Wind Turbine Monitoring Research Articles

Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences

Abstract. This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Efficient anomaly detection method for offshore wind turbines

Time-series anomaly detection plays a crucial role in the operation of offshore wind turbines. Various wind turbine monitoring systems rely on time-series data to monitor and identify anomalies in real-time, as well as to initiate early warning processes. However, for offshore wind turbines with a high data density, conventional methods have high computational overhead in detecting anomalies while failing to accurately detect anomalies due to variations in data scales. To address this challenge, we propose an efficient anomaly detection method with contrastive learning, called Hawkeye. Hawkeye is based on residual clustering, an unsupervised anomaly detection method for multivariate time-series data. To ensure accurate anomaly detection, a trend-capturing prediction module is also combined with an automatic labeling module. As a result, the most common information can be learned from multivariate time-series data to reconstruct data trends. By evaluating Hawkeye on public datasets and real offshore wind turbine operation datasets, the results show that Hawkeye’s F1-score improves by an average of 14% compared with Isolation Forest, and its size shrinks by up to 11.5 times on the largest dataset compared with other methods. The proposed Hawkeye is potential to real-time monitoring and early warning systems for wind turbines, accelerating the development of intelligent operation and maintenance.

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.

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.

Anomaly detection of wind turbines based on stationarity analysis of SCADA data

This study presents a stationarity-based method, based on sliding window principle, for wind turbine monitoring and anomaly detection. Initially, the window is formed with a reference data set collected from a wind turbine under healthy condition. During the monitoring process, the data window is regularly updated with the latest samples recorded by the SCADA system. The Augmented Dickey-Fuller (ADF) test is exploited to compute the stationary statistical characteristics of the data within the window at each updating step. Any abrupt changes in the ADF t-statistics indicate the fault occurrence. The method can simultaneously monitor various parameters without using normal behaviour models. Its monitoring and fault detection mechanism relies on the consecutive accumulation of variations in stationarity of SCADA signals in the data window over time. Through the validation using two SCADA data sets, the method has proven its capability in monitoring wind turbines and detecting faults at the early stage.

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.

Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison

A wind farm is usually equipped with multiple wind turbines of the same type. These wind turbines often work under same complex conditions. Accurate performance degradation monitoring is crucial for ensuring the reliable operation of wind farms and reducing maintenance costs. Motivated by this, this article develops a new wind turbine performance degradation monitoring scheme, which is based on pairwise comparison of the probability power curves of different wind turbines in a wind farm. Firstly, covariate matching is used to eliminate the inherent differences in meteorological variables of different turbines within the same data segment. Next, two probabilistic wind power curves, the quantile power curve and density power curve, model the functional relationship between the meteorological variables and wind power output. Then, deviation vectors are generated by calculating the deviation of probabilistic power curves between each pair of wind turbines. Finally, a directional Hotelling T2 control chart is proposed to monitor the deviation vectors. We apply the new method on the real data of a wind farm in East Britain. Results show that the proposed monitoring technique can monitor wind turbine performance degradation more precisely and comprehensively than the existing approaches.

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

A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy.

Automatic Detection and Intensity Classification of Pitch Misalignment of Wind Turbine Blades: a Learning-based Approach

In recent years, monitoring wind turbine operations, especially in offshore and remote installations, has become crucial. System failures, challenging assessments, and costly maintenance operations are notable concerns. Blade pitch misalignment poses one of the most significant threats, being a leading cause of downtime and energy production reduction. Traditional assessments through frequent inspections are resource-intensive and time-consuming; still they are frequently required as turbines lack routine implementation of effective automatic detection systems.To address this, our study introduces a novel machine learning-based method for autonomous pitch misalignment recognition, which relies on signals measured by a limited set of sensors, already integrated into modern wind turbines. From those signals, frequency domain features derived by harmonic analysis are extracted, ensuring robust detection also in turbulent wind conditions.This innovative method lays the groundwork for early and precise wind turbine diagnostics, promoting the shift from time-based to condition-based inspections. This transition aims to reduce maintenance costs and unexpected downtime, ultimately enhancing energy production.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Structural health monitoring of an onshore steel wind turbine

The study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance).

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed. OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems. METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed. RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data. CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.

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.

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

The service environment of offshore wind turbine (OWT) support structures is harsh, and it is extremely difficult to replace these structures during their operational lifespan, making their failure a catastrophic event. The structural health monitoring (SHM) of OWT support structures is a crucial aspect of operational maintenance for OWT support structures, aiming to mitigate significant financial losses. This paper systematically summarizes the current monitoring methods and technologies for OWT support structures, including towers and foundations. Through the review of monitoring content and the evolution of monitoring techniques for supporting structures, it delves deeper into the challenges faced by wind turbine monitoring and highlights potential avenues for future development. Then, the current damage identification techniques for OWT towers and foundations are analyzed, exploring various methods including model-based, vibration-based, artificial intelligence and hybrid fault diagnosis methods. The article also examines the advantages and disadvantages of each approach and outlines potential future directions for research and development in this field. Furthermore, it delves into the current damage identification techniques for OWT towers and foundations, discussing prevalent challenges and future directions in this domain. This status review can provide reference and guidance for the monitoring design of OWT support structures, and provide support for the fault diagnosis of OWT support structures.

Conditional monitoring and fault detection of wind turbines based on Kolmogorov–Smirnov non-parametric test

This research presents a new method for conditional monitoring based on the wind turbine power curve. The Kolmogorov-Smirnov (K-S) distribution test is employed in the assessment of turbine data and the detection of abnormality (faults) in wind turbines. The process begins with anomaly detection and filtration of faulty SCADA data by a quantile-based filtration approach. Suitable data comprising wind speed, air density, ambient temperature, and pitch angle are utilized in the development of wind turbine power curve models that represents actualities within wind farms. The radial basis function (RBF), multi-layer Perceptron (MLP), and gradient boosting (GBR) methods utilized for model development are compared for predictive accuracy using Mariano-Preve test. The null hypothesis assumes equal predictive ability (EPA); if rejected, an algorithm compares the coefficients of correlation of the models and selects the closest to one (unity). The most accurate model is utilized for the creation of a bin-wise distribution from past data, and bin-wise confidence levels from the plot of wind speed and output power. Cochran’s method was utilized to validate the minimum sample size that will possess a sampling distribution similar to that of the population, and a fault is detected if there is a reasonable difference between the sample distribution and population distribution. The K-S test, having a null hypothesis of equivalent distributions, signals a fault if the null hypothesis is rejected. Two wind turbine SCADA datasets associated with two fault events are used for the assessment of our method. The results indicate that our method effectively discovers abnormalities in power output relating to increased bearing temperature and reduced generator rpm, thereby aiding in the detection of faults long before they occur.

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.

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.