A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Applicationopen access
- Authors
- Sadiq, U.; Mallek, F.; Ur, Rehman S.; Asif, R.M.; Ur, Rehman A.; Hamam, H.
- Issue Date
- Mar-2024
- Publisher
- Science and Information Organization
- Keywords
- Artificial intelligence; DC-DC converter; direct acyclic graph; K-Nearest neighbor; machine learning; maximum power point tracking; multi class support vector machine; one-against-one; one-against-rest; photovoltaic; prognostic analysis; pulse width modulation; support vector machine; zeta c
- Citation
- International Journal of Advanced Computer Science and Applications, v.15, no.3, pp 1290 - 1307
- Pages
- 18
- Journal Title
- International Journal of Advanced Computer Science and Applications
- Volume
- 15
- Number
- 3
- Start Page
- 1290
- End Page
- 1307
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90996
- DOI
- 10.14569/IJACSA.2024.01503127
- ISSN
- 2158-107X
- 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 characterized 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. © (2024), (Science and Information Organization). All Rights Reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.