RT journal article
T1 Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis
A1 Palomares-Salas, José Carlos
A1 Aguado-González, S.
A1 Sierra Fernández, José María
A1 Aguado-González, Sergio
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 power quality
K1 disturbances
K1 machine learning
K1 deep learning
K1 higher-order statistics
AB 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.
PB MDPI
SN 2076-3417
YR 2025
FD 2025-09
LK http://hdl.handle.net/10498/39414
UL http://hdl.handle.net/10498/39414
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026