Optimization of Small Wind Turbines using Genetic Algorithms

2016 ◽  
Vol 7 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Mohammad Hamdan ◽  
Mohammad Hassan Abderrazzaq
Keyword(s):  
Wind Turbines ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Wind Energy Conversion ◽  
Design Alternatives ◽  
Wide Range ◽  
Small Wind Turbines ◽  
Complex Optimization ◽  
Tower Height

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.

Optimization of Small Wind Turbines Using Genetic Algorithms

2017 ◽  
pp. 1484-1499
Author(s):  
Mohammad Hamdan ◽  
Mohammad Hassan Abderrazzaq
Keyword(s):  
Wind Turbines ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Wind Energy Conversion ◽  
Design Alternatives ◽  
Wide Range ◽  
Small Wind Turbines ◽  
Complex Optimization ◽  
Tower Height

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.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

Computational Investigation of a Shrouded Vertical Axis Wind Turbine

2016 ◽  
Author(s):  
K. Vafiadis ◽  
H. Fintikakis ◽  
I. Zaproudis ◽  
A. Tourlidakis
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Urban Areas ◽  
Numerical Study ◽  
Flow Analysis ◽  
Vertical Axis ◽  
Green Energy ◽  
Vertical Axis Wind Turbine ◽  
Wide Range ◽  
Small Wind Turbines

In urban areas, it is preferable to use small wind turbines which may be integrated to a building in order to supply the local grid with green energy. The main drawback of using wind turbines in urban areas is that the air flow is affected by the existence of nearby buildings, which in conjunction with the variation of wind speed, wind direction and turbulence may adversely affect wind energy extraction. Moreover, the efficiency of a wind turbine is limited by the Betz limit. One of the methods developed to increase the efficiency of small wind turbines and to overcome the Betz limit is the introduction of a converging – diverging shroud around the turbine. Several researchers have studied the effect of shrouds on Horizontal Axis Wind Turbines, but relatively little research has been carried out on shroud augmented Vertical Axis Wind Turbines. This paper presents the numerical study of a shrouded Vertical Axis Wind Turbine. A wide range of test cases, were examined in order to predict the flow characteristics around the rotor, through the shroud and through the rotor – shroud arrangement using 3D Computational Fluid Dynamics simulations. The power output of the shrouded rotor has been improved by a factor greater than 2.0. The detailed flow analysis results showed that there is a significant improvement in the performance of the wind turbine.

Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

2020 ◽  
pp. 1181-1198
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Soumya Sahoo ◽  
Bijayalaxmi Panda
Keyword(s):  
Optimization Problems ◽  
Disease Risk ◽  
Optimization Techniques ◽  
Healthcare Sector ◽  
Support Vector ◽  
Vector Machines ◽  
Disease Risks ◽  
Complex Optimization ◽  
Medical And Healthcare ◽  
Timely Treatment

Diabetes has affected over 246 million people worldwide and by 2025 it is expected to rise to over 380 million. With the rise of information technology and its continued advent into the medical and healthcare sector, different symptoms of diabetes are being documented. The techniques inspired from the distributed collective behavior of social colonies have shown worth and excellence in dealing with complex optimization problems and are becoming more popular nowadays. It can be used as an effective problem solving tool for identifying diabetes disease risks. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in data through various swarm optimization techniques by employing Support Vector Machines and Naïve Bayes algorithms. It proposes a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

A Review on Bio-Inspired Migration Optimization Techniques

2015 ◽  
Vol 11 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Jyotsna Verma ◽  
Nishtha Kesswani
Keyword(s):  
Combinatorial Optimization ◽  
Computer Networks ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Problem Size ◽  
Nature Inspired Computing ◽  
Complex Optimization ◽  
Automated Tools

Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.

Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

2016 ◽  
Vol 6 (2) ◽  
pp. 29-43 ◽  
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Soumya Sahoo ◽  
Bijayalaxmi Panda
Keyword(s):  
Optimization Problems ◽  
Disease Risk ◽  
Optimization Techniques ◽  
Healthcare Sector ◽  
Support Vector ◽  
Vector Machines ◽  
Disease Risks ◽  
Complex Optimization ◽  
Medical And Healthcare ◽  
Timely Treatment

Diabetes has affected over 246 million people worldwide and by 2025 it is expected to rise to over 380 million. With the rise of information technology and its continued advent into the medical and healthcare sector, different symptoms of diabetes are being documented. The techniques inspired from the distributed collective behavior of social colonies have shown worth and excellence in dealing with complex optimization problems and are becoming more popular nowadays. It can be used as an effective problem solving tool for identifying diabetes disease risks. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in data through various swarm optimization techniques by employing Support Vector Machines and Naïve Bayes algorithms. It proposes a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

scholarly journals A Comparison of Evolutionary Computation Techniques for IIR Model Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Erik Cuevas ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
Omar Avalos ◽  
Daniel Zaldívar ◽  
...  
Keyword(s):  
Evolutionary Computation ◽  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Model Identification ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Computation Techniques ◽  
Recent Developments ◽  
Complex Optimization

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid ◽  
Abeer Alsadoon ◽  
Nebojsa Bacanin ◽  
Polla Fattah
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2022 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid ◽  
Abeer Alsadoon ◽  
Nebojsa Bacanin ◽  
Polla Fattah ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Scale Optimization ◽  
Benchmark Test Functions

<p></p><p></p><p>The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><p></p><p></p>

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