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Renewable energy is crucial for any nation striving to reduce its carbon footprint and become less dependent on imported energy. Across the world, countries are working toward producing energy from renewable sources such as solar and wind energy. The European Union is working to produce 20 percent of its total energy from renewable sources by 2020.1 To reach this aggressive goal, the National Renewable Energy Action Plan predicts that in 2020, 34 percent of the EU’s total electricity consumption will come from renewable energy sources, including 495 TWh from wind energy.2 The US Department of Energy has stated that the United States is on track to obtain 20 percent of its electricity from wind by 2030.3 China is also increasing its wind energy investment and is expected to more than quadruple installed wind capacity from 41.8 GW in 2010 to over 200 GW by 2020. Wind energy is expected to compose 17 percent of China’s growing energy needs by 2050.4 Ensuring wind energy keeps pace with these goals requires cooperation among the many teams involved in the design, transportation, and monitoring of wind turbines.
Figure 1. A wind turbine in need of repair after sustaining damage during high winds.
The Role of Data
The development lifecycle of a wind turbine is long and intense. Every design iteration requires many tests to evaluate the wind turbines under extreme conditions. These tests help prevent blades from falling off and potentially damaging property or injuring people. While accidents are uncommon, they do occur, which results in both expensive damage and downtime. For example, when a turbine blade at the Crystal Rig complex in Scotland snapped and fell to the ground during strong winds, it resulted in $1.93 million in repair costs and significant downtime for a wind farm that supplied power to over 33,000 homes. To date, there are records of 234 blade failures that have damaged property. Also to date, a combined total of 191 human fatalities have been recorded as a result of wind turbine accidents.5
Turbines are heavily tested during development to prevent these accidents, but damage can still occur when the turbines are being transported to their final destination. Many wind turbine companies design custom tools and procedures to transport the components across standard road, railway, or maritime carriers. Many factors, such as the size and weight of the components, must be considered to ensure that the cost of transport, as well as the regulations and constraints of the transportation method, are met. This often results in a structural challenge for components designed to manage loads and constraints that have significantly different amplitudes, application points, and frequency contents. Aresse Engineering S.L from Spain used a platform from National Instruments to create a system to identify those load cases and their consequences on the transported structure.6
Figure 2. Engineers install the final wind turbine components at an off-shore location.
Aresse Engineering uses numerous sensors to collect data throughout the turbine’s journey and NI DIAdem software to analyze the large amount of data collected. To understand particular events, such as roundabouts, breaking maneuvers, and bumps, that induce demanding loads on traveling wind turbine components, Aresse Engineering uses DIAdem to synchronize GPS maps with raw data that can be visually inspected to identify these key events. This monitoring helps the company analyze any potential damage to the turbines that could affect their construction at the final destination.
Figure 3. Aresse Engineering uses DIAdem to monitor the vibration, altitude, and speed of a traveling wind turbine.
After constructing the new wind turbine at its installation site, additional sensors constantly monitor and record different parameters such as the average wind speed, wind direction, wind shear, structural health of the blades, and more. If the data collected across the lifespan of the wind turbine is not organized and monitored in an efficient manner, then trends cannot be identified to improve the efficiencies of the systems in place or adequately plan for future wind farm expansion.
The Ever-Increasing Data Trend
The increased monitoring of systems during design, transport, construction, and operation are part of the larger challenge of how to manage the ever-increasing digital data pool. In 2011, over 1.8 ZB (1.8 trillion GB) of data were generated. This data comes from numerous sources (measurement files, videos, music files, and so on) and the size of this digital universe doubles approximately every two years.7 To put some numbers on how much raw data a distributed measurement system can generate, take a typical data collection scenario of a wind turbine and focus only on the vibrations. Assuming that the wind turbine instrumentation has eight accelerometers that collect 32 bits of data at a rate of 51.2 kS/s, a little more than 1.64 MB of data is generated every second. That amounts to more than a terabyte of data created every week! Keep in mind that this is monitoring only the vibration sensors on one unit. If you multiply this much data by every type of measurement that is being collected and by the number of turbines in the wind farm, you can clearly see that measurement data is significantly driving the rapid growth of the digital universe.
