Abstract
A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings.The production of extremely high temperatures may lead to the ignition of nearby combustible materials. Theapplications of arc fault diagnosis based machine learning are a global interest due to the immense challengeto create an accurate and efficient detection method. In this paper, a detection and classification method using amultilayer perceptron incorporated with Bi-Directional Long short-term Memory (MLP-BiLSTM) is proposed. Inorder to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observationsfor simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation andexperimental results confirm that the accuracy of the proposed detection and classification method reaches 99%,which results in that the methodology is believed to be accurate for DC series arc fault detection and classification inthe PV system with relatively high accuracy.
Recommended Citation
Omran, Alaa Hamza; Said, Dalila Mat; Hussin, Siti Maherah; Abdulhussein, Sadiq H.; Ahmad, Nasarudin; and Samet, Haidar
(2023)
"Development of an Intelligent Detection Method of DC Series ArcFault in Photovoltaic System Using Multilayer Perceptron andBi-Directional Long Short-Term Memory,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
3, Article 14.
DOI: https://doi.org/10.52866/ijcsm.2023.02.03.014
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss3/14
