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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Lahlou, Cherki
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Computer Science & IT Engineering

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Intelligent assessment of harmonic distortion compliance in reverse osmosis systems Lahlou, Cherki; Bouikhalene, Belaid; Bengourram, Jamaa; Latrache, Hassan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4371-4381

Abstract

This study explores the critical challenge of harmonic distortion compliance in reverse osmosis (RO) desalination systems, with a focus on aligning with international standards, specifically IEC 61000, IEEE 519, and EN 50160. High-power equipment, particularly high-pressure pumps (HPP), introduces significant harmonic distortions, threatening power quality and operational reliability. To address this issue, we integrated advanced machine learning (ML) algorithms, namely decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) to assess harmonic compliance and predict total harmonic distortion (THD) under four operational scenarios. All data used for training and testing were obtained from real-time measurements taken at a large-scale desalination plant using a power quality analyzer (QUALISTAR CA 8336), which guarantees the practical relevance of the analysis. The models were trained on harmonic order and amplitude data and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models, MLP demonstrated superior performance, achieving an accuracy of 99.11% and an F1-score of 98.9%, making it a robust tool for harmonic compliance assessment. SVM and RF also showed commendable results, while DT proved effective for basic analysis. This research underscores the potential of AI-driven approaches in mitigating harmonic-related challenges, optimizing power quality, and enhancing operational efficiency in RO plants. These findings offer a pathway toward more reliable and energy-efficient industrial operations.
Co-Authors Belaid BOUIKHALENE Jamaa Bengourram, Jamaa Latrache, Hassan