Long-term monitoring of wind farms using big data approach

There is a trend to build wind turbines in large
wind farms and in the near future to operate such a farm as an
integrated power production plant. Predictability of individual
turbine behaviour is key in such a strategy. In order to minimize
the influence on the balance of the electricity grid it is necessary
to have stable electricity production by each of the turbines.
Hence all turbines should be available for operation when needed,
which puts significant constraints on owner operators. Failure
of turbine subcomponents should be avoided. This requires
planning in advance of all necessary maintenance actions such
that they can be performed during low wind and low electricity
demand periods. Therefore, it is necessary to anticipate upcoming
component failures, such that spare parts can be ordered and are
available for maintenance personnel during the optimal weather
and market windows. In order to obtain the insights to predict
component failure, it is necessary to have an integrated clean
dataset spanning all turbines of the wind farm for a sufficiently
long period of time. This paper describes the requirements
and challenges related to such a dataset based on experience
acquired during four years of monitoring offshore wind farms.
Furthermore, it suggests a big data based approach for an
integrated no-sql data-storage and data-analytics platform to
tackle these challenges. In addition a failure prognosis approach
using the integrated dataset is proposed to detect failure initiation
in the bearings of gearboxes and generators, which are vital parts
of wind turbines.