Optimization of Small Wind Turbines using Genetic Algorithms

Optimization of Small Wind Turbines using Genetic Algorithms

Mohammad Hamdan (Department of Computer Science, Yarmouk University, Irbid, Jordan and MACS, Heriot-Watt University, Dubai, UAE) and Mohammad Hassan Abderrazzaq (Department of Power Engineering, Yarmouk University, Irbid, Jordan)
Copyright: © 2016 |Pages: 16
DOI: 10.4018/IJAMC.2016100104

Abstract

This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.
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Basics Of Genetic Algoritms

Genetic algorithms are different from other heuristic methods in several ways. The most important difference is that a GA works on a population of possible solutions, while other heuristic methods use a single solution in their iterations. Another difference is that a GA is a probabilistic approach, and not deterministic. Each individual in the GA population represents a possible solution to the problem. The suggested solution is coded into the “genes” of the individual. The values and their position in the “gene string” tell the genetic algorithm what solution the individual represents.

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