1. Introduction
Wind energy is one of most essential substitute energies due its competitive cost and maturity of technology. According to the World Wind Energy Association (WWEA), the total capacity of all wind farms worldwide reached 744 GW in 2020.
Due to the development of wind power production, enhancement of the control of wind energy conversion (WEC) systems is required. For this reason, manufacturers’ efforts have been focused on the improvement of these systems’ lifetimes and the decrease of operation breakdowns (downtime maintenance process), leading to continuous energy production with high power quality [
1,
2].
Wind energy conversion (WEC) systems are composed of various interconnected electrical and mechanical elements. However, unexpected failures usually accompany the operation of these systems. When a fault in a system occurs, it can have an adverse effect on the system’s availability, in addition to the production rate. Indeed, many components of wind turbines (WT) can fail due to harsh environmental and operating conditions, resulting in lengthy downtime maintenance periods [
3,
4]. The most common failures are related to blades [
5,
6], generators [
7,
8], power converters [
1,
9], and gearboxes [
10,
11]. As a crucial component and the heart of these systems, the power converter plays a significant role in transferring the generated power to the grid. It converts electrical energy that varies according to the wind speed to energy with a constant frequency complying with grid specifications [
12]. It was indicated in [
13] that 21% of 25% of the total failures in WEC converters (WECC) are caused by the semiconductor. In order to avoid the WECC collapse, these failures should be detected and diagnosed at an early stage. Therefore, fault detection and diagnosis (FDD) is viewed as essential means to achieve these goals [
14]. The authors of [
15,
16] considered multiple faults in the same-side converter. They address multiple faults in both converter sides at once. The authors of [
17] have studied multiple faults by modeling both converter sides as a state space equation. In [
18], the authors examined two open-switch faults in one sub-module and also addressed the detection of multiple faults in random sub-module elements. However, the linking effects between generator-side and grid-side converters are not taken into account, which could affect considerably the system behavior. The authors in [
14] focused on simple faults in both converter sides. This current work deals with faults in both converter sides, taking into consideration all possible fault scenarios such as simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mix fault on both sides. Each scenario affects the system behavior in a different way, accordingly, considering each of it is a crucial task.
Generally, FDD approaches can be categorized into two main classes: the model-based and the data-driven methods. Model-based FDD uses observers and system identification models of the processes; it demands a precise mathematical model, which is complicated to acquire in reality. Its performance is dramatically impacted by uncertainties and unmodeled noises [
19,
20]. Data-driven methods aim to extract information from the measured signals to train the model, and then use the information for diagnosis in the testing phase [
21,
22,
23]. Numerous studies based on machine learning approaches have been employed in WEC FDD, such as decision tree (DT) [
24], naive Bayes (NB) [
25], support vector machine (SVM) [
26], K-nearest neighbors (KNN) [
27], and random forest (RF) [
14]. In [
2], a WEC fault diagnosis technique based on an RF and kernel principal component analysis (KPCA) approach is developed. In this proposal, KPCA is applied to extract the most informative features from data, with the aim of improving the classification results using an RF classifier. In [
24], the authors introduce five-stage statistical process control and machine learning methods to diagnose wind turbine faults (rotary blades, gearboxes, generators, and hydraulic oil systems) and predict maintenance demands. The five adopted analytical tools in statistical process control are: (1) check lists, (2) Pareto charts, (3) cause and effect diagrams, (4) scatter plots, and (5) control charts. Firstly, the check list comprises information such as the type of wind turbine faults, the duration of faults, causes, and repair events. Authors have classified the repair events by frequency of anomalies in the dataset. Secondly, a Pareto chart is developed based on the classified check list items and presents the repair events with regard to cumulative percentage. Thirdly, an analytical tool, that is, the cause and effect diagram, is presented in order to distinguish the essential causes of principal mechanical issues and produce recommendations to technicians for maintenance. Fourthly, scatter plots are applied to investigate the relationship between features and determine abnormal data. Lastly, control charts are applied to show changes and variation in the observed data over time. After that, a density-based spatial clustering of applications with noise (DBSCAN) approach is used to represent the relationship between the entire amount of wind generation and the five attributes, in addition to ranking normal and abnormal data. Finally, two machine learning techniques—decision tree and random forest—are applied in order to construct a predictive maintenance models for anomalies. The inherent disadvantages of traditional ML-based approaches make them ineffective at representing complex functions due to their unsatisfactory performance and their generalization capabilities. With the explosion of deep learning (DL) algorithms in artificial intelligence (AI) applications, technology has shown a strong ability to surpass conventional intelligent algorithms [
28], whose problems include their dependence on hand-designed feature, as well as their difficulty in understanding sequential data. Thus, many researchers have opted to use DL modes instead of traditional classifiers in fault diagnosis. In fact, the major distinction between AI models and DL models is that the latter can automatically learn precious features directly from raw data. Considering the rapid rise of DL, many architecture have been developed, such as convolutional neural networks (CNN), deep belief networks (DBN), and recurrent neural networks (RNN).