Of course, not all of this data is written to file. To reduce the data volume that you must analyze to find trends about the wind turbine, data is logged only when certain conditions are met, such as a change in some parameter, an event, a period of time, or user input. Drawing conclusions quickly and accurately from the numerous files generated throughout a wind farm can be difficult without the right tools, and can potentially lead to more wind turbine accidents.
Controlling the Data
DIAdem is a software tool that helps manage the increasing number of data files being produced. It can help you meet the demands of current (and future) testing environments that require you to quickly access, process, and report on large volumes of scattered data in multiple custom formats. To monitor long-term data for trends, DIAdem provides distinct benefits over alternative data analysis and reporting tools:
- Regardless of how many instruments or custom file formats you are working with, you can read and load any data file without limitations by using a technology called DataPlugins.
- You can take advantage of patented NI DataFinder technology, which automatically indexes metadata from your files and creates a searchable database. The NI DataFinder is self-configuring and requires no additional IT support to maintain.
- You can find and load the right data in seconds by creating simple and advanced queries. For simple searches, you can just enter search strings as you do for Internet search tools. You can use the advanced search to parameterize searches based on the data file’s descriptive attributes. Both searches return a list of files, groups, or channels that match your query, which helps you to load the correct file or channel quickly and easily.
- You can load and merge data channels from multiple files for immediate comparison with interactive tools such as zooming, curve fitting, and more.
- You can design reusable report templates using a drag-and-drop editor so that what-you-see-is-what-you-get in both report creation and export.
Figure 4. DIAdem performs an advanced search on over 13,000 files collected over 28 years to find turbines that have encountered a maximum wind speed over 90 km/h since 2000.
As a dedicated visualization, analysis, and reporting tool designed to process measurement data sets stored in any format, DIAdem includes many features like the NI DataFinder that help engineers and scientists gain efficiency and overcome today’s data processing challenges. Using the right data management tool to understand what the data you collect is trying to tell you can help to prevent expensive damage and downtime.
Stephanie Orci is a product manager for DIAdem and data management software at National Instruments. She holds a bachelor’s degree in biomedical engineering from The University of Texas at Austin.
References
- Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC Text with EEA relevance. (2009, April). Retrieved May 9, 2012, from EurLex: http://europa.eu/documentation/legislation/index_en.htm
- EU Energy Policy to 2050 – Achieving 80-95% emissions reductions. (2011, March). Retrieved May 15, 2012, from European Wind Energy Association: http://www.ewea.org/fileadmin/ewea_documents/documents/publications/reports/EWEA_EU_Energy_Policy_to_2050.pdf
- 20% Wind Energy By 2030: Increasing Wind Energy’s Contribution to U.S. Electricity Supply.(2008, July). Retrieved June 1, 2012, from U.S. Department of Energy: Energy Efficiency & Renewable Energy: http://www.nrel.gov/docs/fy08osti/41869.pdf
- Lishan, Shi (n.d.) Technology Roadmap: China Wind Energy Development Roadmap 2050. (2011, October). Retreived June 1, 2012, from International Energy Agency: http://www.iea.org/papers/roadmaps/china_wind.pdf
- Summary of Wind Turbine Accident Data to 31st March 2012. Retrieved May 10, 2012, from Caithness Windfarm Information Forum: http://www.caithnesswindfarms.co.uk/page4.htm
- Aguerri, M. A. Transport Event Monitoring for Wind Turbine Components. Retrieved May 10, 2012, from National Instruments: http://sine.ni.com/cs/app/doc/p/id/cs-14393
- Reinsel, J. G. (n.d.). Extracting Value from Chaos. (2011, June). Retrieved May 8, 2012, from EMC: http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
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