Wind Turbine Condition Monitoring Research Articles

Back-propagation-based multivariate state estimation technique: A lightweight adaptive condition monitoring approach for wind turbine

Aimed at the challenge of wind turbine condition monitoring with insufficient available data caused by the harsh conditions, this paper presents a lightweight adaptive condition monitoring approach based on the back-propagation algorithm and the multivariate state estimation technique. In order to maximize the utility of available data, the multivariate state estimation technique is used to extract the features from multiple historical data. Moreover, buffer space is adopted to update dynamically while avoiding the effect of outliers. Then, the operator is modified so that the model can be trained and updated by the back-propagation algorithm, thereby achieving lightweight. The model performs well in condition monitoring with small samples, even discontinuous samples, that are verified through experiments based on field wind turbine datasets. The experimental results and comparative analyses demonstrate the effectiveness, generality, and advantage over others.

Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability

Abstract Condition monitoring of wind turbines with supervisory control and data acquisition (SCADA) data has attracted increasing interest due to its great benefits in improving operation reliability and reducing unplanned downtimes of wind turbines. However, existing SCADA data-based studies focus primarily on anomaly detection, and few studies have attempted to identify the underlying causes of the anomaly. To this end, this paper proposes a new system-level wind turbine anomaly detection and identification method based on an emerging graph neural network with decision interpretability (DIGNN). A correlation-based GNN is first used to capture complex inter-sensor correlations in SCADA data. More importantly, a decision interpretability module is designed to further analyze anomaly causes and effects through a twp-step global and local anomaly decision process. It can greatly improve the interpretability of the model and provide more accurate and meaningful decision results for maintenance purposes. The effectiveness and robustness of the proposed model were verified by four fault cases with SCADA datasets from a real wind farm. The experimental results demonstrated that the proposed model can provide earlier warning of anomalies with lower false alarm rates and accurate anomaly identification with good interpretability, providing valuable help for field maintenance.maintenance.

High-dimensional process monitoring under time-varying operating conditions via covariate-regulated principal component analysis

High-dimensional process monitoring, which aims to raise warnings for faulty events, is a fundamental tool in ensuring the safety of large-scale industrial systems. Over the last few decades, there has been significant interest in Principal Component Analysis (PCA)-based Statistical Process Control (SPC) charts. However, most of them rely on PCA’s optimal properties in homogeneous data, which often fails when dealing with heterogeneous data from industrial processes under time-varying operating conditions. To address this issue, this study proposes a Covariate-regulated PCA model (CrPCA) seamlessly integrated into a process monitoring framework. The main idea is to adjust the principal component directions smoothly according to the time-varying operating conditions, resulting in significantly less information loss in the principal component scores compared to existing methods. This approach further yields a low-dimensional feature vector that follows a distribution with fixed parameters, regardless of covariates, for real-time surveillance. We validate the performance of our methodology through a comprehensive numerical study and two real-world applications: wind turbine condition monitoring and hot rolling process monitoring.

Life Cycle Assessment of Piezoelectric Devices Implemented in Wind Turbine Condition Monitoring Systems

Life Cycle Assessment of Piezoelectric Devices Implemented in Wind Turbine Condition Monitoring Systems

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

Condition monitoring of wind turbine based on a novel spatio-temporal feature aggregation network integrated with adaptive threshold interval

Condition monitoring (CM) technology based on supervisory control and data acquisition (SCADA) data is crucial for ensuring reliable operation and reducing maintenance costs of wind turbine (WT). However, SCADA data is highly dimensional and sophisticated, making it challenging to extract features and determine fault thresholds to improve CM accuracy. In this paper, we propose a new approach for effectively estimating the operating condition and diagnose the faults of WTs, which is founded on the integration of spatio-temporal feature aggregation network and adaptive threshold interval. Briefly, the Kalman filter is used to preprocess the SCADA data and the variables are selected by Pearson correlation coefficient to improve data quality. Then, a novel stacked sample convolution and interaction network (SCINet) embedded in a coordinate attention mechanism (CA) is designed to extract and merge the multi-dimensional and multi-resolution spatial and temporal features. Moreover, an adaptive threshold interval is proposed for early warning of fault occurrence based on Monte Carlo (MC) dropout and kernel density estimation (KDE). The effectiveness of the proposed method is demonstrated through two real cases from a wind farm, which show superior performance compared to other comparative methods.

Testing Topological Data Analysis for Condition Monitoring of Wind Turbines

Testing Topological Data Analysis for Condition Monitoring of Wind Turbines

Wind turbine gearbox oil temperature feature extraction and condition monitoring based on energy flow

Supervisory Control and Data Acquisition (SCADA) data is widely used for wind turbine gearbox condition monitoring (WTGCM) due to its easy access and low cost, thus ensuring the safe and reliable operation of wind turbine gearboxes. However, existing WTGCM methods based on SCADA overlook the fundamental thermal physics and sensor failure. To effectively address the challenges of weak feature generalization and numerous false alarms, we propose a highly robust WTGCM method based on energy flow. Firstly, based on the principles of thermal balance and wind energy conversion, a feature extraction method considering the physical change process of oil temperature is proposed. Secondly, the performance of different feature extraction methods is quantitatively analyzed using data evaluation criteria. The reasons why different feature extraction methods have different effects are revealed through dimensionality reduction analysis. Finally, we compare the proposed method with other feature extraction methods using four models to validate the excellent performance of the proposed method in the early fault warning. The results demonstrate that the proposed method reduces the average root mean square error by 18.21% during the healthy operation of the wind turbine gearboxes. Moreover, the proposed method performs excellently in early warning of mechanical and sensor faults, significantly reducing the number of false alarms. The average warning time for mechanical faults is 32.56 h earlier.

