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.

Property requirements of vibration measurements in wind turbine drivetrain bearing condition monitoring

2021 ◽  
Vol 63 (11) ◽  
pp. 667-674
Author(s):  
D Strömbergsson ◽  
P Marklund ◽  
K Berglund ◽  
P-E Larsson
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Field Data ◽  
Measurement Time ◽  
Frequency Range ◽  
Main Bearing ◽  
Vibration Measurements ◽  
Bearing Failures ◽  
Bearing Condition Monitoring

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data.

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 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 Hardware-in-the-loop simulator of wind turbine emulator using labview

2019 ◽  
Vol 10 (2) ◽  
pp. 971
Author(s):  
Himani Himani ◽  
Navneet Sharma
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Control Strategies ◽  
Hardware In The Loop ◽  
Data Acquisition Card ◽  
Terminal Voltage ◽  
The Uk ◽  
The University ◽  
University Of Manchester

<p><span>This paper describes the design and implementation of Hardware in the Loop (HIL) system D.C. motor based wind turbine emulator for the condition monitoring of wind turbines. Operating the HIL system, it is feasible to replicate the actual operative conditions of wind turbines in a laboratory environment. This method simply and cost-effectively allows evaluating the software and hardware controlling the operation of the generator. This system has been implemented in the LabVIEW based programs by using Advantech- USB-4704-AE Data acquisition card. This paper describes all the components of the systems and their operations along with the control strategies of WTE such as Pitch control and MPPT. Experimental results of the developed simulator using the test rig are benchmarked with the previously verified WT test rigs developed at the Durham University and the University of Manchester in the UK by using the generated current spectra of the generator. Electric subassemblies are most vulnerable to damage in practice, generator-winding faults have been introduced and investigated using the terminal voltage. This wind turbine simulator can be analyzed or reconfigured for the condition monitoring without the requirement of actual WT’s.</span></p>

Comparison of Blind Diagnostic Indicators for Condition Monitoring of Wind Turbine Gearbox Bearings

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fossil Fuels ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Permutation Entropy ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Wind Turbine Gearbox Bearings

Abstract Under the pressure of climate change, renewable energy gradually replaces fossil fuels and plays nowadays a significant role in energy production. The O&M costs of wind turbines may easily reach up to 25% of the total leverised cost per kWh produced over the lifetime of the turbine for a new unit. Manufacturers and operators try to reduce O&M by developing new turbine designs and by adopting condition monitoring approaches. One of the most critical assembly of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset's lifetime, according to the IEC 61400-4 standards but a recent study indicated that gearboxes might have to be replaced as early as 6.5 years. A plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault but often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy, on Negentropy, on Sparsity and on Statistics. The performance of the indicators is evaluated on a wind turbine data set with two different bearing faults. Among the different diagnostic indicators Permutation entropy, Approximate entropy, Samples entropy, Fuzzy entropy, Conditional entropy and Wiener entropy achieve the best results detecting blindly the two failure events.

An Improved 2DCNN With Focal Loss Function for Blade Icing Detection of Wind Turbines Under Imbalanced SCADA Data

2021 ◽  
Author(s):  
Dandan Peng ◽  
Chenyu Liu ◽  
Wim Desmet ◽  
Konstantinos Gryllias
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Turbine ◽  
Loss Function ◽  
Wind Turbines ◽  
Cold Climate ◽  
Detection Methods ◽  
Physical Parameters ◽  
Learning Abilities ◽  
Imbalance Problem

Abstract The deployment of wind power plants in cold climate becomes ever more attractive due to the increased air density resulting from low temperatures, the high wind speeds, and the low population density. However, the cold climate conditions bring some additional challenges as itt can easily cause wind turbine blades to freeze. The frizzing ice on blades not only increases the energy required for the rotation of blades, resulting in a reduction in the power generation, but also increases the amplitude of the blades’ vibrations, which may cause the blade to break, affecting the power generation performance of the wind turbine and poses a threat to its safe operation. Current published blade icing detection methods focus on studying the blade icing mechanism, building the model and then judging if it is iced or not. These models vary with different wind turbines and working conditions, so expertise knowledge is required. However, deep learning techniques may solve the abovementioned problem based on their excellent feature learning abilities but until now, there are only few studies on wind turbine blade icing detection based on the deep learning technology. Therefore, this paper proposes a novel blade icing detection model, named two-dimensional convolutional neural network with focal loss function (FL-2DCNN). The network takes the raw data collected by the Supervisory Control and Data Acquisition (SCADA) system as input, automatically learns the correlation between the different physical parameters in the dataset, and captures the abnormal information, in order to accurately output the detection results. However, the amount of normal data collected by SCADA systems is usually much larger than the one of blade icing fault data, leading to a serious data imbalance problem. This problem makes it difficult for the network to obtain enough features related to the blade icing fault. Therefore the focal loss function is introduced to the FL-2DCNN to solve the aforementioned data imbalanced problem. The focal loss function can effectively balance the importance of normal samples and icing fault samples, so that the network can obtain more icing-related feature information from the icing fault samples, and thus the detection ability of the network can be improved. The experimental results of the proposed FL-2DCNN based on real SCADA data of wind turbines show that the proposed FL-2DCNN can effectively solve the sample imbalance problem and has significant potential in the blade icing detection task compared with other deep learning methods.

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.

How to Improve the Reliability of Wind Turbines

2011 ◽  
Vol 58-60 ◽  
pp. 771-775
Author(s):  
Hai Bo Zhang ◽  
Liang Liu
Keyword(s):  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Lubrication System ◽  
Component Failure ◽  
Time Condition ◽  
Automatic Lubrication ◽  
Potential Component ◽  
Condition Monitoring System

According to the failure of wind turbines in operation, the failure cause and phenomenon of wind turbines is analyzed, combined with the reliability of wind turbine subsystems, measures aiming at cooperation parts and purchased parts are proposed, the reliability of the whole wind turbines is improved in a certain extent. At the same time, condition monitoring system can carry through the early detecting and diagnosing to potential component failure maintain. Besides, automatic lubrication system can realize accurate and timeliness lubrication, also can reduce maintenance workload, preserve correct lubrication and smooth running of all parts.

scholarly journals Remote data acquisition for condition monitoring of wind turbines

2015 ◽  
Vol 6 (2) ◽  
pp. 10
Author(s):  
Bavo De Maré ◽  
Jacob Sukumaran ◽  
Mia Loccufier ◽  
Patrick De Baets
Keyword(s):  
Data Acquisition ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Good Condition ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Offshore Wind Turbines ◽  
Condition Monitoring Systems ◽  
Remote Data

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface.

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