A multi-objective design optimization framework for wind turbines under altitude consideration. (15th October 2020)

• Record Type: Journal Article
• Authors: Mellal, Mohamed Arezki; Pecht, Michael
• Is Part Of: Energy conversion and management. Volume 222(2020)
• Publication Date: 2020-10-15
• Subjects: Wind turbines -- Multi-objective design optimization -- Cost of energy -- Rated power -- Nature-inspired optimization techniques -- Best compromise solution; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113212
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: A framework for best compromise multi-objective design optimization of wind turbine under altitude consideration is proposed. Three nature-inspired optimization techniques are implemented with a mutation strategy. Design constraints are handled and the best optimization technique is identified using comparing metrics. Multi-criteria and fuzzy decision making methods are used to identify the best compromise solution. A case study under two scenarios of altitude is solved in order to highlight the applicability of the proposed framework. Abstract: Wind power is one of the most used renewable energy sources; however, effective wind turbine design remains a challenge. This paper proposes a framework to explore the multi-objective design optimization of wind turbines and find the best compromise solution by considering the altitude. The design objectives are minimization of the cost of energy and maximization of the rated power by considering the rotor radius and hub height with respect to the structural design constraints. The Pareto envelope based selection algorithm II (PESA-II) and two versions of the non-dominated sorting genetic algorithm II and III (NSGA-II & NSGA-III) with a mutation strategy and a constraint handling method are implemented to generate Pareto fronts. Five comparing metrics are used to identify the most efficient optimization technique, and two decision methods are adopted to find the best compromise solution. A case study is solved under two altitude scenarios. The overall results show that NSGA-II performs better than the other algorithms, and the obtained best compromise solution is within the design limits. It is also observed that when the altitude increases, the cost of energy increases, and the rated power decreases.