Positioning of Wind Turbine in a Wind Farm for Optimum Generation of Power Using Genetic Algorithm for Multiple Direction

2021 ◽  
pp. 769-778
Author(s):  
Khalid Anwar ◽  
Sandip Deshmukh
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Wind Farm

Optimization of Wind Farm Micro Sitting Based on Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 3545-3550 ◽  
Author(s):  
Chang Xu ◽  
Yan Yan ◽  
De You Liu ◽  
Yuan Zheng ◽  
Chen Qi Li
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Optimal Method ◽  
Successful Search ◽  
Increasing Trend ◽  
The Given

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting.

Study on Identification of Wind Turbine Power Characteristic Curve by Genetic Algorithm

2014 ◽  
Vol 1070-1072 ◽  
pp. 270-274
Author(s):  
Jia An Zhang ◽  
Xin Yi Meng ◽  
Lin Lin Wu ◽  
Hai Xiang Xu ◽  
Ning Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Wind Farm ◽  
Fitness Function ◽  
Characteristic Curve ◽  
Quadratic Functions ◽  
Power Characteristic ◽  
Piecewise Linearization ◽  
Important Indicator ◽  
Operating Points

Wind turbine power characteristic curve is not only an important indicator to assess the performance of wind turbine and evaluate the power generation performance, but also an important factor of impacting the wind power prediction accuracy. In this article, an identification method of the wind turbine power characteristic curve based on the genetic algorithm is proposed. With the piecewise linearization method, the wind turbine power characteristic curve is divided into several sections that can be described by linear or quadratic functions. Data preprocessing is used to filter the abnormal points in the original data. The distance from the wind turbine operating points to the power characteristic curve is selected as the fitness function. The identification process is to look for the parameters of the linear or quadratic curves that have the shortest distance to the massive operating points. The effectiveness of the algorithm is verified through the computation of a wind farm data.

A Systematic Approach and Genetic Algorithm Method to Power System Stabilizer Design for Wind Turbine Equipped with DFIG

2012 ◽  
Vol 229-231 ◽  
pp. 1095-1099
Author(s):  
Gholamreza Sayyad ◽  
Amin Khodabakhshian ◽  
Rahmatollah Hooshmand
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Wind Turbine ◽  
Dynamic Stability ◽  
Wind Farm ◽  
Optimization Technique ◽  
Power System Stabilizer ◽  
Genetic Algorithm Method ◽  
Doubly Fed ◽  
System Stabilizer

This paper develops a power system stabilizer (PSS) design for a wind turbine equipped with doubly fed induction generator (DFIG) which is based on vector control to improve the performance and dynamic stability of DFIG under fault conditions. The proposed PSS design is combined with genetic algorithm to obtain the higher-fitness answer as a strong optimization technique to the design of PSS parameters. A study network containing a wind farm equipped with DFIG was employed and all simulations will be carried out using MATLAB. It is shown that the employment of a proposed PSS can substantially enhance the contribution of a DFIG-based wind farm to network damping and dynamic stability.

scholarly journals Multi-objective wind farm layout optimization using evolutionary computations

2019 ◽  
Vol 8 (4) ◽  
pp. 293
Author(s):  
Chandra Shekar ◽  
M. R Shivakumar
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Fossil Fuels ◽  
Minimum Spanning Tree ◽  
Evolutionary Computations ◽  
Multi Objective ◽  
Wind Farm Design ◽  
Living Nature

<p class="Abstract">The usage of fossil fuels is actually not good for living nature and in future, this limited source of energy will vanish. Therefore, we need to go with the clean and renewable source of energy such as wind power, solar energy etc. In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. The area-of-convex hull, maximize ‘output power’ and minimum spanning tree distance are our main objective topics, due to their effect in wind farm design. An implementation of modified version of the wind turbine (WT) placement model is uses to estimate the yields of the (wind farm) WF layouts and for simplifying the behavior of wind field, in this paper we uses a simple wake approach. Moreover, to resolve the multi-objective problem here we proposed (Modified Genetic Algorithm) MGA, which is considerably better than the (Genetic Algorithm) GA and for evaluate the performance of MGA we use the multi-objective (EA) evolutionary algorithms such as; Genetic algorithm (GA) and SPEA2 and, produce different number of WT layouts. These methodologies are consider with various ‘problematic specific operators’ that are present in this paper.</p>

scholarly journals Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6905
Author(s):  
Ling Zhou ◽  
Qiancheng Zhao ◽  
Xian Wang ◽  
Anfeng Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Search Algorithm ◽  
Principal Component ◽  
Comparison Results ◽  
Auto Associative Neural Network

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

Binary-real coding genetic algorithm for wind turbine positioning in wind farm

2014 ◽  
Vol 6 (5) ◽  
pp. 053115 ◽  
Author(s):  
K. Chen ◽  
M. X. Song ◽  
X. Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Wind Farm

A Genetic Algorithm-based Robust Control Approach for Wind Turbine System Test Benches

2020 ◽  
Author(s):  
Maximilian Basler ◽  
Thuc Anh Nguyen ◽  
Felix Hruschka ◽  
Uwe Jassmann ◽  
Dirk Abel
Keyword(s):  
Genetic Algorithm ◽  
Robust Control ◽  
Wind Turbine ◽  
Control Approach ◽  
Wind Turbine System

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

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