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

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Titel: Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis.
Autoren: Palomares-Salas, José Carlos, Aguado-González, Sergio, Sierra-Fernández, José María
Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10602, 16p
Schlagwörter: MACHINE learning, DEEP learning, POWER quality disturbances, PYTHON programming language, ROBUST statistics, CLASSIFICATION, SIGNAL-to-noise ratio
Reviews & Products: MATLAB (Computer software)
Abstract: Accurate and robust power quality disturbance (PQD) classification is critical for modern electrical grids, particularly in noisy environments. This study presents a comprehensive comparative evaluation of machine learning (ML) and deep learning (DL) models for automatic PQD identification. The models evaluated include Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), k-Nearest Neighbors (kNN), Gradient Boosting (GB), and Dense Neural Networks (DNN). For experimentation, a hybrid dataset, comprising both synthetic and real signals, was used to assess model performance. The robustness of the models was evaluated by systematically introducing Gaussian noise across a wide range of Signal-to-Noise Ratios (SNRs). A central objective was to directly benchmark the practical implementation and performance of these models across two widely used platforms: MATLAB R2024a and Python 3.11. Results show that ML models achieve high accuracies, exceeding 95 % at an SNR of 10 dB. DL models exhibited remarkable stability, maintaining 97 % accuracy for SNRs above 10 dB. However, their performance degraded significantly at lower SNRs, revealing specific confusion patterns. The analysis underscores the importance of multi-domain feature extraction and adaptive preprocessing for achieving resilient PQD classification. This research provides valuable insights and a practical guide for implementing and optimizing robust PQD classification systems in real-world, noisy scenarios. [ABSTRACT FROM AUTHOR]
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  Data: Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis.
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  Data: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10602, 16p
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  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22POWER+quality+disturbances%22">POWER quality disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22PYTHON+programming+language%22">PYTHON programming language</searchLink><br /><searchLink fieldCode="DE" term="%22ROBUST+statistics%22">ROBUST statistics</searchLink><br /><searchLink fieldCode="DE" term="%22CLASSIFICATION%22">CLASSIFICATION</searchLink><br /><searchLink fieldCode="DE" term="%22SIGNAL-to-noise+ratio%22">SIGNAL-to-noise ratio</searchLink>
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  Label: Abstract
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  Data: Accurate and robust power quality disturbance (PQD) classification is critical for modern electrical grids, particularly in noisy environments. This study presents a comprehensive comparative evaluation of machine learning (ML) and deep learning (DL) models for automatic PQD identification. The models evaluated include Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), k-Nearest Neighbors (kNN), Gradient Boosting (GB), and Dense Neural Networks (DNN). For experimentation, a hybrid dataset, comprising both synthetic and real signals, was used to assess model performance. The robustness of the models was evaluated by systematically introducing Gaussian noise across a wide range of Signal-to-Noise Ratios (SNRs). A central objective was to directly benchmark the practical implementation and performance of these models across two widely used platforms: MATLAB R2024a and Python 3.11. Results show that ML models achieve high accuracies, exceeding 95 % at an SNR of 10 dB. DL models exhibited remarkable stability, maintaining 97 % accuracy for SNRs above 10 dB. However, their performance degraded significantly at lower SNRs, revealing specific confusion patterns. The analysis underscores the importance of multi-domain feature extraction and adaptive preprocessing for achieving resilient PQD classification. This research provides valuable insights and a practical guide for implementing and optimizing robust PQD classification systems in real-world, noisy scenarios. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/app151910602
    Languages:
      – Code: eng
        Text: English
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        PageCount: 16
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      – SubjectFull: MATLAB (Computer software)
        Type: general
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: POWER quality disturbances
        Type: general
      – SubjectFull: PYTHON programming language
        Type: general
      – SubjectFull: ROBUST statistics
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      – SubjectFull: CLASSIFICATION
        Type: general
      – SubjectFull: SIGNAL-to-noise ratio
        Type: general
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      – TitleFull: Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis.
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            NameFull: Palomares-Salas, José Carlos
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            NameFull: Aguado-González, Sergio
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            NameFull: Sierra-Fernández, José María
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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