scholarly journals Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2228 ◽  
Author(s):  
Ángel Encalada-Dávila ◽  
Bryan Puruncajas ◽  
Christian Tutivén ◽  
Yolanda Vidal
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Critical Issue ◽  
Main Bearing ◽  
Bearing Fault ◽  
Bearing Failures ◽  
Relevant Topic ◽  
Fault Prognosis ◽  
And Performance

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

scholarly journals SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on One-Class Support Vector Machines

2021 ◽  
Vol 19 ◽  
pp. 338-343
Author(s):  
A. Insuasty ◽  
◽  
C. Tutivén ◽  
Y. Vidal
Keyword(s):  
Wind Turbine ◽  
Data Driven ◽  
Support Vector ◽  
Main Bearing ◽  
Bearing Fault ◽  
Data Contamination ◽  
Vector Machines ◽  
Unsupervised Approach ◽  
Fault Prognosis ◽  
And Performance

This work proposes a fault prognosis methodology to predict the main bearing fault several months in advance and let turbine operators plan ahead. Reducing downtime is of paramount importance in wind energy industry to address its energy loss impact. The main advantages of the proposed methodology are the following ones. It is an unsupervised approach, thus it does not require faulty data to be trained; ii) it is based only on exogenous data and one representative temperature close to the subsystem to diagnose, thus avoiding data contamination; iii) it accomplishes the prognosis (various months in advance) of the main bearing fault; and iv) the validity and performance of the established methodology is demonstrated on a real underproduction wind turbine.

scholarly journals Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Jürgen Herp ◽  
Niels L. Pedersen ◽  
Esmaeil S. Nadimi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Likelihood Function ◽  
Health Management ◽  
Control Strategies ◽  
Assessment Model ◽  
Prognostic Models ◽  
Early Stopping ◽  
Main Bearing ◽  
Bearing Failures

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines.

scholarly journals Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3406
Author(s):  
Benedikt Wiese ◽  
Niels L. Pedersen ◽  
Esmaeil S. Nadimi ◽  
Jürgen Herp
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Feature Space ◽  
Main Bearing ◽  
Proposed Model ◽  
Bearing Failures ◽  
Gated Recurrent Units ◽  
Useful Lifetime

Condition monitoring for wind turbines is tailored to predict failure and aid in making better operation and maintenance (O&M) decisions. Typically the condition monitoring approaches are concerned with predicting the remaining useful lifetime (RUL) of assets or a component. As the time-based measures can be rendered absolute when changing the operational set-point of a wind turbine, we propose an alternative in a power-based condition monitoring framework for wind turbines, i.e., the remaining power generation (RPG) before a main bearing failure. The proposed model utilizes historic wind turbine data, from both run-to-failure and non run-to-failure turbines. Comprised of a recurrent neural network with gated recurrent units, the model is constructed around a censored and uncensored data-based cost function. We infer a Weibull distribution over the RPG, which gives an operator a measure of how certain any given prediction is. As part of the model evaluation, we present the hyper-parameter selection, as well as modeling error in detail, including an analysis of the driving features. During the application on wind turbine main bearing failures, we achieve prediction in the magnitude of 1 to 2 GWh before the failure. When converting to RUL this corresponds to predicting the failure, on average, 81 days beforehand, which is comparable to the state-of-the-art’s 94 days predictive horizon in a similar feature space.

Fatigue Life Analysis of Offshore Wind Turbine Support Structures in an Offshore Wind Farm

2018 ◽  
Author(s):  
Bryan Nelson ◽  
Yann Quéméner
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
The Body ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Analysis Tool ◽  
Support Structures ◽  
Offshore Wind Farm ◽  
Actuator Disk

This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in space, and a body force distribution representing the aerodynamic characteristics of the rotor is assigned within this virtual disk. Simulations were performed for a range of environmental conditions, which were then combined with preliminary site survey metocean data to produce a long-term statistical environment. The short-term environmental loads on the wind turbine rotors were calculated by an unsteady blade element momentum (BEM) model of the target 4 MW wind turbines. The fatigue assessment of the jacket support structure was then conducted by applying the Rainflow Counting scheme on the hot spot stresses variations, as read-out from Finite Element results, and by employing appropriate SN curves. The fatigue lives of several wind turbine support structures taken at various locations in the wind farm showed significant variations with the preliminary design condition that assumed a single wind turbine without wake disturbance from other units.

scholarly journals Comparison of Electrical and Vibration Analysis Methods for Mechanical Fault Monitoring in Wind Turbine Drivetrains

2019 ◽  
Vol 9 (1) ◽  
pp. 2-7
Author(s):  
P Granjon ◽  
P D Longhitano ◽  
A Singh
Keyword(s):  
Wind Turbine ◽  
Vibration Analysis ◽  
Wind Farm ◽  
Electromechanical System ◽  
Main Bearing ◽  
Monitoring Method ◽  
Fault Monitoring ◽  
Three Phase ◽  
Mechanical Faults ◽  
Electrical Generator

