Dr Bonginkosi Thango, University of Johannesburg, South Africa
Dr. Bonginkosi Allen Thango is a distinguished researcher and Senior Lecturer at the University of Johannesburg, specializing in Electrical Engineering. He holds a Doctor of Engineering (D.Eng.) and is a registered Professional Engineering Technologist with ECSA. Dr. Thango’s expertise encompasses Transformer Engineering, Artificial Intelligence & Machine Learning, Digital Twins, and Metaheuristic Algorithms. His research focuses on predictive maintenance strategies, renewable energy integration, and IoT applications in power systems. He has authored numerous peer-reviewed papers and two books, contributing significantly to advancing transformer diagnostics and optimization. Recognized for his academic achievements, Dr. Thango was awarded the Y2 NRF-rated Researcher status in 2024 and received the M&G 2022 200 Young South Africans Award for Technology and Innovation. He actively engages in teaching, research supervision, and community projects, emphasizing innovation in electrotechnology and technology management.
Publication Profile
Orcid
Education
Dr. Bonginkosi A. Thango has a rich educational background in Electrical Engineering, culminating in his Doctor of Engineering (D.Eng.) degree from Tshwane University of Technology in 2021. His doctoral research focused on assessing harmonic effects on utility-scale photovoltaic plant step-up transformers. Prior to this, he achieved a Master of Engineering (M.Eng.) with Cum Laude honors in 2019, specializing in transformer winding deformation diagnosis through Sweep Frequency Response Analysis (SFRA). He commenced his academic journey with a Bachelor of Technology (B.Tech.) in Electrical Engineering in 2017, exploring practical simulations of underground cable faults using time-domain reflectometry (TDR). His foundational education includes a National Diploma in Electrical Engineering (2016) and a National Senior Certificate (2010), emphasizing mathematics and physical sciences.
Experience
Dr. Thango’s career is distinguished by his roles in academia and industry. Currently serving as a Senior Lecturer at the University of Johannesburg since 2024, he actively contributes to teaching, research, and academic administration in Electrical Engineering. His responsibilities include curriculum development, supervising postgraduate students, securing research funding, and publishing in peer-reviewed journals. Previously, he held positions such as Lecturer at the same institution from 2022 to 2024, where he conducted research, supervised students, and contributed to innovation in his field.
Awards and Honors
Acknowledged as a leader in his field, Dr. Thango received the Mail and Guardian 2022: 200 Young South Africans award in the category of Technology and Innovation. He is also recognized as a Y2 NRF-rated researcher, highlighting his substantial contributions to engineering research. His academic achievements include scholarships during his postgraduate studies and awards for academic excellence during his early education.
Research focus
Dr. Bonginkosi A. Thango’s research is dedicated to advancing the field of electrical power transformers, focusing on diagnostics, predictive maintenance, and optimization strategies. His work spans various critical areas aimed at enhancing transformer reliability and performance using state-of-the-art technologies and methodologies. He has contributed significantly to understanding and classifying partial discharge sources in power transformers through systematic literature reviews and empirical studies. Dr. Thango’s research also delves into predictive modeling for transformer insulation, particularly using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the remaining life cycle of cellulose insulation. Additionally, he explores the integration of solar photovoltaic (PV) systems with transformers, investigating the techno-economic implications and optimizing PV transformer designs
Publication Top Notes
🔍 “Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review” (2024) – Investigates methods for categorizing partial discharge sources in transformers.
🔬 “A Technique for Transformer Remnant Cellulose Life Cycle Prediction Using Adaptive Neuro-Fuzzy Inference System” (2023) – Develops a predictive model for assessing cellulose insulation life in transformers.
☀️ “Techno-Economic Modelling of Solar Photovoltaic (PV) Power Transformers in South Africa” (2023) – Explores economic implications of integrating PV systems with transformers.
🛠️ “Dissolved Gas Analysis and Application of Artificial Intelligence Technique for Fault Diagnosis in Power Transformers: A South African Case Study” (2022) – Applies AI to fault diagnosis via dissolved gas analysis.
📊 “Feedforward Artificial Neural Network (FFANN) Application in Solid Insulation Evaluation Methods for the Prediction of Loss of Life in Oil-Submerged Transformers” (2022) – Utilizes FFANN for predicting transformer insulation degradation.
🔧 “Application of the Analysis of Variance (ANOVA) in the Interpretation of Power Transformer Faults” (2022) – Analyzes transformer fault data using ANOVA techniques.
🌐 “On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers” (2022) – Focuses on using neural networks for early fault detection in transformer gases.