Wind Turbine Design: Multi-Objective Optimization

TL;DR

This paper discusses the application of multi-objective optimization techniques, specifically genetic algorithms, to wind turbine design to improve performance and cost-effectiveness by identifying optimal trade-offs.

Contribution

It introduces the fundamental principles of multi-objective optimization in wind turbine design and demonstrates their application through a genetic algorithm approach.

Findings

Identification of Pareto front for wind turbine design trade-offs

Genetic algorithm effectively finds optimal design solutions

Highlights weaknesses and strengths of different design targets

Abstract

Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of manufacturers has been to minimize the cost of energy of wind turbines in order to compete with fossil-fuel sources. Therefore, it has been argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi-objective optimization algo- rithms, the designers are able to identify a trade-off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. In this chapter, we present the fundamental principles of multi-objective optimization in wind turbine design and solve a classic multi-objective wind turbine optimization problem using a genetic algorithm.