Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

International Conference on Advances in Manufacturing, Materials and Energy Engineering 2018

材料科学;能源学

Shetty, Rashmi P.^1 ; Sathyabhama, A.^1 ; Srinivasa Pai, P.^1

Department of Mechanical Engineering, NITK, Surathkal, India^1

关键词: Artificial bee colonies (ABC);    Cuckoo searches;    Horizontal axis wind turbines;    Maximum power output;    Meta heuristic algorithm;    Objective functions;    Optimization algorithms;    Power Optimization;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/376/1/012021/pdf
DOI  :  10.1088/1757-899X/376/1/012021

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The wind being a most promising renewable energy, has become a strong contender for fossil fuels. Optimizing the blade pitch angle of a wind turbine is important to obtain the maximum power output, as the other variables are considered to be uncontrollable. In this paper an effort has been made to compare performances of three different optimization algorithms namely Particle swarm optimization (PSO), Artificial bee colony (ABC) and cuckoo search (CS) for optimizing the blade pitch angle and hence optimize the power output of a 1.5 MW capacity, pitch regulated, three-bladed horizontal axis wind turbine operating at a large wind farm in central dry zone of Karnataka. The objective function development is done using Artificial Neural Network. The CS algorithm is found to be faster and more efficient as compared to ABC and PSO for the problem under consideration.