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DOI: 10.14569/IJACSA.2024.01503127
PDF

A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application

Author 1: Umaiz Sadiq
Author 2: Fatma Mallek
Author 3: Saif Ur Rehman
Author 4: Rao Muhammad Asif
Author 5: Ateeq Ur Rehman
Author 6: Habib Hamam

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 3, 2024.

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Abstract: The integration of renewable energy sources and the advancement of smart grid technologies have revolutionized the power distribution landscape. As the smart grid evolves, the monitoring and control of power converters play a crucial role in ensuring the stability and efficiency of the overall system. This research paper introduced a converter monitoring system in photovoltaic systems, the main concern is to protect the electrical system from disastrous failures that occur when the system is in operating condition. The reliability of the converters is significantly influenced by the degradation of their passive components, which can be characterized in various ways. For instance, the aging of inductors and capacitors can be char-acterized by a decrease in their inductance and capacitance values. Identifying which component is undergoing degradation and assessing whether it is in a critical condition or not, is crucial for implementing cost-effective maintenance strategies. This paper explores a set of classification algorithms, leveraging machine learning, trained on data collected from a Zeta converter simulated in Matlab Simulink. the report presents observations on how each algorithm effectively predicts the component and its condition and Graphical Performance Comparison for different ML Techniques serves as a crucial endeavor in evaluating and understanding the effectiveness of various ML approaches. The goal is to provide a comprehensive overview of how these techniques fare concerning criteria such as accuracy, precision, recall, F1 score, and Specificity among others. Quadratic Support Vector Machine (SVM) yields superior results compared to other machine learning techniques employed in training our dataset.

Keywords: Artificial intelligence; photovoltaic; support vector machine; machine learning; K-Nearest neighbor; maximum power point tracking; pulse width modulation; prognostic analysis; one-against-rest; one-against-one; direct acyclic graph; multi class support vector machine; DC-DC converter; zeta c

Umaiz Sadiq, Fatma Mallek, Saif Ur Rehman, Rao Muhammad Asif, Ateeq Ur Rehman and Habib Hamam, “A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01503127

@article{Sadiq2024,
title = {A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01503127},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01503127},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Umaiz Sadiq and Fatma Mallek and Saif Ur Rehman and Rao Muhammad Asif and Ateeq Ur Rehman and Habib Hamam}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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