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This paper presents an imaging radar system for structural health monitoring (SHM) of wind turbine blades. The imaging radar system developed here is based on two frequency modulated continuous wave (FMCW) radar sensors with a high output power of 30 dBm. They have been developed for the frequency bands of 24,05 GHz-24,25 GHz and 33.4 GHz-36.0 GHz, respectively. Following the successful proof of damage detection and localization in laboratory conditions, we present here the installation of the sensor system at the tower of a 2 MW wind energy plant at 95 m above ground. The realization of the SHM-system will be introduced including the sensor system, the data acquisition framework and the signal processing procedures. We have achieved an imaging of the rotor blades using inverse synthetic aperture radar techniques under changing environmental and operational condition. On top of that, it was demonstrated that the front wall and back wall radar echo can be extracted from the measured signals demonstrating the full penetration of wind turbine blades during operation.

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This paper presents the design and experimental realization of a cooperative radar network for structural health monitoring (SHM) of wind turbine blades. For this purpose, 40 FMCW (frequency-modulated continuous wave) radar sensors operating from 58 to 63.5 GHz have been installed in a 31-m-long blade during manufacturing. A subset of 10 sensors is material-embedded in the core material of the blade, and the remaining thirty sensors are placed inside the blade on an inner rotor blade surface. The sensors are distributed over the entire blade based on previous high-frequency electromagnetic simulations. A full-scale fatigue test has been performed under controlled laboratory conditions. In addition, holes have been drilled into the blade by hand to represent a well-defined and relatively small damage. During the experimental campaign, measurements from the complete radar network have been transferred to a base station through a wireless communication link. Finally, it was demonstrated that fatigue as well as artificial damage could be detected accurately using the proposed damage indicator (DI) approach.