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October 1, 2025Applied SciencesOpen Access

Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis

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Authors

JPJosé-Carlos Palomares-SalasSASergio Aguado-GonzálezJSJosé-María Sierra-Fernández

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Overview

Comparative analysis of ML and DL models shows over 95% accuracy for power quality disturbance classification, highlighting robustness variation with noise levels.

Key Points

  • Machine learning models achieved over 95% accuracy under specific conditions and exhibited strong performance.
  • Deep learning models maintained 97% accuracy for signal-to-noise ratios above 10 dB but struggled with lower ratios.
  • Model robustness was tested using both synthetic and real signals across multiple platforms, revealing strengths and weaknesses.
  • The study emphasizes the role of feature extraction and preprocessing in enhancing the resilience of classification systems.

Cite This Study

Palomares-Salas et al. (2025) studied this question.

synapsesocial.com/papers/68dd91dafe798ba2fc499512https://doi.org/10.3390/app151910602
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