Method used for fault prediction and diagnosis of wind power plant unit gearbox

Abstract

The invention discloses a method for optimizing the fault prediction and diagnosis of a wind power plant unit mainshaft bearing of an SVM (Support Vector Machine) on the basis of a Cuckoo algorithm. Historical moment sampling data is subjected to principal component characteristic extraction to establish an SVM model, and the Cuckoo algorithm is used for optimizing the performance parameter of the SVM. After the sampling data which contains fault information is subjected to real-time prediction, an expert system makes an effective fault diagnosis, and a diagnosis result is presented on a human-computer interaction interface. PCA (Principal Component Analysis) is used for carrying out dimensionality reduction on the data, classification accuracy is improved, and the training time of the classifier is greatly shortened. Meanwhile, compared with other traditional optimizing methods, the Cuckoo algorithm has the advantages that a global optimal value is obtained by quick convergence, and has an obvious advantage on an aspect of prediction accuracy, and a guarantee is provided for the expert system to accurately obtain a diagnosis result.

Description

technical field [0001] The invention relates to the field of fault diagnosis of wind turbines, in particular to a method for fault prediction and diagnosis of a gearbox of a wind turbine. Background technique [0002] As a key mechanical component of wind power generation equipment, the main shaft bearing of wind turbines has high maintenance costs and long maintenance time. Abnormalities during the non-maintenance period are likely to cause main shaft bearing failure. The traditional SCADA system cannot timely and accurately locate hidden troubles , affecting the normal operation of wind turbines and the stability of wind power grid connection. Therefore, it is necessary to carry out in-depth research on the real-time operating status and fault diagnosis of wind turbine main shaft bearings. [0003] The traditional spindle bearing fault diagnosis method is based on the vibration signal of the spindle bearing in the running state to carry out pattern recognition on the gras...

AI Technical Summary

Problems solved by technology

However, the neural network is prone to fall into the local minimum problem, and there will be over-adaptation phenomenon.

On the other hand, the parameter optimization of support vector machine (SVM) determines the convergence accuracy and prediction accuracy. The parameter optimization algorithm of the existing support vector machine model has a poor convergence speed, which greatly affects the accuracy and speed of fault diagnosis.

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