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.

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.

Study on a global control strategy for rotation speed with different work states in variable-speed constant-frequency wind turbines

2011 ◽  
Vol 225 (7) ◽  
pp. 968-976 ◽  
Author(s):  
G Zheng ◽  
H Xu ◽  
X Wang ◽  
J Zou
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Control Strategy ◽  
Control Strategies ◽  
Stable Operation ◽  
Constant Frequency ◽  
Global Control ◽  
Control Rules ◽  
The Individual ◽  
And Control

This paper studies the operation of wind turbines in terms of three phases: start-up phase, power-generation phase, and shutdown phase. Relationships between the operational phase and control rules for the speed of rotation are derived for each of these phases. Taking into account the characteristics of the control strategies in the different operational phases, a global control strategy is designed to ensure the stable operation of the wind turbine in all phases. The results of simulations are presented that indicate that the proposed algorithm can control the individual phases when considered in isolation and also when they are considered in combination. Thus, a global control strategy for a wind turbine that is based on a single algorithm is presented which could have significant implications on the control and use of wind turbines.

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>

Using Extremum Seeking Control to Improve the Power Capture of Midsize Hydrostatic Wind Turbines

2021 ◽  
Author(s):  
Daniel Escobar-Naranjo ◽  
Biswaranjan Mohanty ◽  
Kim A. Stelson
Keyword(s):  
Adaptive Control ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Control Strategies ◽  
Torque Control ◽  
Extremum Seeking ◽  
Extremum Seeking Control ◽  
Model Free ◽  
Point Tracking ◽  
Power Capture

Abstract Adaptive control strategies are commonly used for systems that change over time, such as wind turbines. Extremum Seeking Control (ESC) is a model-free real-time adaptive control strategy commonly used in conventional gearbox wind turbines for Maximum Power Point Tracking (MPPT). ESC optimizes the rotor power by constantly tuning the torque control gain (k) when operating below rated power. The same concept can be applied for hydrostatic wind turbines. This paper studies the use of ESC for a 60-kW hydrostatic wind turbine. First, a systematic approach to establish the ideal ESC is shown. Second, a comparison of the power capture performance of ESC versus the conventional torque control law (the kω2 law) is shown. The simulations include a timesharing power capture coefficient (Cp) to clearly show the advantages of using ESC. Studies under steady and realistic wind conditions show the main advantages of using ESC for a hydrostatic wind turbine.

scholarly journals An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1512
Author(s):  
Mattia Beretta ◽  
Anatole Julian ◽  
Jose Sepulveda ◽  
Jordi Cusidó ◽  
Olga Porro
Keyword(s):  
Ensemble Learning ◽  
Wind Turbines ◽  
Wind Farms ◽  
Main Bearing ◽  
Predictive Algorithms ◽  
Bearing Failures ◽  
Innovative Solution ◽  
Single Complex ◽  
Failure Predictions ◽  
Learning Principles

A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.

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.

A Prognostics and Health Management Framework for Wind

2019 ◽  
Author(s):  
Shuangwen Sheng ◽  
Yi Guo
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Health Management ◽  
Remaining Useful Life ◽  
Rolling Contact ◽  
Design Stage ◽  
Series Data ◽  
Prognostics And Health Management ◽  
Wind Turbine Gearbox ◽  
Operation And Maintenance

Abstract Operation and maintenance costs are a major driver for levelized cost of energy of wind power plants and can be reduced through optimized operation and maintenance practices accomplishable by various prognostics and health management (PHM) technologies. In recent years, the wind industry has become more open to adopting PHM solutions, especially those focusing on diagnostics. However, prognostics activities are, in general, still at the research and development stage. On the other hand, the industry has a request to estimate a component’s remaining useful life (RUL) when it has faulted, and this is a key output of prognostics. Systematically presenting PHM technologies to the wind industry by highlighting the RUL prediction need potentially helps speed up its acceptance and provides more benefits from PHM to the industry. In this paper, we introduce a PHM for wind framework. It highlights specifics unique to wind turbines and features integration of data and physics domain information and models. The output of the framework focuses on RUL prediction. To demonstrate its application, a data domain method for wind turbine gearbox fault diagnostics is presented. It uses supervisory control and data acquisition system time series data, normalizes gearbox temperature measurements with reference to environmental temperature and turbine power, and leverages big data analytics and machine-learning techniques to make the model scalable and the diagnostics process automatic. Another physics-domain modeling method for RUL prediction of wind turbine gearbox high-speed-stage bearings failed by axial cracks is also discussed. Bearing axial cracking has been shown to be the prevalent wind turbine gearbox failure mode experienced in the field and is different from rolling contact fatigue, which is targeted during the bearing design stage. The method uses probability of failure as a component reliability assessment and RUL prediction metric, which can be expanded to other drivetrain components or failure modes. The presented PHM for wind framework is generic and applicable to both land-based and offshore wind turbines.

Load Alleviation on Wind Turbine Blades Using Variable Airfoil Geometry

2005 ◽  
Vol 29 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Santiago Basualdo
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Theoretical Study ◽  
Control Strategies ◽  
Effective Means ◽  
Turbine Blades ◽  
Two Dimensional ◽  
Variable Speed ◽  
Wind Turbine Blades ◽  
Second Derivatives

A two-dimensional theoretical study of the aeroelastic behaviour of an airfoil has been performed, whose geometry can be altered using a rear-mounted flap. This device is governed by a controller, whose objective is to reduce the airfoil displacements and, therefore, the stresses present in a real blade. The aerodynamic problem was solved numerically by a panel method using the potential theory, suitable for modelling attached flows. It is therefore mostly applicable for Pitch Regulated Variable Speed (PRVS) wind turbines, which mainly operate under this flow condition. The results show evident reductions in the airfoil displacements by using simple control strategies having the airfoil position and its first and second derivatives as input, especially at the system's eigenfrequency. The use of variable airfoil geometry is an effective means of reducing the vibration magnitudes of an airfoil that represents a section of a wind turbine blade, when subject to stochastic wind signals. The results of this investigation encourage further investigations with 3D aeroelastic models to predict the reduction in loads in real wind turbines.

Sign in / Sign up

Export Citation Format

Share Document