Abstract
Structural health monitoring (SHM) will be pivotal for safe and economic operation of wind turbines. Timely discovery of changes within the structure and means of prediction of required maintenance will reduce production costs of electricity and catastrophic failures. Long-term structural acceleration recording can support damage detection on turbine towers and document progression of fatigue. Conventional acceleration recordings are based on wired sensor nodes at fixed positions with privileged accessibility and electric power supply. However, such positions might be near vibration nodes and not necessarily experience the maximum vibration amplitude. Shifts in eigenfrequencies can be an indicator of changes in structural stiffness, hence damage, but also be caused by environmental effects, e.g., temperature. Damages generate local effects while the structure’s vibration spectrum is a global evaluation. If a sensor is close to the location of damage, the probability of detection is increased. Wireless sensors powered by batteries are advantageous for this task as they are independent of cabling for power supply and data transmission. Such monitoring of turbine tower structures is not common in practice and requires new data-enabled techniques to discover deviations from the optimal way of wind turbine operation. This paper proposes a new approach using wireless high-resolution acceleration measurement sensor nodes, exploiting the vibration response of wind turbine towers. Influences of acceleration resolution and sensor node locations onto the accuracy of eigenfrequency determination are demonstrated. A comparison between acceleration recordings by wireless sensor nodes and their wired counterparts is presented to prove the equivalence of the wireless sensing method. Finally, new data compression techniques used with the sensor nodes are discussed to reduce wireless transmission to a minimum.
Highlights
In 2017, the wind power capacity installed worldwide exceeded the level of 500 GW [1]
Due to the higher sensitivity of the sensor nodes of the second generation, the wind speed of 5 to 11 m/s and the additional sensor location at the intermediate steel platform, the fast Fourier transform (FFT) of the recorded acceleration values generated frequency spectra with several frequency peaks, as shown in Figs. 11 and 12
In order to exclude a side effect caused by the loss of transmitted data during the first measurement period, the vibration spectra from the wireless sensor nodes for the first measurement period and the second measurement period were compared, as the loss during the second period was only 0.1% of the data
Summary
In 2017, the wind power capacity installed worldwide exceeded the level of 500 GW [1]. Due to the large number of installed wind turbines and the standard design lifetime of 20 years, a large percentage encounters its end of service life within the few years. Not every wind turbine experienced the load conditions assumed at its design, but preserved significant remaining useful lifetime (RUL). Research projects such as the MISTRALWIND project aim at the extension of the RUL [3]. To warrant a safe long-term operation of wind turbines and to predict the RUL from the evolvement of fatigue, changes in the structural integrity need to be detected, analyzed, and
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