Wind Turbine Fault Research Articles

RlaNet: A Residual Convolution Nested Long-Short-Term Memory Model with an Attention Mechanism for Wind Turbine Fault Diagnosis

This paper proposes a new fault diagnosis model for wind power systems called residual convolution nested long short-term memory network with an attention mechanism (rlaNet). The method first preprocesses the SCADA data through feature engineering, uses the Hermite interpolation method to handle missing data, and uses the mutual information-based dimensionality reduction technique to improve data quality and eliminate redundant information. rlaNet combines residual networks and nested long short-term memory networks to replace traditional convolutional neural networks and standard long short-term memory architectures, thereby improving feature extraction and ensuring the abstractness and depth of the extracted features. In addition, the model emphasizes the weighted learning of spatiotemporal features in the input data, enhances the focus on key features, and improves training efficiency. Experimental results show that rlaNet achieves an accuracy of more than 90% in wind turbine fault diagnosis, showing good robustness. Furthermore, noise simulation experiments verify the model’s resistance to interference, providing a reliable solution for wind turbine fault diagnosis under complex operating conditions.

Influence of wind farm fault current control strategies on distance protection during unsymmetrical faults

Abstract This paper evaluates the performance of distance protection near a wind farm with type IV wind turbines with various basic fault current control strategies implemented in the power converters. The considered fault control strategies include control of purely positive sequence currents or combined positive and negative sequence currents for reactive power support of various magnitudes. A multi-variable study is performed in PSCAD to show performance of a basic algorithm of distance protection at a 420 kV line in a vicinity of the wind farm. The results show that the majority of relay failures occur for phase-phase-ground faults, that reactive current control has less influence in strong grid than in weak grid, and that standard wind turbine fault control procedure of providing balanced current might result in fault currents much smaller than maximum allowed (usually 1.2 pu).

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

A novel memristor chaotic circuit and its application in weak signal detection of wind turbine fault

Dynamic responses of monopile offshore wind turbines under different wind-wave misalignment angles

Wind-wave misalignment is a common occurrence for monopile offshore wind turbines during extreme typhoon conditions. This phenomenon has a significant impact on the structural dynamic response of turbine structures and poses a threat to their structural integrity. In this study, finite element analysis is conducted on 75 sets of working conditions of monopile offshore wind turbines to investigate their dynamic response under different wind turbine operating states, wind-wave misalignment angles, and randomness of wind and wave load conditions. The results of the analysis indicate that the impact of wind-wave misalignment angles on the dynamic response of the monopile offshore wind turbine varies depending on the operational conditions. Furthermore, the study reveals that there are variations in the dynamic response of the monopile offshore wind turbine under different combinations of wind and wave loads. During the parked state and yaw system fault of the monopile offshore wind turbine, the most adverse working conditions need to consider wind-wave misalignment angles of 0° and 180°. In contrast, the most adverse working conditions for operational states emerge when the wind-wave misalignment angle is 90°. These findings highlight the importance of considering wind-wave misalignment angles and the randomness of wind and wave load conditions when assessing the dynamic response and structural integrity of monopile offshore wind turbines.

Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow

Effective wind turbine (WT) condition monitoring is significant to improve wind power generation efficiency and reduce operation and maintenance costs. Supervisory control and data acquisition (SCADA) data are widely utilized for WT condition monitoring due to their low cost and accessibility. However, the intricate interdependencies among SCADA variables affect the accuracy of WT fault detection, and few methods provide identification for the anomaly cause. To solve these issues, this paper proposes an unsupervised fault detection and identification method based on self-attention-based dynamic graph representation learning and variable-level normalizing flow. Firstly, a dynamic graph representation learning model based on spatial and temporal self-attention mechanisms is proposed. It can effectively learn the dynamic and mutual relations among variables for early fault detection. Secondly, a variable-level normalizing flow is proposed for discriminative density estimation of variables, which can realize component fault localization. Finally, a node deviation index based on contrast graph is proposed to identify the root cause of anomalies. Experimental results using WT data from a wind farm in Northwest China prove that the proposed method has better accuracy and interpretability in WT fault detection and identification, which displays better effectiveness in practical wind energy applications.

Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal

Wind Turbines are the most crucial devices in wind energy conversion systems to increase energy generation efficiency from wind sources. Blade failure in wind turbines is due to the induced vibrations of change in ecological variables. It is necessary to periodically inspect the state of the wind turbine blades to enhance safety by decreasing downtime, lessening the likelihood of unplanned failures requiring extensive repair, and progressively increasing power generation with reduced logistical costs. The primary goal of the proposed research is to adopt a hybrid strategy that combines convolutional neural network models and continuous wavelet transform. The bump wavelet-based continuous wavelet transform with a convolutional neural network model is employed to classify the faulty wind turbine blades based on the extracted vibration signals of turbine blades. This approach distinguishes between different states of blade faults affecting wind turbine blades during operational phases. The research considers blade faults in the horizontal axis wind turbine, including blade bending, erosion, and the connection looseness between the hub and blade. The cross-validation and hold-out methods are used to validate the classification accuracy and are compared with other existing popular methods. The hold-out method has a better classification accuracy of 97.916%.

Maximum subspace transferability discriminant analysis: A new cross-domain similarity measure for wind-turbine fault transfer diagnosis

In the field of fault transfer diagnosis, many approaches only focus on the distribution alignment and knowledge transfer between the source domain and target domain. However, most of these approaches ignore the precondition of whether this transfer task is transferable. Current mainstream transferability discrimination methods heavily depend on expert knowledge and are extremely vulnerable to the noise interference and variations in feature scale. This limits their applicability due to the intelligent requirements and complex industrial environment. To address the challenges mentioned previously, this paper introduces a novel cross-domain similarity measure called maximum subspace transferability discriminant analysis (MSTDA) with zero-label prior knowledge. MSTDA is comprised of a maximum subspace representation and a similarity measurement criterion. During the phase of maximum subspace representation, a new kernel-induced Hilbert space is designed to map the low-dimensional original samples into the high-dimensional space to maximize the separability of different faults and then solve the separable intrinsic fault features. Following that, a novel similarity measurement criterion that is resistant to variations in feature scale is developed. This criterion is based on the orthogonal bases of intrinsic feature subspaces. The mini-batch sampling strategy is used to ensure the timeliness of MSTDA. Finally, the experimental results on three cases, particularly in the actual wind turbine dataset, confirm that the proposed MSTDA outperforms other well-known similarity measure methods in terms of transferability evaluation. The related code can be downloaded from https://qinyi-team.github.io/2024/09/Maximum-subspace-transferability-discriminant-analysis.

A clustered federated learning framework for collaborative fault diagnosis of wind turbines

Data-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance and reduce communication costs in federated learning with data heterogeneity among different clients, we introduce a clustered federated learning framework to wind turbine fault diagnosis. Initially, a lightweight multiscale separable residual network (LMSRN) model is proposed for each local client. The LMSRN model integrates a multiscale spatial feature derivation unit and a depthwise separable feature extraction unit. Subsequently, to tackle data heterogeneity among clients, canonical correlation coefficients of representations are extracted from the intermediate layers of local LMSRN models, and a representational canonical correlation clustering (RCCC) method is proposed to assess the similarity of local LMSRN models and group them into clusters. Finally, a global model is trained for each cluster. Real-world wind turbine data experiments showcase the superior performance of the proposed clustered federated learning framework over traditional methods in terms of diagnostic accuracy and computational speed. Additionally, the optimal choice of the number of clusters is also discussed.

A novel wind turbine fault diagnosis method based on compressive sensing and lightweight SqueezeNet model

The fault diagnosis method based on model and signal processing has some problems, such as difficulty in modeling and difficulty in extracting signal features; As the depth of the neural network deepens, there will be the problem of gradient vanishing or mass, and directly converting the fault signal into a one-dimensional or two-dimensional image as the network input cannot retain the time information between the signals, resulting in the loss of information. In order to solve the above problems, this paper proposes a fault diagnosis method based on Compressed Sensing (CS) and lightweight SqueezeNet model. Firstly, the Compressive Sensing (CS) technology was used to sparse the original signal, and the Compressed Sampling Matching Pursuit (CoSaMP) algorithm was used to compress and reconstruct the signal to complete the signal noise reduction and remove the data redundancy. Secondly, the compressed and reconstructed signal was encoded by recursive graph (RP) to generate a two-dimensional image, and the temporal correlation characteristics of the signal were retained. Thirdly, the traditional SqueezeNet model is improved by using the residual idea to enhance the feature extraction ability. The RP dataset was input into the improved SqueezeNet model for fault feature extraction and fault classification. Finally, the experimental results show that compared with other methods, the proposed method can identify faults and have high diagnostic accuracy on the rolling bearing data samples of wind turbines.

