Big Data Analytics for Wind Farms
Dina Fawzy Mahmoud Abu-Elela;
Abstract
ABSTRACT
Recently big data have become a buzzword, which forced the
researchers to expand the existing techniques to cope with the evolved nature
of data and to develop new analytic techniques to process and analyze such
data. Big data analytic techniques are serving many domains such as the
renewable energy (RE). RE has become an important discipline for sciences
and technologies. Wind power is a part of RE that has encountered a rapid
development in the recent years. However, Wind turbines are used to
transform wind power into electrical power, which are grouped together into
specifically-designed wind farms. Wind farms output needs management to
cover the required energy for consumption. The generated data from the
sensors detecting a potential land can be very huge, fast in generation,
heterogeneous, and incomplete, which considered as big data. Due to the
huge costs of wind farms establishment, the location for wind farms should
be carefully determined to achieve the optimum return of investment, in
addition to the design of the turbines’ layout distribution that directly affects
the power generated from such design.
On the other hand, wind farms maintenance and production reliability are
of high necessity to ensure the sustainability of wind farms. The continuous
evolution of turbines industry has a serious impact on the operation and
maintenance costs. Thus, monitoring wind turbines performance and early
prediction of deterioration are highly required. During the operational life of
turbines, some components are persistently exposed to extreme
environmental influences that result in their edge erosion. This led
researchers to dig more into managing the costs of wind farms. The operation
and maintenance (O&M) costs have a great effect on the success of wind
farm projects to build profitable wind farms. Thus, managing O&M costs has
become of a crucial demand to manage wind farms economics.
In this thesis, we propose Trio-V Wind Analyzer, a big data analytic
system for wind farms management. The system can determine the suitability
of spatial locations for cost-effective wind energy production through
analyzing many factors such as wind factors (ex: speed and direction), Land
Recently big data have become a buzzword, which forced the
researchers to expand the existing techniques to cope with the evolved nature
of data and to develop new analytic techniques to process and analyze such
data. Big data analytic techniques are serving many domains such as the
renewable energy (RE). RE has become an important discipline for sciences
and technologies. Wind power is a part of RE that has encountered a rapid
development in the recent years. However, Wind turbines are used to
transform wind power into electrical power, which are grouped together into
specifically-designed wind farms. Wind farms output needs management to
cover the required energy for consumption. The generated data from the
sensors detecting a potential land can be very huge, fast in generation,
heterogeneous, and incomplete, which considered as big data. Due to the
huge costs of wind farms establishment, the location for wind farms should
be carefully determined to achieve the optimum return of investment, in
addition to the design of the turbines’ layout distribution that directly affects
the power generated from such design.
On the other hand, wind farms maintenance and production reliability are
of high necessity to ensure the sustainability of wind farms. The continuous
evolution of turbines industry has a serious impact on the operation and
maintenance costs. Thus, monitoring wind turbines performance and early
prediction of deterioration are highly required. During the operational life of
turbines, some components are persistently exposed to extreme
environmental influences that result in their edge erosion. This led
researchers to dig more into managing the costs of wind farms. The operation
and maintenance (O&M) costs have a great effect on the success of wind
farm projects to build profitable wind farms. Thus, managing O&M costs has
become of a crucial demand to manage wind farms economics.
In this thesis, we propose Trio-V Wind Analyzer, a big data analytic
system for wind farms management. The system can determine the suitability
of spatial locations for cost-effective wind energy production through
analyzing many factors such as wind factors (ex: speed and direction), Land
Other data
| Title | Big Data Analytics for Wind Farms | Authors | Dina Fawzy Mahmoud Abu-Elela | Issue Date | 2017 |
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