As a wind blade is getting larger, its structural behavior becomes more complex and thus the risk of blade structural failure becomes higher. Generally, structural integrity is proved through blade testing with contact measurement devices such as strain gauges. However, contact measurement can only measure the physical quantities from installation points and requires lengthy cables to transmit the electrical signals to a data acquisition system, which can cause signal noise and add unnecessary weight. Recently, noncontact measurement techniques such as digital image correlation (DIC) have been widely used in various industries to process images captured from cameras and generate full-field strains and displacements without any mechanical connections such as cables. In this study, the application of three-dimensional DIC to wind blades was investigated; in addition, the material properties of glass-fiber-reinforced plastics used in blades for the measurement of the first frequency were also studied. Tensile testing was performed using coupons equipped with strain gauges and speckle patterns. In addition, a small blade from a 10 kW wind turbine was utilized to measure the first flapwise frequency using DIC and modal tests; the results showed a 2.1% difference between the two different measurements. This research showed that the DIC technique could be an alternative replacement for the traditional measurement technique upon further validation tests.

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.