Boosting field data using synthetic SCADA datasets for wind turbine condition monitoring

State-of-the-art Deep Learning (DL) methods based on Supervisory Control and Data Acquisition (SCADA) system data for the detection and prognosis of wind turbine faults require large amounts of failure data for successful training and generalisation, which are generally not available. This limitation prevents benefiting from the superior performance of these methods, especially in SCADA-based failure prognosis. Data augmentation approaches have been proposed in the literature for generating failure data instances within a SCADA sequence to reduce the imbalance between healthy and faulty state data points, which is relevant to fault detection tasks. However, the successful implementation of DL-based failure prognosis methods requires the availability of multiple run-to-failure SCADA sequences. This paper proposes a data-driven method for generating synthetic run-to-failure SCADA sequences with custom operational and environmental conditions and progression of degradation. An Artificial Neural Network (ANN) is trained with signals that represent these factors to reconstruct the SCADA signals. Then, it is used to generate synthetic SCADA datasets based on data available from a wind turbine that experienced a gearbox failure. Synthetic data sets generated are evaluated on the basis of the similarity of their signal distributions, the temporal dynamics within each signal, and the temporal dynamics among different SCADA signals with those in similar field datasets. The results show that the generated synthetic datasets are consistent with their field counterparts, with a comparatively lower diversity in their dynamic behaviour in time.

Data-driven wind turbine sensor health validation

This paper presents a data-driven approach for detecting anomalies in wind turbine sensors, specifically anemometers and wind vanes, and the development of a smart alerting system. The study focuses on utilizing SCADA and reanalysis (ERA5) data for accurate anomaly detection and reducing false alarms through smart change point detection algorithms. The methodology involves modeling normal behavior, detecting change points, and comparing power curves before and after these points. For anemometer anomaly detection, a three-year SCADA dataset from an offshore wind farm and a synthetic dataset is used, employing an XGBoost model and the PELT algorithm for change point detection. Wind vane anomalies are identified using a nine month dataset from seven turbines, with synthetic alterations to simulate misalignments. Results show successful detection of sudden changes in wind speed and direction, with smart alarms assisting operators in decision-making. This research enhances wind turbine condition monitoring, improving reliability and efficiency.

Offshore field experimentation for novel hybrid condition monitoring approaches

This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of components and highly variable operating conditions, makes drive train condition monitoring a challenging task. This paper details the full data measurement and analysis flow from sensor to insights and proposes a hybrid automated pipeline with signal processing and data-driven techniques to address the complexity of dealing with wind turbine vibration data. The vibration signals are directly employed to estimate the wind turbine’s instantaneous angular speed to compensate for any rotation speed fluctuations. Pre-processing is performed on the speed-independent signals to evaluate condition indicators in both the time and spectral domain for the vibration signals and their envelopes. Machine learning is then employed to distinguish the healthy state of the machine from a faulty one using the computed condition indicators. Besides the scalar indicators, also two-dimensional vibration decompositions such as the cyclic spectral correlation maps are used as inputs to the machine learning pipeline. This comprehensive and automated approach ensures both an early and reliable fault detection. Experimental results demonstrate that the fully automated hybrid pipeline can effectively be used for fleet-based health tracking of offshore wind turbine drivetrains.

Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring

Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring

Wind turbine condition monitoring based on three fitted performance curves

AbstractBased on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM‐KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM‐KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM‐KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection.

A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks.

Wind power curve modeling: A probabilistic Beta regression approach

Wind turbine power curves play a key role in various aspects during the life of a wind farm. Typical uses range from wind power forecasting to wind turbine condition monitoring. This paper addresses the identification of probabilistic models of wind power curves from observed data. The main challenge is the need to handle a statistical distribution of wind energy whose shape not only may be highly skewed, but can also change with wind speed. To address these issues, we resort to the framework of Generalized Linear Models (GLMs), proposing a Beta regression approach, with constant or variable dispersion and an appropriate preconditioning step. The proposed methodology was tested on three real SCADA measurements retrieved from public datasets, including a comparison with Quantile Regression Forests (QRFs), also in terms of robustness to outliers. The results suggest that Beta regression can be a valuable resource in the development of probabilistic models for wind energy, as it provides a high degree of flexibility while preserving an interpretable structure.

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique.

A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques

A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques

Probabilistic Prognosis of Wind Turbine Faults With Feature Selection and Confidence Calibration

With the ever-increasing expansion of the installed capacity of wind power generation, reliable condition monitoring for wind turbines (WTs) has become increasingly important. To this end, this paper proposes a probabilistic WT fault prognosis (WTFP) scheme to output reliable fault probabilities in addition to class predictions. First, a multivariate time series (MTS) based mutual information estimator (MIE) is developed to integrate with combinational optimization, selecting features that contain more beneficial temporal data patterns for WTFP. Then, a multi-fault prognosis model for WTs is trained based on the selected features and MTS learning network. Next, a confidence calibration (CC) post-processing module is appended to re-construct the mapping relation between network logits and posterior probabilities by minimizing negative log-likelihood, thereby calibrating confidence estimates to approximate the true correctness likelihood as much as possible while keeping the high accuracy of the trained MTS learning network. These components finally drive a comprehensive WTFP model. Test results on the field data of WT verify the efficacy of MTS-based MIE and CC, and show that the proposed probabilistic WTFP scheme can provide more reliable probability estimates, contributing to better decision-making.

Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data

Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data