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Updated: Jun 30, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation

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This study introduces a new method for variational mode decomposition (VMD) to improve time-series forecasting accuracy. The adaptive approach optimizes the mode number, enhancing predictions for tasks like wind power generation.

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Area of Science:

  • Time-series analysis
  • Signal processing
  • Renewable energy forecasting

Background:

  • Variational Mode Decomposition (VMD) is a common tool in hybrid time-series forecasting models.
  • Its accuracy is limited by the manual selection of the mode number, leading to under- or over-decomposition.
  • Optimizing VMD's mode number is crucial for improving forecasting performance.

Purpose of the Study:

  • To develop an adaptive method for determining the mode number in VMD.
  • To enhance the accuracy of time-series forecasting hybrid models.
  • To apply the improved VMD method to wind power generation forecasting.

Main Methods:

  • A novel Branch Error Reduction (BER) criterion was developed.
  • An adaptive VMD-based recursive decomposition method was implemented using the BER criterion.
  • The adaptive VMD method was integrated with standard forecasting models.

Main Results:

  • The proposed adaptive VMD method effectively determines the optimal mode number.
  • Integrating the adaptive VMD with forecasting models significantly improved accuracy.
  • Experimental results confirmed the method's effectiveness in wind power forecasting.

Conclusions:

  • The adaptive VMD approach overcomes the limitations of manual mode number selection.
  • This method offers a more robust and accurate solution for time-series forecasting.
  • The study validates the proposed technique for practical applications in renewable energy prediction.