Fault detection framework in wind turbine pitch systems using machine learning: Development, validation, and results

This work develops a methodology for detecting faults in wind turbines through supervised machine-learning classification methods focusing in two modes of operation. The Normal mode will be related to the regular operation of the wind turbines and in the case of the abnormal mode, the system will be cataloged as Pitch system failure. This methodology uses real data through the supervisory control and data acquisition (SCADA) system and the recording of events and actions (LOG alarm system). Sixteen classifications artificial intelligence (AI) models were tested, such as Random Forest Classifier (RF), Gradient Boosting Classifier (GBC), Extra Trees Classifier (ET), Extreme Gradient Boosting (XGBOOST), Light Gradient Boosting Machine (LIGHTGBM), Categorical Boosting Classifier (CATBOOST), Adaptative Boosting Classifier (ADA), Decision Tree Classifier (DT), Linear Discriminant Analysis (LDA), K-Nearest Neighbors Classifier (KNN), Logistic Regression (LR), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Dummy Classifier (DC), Super vector machine (SVM), Ridge Classifier (RC). The training process model was carried out by structuring the dataset in 75% for training and validation and 25% for the final test, considering cross-validation and optimization of the hyperparameters. The Random Forest Classifier (RF) and Extra Trees Classifier (ET) classifiers models presented the best performances, and the models with the lowest performances were K-Nearest Neighbors Classifier (KNN) and Linear Discriminant Analysis (LDA) ones. The average hit effectiveness for both Healthy and Faulty operating modes was approximately 80% across most of the models developed.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

CTNet: A data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults.

Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting

AbstractIntelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environment. This article addresses some of the challenges of fault classification in wind energy systems by proposing an integrated approach that combines deep learning features with a resampled supervisory control and data acquisition (SCADA) dataset. The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near‐miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite‐particle swarm optimization (AEPSO). The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. Furthermore, the proposed integrated approach shows significance for fault diagnosis enhancement in wind energy systems and advancing the field towards more efficient and reliable operation.

Wind Turbine Fault Diagnosis Based on Cyclostationary Feature Detection From Compressive Samples

Wind Turbine Fault Diagnosis Based on Cyclostationary Feature Detection From Compressive Samples

Early anomaly detection of wind turbine gearbox based on SLFormer neural network

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability.

Adaptive Optimal Resonance Fault Feature Enhancement for Wind Turbine Based on Frequency Search and Uncertainty Analysis

The wind turbine fault feature has strong uncertainty due to various factors such as wind speed and equipment parameters. Based on the analysis of the vibration characteristics of wind turbines with unknown prior conditions, this paper conducts research on the enhancement method of fault features. First, analyzing the change in fault features caused by the change in operating conditions to lay the foundation for subsequent research; Second, for the uncertainty of wind turbine fault feature frequency, searching for fault features by constructing the energy in each spectral interval, then use signal noise optimal resonance to transfer noise energy to low frequency fault features to achieve the purpose of adaptive optimal resonance enhancement of fault feature; Finally, validation of the proposed method by simulation experiments and fault signals of actual wind turbine rolling bearings. The case shows that the proposed algorithm can effectively realize the uncertain fault feature of wind turbines search and enhancement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

LTFM-net framework: Advanced intelligent diagnostics and interpretability of insulated bearing faults in offshore wind turbines under complex operational conditions

