Wind generation set fault detection method and device based on quick gradient elevator

Abstract

The invention discloses a wind generation set fault detection method and device based on a quick gradient elevator. Target state features are selected from a state feature set based on a maximum information coefficient correlation analysis method, and a fault detection model is obtained according to a cost sensitive quick gradient elevator, so that in the wind generation set fault detection process, the calculation amount is reduced, consumed calculation time is shortened, and the fault detection efficiency and real-time performance of a wind generation set are improved. The method comprises the steps that the state feature set of the wind generation set is obtained, wherein the state feature set comprises at least one state feature; target state features are selected from the state feature set based on the maximum information coefficient correlation analysis method; the fault detection model is obtained according to the cost sensitive quick gradient elevator; and according to the target state features and the fault detection model, a fault detection result of the wind generation set is obtained through prediction.

Description

technical field [0001] The invention relates to the field of wind power generation, in particular to a fault detection method and device for a wind turbine based on a fast gradient hoisting machine. Background technique [0002] Wind power generation technology is an important direction in the field of new energy, and places rich in wind resources are often located in remote areas, and the harsh external environment may easily cause wind turbine failures. The failure of the gearbox of the wind turbine is the cause of the longest downtime and the largest economic loss. The failure of the gearbox will directly affect the overall performance of the equipment. Therefore, it is of great significance to detect and quickly identify faults in the gearbox components of wind turbines for reducing the operation and maintenance costs of wind turbines and improving the production efficiency of the entire wind farm. [0003] Machine learning methods have been widely used in the field of ...

AI Technical Summary

Problems solved by technology

However, because the traditional boosting algorithm is very sensitive to outliers, when the data sample is an outlier, it will greatly interfere with the learning effect of the base classifier; the traditional boosting algorithm is not efficient in training and takes up a lot of memory; in the actual wind turbine fault diagnosis In the process, due to the existence of many eigenvectors, the traditional lifting algorithm has a large complexity in calculation and cannot handle massive big data, which in turn affects the calculation efficiency and the real-time performance of fault detection.

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