scholarly journals Multi-Objective Random Search Algorithm for Simultaneously Optimizing Wind Farm Layout and Number of Turbines

2016 ◽  
Vol 753 ◽  
pp. 032011 ◽  
Author(s):  
Ju Feng ◽  
Wen Zhong Shen ◽  
Chang Xu
Keyword(s):  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Wind Farm Layout ◽  
Multi Objective ◽  
Random Search Algorithm

scholarly journals An Optimization Framework for Wind Farm Design in Complex Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2053 ◽  
Author(s):  
Ju Feng ◽  
Wen Shen ◽  
Ye Li
Keyword(s):  
Complex Terrain ◽  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Wind Farms ◽  
Minimal Distance ◽  
Original Design ◽  
Optimization Framework ◽  
Random Search Algorithm ◽  
Wind Resource

Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

scholarly journals Wind Farm Yaw Optimization via Random Search Algorithm

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 865
Author(s):  
Jim Kuo ◽  
Kevin Pan ◽  
Ni Li ◽  
He Shen
Keyword(s):  
Real Time ◽  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Computational Cost ◽  
Solution Quality ◽  
Farm Production ◽  
Wake Interactions ◽  
Random Search Algorithm ◽  
Significant Attention

One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

Multi-objective lightning search algorithm applied to wind farm layout optimization

Energy ◽  
2021 ◽  
Vol 216 ◽  
pp. 119214 ◽  
Author(s):  
Sinvaldo Rodrigues Moreno ◽  
Juliano Pierezan ◽  
Leandro dos Santos Coelho ◽  
Viviana Cocco Mariani
Keyword(s):  
Wind Farm ◽  
Search Algorithm ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Multi Objective ◽  
Wind Farm Layout Optimization

scholarly journals Constructing Synthetic Samples

2016 ◽  
Vol 32 (1) ◽  
pp. 113-127
Author(s):  
Hua Dong ◽  
Glen Meeden
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Random Search ◽  
The United States ◽  
Simulation Studies ◽  
Synthetic Sample ◽  
Population Means ◽  
Adaptive Random Search ◽  
Random Search Algorithm ◽  
Two Populations

Abstract We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

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