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This study represents an effort in conducting wind resource assessment for a coastal location in India by optimizing the Weibull parameters. The analysis relies on a 22-year reanalysis dataset from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), NASA. Wind characteristics are assessed using the Weibull probability distribution, with shape (k) and scale (A) parameters determined through three distinct numerical techniques at a 50 m height level. This is done to calculate wind power density using MERRA-2 reanalysis data. Performance evaluation is carried out using goodness of fit tests. The outcomes indicate that all employed methods for parameter estimation are suitable. However, for assessing wind potential, the Maximum Likelihood Method (MLM) emerges as the most accurate. These findings are expected to contribute to the existing literature on probabilistic modeling of wind resources and utility of MERRA-2 in wind resource assessment.

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Estimation of Optimal Weibull Parameters for Wind Power Potential Assessment of Indian Coastal Site Using Reanalysis Data

  • 会议论文

  • 发表日期：

    2025年

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  • Harsh Patidar,
  • Vikas Shende,
  • Prashant Baredar,
  • Archana Soni

摘要

This study represents an effort in conducting wind resource assessment for a coastal location in India by optimizing the Weibull parameters. The analysis relies on a 22-year reanalysis dataset from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), NASA. Wind characteristics are assessed using the Weibull probability distribution, with shape (k) and scale (A) parameters determined through three distinct numerical techniques at a 50 m height level. This is done to calculate wind power density using MERRA-2 reanalysis data. Performance evaluation is carried out using goodness of fit tests. The outcomes indicate that all employed methods for parameter estimation are suitable. However, for assessing wind potential, the Maximum Likelihood Method (MLM) emerges as the most accurate. These findings are expected to contribute to the existing literature on probabilistic modeling of wind resources and utility of MERRA-2 in wind resource assessment.