Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.
Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Dimitri Foussekis (1) F
(1) C.R.E.S., Pikermi, Greece
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
D. Foussekis received his Bachelor's of Science in Physics in Greece and his MSc and PhD in Fluid Mechanics in France. He is a senior Research Engineer at C.R.E.S. and his primary research interests lie in the fields of i) wind potential studies (member of MEASNET’s Expert Group for Site Assessment), ii) LIDAR and SODAR performance evaluation in complex terrains and iii) design and implementation of remotely controlled measurement systems for mission critical applications (wind farm monitoring, load and power performance measurements of wind turbines). He has more than 50 papers, presentations and announcements in scientific journals, conferences and workshops.
PosterDownload poster (11.95 MB)
The benefits of using FPGA-based data loggers in wind energy applications
An FPGA (Field-Programmable Gate Array) is reprogrammable silicon chip offering high reliability, determinism and true parallel processing. When used in wind energy applications, usually running in harsh environments, they significantly increase measurement accuracy and reliability, extending to new levels the online detection of faulty signals.
Now that the desired accuracy for cup anemometers intercompatisons (or cup-LiDARs comparisons) is below 1%, FPGA-based wind data loggers excel in achieving this.
A concrete example of this is the online detection and rejection of wrong intermittent digital pulses, affecting the accuracy of cup anemometers. This situation is likely to occur in outdoor areas with electromagnetic noisy environments, where very long cables are used, next to huge metal bodies.
Online detection occurs in the level of the FPGA chip, through software configured logic, performing digital signal debouncing. Today’s (high-end) data-loggers do not follow this approach, but they filter out (delete) the measurement by statistical processing, based on preset error limits.
Main body of abstract
A FPGA chip is made up of a finite number of predefined resources with programmable interconnects to implement a reconfigurable digital circuit and I/O blocks to allow the circuit to access the outside world. One of the benefits of FPGAs over processor-based systems is that the application logic is implemented in hardware circuits rather than executing on top of an OS, drivers, and application software.
Hardware execution provides greater performance and determinism than most processor-based software solutions. Once the code is compiled and running on the FPGA, it will run without the jitter associated with software execution and thread prioritization typical to most common operating systems and even present (to a much lesser extent) in real-time operating systems. It is generally used in high-end applications and, up to now, was available to only engineers with a deep understanding of digital hardware design. The rise of high-level system design tools, such as NI LabVIEW software, changed the rules of FPGA programming, delivering new technologies that convert graphical block diagrams into digital hardware circuitry
Apart the benefits brought by this technology to the measurement of the wind speed, other measured quantities benefit also from it. For example, wind direction measurements have increased accuracy because real-time compensation takes place for input and output signals of the potentiometer-based wind vanes. Also, any analogue signal benefits from oversampling and averaging of each single raw data.
Today, that maximum accuracy is needed for wind speed measurements, FPGA-based data loggers provide a reliable solution to the wind resource assessment campaigns. Even though, most of their uses are reserved to high-end applications, the proposed solution demonstrates that not-only accuracy is increased, but also reliability, data-connectivity options and modularity.
The proposed data-logger architecture, implemented in hardware circuits rather than executing on top of an OS, drivers, and application software, unifies the hardware requirements for data-logging in wind energy applications. These data loggers easily meet the requirements, not only for resource assessment, but also of power verification and load measurement campaigns.