An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

Abstract

Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

An Abnormal Data Processing Method Based on An Ensemble Algorithm for Early Warning of Wind Turbine Failure

“…Therefore, in this study, the Otus algorithm is considered to distinguish the normal cluster from the abnormal cluster. In addition, according to expert knowledge, the gearbox oil temperature and gearbox shaft temperature are limited to 75 • C and 80 • C, respectively [12]. DBSCAN is a common clustering algorithm based on density, which can distinguish the sparse data from the dense data [41].…”

“…Because it does not need complex mathematical models and profound expert knowledge, data-driven methods have been developed rapidly in recent years. Supervisory control and data acquisition (SCADA) data, which contain abundant operational status information, has been widely proved to be effective in the field of wind turbine fault research [12], [13]. In the research on imminent fault early warning using SCADA data, methods based on the normal behavioral model are the most commonly used modeling methods [14].…”