scholarly journals Multi-target normal behaviour models for wind farm condition monitoring

2021 ◽  
Vol 300 ◽  
pp. 117342
Author(s):  
Angela Meyer
Keyword(s):  
Condition Monitoring ◽  
Wind Farm ◽  
Normal Behaviour ◽  
Behaviour Models

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

scholarly journals Value-Centric Approaches to Design, Operations, and Maintenance of Wind Turbines

2013 ◽  
Author(s):  
Madhur A. Khadabadi ◽  
Karen B. Marais
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Net Present Value ◽  
Operating Cost ◽  
Optimal Level ◽  
Two Dimensions ◽  
Alternative View ◽  
Operations And Maintenance ◽  
Farm Operators

Wind turbine maintenance is emerging as an unexpectedly high component of turbine operating cost and there is an increasing interest in managing this cost. Here, we present an alternative view of maintenance as a value-driver, and develop an optimization algorithm to maximize the value delivered by maintenance. We model the stochastic deterioration of the turbine in two dimensions: the deterioration rate, and the extent of deterioration, and view maintenance as an operator that moves the turbine to an improved state in which it can generate more power and so earn more revenue. We then use a standard net present value (NPV) approach to calculate the value of the turbine by deducting the costs incurred in the installation, operations and maintenance from the revenue due to the power generation. The application of our model is demonstrated using several scenarios with a focus on blade deterioration. We evaluate the value delivered by implementing blade condition monitoring systems (CMS). A higher fidelity CMS allows the blade state to be determined with higher precision. With this improved state information, an optimal maintenance strategy can be derived. The difference between the value of the turbine with and without CMS can be interpreted as the value of the CMS. The results indicate that a higher fidelity (and more expensive) condition monitoring system (CMS) does not necessarily yield the highest value, and, that there is an optimal level of fidelity that results in maximum value. The contributions of this work are twofold. First, it is a practical approach to wind turbine valuation and operation that takes operating and market conditions into account. This work should therefore be useful to wind farm operators and investors. Second, it shows how the value of a CMS can be explicitly assessed. This work should therefore be useful to CMS manufacturers and wind farm operators.

Condition Monitoring of Wind Turbine Drivetrain Bearings

2019 ◽  
Author(s):  
Konstantinos Gryllias ◽  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Chenyu Liu
Keyword(s):  
Wind Energy ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
The Other ◽  
Offshore Wind ◽  
Center Frequency ◽  
Classical Statistic ◽  
Offshore Wind Turbines ◽  
Other Hand

Abstract The current pace of renewable energy development around the world is unprecedented, with offshore wind in particular proving to be an extremely valuable and reliable energy source. The global installed capacity of offshore wind turbines by the end of 2022 is expected to reach the 46.4 GW, among which 33.9 GW in Europe. Costs are critical for the future success of the offshore wind sector. The industry is pushing hard to make cost reductions to show that offshore wind is economically comparable to conventional fossil fuels. Efficiencies in Operations and Maintenance (O&M) offer potential to achieve significant cost savings as it accounts for around 20%–30% of overall offshore wind farm costs. One of the most critical and rather complex assembly of onshore, offshore and floating wind turbines is the gearbox. Gearboxes are designed to last till the end of the lifetime of the asset, according to the IEC 61400-4 standards. On the other hand, a recent study over approximately 350 offshore wind turbines indicate that gearboxes might have to be replaced as early as 6.5 years. Therefore sensing and condition monitoring systems for onshore, offshore and floating wind turbines are needed in order to obtain reliable information on the state and condition of different critical parts, focusing towards the detection and/or prediction of damage before it reaches a critical stage. The development and use of such technologies will allow companies to schedule actions at the right time, and thus will help reducing the costs of operation and maintenance, resulting in an increase of wind energy at a competitive price and thus strengthening productivity of the wind energy sector. At the academic level a plethora of methodologies have been proposed during the last decades for the analysis of vibration signatures focusing towards early and accurate fault detection with limited false alarms and missed detections. Among others, Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclostationary Analysis and corresponding methodologies, i.e. the Cyclic Spectral Correlation and the Cyclic Spectral Coherence, have been proved as powerful tools for condition monitoring. On the other hand the application, test and evaluation of such tools on general industrial cases is still rather limited. Therefore the main aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic time and frequency indicators, i.e. Kurtosis, Crest Factor etc. and their effectiveness is evaluated based on the successful detection of two failure events.

Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model

2017 ◽  
Vol 231 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Peyman Mazidi ◽  
Lina Bertling Tjernberg ◽  
Miguel A Sanz Bobi
Keyword(s):  
Condition Monitoring ◽  
Wind Farm ◽  
Stress Condition ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Hybrid Approach ◽  
Principal Component ◽  
Mathematical Form ◽  
Hazards Model ◽  
Normal Behavior

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.

scholarly journals Three-Stage Wind Turbine Assessment Method: Condition Monitoring, Failure Prediction, And Health Assessment

2021 ◽  
Author(s):  
Yongsheng Qi ◽  
Tongmei Jing ◽  
Chao Ren ◽  
Xuejin Gao
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Health Management ◽  
Health Assessment ◽  
Failure Prediction ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Generalized Regression Neural Network ◽  
Monitoring Method

Abstract To improve the wind turbine shutdown early warning ability, we present a generalized model for wind turbine (WT) prognosis and health management (PHM) based on the data collected from the SCADA system. First, a new condition monitoring method based on kernel entropy component analysis (KECA) was developed for nonlinear data. Then, an aggregate statistic T was designed to express the state change of the monitoring parameters. As the features were submerged because of the diversity and nonlinearity of SCADA data, an enhanced generalized regression neural network (GRNN) method—KECA-GRNN—for failure prediction was developed by adding KECA for feature extraction to improve the predictive performance. Finally, the results of the KECA-GRNN model were visualized by a bubble chart, which made the health assessment results of the WT more intuitive. Similarly, the fusion residual was defined to analyze the health trend of the WT, and the health status of the WT was represented by two visualization methods—bubble chart and fuzzy comprehensive evaluation. Furthermore, they were evaluated using SCADA data that were collected from a wind farm. Observations from the results of the model indicated the ability of the approach to trend and assess turbine degradation before known downtime occurrences.

Use of Seismic Analyses for the Wind Energy Industry

2017 ◽  
Author(s):  
Weifei Hu ◽  
S. C. Pryor ◽  
F. Letson ◽  
R. J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Spectral Power ◽  
Operating Conditions ◽  
Energy Industry ◽  
Ground Vibrations ◽  
Power Spectral ◽  
Sonic Anemometers

This paper proposes new seismic-based methods for use in the wind energy industry with a focus on wind turbine condition monitoring. Fourteen Streckeisen STS-2 Broadband seismometers and two 3D sonic anemometers are deployed in/near an operating wind farm to collect the data used in these proof-of-principle analyses. The interquartile mean (IQM) value of power spectral density (PSD) of the seismic components in 10-minute time series are used to characterize the spectral signatures (i.e. frequencies with enhanced variance) in ground vibrations deriving from vibrations of wind turbine subassemblies. A power spectral envelope approach is taken in which the probability density function of seismic PSD is developed using seismic data collected under normal turbine operation. These power spectral envelopes clearly show the energy distribution of wind-turbine-induced ground vibrations over a wide frequency range. Singular PSD lying outside the power spectral envelopes can be easily identified, and are used herein along with SCADA data to diagnose the associated sub-optimal turbine operating conditions. Illustrative examples are given herein for periods with yaw-misalignment and excess tower acceleration. It is additionally shown that there is a strong association between drivetrain acceleration and seismic spectral power in a frequency band of 2.5–12.5 Hz. The long-term goal of the research is development of seismic-based condition monitoring (SBCM) for wind turbines. The primary advantages of SBCM are that the approach is low-cost, non-invasive and versatile (i.e., one seismic sensor monitoring for multiple turbine subassemblies).

scholarly journals Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4745
Author(s):  
Conor McKinnon ◽  
Alan Turnbull ◽  
Sofia Koukoura ◽  
James Carroll ◽  
Alasdair McDonald
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Signal Reconstruction ◽  
Significant Proportion ◽  
Model Performance ◽  
Time History ◽  
Normal Behaviour ◽  
Training Time ◽  
Behaviour Modelling ◽  
Cost Of Energy

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

scholarly journals Assessment of Condition Monitoring Techniques for Offshore Wind Farms

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Edwin Wiggelinkhuizen ◽  
Theo Verbruggen ◽  
Henk Braam ◽  
Luc Rademakers ◽  
Jianping Xiang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Wind Farm ◽  
Early Stage ◽  
Wind Farms ◽  
Added Value ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Power Performance ◽  
Offshore Wind Farms ◽  
Condition Monitoring Systems

This paper discusses the results of an extensive investigation to assess the added value of various techniques of health monitoring to optimize the maintenance procedures of offshore wind farms. This investigation was done within the framework of the EU funded Condition Monitoring for Offshore Wind Farms (CONMOW) project, which was carried out from 2002 to 2007. A small wind farm of five turbines has been instrumented with several condition monitoring systems and also with the “traditional” measurement systems for measuring mechanical loads and power performance. Data from vibration and traditional measurements, together with data collected by the turbine’s system control and data acquisition (SCADA) systems, have been analyzed to assess (1) if failures can be determined from the different data sets; (2) if so, if they can be detected at an early stage and if their progress over time can be monitored; and (3) if criteria are available to assess the component’s health. Several data analysis methods and measurement configurations have been developed, applied, and tested. This paper first describes the use of condition monitoring if condition based maintenance is going to be applied instead of only scheduled and corrective maintenance. Second, the paper describes the CONMOW project and its major results, viz., the assessment of the usefulness and capabilities of condition monitoring systems, including algorithms for identifying early failures. Finally, the economic consequences of applying condition monitoring systems have been quantified and assessed.

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