Adjunct Academic
| College of Science, Engineering and Technology
School of Engineering
| Department: Electrical Engineering
Feature Extraction Technique for Fault Detection in Microgrid Using Principal Component Analysis
He possesses a Master's degree in Electrical and Smart Systems Engineering and a Bachelor of Technology degree in the same discipline, both obtained from the University of South Africa (UNISA). His pursuit has been characterized by his commitment to enhancing his understanding of creative and sustainable energy solutions. His enthusiasm for renewable energy is demonstrated by his proficiency and practical expertise as an approved PV green card installer powered by SAPVIA. He has an extensive understanding and scholarly accomplishment in Machine Learning Algorithms for Microgrid Optimization, coupled with an innovative outlook on the efficiency, reliability, and resilience of Microgrids. positioning him as a strong advocate for advancing machine learning techniques for real-time energy management and investigating new applications of artificial intelligence in autonomy.