This thesis was submitted for the award of Docctor of Philosophy and was awarded by Brunel University LondonCurrent wind turbine and power grid industry have relatively little research and
development with regards to implementing novel communication network and intel-
ligent system to overcome issues that pertain to network failure and lack of monitor-
ing. Wind turbine location could be a big concern when it comes to identifying an
efficient location for future wind turbine and the impact of a site with non-efficient
meteorological parameters can result in relocation of a wind turbine and revenue-
loss. Unplanned wind turbine shutdowns that are considered to be one of the major
revenue-loss factors of a modern wind farm business. Typically, the unplanned wind
turbine shutdown is a result of sensors fail due to harsh environment challenges that
prevent hardware status from being available on the monitoring system. The above
mentioned research problems pertain to wind turbine site assessment and predic-
tion of power. In this thesis, a novel programmable software-defined robotics and
IoT testbeds are proposed with the fusion of Artificial Intelligence and optimiza-
tion methods to solve specific problems related to wind turbine site assessment and
fault management. The site selection process is implemented using proposed aerial
and ground robotic systems that are incorporated with Software-Defined Networks
and OpenFlow switching capabilities. A second stage development of the system is
proposing a prediction platform that run on the aerial robot cluster using neural net-
works optimization regression techniques. To overcome the unplanned wind turbine
network outage, an IoT micro cloud cluster system is proposed that act as immedi-
ate fail-over platform to provide continuous health readings of the wind turbine to
ensure the turbine in question will not get shutdown unnecessarily. The proposed
system help in minimizing revenue-loss caused by stopping a wind turbine from op-
eration and help maintain generated power stability on the grid. Additionally, since
large wind farms require an agile and scalable management of selecting the most
efficient wind turbine location install. Thus, a softwarized cognitive routing proto-
col is proposed. The group of quadcopters is a redundant failover Software-Defined
Network/OpenFlow system that can cover every single way point of the farm land.
Although, power consumption is essential for the continuity the service, a Software-
Defined charging system testbed is proposed that uses inductive power transfer wit