@inproceedings{7df8e1900ea14616a8d9b70425e7e2ef,
title = "Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis",
abstract = "This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.",
keywords = "Classification",
author = "Amal Hichri and Wajdi Saadaoui and Mansour Hajji and Majdi Mansouri and Mohamed Nounou and Kais Bouzrara",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th International Conference on Control, Decision and Information Technologies, CoDIT 2024 ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1109/CoDIT62066.2024.10708329",
language = "English",
isbn = "979-8-3503-7398-1",
series = "International Conference On Control Decision And Information Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2995--3001",
booktitle = "2024 10th International Conference On Control, Decision And Information Technologies, Codit 2024",
address = "United States",
}