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<p>The evaluation of output statistics in systems with high-dimensional uncertain parameters is important for real-time decision-making in large-scale systems. In this paper, we develop an MPCM-Taguchi method that combines the Multivariate Probabilistic Collocation Method (MPCM) with the Taguchi method to accurately estimate system output statistics while significantly reducing the number of simulations. The estimation algorithm is developed, and its theoretical analysis is provided. Numerical studies and the application to power buffers in Direct Current (DC) Microgrids with uncertain loads validate the MPCM-Taguchi uncertainty evaluation method.</p>

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MPCM-Taguchi: an efficient method for estimating output statistics in high-dimensional uncertain systems

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Siyu Zhou,
  • Yan Wan,
  • Jeff White

摘要

The evaluation of output statistics in systems with high-dimensional uncertain parameters is important for real-time decision-making in large-scale systems. In this paper, we develop an MPCM-Taguchi method that combines the Multivariate Probabilistic Collocation Method (MPCM) with the Taguchi method to accurately estimate system output statistics while significantly reducing the number of simulations. The estimation algorithm is developed, and its theoretical analysis is provided. Numerical studies and the application to power buffers in Direct Current (DC) Microgrids with uncertain loads validate the MPCM-Taguchi uncertainty evaluation method.