Fault Diagnosis of Multi Wind Turbine Based on CNN-Ensemble Learning

doi:10.3969/j.issn.1007-7375.2022.01.017

Abstract

Abstract: Offshore wind farms are located in remote environment and have been constantly corroded by saline alkali. In order to solve the problems of multiple-fault detection and identification in the operation process of wind turbines, a model is established based on the traditional convolution neural network LeNet-5. The model adopts the ReLU function as the activation function, and a convolutional layer, a pooling layer and a full connection layer are incepted. Aiming at the datasets of the wind turbine supervisory control and data acquisition (SCADA) system and the condition monitoring (CM) system, a multi-category fault diagnosis is carried out. A cluster analysis is implemented on several wind turbines, followed by ensemble learning to build a multi-machine wind turbine fault diagnosis model. The experimental results indicate that the diagnostic accuracy of the proposed method is 97% ~ 99%. By comparing the experimental results and other algorithms, the effectiveness of the proposed method is verified.

Key words: fault diagnosis, LeNet-5 network, supervisory control and data acquisition (SCADA), multi-category, ensemble learning

CLC Number:

TP312
TK83

YE Yini, LI Yanting. Fault Diagnosis of Multi Wind Turbine Based on CNN-Ensemble Learning[J]. Industrial Engineering Journal, 2022, 25(1): 136-143.

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