Wind Farm Layout Optimization using Real Coded Multi-population Genetic Algorithm

Hassoine, Mohammed Amine; Lahlou, Fouad; Addaim, Adnane; Madi, Abdessalam Aït

doi:10.1109/wits.2019.8723810

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…HVDC transmission is the best method to transmit electrical power concerning technical and financial perspectives [3,4]. HVDC is more feasible and efficient for power transfer for distances greater than 83 km than HVAC [5][6][7][8]. HVDC efficiency depends on a wellconfigured converter where it offers a complete control over transmitted power.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Equation (7), M aj represents the active gravitational mass attributed to agent j, M pi represents the passive gravitational mass associated with agent i. The term G(t) within Equation ( 7) denotes the gravitational constant at time t. Additionally, ε is a small constant, and D(t) represents the Euclidean distance between agents i and j.…”

Section: Hpsogsa Proceduresmentioning

confidence: 99%

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Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

et al. 2023

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This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Hpsogsa Proceduresmentioning

confidence: 99%

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

et al. 2023

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“…Chen et al [10] used the greedy algorithm to optimize the placement of WTs. Gao et al [11] and Hassoine et al [12,13] proposed a multi-population genetic algorithm for solving WF layout optimization problem. Using the same WF and cost models as Mosetti, Pookpunt [14] and Asaah et al [15] demonstrated optimal placement using particle swarm optimization to maximize energy production.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Wind Farm Design by Using Biogeography based Optimization

Hassoine¹

2021

IJIES

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This paper investigates the optimal layout of wind turbines (WTs) within a wind farm. Finding the best placement of WTs in a wind farm is a challenging process due to the existence of multiple wake effects. A biogeography based optimization (BBO) algorithm method is proposed to search for the optimal location of WTs in a wind farm (WF), to maximize the power produced by the WF and improve the annual economic performance of the WF. A wind turbine (WT) that operates in the wake of one or more other turbines is subject to lower flow and therefore produces less power. When designing a wind project, the arrangement of the turbines with each other in a wind farm is a very important factor. The best layout of a wind farm is to achieve the optimal placement of the turbines in relation to each other in a given area to maximize the efficiency of the whole wind farm and reduce its cost. A dense configuration would result in considerable power losses. Each turbine must have a sufficient distance from other turbines in the WF where the optimal number of turbines should be placed. The BBO approach is conducted on a 2 km x 2 km wind farm assuming a constant wind speed of 12 m/s with a fixed wind direction, for solving the wind farm layout optimization problem in two different configurations which include 26 and 30 WTs respectively. A comparison of the results obtained with the previous studies shows that the BBO is more efficient in terms of maximizing power output and economic profitability of the same wind farm model, which validates that BBO performs effectively in optimizing WTs placement within WF. BOO provides the greatest improvement in the optimal layout, for example, in the case of the layout for 30 WTs. The power output reaches 15,383 KW, the agreement between the ideal and the optimal layout is more favorable. The difference in output power is only 169 KW (1%). Knowing that the ideal layout is reached if all WTs receive a wind flow with a maximum wind speed of 12 m/s. Furthermore, a case study of wind turbine layout optimization using the BBO program on the Alta X wind farm has been performed under variable wind speed and variable wind direction. The results indicate that the optimized layout of the Alta X wind farm achieves a 12% increase in the power output for a similar cost when compared to the original layout of Alta X. It is also more appropriate for evaluating the wind farm layout in the Wind Power Project (WPP).

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“…This optimization is known as the WF layout optimization problem (WFLOP). Some typical work using an approach based on genetic algorithms was performed by Mosetti et al [8], Grady et al [9], Emami et al [10], as well as Mittal [11] , Rajper [12] and Hassoine et al [13,14]. Using the same models of the WF and cost, Wan et al [15] and Pookpunt [16,17] demonstrated the optimal placement using Particle Swarm Optimization to maximize power production.…”

Section: Introductionmentioning

confidence: 99%

Improved Design of a Large Offshore Wind Farm by Using Biogeography based Optimization

Hassoine

Lahlou

Addaim

et al. 2021

IJRER

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This paper investigates optimal placement of wind turbines (WTs) within a large offshore wind farm (LOFWF). 88 wind farm (WF) configurations are invested to search the optimal layout for the Horns rev 1 offshore WF using twenty years of wind data knowing that wind characteristics are modeled from long term reanalysis data based on MERRA-2. The regular and the irregular placement of the Horns Rev 1 (HR1) offshore WF are investigated with Jensen wake model. Therefore, the objective is to assess the effect of wind turbine spacing (WTS) on the power output loss in a LOFWF and, also to find the best configuration that gives the maximum power with minimum investment cost. The use of the Biogeography based optimization (BBO), as a bio-inspired evolutionary approach, represents the advantage of being effective on strongly non-convex spaces such as a large offshore WF. Due to the iterative process applied to the initial population, and the multiplicity of the population, the BBO process limit the risk of getting stuck in a local optimum, by distributing the individuals in the whole solution space. The results obtained show that the wind data extracted from MERRA-2 can be applied reliably to any existing WF to simulate wind power production. The results also demonstrate that significant power losses occur when the turbines are arranged in a condensed manner and the significant power gains are not obtained from too large configurations, but rather from the best placement in configurations. The proposed approach shows promise in terms of applicability with MERRA-2 and is effectively suitable for arranging turbines and assessing wind resources in an offshore wind farm project (OFWFP).

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Wind Farm Layout Optimization using Real Coded Multi-population Genetic Algorithm

Cited by 4 publications

References 11 publications

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

Enhancement of Wind Farm Design by Using Biogeography based Optimization

Improved Design of a Large Offshore Wind Farm by Using Biogeography based Optimization

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