High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine

• Published in: Renewable energy Vol. 126; pp. 1055 - 1063
• Main Authors: Mokhtari, Yacine, Rekioua, Djamila
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2018
• Subjects: algorithms; Ant colony algorithm; Artificial intelligence; electric power; environmental impact; MPPT; Optimization; power generation; system optimization; Wind energy; wind power; Wind turbine; wind turbines; MPPT; Wind energy; Wind turbine; Artificial intelligence; Ant colony algorithm; Optimization
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2018.03.049

The growing interest in wind power as a source of electric power generation with minimal environmental impact and the advancement of aerodynamic designs, including wind turbines, have been the subject of numerous studies. When wind energy is integrated into the grid, this gives a significant amount of power added to the one produced by other types of plants. Several researchers aim to achieve high efficiency in wind power systems using maximum power point tracking (MPPT) of a variable-speed turbine but this technique is complicated because the different approximations that occur during the online calculations. The main objective of this work is to develop and improve a maximum power tracking control strategy using metaheuristic methods. Ant colony optimization (ACO) algorithm is used to determine the optimal PI controller parameters for speed control. The optimization of the speed gets a better value of power coefficient therefore the extracting power. •Achieve high efficiency in wind power systems using maximum power point tracking (MPPT) of a variable-speed turbine.•Develop and improve a maximum power tracking control strategy using metaheuristic methods.•Ant colony optimization (ACO) algorithm.•Determine the optimal PI controller parameters for speed control.