The rapid advancement of intelligent theories and models, exemplified by deep learning, has achieved remarkable success across numerous fields. However, given that the complexity and variability of offshore wind turbine systems, particularly in the context of high-power variable-frequency control of insulated bearings in offshore wind turbines, the problem of fault identification has become a recognized technical challenge. Additionally, fully exploiting the temporal characteristics of insulated bearing fault data poses a key problem that demands urgent resolution. To address these gaps, this paper pioneers a novel Lightweight Temporal Feature-focused framework, named LTFM-Net, aimed at solving the difficult problem of identifying insulated bearing faults in offshore wind turbines in practical engineering applications. Specifically, this framework enables the intelligent identification of insulated bearing faults under harsh operating conditions such as alternating voltage and variable loads, marking a first in this field. Furthermore, an innovative strategy named Weighted Diminish Recurrent Unit (WDRU) was developed, along with the derivation of its backpropagation formula, which is innovatively applied to the feature extraction module of the LTFM-Net framework for the first time. Thus, an efficient method for acquiring fault data from insulated bearings in offshore wind turbines was proposed. Based on a unified dataset, the diagnostic performance of the LTFM-Net framework was evaluated and compared with seven advanced methods. The results demonstrate that the LTFM-Net achieves precise identification of insulated bearing faults, confirming its excellent generalization, robustness, and superiority. The introduction of t-SNE for visualizing the fault characteristics of insulated bearings uncovered by the LTFM-Net framework further enhances its reliability, accuracy, and credibility.

A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines

AbstractFaulty gears are a common cause of wind turbine failures. For this sake, this work was developed as a reliable diagnostic tool for wind turbines to improve wind power stability accordingly. A convolutional extension neural network (CENN) was proposed to identify vibration and audio signals captured from a gearbox. According to the status of the contained faulty gears, a gearbox was categorised as one of the three types: (i) broken, (ii) rusty and (iii) a combination of (i) and (ii). It was further assigned one of the three severity levels: mild, moderate and severe. Therefore, there were a total of nine combinations for identification. Captured raw vibration and audio signals were applied to a chaotic synchronisation detector by which 3D chaotic error scatter feature images were generated to train and test the CENN. The recognition rate provided by CENN and the majority rule reached 99.6%, and then slightly fell to 97.4% in a noise robustness test, and consequently CENN outperformed counterparts in terms of the recognition rate and the robustness against noise. Accordingly, multiple gearbox faults can be well diagnosed for the first time in the literature. Finally, this paper concludes with a simplified version of the original proposal.

Wind turbine real-time data analysis and monitoring and warning system based on Storm

Abstract This paper introduces a real-time data analysis and monitoring and warning system for wind turbines based on Storm platform. Firstly, the framework and core components of Storm technology are introduced in detail, as well as the related Kafka technology. At the same time, the method of data forecasting is introduced. In state prediction algorithm based on naive Bayes, the basic idea of naive Bayes algorithm is introduced in detail, and the pseudo-code setting meter of the algorithm is given. In addition, the wind turbine condition warning algorithm is designed and implemented. In the chapter of real-time data analysis and early warning system design and implementation for wind turbine, the database is designed, including the design of MySQL database and HBase database, the design and reality of real-time data analysis and visualization system, and the design and reality of wind turbine fault early warning system. Finally, in the chapter of system test and analysis, the system environment and test data set are described, and the prediction algorithm based on naive Bayes is tested and the system is tested.

A Review of Machine Learning

The ever-growing complexity of engineering systems and the vast amount of data generated necessitate advanced data analysis techniques like machine learning (ML). This review explores the motivations and advantages of integrating ML into real-world engineering applications. The paper highlights the limitations of traditional deterministic models in handling intricate interactions within modern engineering systems. ML offers significant benefits including improved efficiency, enhanced decision-making, advanced automation and control, adaptability, and cost savings. Real-world applications across various disciplines are explored, including image recognition and computer vision in self-driving cars, predictive maintenance for optimizing equipment lifespans, structural health monitoring for identifying potential damage, and signal processing for control systems in airplanes and traffic lights. The review concludes by summarizing key findings from case studies on autonomous vehicle navigation, smart grid optimization, wind turbine fault detection, and HVAC system optimization in smart buildings. These case studies showcase the power of machine learning techniques like deep learning, reinforcement learning, sensor fusion, and ensemble learning in achieving superior performance and efficiency in various engineering domains. Key Words: Machine Learning, Engineering Applications, Artificial Intelligence, Big Data, Predictive Maintenance, Robotics, Structural Design, Smart Grids, Fault Detection, Optimization, Control Systems.