Sea Technology

APR 2017

The industry's recognized authority for design, engineering and application of equipment and services in the global ocean community

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26 st / April 2017 in identifying equipment that has failed, with significant re- ductions in fault-finding time and costs. Predicting Future States Output information from the real-time analysis can be used as an input to further studies to identify issues com- promising long-term integrity. An alarm and its root cause can indicate a particular problem, but investigation over time can reveal valuable insights and predict future states. Changes over time can be investigated to predict future ef- fects before an alarm occurs. For example, combining structural data with data from the meteorological systems might show changes in struc- tural behavior caused by changes to the seabed. Further ex- amination of data over time and modeling of the evolution of the structural system might reveal that the structure will continue to perform satisfactorily for the design lifetime. Al- ternatively, it might reveal that the structure will not perform satisfactorily after a certain time. Monitoring the evolution of system variables helps to pre- dict future states of the systems. Intervention can be planned to maximize effectiveness and minimize cost, such as dur- ing a campaign in the most benign periods of the year. The structural system used as an example is only one of several systems in a wind farm. The mechanical and electrical sys- tems can and should also be monitored, and similar inves- tigations performed and appropriate interventions planned. This ensures a cost-saving, integrated structural monitoring system to improve the overall integrity, and life extension possibilities, of offshore wind structures. Future Outlook Wind farms have changing and evolving technology to meet the demands of capacity, location and reductions in electricity generation costs. The latest developments in the U.K. and U.S. are substantial in size, with expensive assets that represent considerable investments in time, effort and money. Reducing operating and maintenance costs, as well as managing the risk of disruption to supply, is a challenge for the operators of these assets. Real value can be added and costs reduced by adopt- ing real-time monitoring and data acquisition with initial system modeling and subsequent data processing and inter- pretation. This combination of data, modeling and interpre- tation can generate useful alarms, provide the information for RCA, and predict the future states of the various systems. It is this combination that provides real measurement with meaning. ST Offshore Wind Energy Monitoring System With global offshore wind expenditure forecast to reach £210 billion over the next 10 years, Proeon and Aquaterra have developed a special package for such inertial sensing that combines acceleration with angular rate and inclina- tion, and this package has been field-proven in the North Sea. The offshore wind energy monitoring system will im- prove inspection and repair planning and lower the fre- quency of offshore failures. The angular rate and the inclination data can be used to elaborate the picture, and together the companies have developed novel algorithms for determining structural be- havior from these additional data. Determining the state of any subsystem only solves part of the puzzle, and the next question is which avenue of in- vestigation to follow. There are three avenues to explore using the knowledge gained: alarms, root cause analysis (RCA) and predictions of future condition. If the measured behavior of the system deviates from the expected behavior, then an alarm can be triggered. The alarm may require a quick response or it might be flagged for longer term review and action. An alarm for the electri- cal or mechanical systems might require on-site inspection and intervention or for equipment to be temporarily taken out of commission until intervention is possible. Converse- ly, an alarm for the structure might indicate that the system behavior is degrading from the expected behavior, but the change might be occurring sufficiently slowly that immedi- ate intervention is not required. Root Cause Analysis The use of real-time data with the resulting alarms and events is a key window into the state of the structure but is only one part of a comprehensive condition monitoring sys- tem. It is likely that alarms and events created by the dispa- rate systems for a wind farm may have causal links that can result in either alarm floods or what can appear as unrelated alarms and events. Such alarms and event floods can be confusing to operators who may be dealing with many tens of thousands of points of data. One solution to this problem is to use root cause analysis techniques to drill down into data derived from the condi- tion monitoring system or other systems on the wind farm such as the SCADA or meteorological systems. The goal of the RCA is to identify a single source of failure (the root cause) and generate an appropriate actionable alarm for the detected failure. Real-time RCA techniques may include a variety of tech- niques that are employed to solve different problems and conditions. In all cases, the techniques will interpret a set of symptoms and events and pinpoint the source that is caus- ing those occurrences. While conceptually simple, this can be surprisingly dif- ficult to achieve in the real world with real-time feeds of data from a number of sources. The RCA system is required to understand the relationship between information within the infrastructure together with the systems and users that rely on that information. Efficient real-time performance of the RCA should gen- erate unambiguous alarms and events to identify the root cause of the observed condition. RCA can be a key tool Nick Stringer is the general technical manager at Aquaterra Energy and is a chartered mechanical en- gineer with 15 years' experience in analysis. He is re- sponsible for the technical engineering teams, includ- ing riser analysis, structural and design engineering. Kevin Magee is the managing director of Proeon Sys- tems and is a chartered electrical engineer and CFSP with more than 25 years' experience in detailed de- sign, project engineering, project management, com- missioning and engineering management.

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