Authors in [
29] propose an ensemble transfer CNN driven by multi-channel signals for fault diagnosis of rotating machinery. In this case, modified CNNs based on stochastic pooling and leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Then, the target CNN is initialized using the learned parameter knowledge of each individual source CNN with the help of parameter transfer. Lastly, in order to achieve the comprehensive result, a new decision fusion procedure is constructed to flexibly fuse each individual target CNN. An FDD approach based on the convolutional neural network long short-term memory attention mechanism (CNN-LSTM-AM) for anomaly recognition and fault detecting of wind turbine is suggested in [
30]. The CNN is used to extract features of state space from wind turbine, LSTM is applied to improve the time characteristics fusion of different part states, and AM is used to help the model make more accurate judgments through mapping weight and parameter learning. The authors of [
31] propose an approach to regularize the discriminant structure of the deep network with both intrinsic and extrinsic generalization goals in order to improve the learning of robustness features and to generalize to unseen domains. In [
32], the authors develop an improved RNN techniques for fault detection and diagnosis for wind energy conversion (WEC) systems. In the beginning, a reduced RNN-based hierarchical K-means clustering is adopted in order to simplify the complexity of the model in terms of training and computation time. It is used to treat the correlations between samples and extract a reduced number of observations from the training data matrix. Then, two reduced RNN-based interval-valued-data methods are developed for classification purposes.
With the RNN, sequence inputs of variable length can be handled due to the recurrent hidden states, whose activation at any particular time is dependent on that of the previous moment. Other research proposes long short-term memory (LSTM) to directly learn features and time-series data [
33]. In fact, the recursive behavior of the LSTM gate architecture allows it to capture long-term dependencies and efficiency figures without the gradient vanishing problem of recurrent neural networks (RNNs) [
34].
In the current work, we propose an innovative fault diagnosis paradigm using KPCA-based BiLSTM. In fact, the previous studied LSTM-based fault diagnosis approaches were applied directly to raw data without taking account the impact on the extracted and selected features on the classification accuracy, as well as the nonlinear behavior of features. To address these issues, a KPCA-based bidirectional LSTM (KPCA-based BiLSTM) FDD approach is proposed to detect the faults and distinguish between the working modes in the WTC systems. The KPCA model is able to deal with noisy, nonlinear, multivariate, and statistical features [
35]. In comparison to other nonlinear techniques, KPCA has the advantages of not involving nonlinear optimization, requiring no prior specification of reduced space dimensions, and being able to handle a wide range of nonlinearities due to its ability to use different kernels [
36]. Therefore, in this work, the KPCA feature extraction/selection paradigm and the BiLSTM classification model are applied to detect and classify the WTC faults. The proposed approach makes full use of the KPCA for powerful feature extraction/selection and BiLSTM for fault diagnosis, which can solve the problem of nonlinear, statistical, and multivariate feature extraction and fault diagnosis in WTC systems.
This paper is organized as follows:
Section 2 is dedicated to a brief description of the KPCA tool used in feature extraction and selection and of the BiLSTM technique for classification purposes.
Section 3 presents the application of the developed methodology for fault detection and diagnosis. Finally, the conclusions are illustrated in
Section 4.
5. Conclusions and Future Works
In this paper, an enhanced KPCA-based BiLSTM method was presented for wind energy conversion (WEC) system fault detection and diagnosis (FDD). The proposed FDD approach was addressed in such a way that the extracted and selected features using the KPCA model are introduced as input for the BiLSTM for classification purposes. In fact, the effectiveness of the proposed classifier was validated by comparing it with several classical methods, including NN, FFNN, CFNN, RNN, and CNN. In order to evaluate the performance of the developed KPCA-based BiLSTM approach, we used data obtained from healthy WEC converters (WECC) that were then injected with several fault scenarios of fault: simple fault generator-side, simple fault grid-side, multiple faults generator-side, multiple faults grid side, and mixed faults both side. The obtained results showed the effectiveness and robustness of the proposed FDD approach in terms of accuracy, recall, precision, and computation time. The fault diagnosis accuracy when using the proposed tools showed some missed detection and false alarm results, and some faults were not correctly classified. Thus, one future research direction is to develop adaptive BiLSTM-based tools to update the model in order to reduce missed classification results. Another future direction is to develop adaptive BiLSTM-based approaches dealing with uncertainties in WTC systems using interval-valued data representation. Additionally, ensemble-based models will be developed using multiple models in order to enhance decision-making accuracy. Ensemble-based models merges multiple learning models in order to produce one optimal predictive model that gives effective diagnosis results. Furthermore, in this study, we considered a wind profile where the mean value of the speed, as well as the pitch angle, is constant. In the real world, the wind has a variable profile according to climatic conditions. Thus, one future research direction is to implement an FDD approach while taking into account wind variations.