Fault classification in wind turbine based on deep belief network optimized by modified tuna swarm optimization algorithm

Abstract

The use of failure recognition technology can detect unusualness and deal with it properly to ensure the safe and stable operation of wind turbines (WT). An effective troubleshooting method can quickly distinguish the type of WT fault and reduce wind farm operation and maintenance costs. At present, the relevant data required for fault diagnosis methods comes from the supervisory control and data acquisition (SCADA) system, because the SCADA data contains information associated with the operating characteristics of WT, which can provide a rich source of data for WT fault diagnosis. A deep belief network (DBN) is commonly used as a deep learning method. In the present study, an optimized DBN based on the modified tuna swarm optimization (MTSO) algorithm was established to construct an MTSO-DBN WT fault diagnostic model so as to address the problem that the selection of DBN hyperparameters may affect the classification results. After preprocessing the WT fault data acquired by SCADA, the MTSO-DBN model was used to classify the WT faults. The experimental results reveal that, compared with the support vector machine, extreme learning machine, DBN, particle swarm optimization-DBN, and TSO-DBN classification models, the MTSO-DBN model could effectively improve the accuracy of WT faults for wind farms.