A two step optimization algorithm for wind turbine generator placement considering maximum allowable capacity

Safaei, A.; Vahidi, B.; Abyaneh, Hossein Askarian; Azad‐Farsani, Ehsan; Ahadi, Seyed Mohammad

doi:10.1016/j.renene.2016.01.093

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…Particle swarm optimisation algorithm (PSO) has been applied to the optimal planning and dispatching of distribution systems in recent years [36,37]. In order to solve the precocious problem of PSO, adaptive particle swarm optimisation (APSO) has been widely used [38,39]. The proposed bi-level optimal dispatching model for PVHCM enhancement is solved with the developed IPSO algorithm.…”

Section: Iterative-particle Swarm Optimisation Algorithm For Bod Modementioning

confidence: 99%

Bi‐level optimisation dispatch method for photovoltaic hosting capacity enhancement of distribution buses

Wang

Zhao

et al. 2019

IET Generation, Transmission & Distribution

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Section: Iterative-particle Swarm Optimisation Algorithm For Bod Modementioning

confidence: 99%

Bi‐level optimisation dispatch method for photovoltaic hosting capacity enhancement of distribution buses

Wang

Zhao

et al. 2019

IET Generation, Transmission & Distribution

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“…It should be noted that P i (t) and P gb (t) are updated in each t period. Changes in time at t + 1 (changes in the site and rate of position change of each particle) are defined as relations (15) and (16). In the process of this algorithm and in each repetition of the process, each member remembers its best position and the best position in the entire community and proceeds to change its position based on these two.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The problem is expressed as bound and has a multi-criteria objective function, which is to minimize losses and improve voltage stability. In Reference [16], the location of wind turbines and solar panels has been proposed to reduce power losses and improve voltage stability using PSO.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Problem for Optimal Site Selection of Wind Turbines for Reduce Losses and Improve Voltage Profile of Distribution Grids

Naderipour

Abdul‐Malek

Nowdeh³

et al. 2019

Energies

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In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved more considering WTs maximum allowable size versus not considering this constraint. Additionally, the multi-objective results showed that there is a compromise between the objectives in the multi-objective WTs site selection and the multi-objective problem solution is a more realistic and accurate approach in comparison with the single-objective problem solution.

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“…Many researchers have also been using the PSO algorithm for location optimization. Safaei et al [17] proposed the new two-step PSO algorithm for the placement of wind turbine generators for maximum allowable capacity and minimizing power loss in wind turbines. Wind turbine placement at a wind farm was optimized by Wan et al [18].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

et al. 2019

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Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient ( C p ) of HAWT system. In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of C p for HAWT. The parameters are tip speed ratio, blade radius, lift to drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining the optimal blade design based on the C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p of wind turbine blades for investigation of algorithm performance based on the coefficient determination (R2) and root mean square error (RMSE). The optimized blade design parameters are validated with experimental results from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. The predicted C p using ANFIS-ABC also outperformed the ANFIS-ACO and ANFIS-PSO. The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms.

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A two step optimization algorithm for wind turbine generator placement considering maximum allowable capacity

Cited by 21 publications

References 19 publications

Bi‐level optimisation dispatch method for photovoltaic hosting capacity enhancement of distribution buses

Bi‐level optimisation dispatch method for photovoltaic hosting capacity enhancement of distribution buses

A Multi-Objective Optimization Problem for Optimal Site Selection of Wind Turbines for Reduce Losses and Improve Voltage Profile of Distribution Grids

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

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