1. Introduction
Wind energy, as a clean and renewable energy, now has been one of the major potential and practical renewable resources. In recent years, the installed capacity of wind turbines all over the world has increased rapidly [
1,
2]. With the increase of installed capacity and wind turbine complexity, frequent malfunctions result in low reliability and expensive maintenance costs of wind turbines. According to statistics, the cost of operation and maintenance of onshore wind farms and offshore wind farms account for about 15–20% and 30–35% of the total revenue, respectively [
2,
3]. To raise the availability and reliability of wind turbines, monitoring the operation status of wind turbines and detecting potential faults are increasingly significant. Gearbox, as a key component of wind turbines, often occurs various faults, which leads to high maintenance costs. Statistically, the maintenance cost caused by gearbox is as high as 13% of the total cost [
4]. In recent years, monitoring the operation status of the gearbox has attracted wide attention.
With the development of the wind power industry, there are numerous studies on wind turbines fault diagnosis and condition monitoring. According to the methods adopted by these studies, they can be roughly classified into two types: model-based methods and data-driven methods [
5]. In addition to classical methods such as state estimation and parameter estimation, many new model-based studies have been proposed in recent years [
6,
7,
8,
9,
10]. In [
8], a set-valued approach is proposed for wind turbine fault diagnosis. In order to ensure the performance of fault diagnosis, model-based methods need to establish accurate mathematical models of wind turbines system. However, due to the complexity of wind turbine systems, it is difficult to establish an accurate mathematical model, which leads to the difficulty of model-based in practical application [
5]. In contrast, data-driven methods do not require accurate mathematical models, and most wind turbines are equipped with a supervisory control and data acquisition (SCADA) system, which makes it easy to obtain data. Therefore, the data-driven method is a very worthwhile aspect to be studied for wind turbine fault diagnosis and condition monitoring. The temperature of gearbox components is closely related to the operation state of the gearbox. Excessive temperature will cause the occurrence of faults. Similarly, the occurrence of faults in a component will also be accompanied by a significant change in temperature [
11]. Therefore, high temperature warning of gearbox components is crucial for condition monitoring of wind turbines and reduction of operational and maintenance costs. The key of high-temperature warning is to improve the accuracy of the temperature prediction model as much as possible. In this paper, a data-driven method based on temperature prediction is studied to monitor the operation status of the gearbox.
Generally, according to the sources of data, the time series prediction models can be divided into two categories as the multi-variable models and single-variable models in the wind turbines system. At present, most temperature prediction models adopt multi-variable data based on SCADA system [
12,
13]. Huang et al. [
12] put up with a hybrid method combining principal component analysis (PCA) and nonlinear autoregressive dynamic neural network to establish a gearbox oil temperature prediction model. Wang et al. [
13] presented a condition monitoring method of wind turbine main bearing based on the deep belief network (DBN), where DBN is adopted to establish the normal temperature prediction model, so as to realize the condition monitoring of wind turbine main bearing. However, the use of multi-variate data may increase the complexity and uncertainty of the modeling process, which will reduce the performance of the prediction model. Compared to the multi-variable model, the single-variable model has lower computational complexity and easier data acquisition [
14].
Although single-variable methods are seldom used in temperature prediction of gearbox components, many prediction methods have been proven to be effective in other aspects of wind energy systems, such as wind speed and wind power. The prediction methods can be roughly classified into three categories: the statistical methods [
15,
16], conventional machine learning methods [
17,
18] and deep learning methods [
19,
20]. Among the statistical methods, autoregressive integrated moving average (ARIMA) is the most classical and widely adopted model. However, most statistical methods are difficult to deal with the non-linear characteristics of the time series, which results in low prediction accuracy. In addition, the conventional machine learning methods are also widely chosen in time series prediction, which mainly include back propagation (BP) neural networks, radial basis function (RBF) neural network, extreme learning machine (ELM), support vector machine (SVM) methods and so on. Nevertheless, although the traditional machine learning method is an intelligent method, its ability of learning data nonlinearity and non-stationarity is not strong because of its shallow structure. In recent years, with the breakthrough of neural network technology, deep learning approaches have attracted wide attention because of its better performance in many tasks. Compared with the shallow methods, the deep learning methods can have a better ability of non-linear expression and data feature extraction [
21]. Wang et al. [
19] carried out a novel hybrid deep learning-based approach. The comparison results indicate that the hybrid model can better learn the non-linear and non-stationary characteristics.
