Information

Name: Siwan Shachin Narayan
Position: Teaching Assistant
Email: siwan.narayan@usp.ac.fj
Phone: (+679) 8740243

Detail Information

Research Interest


With a degree in Electrical and Electronics Engineering, I am very interested in working with electrical motors, diagnosing faults, and power electronics, especially in renewable energy. I enjoy finding ways to make electrical systems more efficient and reliable. My goal is to improve how we detect and fix issues in electric motors and to use power electronics to enhance renewable energy systems. By combining my knowledge of electrical motors with new advancements in power electronics, I hope to help create better and more sustainable energy solutions.

Members of an organization

Professional member of Institute of Electrical and Electronics Engineering (IEEE), member of Fiji Institute of Engineers (FIE)

 Awards and Prizes

Recipient of Young Entrepreneurship Scheme (YES)

Undergraduate & Postgraduate Course:

EE102 – Fundamentals of Electrical and Electronics Engineering

EE211 – Electrical Machines

EE224 – Signals and Systems

EE222 – Digital Logic Design

EE321 – Power System Analysis

EE325 – Power Electronics and Drives

EE464 – Power Electronic for Distributed Generation and Renewable Energy

EE461 – Selected topics in Electrical and Electronics Engineering

EE491 – Maintenance, Reliability & Engineering Economics

EE481 – Professional Engineering & Project Management

EE492 – Digital Signal Processing

  1. M. Kumar, S. Narayan, P. Catrini, M. Cirrincione, A. Piacentino and A. Fagiolini, “Online Estimation of the Parameters and Diagnosis of Faults in an Air-cooled Chiller using Synchronous Reluctance motor drive,” 2023 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2023 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), Istanbul, Turkiye, 2023, pp. 1-5, doi: 10.1109/ACEMP-OPTIM57845.2023.10287042.
  1. Narayan, R. R. Kumar, G. Cirrincione and M. Cirrincione, “Detection of Stator Fault in Synchronous Reluctance Machines Using Shallow Neural Networks,” 2021 IEEE Energy Conversion Congress and Exposition (ECCE), Vancouver, BC, Canada, 2021, pp. 1347-1352, doi: 10.1109/ECCE47101.2021.9595518.
  1. -C. Liu, S. Laghrouche, A. N’Diaye, S. Narayan, G. Cirrincione, and M. Cirrincione, “Sensorless control of synchronous reluctance motor drives based on the TLS EXIN neuron,” in 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019, 2019. doi: 10.1109/IEMDC.2019.8785335.