M
Muhammad Hamza Zafar
Researcher at Capital University
Publications - 25
Citations - 237
Muhammad Hamza Zafar is an academic researcher from Capital University. The author has contributed to research in topics: Maximum power point tracking & Computer science. The author has an hindex of 3, co-authored 10 publications receiving 33 citations.
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Journal ArticleDOI
A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition
Muhammad Hamza Zafar,Noman Mujeeb Khan,Adeel Feroz Mirza,Majad Mansoor,Naureen Akhtar,Muhammad Usman Qadir,Nauman Ali Khan,Syed Kumayl Raza Moosavi +7 more
TL;DR: A novel search and rescue optimization algorithm based MPPT control of PV systems to circumvent these shortcomings is presented, which achieves up to 8% more power and 5% more energy and the settling time and tracking time are shortened.
Journal ArticleDOI
Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading
Muhammad Hamza Zafar,Thamraa Alshahrani,Noman Mujeeb Khan,Adeel Feroz Mirza,Majad Mansoor,Muhammad Usman Qadir,Muhammad Imran Khan,Rizwan Ali Naqvi +7 more
TL;DR: A novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading conditions and solidified the superior performance of the proposed GTOA based MPPT technique.
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Bio-inspired optimization algorithms based maximum power point tracking technique for photovoltaic systems under partial shading and complex partial shading conditions
TL;DR: Improvements in the tacking time of up to 45% and efficiency greater than 99.9% has been observed in the proposed technique and oscillations have been reduced to as low as 1 W along with extreme reduction in power loss as well.
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High-efficiency hybrid PV-TEG system with intelligent control to harvest maximum energy under various non-static operating conditions
Adeel Feroz Mirza,Majad Mansoor,Kamal Zerbakht,Muhammad Yaqoob Javed,Muhammad Hamza Zafar,Noman Mujeeb Khan +5 more
TL;DR: A novel implementation of an arithmetic optimization algorithm (AOA) is utilized as an active maximum power point tracking (MPPT) controller for hybrid PV-TEG system power control, demonstrating the robustness of the proposed technique.
Journal ArticleDOI
Towards green energy for sustainable development: Machine learning based MPPT approach for thermoelectric generator
TL;DR: In this paper , a feed-forward neural network (FNN) trained by a novel flow direction algorithm (FDA) with a tuned PID controller was used to harvest the energy under non-uniform temperature gradient conditions.