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.
Highlights
Wind energy, as a clean and renewable energy, has been one of the major potential and practical renewable resources
A more suitable deep learning model for time series analysis, long short term memory (LSTM) model, is adopted, which can better learn the non-linear and non-stationary characteristics of temperature series; (b) in view of the problem of drastic temperature drop caused by the above mentioned downtime phenomenon, an adaptive error correction model is designed to improve the precision of prediction model; (c) to avoid the weakness of some decomposition algorithms mentioned above such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), fast ensemble empirical mode decomposition (FEEMD) and CEMDAN, the variational mode decomposition (VMD) decomposition algorithm is employed in this paper, which can effectively reduce the chaotic characteristics and non-stationary of error series; (d) in view of the above mentioned the modeling data construction problems, a rolling data decomposition process which can be applied in practice is proposed
Preliminary prediction model and adaptive error correction algorithm based on the VMD method
Summary
As a clean and renewable energy, has been one of the major potential and practical renewable resources. A more suitable deep learning model for time series analysis, long short term memory (LSTM) model, is adopted, which can better learn the non-linear and non-stationary characteristics of temperature series; (b) in view of the problem of drastic temperature drop caused by the above mentioned downtime phenomenon, an adaptive error correction model is designed to improve the precision of prediction model; (c) to avoid the weakness of some decomposition algorithms mentioned above such as EMD, EEMD, FEEMD and CEMDAN, the VMD decomposition algorithm is employed in this paper, which can effectively reduce the chaotic characteristics and non-stationary of error series; (d) in view of the above mentioned the modeling data construction problems, a rolling data decomposition process which can be applied in practice is proposed.
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