction can be improved.

Description

technical field [0001] The application belongs to the technical field of early warning and prediction of wind power failure, and in particular relates to a method for early warning and prediction of wind power failure. Background technique [0002] As a very important part of the new energy field, wind power technology has made great progress in recent years. [0003] Because wind farms are generally located in remote areas and the working environment is complex and harsh, the probability of failure of wind turbines is relatively high, and many operational failures have occurred. With the operation of large-scale wind farms, in order to reduce the risk of wind turbine operation and maintain the safe and economical operation of wind turbines, fault diagnosis, early warning and prediction play an increasingly important role in the operation of wind power generation. [0004] The accumulation of wind farm operation and monitoring data makes the early warning and prediction of ...

AI Technical Summary

Problems solved by technology

[0004] The accumulation of wind farm operation and monitoring data makes the early warning and prediction of wind power failure possible. However, due to the uneven data quality, the existing wind power failure prediction analysis lacks high-quality features that are accurate and effective and can be combined with actual business. Reduced interpretability of the obtained data for early warning and prediction of wind power faults

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