Sungmok Hwang

Power generation using a photovoltaic (PV) system is increasing to achieve the carbon neutrality. Module-level power electronics (MLPEs) can optimize power generation for each PV panel. However, it increases the possibility of dc series arc fault condition between the PV panel and MLPEs. The broken and loose connector and damaged cable can induce a dc series arc fault condition. Previously, frequency domain analysis using PV current, such as Fourier transform and wavelet transform, was widely employed for arc fault detection. However, it has limited performance in accuracy according to operating conditions. This article proposes the dc series arc fault detection and extinguishing algorithm with coordination of time and frequency domain analysis, and dc current modulation. The arc resistance characteristics is analyzed to detect the fault condition according to the power rating. The MLPE modulates the dc current to optimize the arc fault detection. In addition, it can extinguish the arc fault condition using the current modulation. Therefore, the dc series arc fault condition is double detected through signal processing and current modulation. Experimental results can verify the fault detection and extinguishing capability.

The structural stiffness of a blade changes when it is defective, which affects its modal parameters. Consequently, modal parameters can be crucial to investigating defects. Therefore, developing a model that can accurately analyze the modal parameters of healthy and defective blades is essential. This study proposes a high‐fidelity finite element (FE) model development method based on multiobjective optimization. Three 10‐kW‐class wind turbine blades were manufactured, and the modal parameter changes due to debonding damage were measured. The initial FE model was constructed using structural drawings and considering specific material properties. Subsequently, the modal analysis results were compared with the experimental results. The analysis errors of normal and abnormal blades were expressed as cost functions and multiobjective optimization was performed. Machine learning–based predictive models facilitated time‐ and cost‐effective optimization, enabling modal updating that simultaneously reflects modal parameter changes for various blade models. Because accurately predicting the dynamic characteristics of blades by various defect locations and sizes is crucial for blade damage detection, the proposed method is significant for damage detection research.

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.

The noise from a 1.5 MW wind turbine was measured and the apparent sound power level, tonal audibility, and spectrum balance were analyzed. The apparent sound power level and tonal audibility were analyzed according to IEC 61400-11: ed3. (2012-11). The measured noise data at the turbine site was mainly in the north-west (NW) and north-north-west (NNW) directions, and approximately 500 and 250 samples of total noise and background noise data were obtained. Three tone components were observed in the low frequency region below 100 Hz. The tones in the low frequency critical band with a center frequency of 78.1 Hz were found to have a higher level than the hearing threshold. The possibility of wind turbine noise annoyance was also analyzed through the spectrum balance analysis.

Energy flow analysis has been developed and studied by many researchers to analyze acoustic energy in the medium to high frequency ranges. In this paper, a method to solve the 3D acoustic multi-domain problem by adopting an indirect approach to the EFBEM (Energy Flow Boundary Element Method) is proposed. A joint matrix comprised of power transmission and reflection coefficients is used to describe the energy exchanges between domains and absorption coefficients are applied to consider an energy absorbing boundary condition. Finally, coupled small reverberation rooms and a mock-up system of ship cabins were analyzed using the method to compare the numerical analysis with the experimental results. They show good agreement, thereby validating the method. The indirect EFBEM method allows us to use spherical wave Green's functions, which are not available in the direct method. Therefore, it is expected that the method can be applied to almost any acoustic multi-domain situation, from room to room cases to radiation problems.

The authors already have proposed a novel method of HRIR customization for synthesizing stationary sounds in the median plane based on subjective tuning of three parameters at each static source position. For synthesizing both stationary and dynamic sounds, this study extends the previous study to customize spatially continuous HRIRs in the median plane. The median plane in the upper hemisphere was systematically divided into two sectors, and three parameters were tuned at three positions (0°, 70°, and 180°), which are the endpoints of the two sectors, with listening to moving sounds synthesized by filtering the obtained spatially continuous HRIRs. Thus, the entire median-plane HRIRs in the upper hemisphere can be customized by subjective tuning of nine parameters only. Subjective localization listening tests were carried out to assess the customization performance using both stationary and moving sounds. When the stationary sounds were presented to nine subjects, the localization performance with the customized HRIRs was comparable to that with the individual HRIRs, but it was significantly better than that with the Kemar HRIRs. When the moving sounds were presented to four subjects, all subjects perceived the center position of the target trajectory more accurately with the customized HRIRs than with the Kemar HRIRs. The error with the customized HRIRs was comparable to that with the individual HRIRs. Therefore, it can be concluded that to make desired perception of both stationary and moving sounds is possible by the proposed customization methods.