This paper describes the insulation breakdown characteristics of 72.5 kV dry-air insulated switchgear under development for installation in a wind power generator when a lightning impulse voltage is applied. For this study, the weak point of insulation due to the electric field concentration of the switchgear's internal shape was identified by finite element method (FEM) analysis, and the shape was actually simulated to measure and analyze the polarity of the lightning impulse voltage and the insulation breakdown characteristics according to the gas pressure at dry-air pressures of 0.1 Mpa to 0.45 Mpa. This study derives the maximum electric field with a 50 % discharge probability for each switchgear internal insulation vulnerable point based on the actual test and electrical simulation, which will be useful as reference data for supplementing and changing insulation design in the future.

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

In this study, a health monitoring system was developed for the two most vulnerable parts of a wind tower support structure: the connection between steel towers (L-Flange) and the concrete foundation-steel tower connection. To select assessment parameters for health monitoring, detailed FEM analysis was conducted using the ABAQUS program. Additionally, a testbed was established near the Jeju Woljeongri wind turbine farm to evaluate the applicability of measurement data by installing sensors. Through computational analysis and relevant criteria review, we defined limits for measurement parameters by vulnerable section. We categorized the structural safety evaluation into four stages: normal, caution, warning, and danger, and selected management criteria for each stage. From this, an algorithm to evaluate safety was developed, and a visualized monitoring platform based on the established critical parts monitoring system was developed.

In this study, a tensile test and FE analysis were conducted on a bolt-connected L-shaped flange to evaluate its behavior and load resistance. A total of five specimens were manufactured using the inner and outer distances and bolt diameters of the L-type flange as experimental variables. As a result of the tensile test of the L-shaped flange, as the internal and external length ratio (b/a) increased, the maximum load decreased and the maximum displacement increased. As the diameter (d) of the bolt increased, the maximum load and the deformation of the wall increased. The shapes of the destruction specimens showed two forms of destruction: one due to the fall of the nut and the surrender of the bolt as the thread of the bolt and nut was worn out, followed by the surrender of the wall. As a result of FE analysis, it was found that elasto-plastic model (EPM) analysis similarly tracks the behavior of the tensile test results.