Adnan Qahtan Adnan is an Assistant Lecturer in the Department of Energy Engineering at University of Baghdad. He received his B.Sc. and M. Sc. degrees in Energy Engineering from College of Engineering - University of Baghdad, Iraq, in 2020 and 2024, respectively. The research interests include intelligent control systems, renewable energy systems, optimization algorithm, and Wind Turbine systems.
1- M.Sc., University of Baghdad (Iraq), Major: Energy Engineering, 2024
2- B.Sc., University of Baghdad (Iraq), Major: Energy Engineering, 2020
Lecturer (Assistant) of Energy Engineering/University of Baghdad/Departement of Energy Engineering
- Intelligent Control Systems
- Renewable Energy Systems
- Optimization Algorithm
- Wind Turbine systems
- Neural Networks
- Fuzzy Logic Controllers
- Learning Theory
- Evolutionary & Bio-Inspired Computation
Energy Engineering and Energy Processes
Wind turbine (WT) is now a major renewable energy resource used in the modern world. One of the most significant technologies that use the wind speed (WS) to generate electric power is the horizontal-axis wind turbine. In order to enhance the output power over the rated WS, the blade pitch angle (BPA) is controlled and adjusted in WT. This paper proposes and compares three different controllers of BPA for a 500-kw WT. A PID controller (PIDC), a fuzzy logic controller (FLC) based on Mamdani and Sugeno fuzzy inference systems (FIS), and a hybrid fuzzy-PID controller (HFPIDC) have been applied and compared. Furthermore, Genetic Algorithm (GA) and Particle swarm optimization (PSO) have been applied and compared in order to identify the optimal
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In the modern world, wind turbine (WT) has become the largest source of renewable energy. The horizontal-axis wind turbine (HAWT) has higher efficiency than the vertical-axis wind turbine (VAWT). The blade pitch angle (BPA) of WT is controlled to increase output power generation over the rated wind speed. This paper proposes an accurate controller for BPA in a 500-kw HAWT. Three types of controllers have been applied and compared to find the best controller: PID controller (PIDC), fuzzy logic type-2 controller (T2FLC), and hybrid type-2 fuzzy-PID controller (T2FPIDC). This paper has been used Mamdani and Sugeno fuzzy inference systems (FIS) to find the best inference system for WT controllers. Furthermore, genetic algorithm (GA) and particl
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