A data-mining approach for wind turbine fault detection based on scada data analysis using artificial neural networks

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20-30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm

Wind turbine gearbox condition monitoring based on class of support vector regression models and residual analysis

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models

On the Development of Offshore Wind Turbine Technology:An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market

Offshore wind turbine drive train technology is evolving as developers increase size, aim to maximise availability and adapt to changing electricity grid requirements. This work first of all explores offshore technology market trends observed in Europe, providing a comprehensive overview of installed and planned capacity, showing a clear shift from smaller high-speed geared machines to larger direct-drive machines. To examine the implications of this shift in technology on reliability, stop rates for direct-drive and gear-driven turbines are compared between 39 farms across Europe and South America. This showed several key similarities between configurations, with the electrical system contributing to largest amount of turbine downtime in either case. When considering overall downtime across all components, the direct-drive machine had the highest value, which could be mainly attributed to comparatively higher downtime associated with the electrical, generator and control systems. For this study, downtime related to the gearbox and generator of the gear-driven turbine was calculated at approximately half of that of the direct-drive generator downtime. Finally, from a perspective of both reliability and fault diagnostics at component level, it is important to understand the key similarities and differences that would allow lessons learned on older technology to be adapted and transferred to newer models. This work presents a framework for assessing diagnostic models published in the literature, more specifically whether a particular failure mode and required input data will transfer well between geared and direct-drive machines. Results from 35 models found in the literature shows that the most transferable diagnostic models relate to the hydraulic, pitch and yaw systems, while the least transferable models relate to the gearbox. Faults associated with the generator, shafts and bearings are failure mode specific, but generally require some level of modification to adapt features to available data

Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.publishedVersio

Data insights from an offshore wind turbine gearbox replacement

This is the final version. Available from IOP Publishing via the DOI in this record.Gearboxes are a complex, yet vital assembly for non-direct-drive offshore wind turbines, which are designed to last for the lifetime of the asset. However, recent studies indicate that they may have to be replaced as early as 6.5 years. Moreover, their contribution to offshore wind farm failures and downtime has been shown to be amongst the three most critical assemblies with the highest material cost required. An improved understanding of these premature failures and the ability to predict them in advance could reduce inspection and maintenance costs, as well as to help overcome many logistical and planning challenges. The objective of this paper is to present the lessons learnt from a gearbox exchange performed in one of the offshore wind turbines at Teesside offshore wind farm, comprising 27 2.3MW wind turbines. The paper takes a condition monitoring perspective and uses the identified spalling at the inner part of the planetary bearing as the governing failure mode. A data management system has been setup, incorporating all the operational data received, including maintenance log information and sensor data. A period of up to 2.5 years, prior to the the gearbox exchange, is examined for this study. SCADA and CMS data of the faulty turbine are compared against the wind farm, using statistical methods and machine learning techniques. Supervised learning models are built, which will help predict similar failures in the future. Results show how different data sources can contribute in gearbox failure diagnosis and help to expedite failure detection for Teesside offshore wind farm and similar wind turbine and gearbox types. This paper will be of interest to wind farm developers and operators to build predictive models from monitoring data that can forecast potential gearbox failures.Energy Technology InstituteResearch Council Energy ProgrammeEuropean Unions Horizon 202

Challenges in using operational data for reliable wind turbine condition monitoring

Operational data of wind turbines recorded by the Supervisory Control And Data Acquisition (SCADA) system originally intended only for operation and performance monitoring show promise also for assessing the health of the turbines. Using these data for monitoring mechanical components, in particular the drivetrain subassembly with gearbox and bearings, has recently been investigated with multiple techniques. In this paper the advantages and drawbacks of suggested approaches as well as general challenges and limitations are discussed focusing on automated and farm-wide condition monitoring

Anomaly-based fault detection in wind turbine main bearings

Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.</p

Comparison of new anomaly detection technique for wind turbine condition monitoring using gearbox SCADA data

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O&M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE