scholarly journals Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability

2021 ◽  
Vol 208 ◽  
pp. 107404
Author(s):  
Sofia Koukoura ◽  
Matti Niclas Scheu ◽  
Athanasios Kolios
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Monitoring Systems ◽  
Functional Failure ◽  
Condition Monitoring Systems

scholarly journals Economic analysis of condition monitoring systems for offshore wind turbine sub‐systems

2015 ◽  
Vol 9 (8) ◽  
pp. 900-907 ◽  
Author(s):  
Allan May ◽  
David McMillan ◽  
Sebastian Thöns
Keyword(s):  
Economic Analysis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Monitoring Systems ◽  
Condition Monitoring Systems

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.

Quantifying the Economic Benefits of Wind Turbine Condition Monitoring

2014 ◽  
Author(s):  
Richard Williams ◽  
Christopher Crabtree ◽  
Simon Hogg
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Economic Benefits ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Financial Benefit ◽  
Condition Monitoring Systems ◽  
Detection Ratio

This paper presents a cost benefit analysis for wind turbine condition monitoring systems. It is widely acknowledged that performing proactive maintenance actions can reduce the number and severity of wind turbine failures. However, the use of condition monitoring systems to determine the health of the system is often viewed as costly and of little financial benefit. In this analysis the increased costs associated with condition monitoring were offset by the positive effect of early fault detection, with faults being detected before they reach a critical stage. The continual growth in turbine output and the emergence of far-offshore wind farm sites make the economic case for cost of energy reduction from timely and accurate fault detection ever stronger. An assessment of the capability of the monitoring system was undertaken through allowance for the true to false condition monitoring detection ratio and the ability of the system to detect the severity of a fault. The analysis also compared onshore and offshore assets where the access availability can severely influence the downtime. The results show a clear financial justification for wind turbine condition monitoring and indicate the successful detection ratio required before a condition monitoring system can offer a financial benefit.

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.

scholarly journals SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

2020 ◽  
Vol 10 (23) ◽  
pp. 8685
Author(s):  
Ravi Pandit ◽  
Athanasios Kolios
Keyword(s):  
Support Vector Machine ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Offshore Wind Turbine ◽  
Power Curve ◽  
Power Curves

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

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