The performance of gearbox condition monitoring depends on a high precision temperature prediction model, especially in the part of the high-temperature series. To this end, it is of great significance to develop optimization methods for promoting prediction performance. The existing optimization algorithms have three main aspects, including signal processing techniques [
22,
23,
24], parameters optimization techniques [
25,
26] and error correction techniques [
27,
28]. As shown in
Table 1, it is a summary of the above-mentioned and related algorithms.
In signal processing techniques, the signal decomposition method is widely used, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), fast ensemble empirical mode decomposition (FEEMD) and complete ensemble empirical mode decomposition (CEEMDAN). Various literatures have proved the effectiveness of decomposition algorithms. However, these traditional decomposition methods have some shortcomings. For example, sometimes it is difficult to decompose multiple low-frequency components for wavelet decomposition (WD) and wavelet packet decomposition (WPD), while other decomposition algorithms, including EMD, EEMD, FEEMD and so on, currently lack the strict mathematical proof [
29]. In order to overcome these drawbacks, some new decomposition algorithms are adopted in time series prediction, such as empirical wavelet transform (EWT) and variational mode decomposition (VMD). In [
24], the VMD approach is chosen to decompose the corresponding time-series signals, which avoids the interaction between different modes. In addition to the decomposition algorithm mentioned above, error correction is also a method to improve the performance of the prediction model [
30]. In [
28], an error correction model based on ICEEMDAN and ARIMA algorithm is proposed to promote the prediction accuracy.
In addition, there are still some deficiencies in the field of research, which need to be further studied. First, many literatures decompose training data and testing data together [
31,
32], which is not feasible in the process of real-time prediction. Regretfully, other literature does not clearly explain the construction process of the modeling data. Second, different from the wind speed prediction, the temperature will drop dramatically due to shutdown and other factors in the operation of wind turbines, which will result in inaccurate prediction results.
In the study, a new hybrid forecasting method is proposed, which consists of a preliminary temperature prediction model and an adaptive error correction model. The innovations and contributions of the proposed hybrid model are as follows: (a) with aims to avoid the complexity and uncertainty of multi-variable prediction model, a prediction model based on single-variable data is proposed. In this paper, 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.
The organizational structure of the paper is as follows: (a) the framework and algorithms of the hybrid prediction model are explained in
Section 2; (b) gearbox components temperature forecasting case studies are presented in
Section 3; and (c) conclusions are drawn in
Section 4.
4. Conclusions
The accuracy of the prediction model directly affects the high-temperature warning performance of the wind turbines gearbox components. In order to achieve higher forecasting accuracy, a novel hybrid model, named the LSTM-AEC, is proposed in the study, which consists of the LSTM preliminary prediction model and adaptive error correction algorithm based on the VMD method. Besides, the dynamic and real-time data decomposition process of the VMD algorithm ensures that the proposed model can be used in the online process. To demonstrate the effectiveness and superiority of the proposed hybrid model, three wind turbine prediction experiments are given in this paper. The prediction models for performance comparison include the hybrid model (LSTM-AEC), BP, ELM, LSTM, ELM-EC, LSTM-EC, and ELM-AEC. Based on the comparative analysis of the prediction performance of different models, the following conclusions can be drawn. (a) By comparing LSTM with ELM and BP algorithms, it can be found that LSTM is superior to other models to some extent; (b) by comparing the two sets of models which contains ELM, ELM-EC, ELM-AEC, LSTM, LSTM-EC and LSTM-AEC, it is found that the adaptive error correction algorithm can optimize the preliminary prediction results to a certain extent; (c) according to the prediction results of three wind turbines, the proposed hybrid model has better performance than other comparative models. Moreover, the prediction accuracy of the proposed hybrid model in the high-temperature series part is high, which lays a solid foundation for the high-temperature warning of the wind turbines gearbox components.
Although the current research shows that the hybrid model has better prediction performance in temperature prediction of gearbox components, there are still some limitations of the model which need further study. The influence of model parameter initialization results in the fluctuation of prediction performance. Although this fluctuation does not affect the conclusions drawn in this paper, it shows that the hybrid model proposed in this paper has the possibility of further improvement. In addition, the hybrid model proposed in this paper only predicts the temperature of gearbox components in one step, but in practical applications, the multi-step prediction is more greatly needed, which can provide more maintenance time. In future work, the problem of parameter initialization will be studied to further improve the performance and robustness of the prediction model, and the development of multi-step temperature prediction model is needed, which makes the prediction model more practical.