Tuning Fractional Order PI Using Genetic Algorithm for Maximum Power Extraction of Wind Turbine
| dc.contributor.advisor | Dereje, Shiferaw (PhD) | |
| dc.contributor.author | Belete, Yadessa | |
| dc.date.accessioned | 2021-11-15T06:51:06Z | |
| dc.date.accessioned | 2023-11-28T14:20:36Z | |
| dc.date.available | 2021-11-15T06:51:06Z | |
| dc.date.available | 2023-11-28T14:20:36Z | |
| dc.date.issued | 2021-10 | |
| dc.description.abstract | Energy is in high demand and fossil fuels having problems, so current solution is to use renewable out of which wind is one. In using wind, the problem is how to extract the maximum power available in the wind stream. Due to the randomness of wind speed and the fluctuations of wind power. So, to extract the maximum power from the wind well-designed control systems are required. In this thesis, a 1.5 MW double fed induction generator wind turbine is presented along with new controllers designed to maximize the wind power capturer. The proposed designs mainly focus on controlling the double fed induction generator rotor current in order to allow the system to operate at a certain current value that maximizes the energy capture at different wind speeds. A vector controller for current loop is designed to control the rotor side converter. The control system design technique considered in this work is genetic algorithm which is used to tune integer order proportional integral and fractional order proportional integral controllers’ parameter. The controllers’ performance is evaluated using MATLAB/Simulink in related to step response and in overall simulation block of double fed induction generator wind turbine. The obtained results are analyzed to show the performance of the proposed control which is fractional order proportional integral. The step response for rotor current controller evaluation in terms of settling time, rise time, maximum percentage overshoot and steady state error, the responses based on fractional order proportional integral controller has shown better performance when compared to the integer one. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/28648 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | IOPI | en_US |
| dc.subject | Genetic algorithm | en_US |
| dc.subject | FOPI | en_US |
| dc.subject | Wind generator | en_US |
| dc.subject | PI Controller | en_US |
| dc.subject | Tuning | en_US |
| dc.subject | DFIG | en_US |
| dc.title | Tuning Fractional Order PI Using Genetic Algorithm for Maximum Power Extraction of Wind Turbine | en_US |
| dc.type | Thesis | en_US |