  Modern wind turbine control algorithms typically utilize rotor effective wind
speed measured from an anemometer on the turbine's nacelle. Unfortunately, the
measured wind speed from such a single measurement point does not give a good
representation of the effective wind speed over the blades, as it does not take
the varying wind condition within the entire rotor area into account. As such,
Blade Effective Wind Speed (BEWS) estimation can be seen as a more accurate
alternative. This paper introduces a novel Subspace Predictive Repetitive
Estimator (SPRE) approach to estimate the BEWS using blade load measurements.
In detail, the azimuth-dependent cone coefficient is firstly formulated to
describe the mapping between the out-of-plane blade root bending moment and the
wind speed over blades. Then, the SPRE scheme, which is inspired by Subspace
Predictive Repetitive Control (SPRC), is proposed to estimate the BEWS. Case
studies exhibit the proposed method's effectiveness at predicting BEWS and
identifying wind shear in varying wind speed conditions. Moreover, this novel
technique enables complicated wind inflow conditions, where a rotor is impinged
and overlapped by wake shed from an upstream turbine, to be estimated.
