초록
Long-term time series of wind data are essential to estimate the control and output performance of wind turbine systems. In this work, we propose and verify the use of Markov chains to generate long-term synthetic wind data using measured data at Wido Island. We first identify the Weibull distribution, the daily and monthly wind speed variations of the Wido region. Statistical parameters of the synthetic wind data such as maximum wind speed, minimum wind speed, average wind speed, and standard deviation are compared with those of observed data via met mast. In addition, both plots of autocorrelation and histogram are examined to verify the coincidence between synthetic and observed wind data. The transition matrix reflects the characteristics of wind speed variations over time at the measured site. A cumulative state transition matrix is obtained from the state transition matrix to determine the next state with a random number generator. The size of the synthetic wind data was iteratively determined to minimize statistical parameter errors. Through this study, long-term synthetic wind data can be adopted to simulate both the control performance and capacity factor of wind turbine systems scheduled to be installed in the site of interest.
키워드
마르코프 사슬, 상태전이 확률 행렬, 인공 바람 자료, 와이블 분포, 자기상관
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