Dr Alireza Gheitasi

PhD (Electrical Engineering), Auckland University of Technology, Master of Engineering Science in Signal Processing and Data Analysis in Power Systems, University of Malaya, Bachelor of Engineering in Electrical Engineering (Power), Shahid Chamran University

Position

Senior Lecturer

Teaching area

Electrical Engineering

Biography

Dr Alireza Gheitasi is a Senior Lecturer in Electrical Engineering at Manukau Institute of Technology with more than 20 years of industry and tertiary education experience in New Zealand and internationally.

His professional and academic experience spans electrical engineering, power systems, electrical machines, industrial automation, intelligent condition monitoring, distributed sensing, artificial intelligence, and renewable energy systems. Dr Gheitasi is actively involved in teaching, curriculum development, engineering project supervision, applied research, and industry engagement.

Why I love MIT

I enjoy working at MIT because of its strong practical and industry-focused approach to engineering education. I particularly value working alongside students on real-world engineering projects and helping them develop the technical, analytical, and problem-solving skills needed for successful engineering careers.

I also appreciate the opportunity to connect teaching, applied research, and industry engagement, ensuring that learning remains relevant to current industry practice.

Publications

  • A Digital-Twin Framework for Integrated Electro-Thermal-Economic Evaluation of Superconducting Electric Motors (SPIES, Australia, 2026)
  • Current Signature Analysis Using Distributed Sensor Networks and AI: A Practical Build for Multi-Node Fault Detection in Electrical Systems (SPIES, Australia, 2026)
  • Cyber-Physical Distributed Intelligent Motor Fault Detection (Sensors, 2024)
  • Distributed Motor Current Signature Analysis in an IoT Environment (AUPEC, 2019)
  • Distributed Monitoring of Low Voltage Grids Using Signature Analysis (IEEE ICASI, 2018)
  • Hopf Bifurcation Control of Subsynchronous Resonance Utilizing UPFC (Engineering, Technology & Applied Science Research, 2017)
  • Development of an Automatic Cleaning System for Photovoltaic Plants (IEEE PES APPEEC, 2015)
  • Distributed Signature Analysis of Induction Motors Using Artificial Neural Networks (ICARCV, 2014)

Research interests

  • Intelligent condition monitoring of electrical machines
  • Motor Current Signature Analysis (MCSA)
  • Distributed sensor networks and fault diagnosis
  • Artificial intelligence and machine learning for electrical engineering
  • Cyber-physical electrical systems
  • Industrial automation and control
  • Electrical drives and motor control
  • Power systems protection, stability, and power quality
  • Renewable energy and photovoltaic systems
  • Digital twins and electro-thermal-economic modelling
  • High-power electric motors and transport electrification
  • Industrial IoT, SCADA, and data acquisition systems

Award and grants

  • Best Paper Award, International Conference on Power and Energy Systems (2022)
  • Research funding supporting intelligent motor condition monitoring and AI-based fault diagnosis
  • MIT research funding supporting electrical machine, sensing, and industrial automation research
  • Supervision of student projects that have received national and international recognition, including projects recognised by the Australian Ambassador to Kuwait and Maker Faire
  • Co-inventor of a patented vibration data acquisition system developed for turbine monitoring at Ramin Power Station

 

Memberships and affiliations

  • Engineering New Zealand (MEngNZ 1016153)
  • Electrical Workers Registration Board (Electrical Engineering Class Registration EW00070522)
  • Active involvement in professional and academic communities related to electrical engineering, industrial automation, and intelligent condition monitoring