We have studied a method of evaluating antifouling properties for biofouling occurring in offshore wind farms. To evaluate the antifouling properties, surface energy was analyzed through contact angle measurements and biofouling characteristics were analyzed through marine environment tests. Test samples were fabricated depending on the type of material and whether it had self-polishing properties. The surface energy showed relatively low results in self-polishing and polyurethane film samples, and in marine environment tests, the incidence of biofouling was lowest in self-polishing samples. In order to evaluate antifouling properties to prevent biofouling in offshore wind farms, it is believed that in addition to surface energy analysis using the contact angle, an evaluation of self-polishing properties is also necessary.

As large-scale wind power integration accelerates worldwide, accurately modeling of transient phenomena through EMT simulations has become crucial for maintaining power system stability and reliability. While conventional RMS-based models are adequate for certain steady-state and low-frequency analyses, they often fail to capture high-order harmonic phenomena and resonance phenomena. As a result of these issues, the necessity for EMT-based models has become more apparent. Many system operators and regulatory authorities worldwide have established grid codes that require PSCAD/EMTDC models for new renewable energy facilities. In Korea, KEPCO requires PSCAD/EMTDC models, but there are no official guidelines for PSCAD-based modeling of renewable energy systems. In particular, there are a lack of guidelines for Type-4 wind turbine systems, which are more complex. This remains a significant issue. This paper proposes an electromagnetic transient (EMT) modeling framework for a Type-4 wind turbine system. It comprises detailed representation of both the machine-side and grid-side converters as well as an active damping technique applied to the LCL filter. Various inverter modeling approaches such as switching model (SW), average-value model (AV), and controlled current injection model (CCI) are discussed, with guidance on selecting the appropriate inverter model based on the transient phenomenon of interest. Finally, as an example of using the EMT approach, we analyzed the harmonic resonance caused by the output filter in a Type-4 wind turbine system and verified that applying a mitigation strategy suppressed the harmonic resonance. This shows that the EMT model is an effective way to capture transient phenomena and harmonic resonance behaviors, as well as to find practical solutions.

This study proposes an innovative method for improving the energy production of an onshore wind farm by integrating Bayesian algorithm-based wake steering control. The proposed method dynamically adjusts turbine yaw angles to mitigate wake effects, thereby optimizing the wake across the wind farm. By applying the predictive optimization capabilities of Bayesian algorithms, the method significantly improves wind farm power output and shows strong potential for large-scale implementation. FLORIDyn, the dynamic version of the FLORIS model, is used to simulate wake interactions and to evaluate the performance of the proposed control strategy compared to existing methods. Bayesian optimization effectively strikes a good balance between computational effort and performance. Simulation results are presented that demonstrate a considerable increase in overall energy production by minimizing wake effects.

In this work, the design and analysis of small wind turbine blades was performed. The target system is a 1.5 kW class small wind turbine. The number of blades are three. Most wind turbine blades are manufactured using composite materials. The composite laminate structures are vulnerable to damage such as delamination. Therefore, a small wind turbine blade was selected and design was performed. The damage diagnosis was considered during the structural design. Damage diagnosis was analyzed using optical fiber sensors. A design method is proposed in which damage is detected using an optical fiber sensor and a damage diagnosis technique is applied.

This study proposes an empirical turbulence spectrum-based approach to improve the temporal resolution of Local Data Assimilation and Prediction System (LDAPS) wind forecasts. While LDAPS forecasts provide valuable large-scale wind predictions, their inherent temporal resolution of one hour limits accurate modeling of short-term wind variability, which is critical for wind power applications. To overcome this, the measured power spectral density (PSD) from 14 days of lidar observations was averaged and used to complement the high-frequency components of the LDAPS forecasts, resulting in high-resolution wind speed time series with a one-minute resolution. Comparative analyses with Kaimal and von Kármán spectral models demonstrated that the empirical PSD model effectively suppresses unrealistic high-frequency variability and aligns more closely with the measured wind speed fluctuations. This approach significantly reduced the mean absolute error (MAE) and root mean square error (RMSE) when compared to traditional models, underscoring its potential as a reliable tool for high-resolution wind forecasting in operational contexts.