Titolo della tesi: Wind Flow Observation at Different Spatio-Temporal Scales Based on SCADA Operational Wind Turbine Data and Satellite Remote Sensing
This doctoral thesis investigates the potential use of operational data from
wind turbines and wind farms to observe and characterize wind flow distributions
around and within the plant at multiple spatial and temporal scales. A major
objective is to explore how turbine-level measurements—typically used for operational
diagnostics—can be leveraged to enhance wind flow analysis, from the scale of
individual turbines to broader wind farm and mesoscale wind field distributions
investigated.
At the turbine scale, the study explores the use of operative data to improve the
characterization of intra-wind-farm wake interactions, with particular emphasis on
applications for wind farm control and real-time performance monitoring. Particular
emphasis is placed on the evolution and implementation of engineering wake models
and their integration with operational data to capture localized flow dynamics within
the wind farm layout.
At the wind farm and mesoscale levels, the thesis examines the integration
of SCADA (Supervisory Control and Data Acquisition)-derived wind data with
satellite-based wind field observations, particularly those obtained from Synthetic
Aperture Radar (SAR) imagery. This integration aims to improve the accuracy
and reliability of satellite-derived wind estimates, with significant implications for
regional-scale wind monitoring and site assessment in complex coastal environments.
A comprehensive review of current satellite wind retrieval techniques is presented,
highlighting their limitations and potential, and proposing a novel methodology to
combine remote sensing data with in-situ turbine measurements.
Case studies demonstrate how readily accessible operational data can effectively
enhance both wind farm control and planning. By leveraging sensors already installed
on turbines, this approach presents a cost-efficient strategy that maximizes existing
resources. This method not only underscores significant economic advantages but
also highlights its potential to drive technological advancements in wind energy
management. Overall, this thesis provides practical examples of how turbine SCADA
data—typically employed for operational diagnostics—can serve as a valuable ob
servational resource for wind flow characterization across different spatio-temporal
scales, with a broad range of applications for the wind energy industry. Furthermore,
by integrating these in-situ measurements with remote sensing data—particularly
satellite-based wind observations—the proposed approach expands its applicability
to wind flow analysis at the wind farm scale, without requiring additional on-site
instrumentation.