Mechanical faults occurring in drivetrains are traditionally monitored through vibration analysis and, more rarely, by analysing electrical quantities measured on the electromechanical system involved. However, a monitoring method that is able to take into account all of the information contained in three-phase electrical quantities was recently proposed. The goal of this paper is to compare this threephase electrical approach with the usual vibration-based method in terms of its capability to detect mechanical faults in drivetrains. In this context, a 2 MW geared wind turbine operating in an industrial wind farm was equipped with accelerometers near the main bearing and electrical sensors on the stator of the electrical generator for several months. During this period, an important mechanical fault occurred in the main bearing of the system. The evolution of the fault indicators computed by the two previous approaches were compared throughout this period of time. All of the indicators behaved similarly and showed the development of an inner bearing fault in the main bearing. This demonstrated that a mechanical fault occurring in a drivetrain can be monitored and detected by analysing electrical quantities, even if the fault is located some distance from the electrical generator.

scholarly journals Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Flow Conditions ◽  
Output Fluctuations ◽  
Power Difference ◽  
Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

Health Assessment Methods for Wind Turbines Based on Power Prediction and Mahalanobis Distance

2018 ◽  
Vol 33 (02) ◽  
pp. 1951001 ◽  
Author(s):  
Jun Zhan ◽  
Ronglin Wang ◽  
Lingzhi Yi ◽  
Yaguo Wang ◽  
Zhengjuan Xie
Keyword(s):  
Health Status ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Mahalanobis Distance ◽  
Wind Farm ◽  
Health Status Assessment ◽  
Power Prediction ◽  
Residual Set ◽  
Scada System ◽  
Status Assessment

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.

scholarly journals Floating Performance of a Composite Bucket Foundation with an Offshore Wind Tower during Transportation

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 882 ◽  
Author(s):  
Hongyan Ding ◽  
Zuntao Feng ◽  
Puyang Zhang ◽  
Conghuan Le ◽  
Yaohua Guo
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Interaction Force ◽  
Offshore Wind ◽  
Construction Technique ◽  
Bucket Foundation ◽  
Offshore Wind Turbines ◽  
One Step

The composite bucket foundation (CBF) for offshore wind turbines is the basis for a one-step integrated transportation and installation technique, which can be adapted to the construction and development needs of offshore wind farms due to its special structural form. To transport and install bucket foundations together with the upper portion of offshore wind turbines, a non-self-propelled integrated transportation and installation vessel was designed. In this paper, as the first stage of applying the proposed one-step integrated construction technique, the floating behavior during the transportation of CBF with a wind turbine tower for the Xiangshui wind farm in the Jiangsu province was monitored. The influences of speed, wave height, and wind on the floating behavior of the structure were studied. The results show that the roll and pitch angles remain close to level during the process of lifting and towing the wind turbine structure. In addition, the safety of the aircushion structure of the CBF was verified by analyzing the measurement results for the interaction force and the depth of the liquid within the bucket. The results of the three-DOF (degree of freedom) acceleration monitoring on the top of the test tower indicate that the wind turbine could meet the specified acceleration value limits during towing.

scholarly journals Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1474 ◽  
Author(s):  
Francesco Castellani ◽  
Luigi Garibaldi ◽  
Alessandro Paolo Daga ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Statistical Tests ◽  
Novelty Detection ◽  
General Idea ◽  
Data Set ◽  
Vibration Measurements ◽  
Bearing Fault ◽  
Operation Conditions

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

Floating Offshore Wind Turbines: Responses in a Seastate Pareto Optimal Designs and Economic Assessment

2008 ◽  
Author(s):  
Paul Sclavounos ◽  
Christopher Tracy ◽  
Sungho Lee
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Economic Assessment ◽  
Offshore Wind ◽  
Pareto Optimal ◽  
Mooring System ◽  
Floating Structure ◽  
Water Depths

Wind is the fastest growing renewable energy source, increasing at an annual rate of 25% with a worldwide installed capacity of 74 GW in 2007. The vast majority of wind power is generated from onshore wind farms. Their growth is however limited by the lack of inexpensive land near major population centers and the visual pollution caused by large wind turbines. Wind energy generated from offshore wind farms is the next frontier. Large sea areas with stronger and steadier winds are available for wind farm development and 5MW wind turbine towers located 20 miles from the coastline are invisible. Current offshore wind turbines are supported by monopoles driven into the seafloor at coastal sites a few miles from shore and in water depths of 10–15m. The primary impediment to their growth is visual pollution and the prohibitive cost of seafloor mounted monopoles in larger water depths. This paper presents a fully coupled dynamic analysis of floating wind turbines that enables a parametric design study of floating wind turbine concepts and mooring systems. Pareto optimal designs are presented that possess a favorable combination of nacelle acceleration, mooring system tension and displacement of the floating structure supporting a five megawatt wind turbine. All concepts are selected so that they float stably while in tow to the offshore wind farm site and prior to their connection to the mooring system. A fully coupled dynamic analysis is carried out of the wind turbine, floater and mooring system in wind and a sea state based on standard computer programs used by the offshore and wind industries. The results of the parametric study are designs that show Pareto fronts for mean square acceleration of the turbine versus key cost drivers for the offshore structure that include the weight of the floating structure and the static plus dynamic mooring line tension. Pareto optimal structures are generally either a narrow deep drafted spar, or a shallow drafted barge ballasted with concrete. The mooring systems include both tension leg and catenary mooring systems. In some of the designs, the RMS acceleration of the wind turbine nacelle can be as low as 0.03 g in a sea state with a significant wave height of ten meters and water depths of up to 200 meters. These structures meet design requirements while possessing a favorable combination of nacelle accleration, total mooring system tension and weight of the floating structure. Their economic assessment is also discussed drawing upon a recent financial analysis of a proposed offshore wind farm.

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