Data-Driven SiC MOSFET Active Gate Driving for 380V/3A LVDC System Protection Employing Recurrent Deep Neural Network

Abstract

This article proposed a Gated-Recurrent Learning Deep Neural Network (GRL-DNN) to predict the gate driving sequence of SiC-MOSFET in a 380 V/3A Low Voltage DC microgrid (LVDC). It is devised that the proposed intelligent fuse Active Gate Driving (AGD) requires a current limitation of 3 A to avoid fault and excess power loss. During over-current conditions in microgrid the proposed data-driven Intelligent Fuse (iFuse) acts as a circuit breaker with current limitation. The proposed GRL-DNN switching model training dataset is obtained for various operating condition in MATLAB. Training features like gate triggering, temperature, over-current magnitude, and tripping time are obtained by the proposed circuitry sequence predictor model in LTspice. By obtaining large solution space, without any delay automatic AGD can cutoff over-current in LVDC microgrid system. By comparing with conventional gate driving solid state fuse by Angelov model, the proposed iFuse integrated merits like fast reaction, accurate tripping, and high reliability to any system for improving fault protection. Online data analysis was carried to validate the outcome of the proposed iFuse AGD in hardware-in-loop simulation. The conducted real-time scaled-down experiment under over-current conditions on 380 V/3A LVDC system shows the proposed GRL-DNN based iFuse as a valuable active gate driving component for protection.

Keywords:

• Deep neural network
• recurrent learning
• active gate driving
• iFuse
• SiC-MOSFET
• over-current isolation

ACKNOWLEDGMENTS

The authors would like to express their profound gratitude to Department of Electrical and Electronics Engineering of Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu and School of Electrical Engineering, Vellore Institute of Technology, Chennai for providing us the necessary facilities to carry out our research work. This research work is fully supported by DST-SERB project under TARE scheme (TAR/2021/000396).

Availability of Data and Materials

The data presented in this study are available by reasonable request to the corresponding author.

Code Availability

The deep learning code presented in this study is available by reasonable request to the corresponding author.

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

No, generative AI and AI-assisted technologies used in the writing process.

Disclosure Statement

No potential competing interest was reported by the authors.

Human and Animal Rights

In this research, No Human Participants and Animals are involved.

Informed Consent

No Informed consent.

Additional information

Notes on contributors

Pandia Rajan Jeyaraj

Pandia Rajan Jeyaraj received his B.E. from Kamaraj College of Engineering and Technology, in 2009 with First class distinction, the M.Tech. degree in Control and Instrumentation from Thiagarajar College of Engineering, Madurai in 2011 with First class distinction, and Ph.D. from Anna University, Chennai in 2020. He currently working as an Assistant professor (Selection Grade) at Mepco Schlenk Engineering College, Sivakasi. He has authored over 26 research papers in the reputed International SCI Journals. He acts as a reviewer for various reputed publishers, such as Wiley, IEEE Transactions, IEEE ACCESS, IET, Springer, Sage, and Journals. He has published 8 Indian patents and 3 copyrights. His research area includes the Internet of Things, Smart Grid, Image Processing, Process Control, Power system Planning, and Deep Learning Algorithm applications. Dr. Pandia was a recipient of a fellowship from the Indian Science Academies at the Department of Electrical Engineering at the Indian Institute of Technology, Delhi. He is a life member of the Indian Society for Technical Education (ISTE).

Brindhu Kumari Albert

Brindhu Kumari Albert received her B.E. degree in Electrical and Electronics Engineering in 2006, and her M.E. degree in Embedded System technologies in 2009 from Anna University, Chennai, India. Currently, she is a full-time research scholar at Mepco Schlenk Engineering College, Sivakasi, India. Her research area includes power converters, Microgrid/Smart Grid, intelligent controllers, and renewable energy systems.

Gnanavadivel Jothimani

Gnanavadivel Jothimani received his B.E degree in Electrical and Electronics Engineering from Madras University, and his M.E. degree in Power Electronics and Drives from Bharathidhasan University. He completed a Ph.D degree in Electrical Engineering from Anna University, Chennai. He is working as an Associate Professor (Sr. Gr) at Mepco Schlenk Engineering College, Sivakasi, India. He has published research papers in international journals and conferences. His research area includes power converters, power quality, intelligent controllers, and renewable energy sources.

Aravind Chellachi Kathiresan

Aravind Chellachi Kathiresan received the Diploma in Electrical and Electronics Engineering from Noorul Islam Polytechnic College, Nagercoil, India in 2003 and, a B.E degree from Anna University, Chennai, India in 2006. He received his Master’s degree from VIT University, Vellore, India in 2009. He received his Ph. D. degree in 2015 from the National Institute of Technology, Tiruchirappalli, India. He has 16 years of teaching and research experience at various engineering institutions. Since December 2023, he has been an Senior Associate Professor at the School of Electrical Engineering, Vellore Institute of Technology, Chennai, India. His areas of interest include Power Converters, Renewable Energy systems, Power flow Management in Hybrid Systems, and AC/DC microgrids and their protection.

Senthil Kumar Subramanium

Senthil Kumar Subramanium (M’17) received the B.E. degree in electrical and electronics engineering from Madurai Kamaraj University, Madurai, India, in 1999, the M.Tech. degree in electrical drives and control from Pondicherry University, Puducherry, India, in 2005, and the Ph.D. degree in electrical engineering from the National Institute of Technology, Tiruchirappalli, India, in 2013. He has 17 years of teaching experience at various engineering institutions. Since April 2006, he has been an Assistant Professor at the National Institute of Technology. He has extensively researched on self-excited induction generators for standalone and grid-connected applications. His current research interests include the development of new power converter topologies for renewable energy system