Collection line optimisation in wind farms using improved ant colony optimisation

Wu, Yiwen; Wang, Yong

doi:10.1177/0309524x20917319

Cited by 5 publications

(16 citation statements)

References 15 publications

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“…𝑃 √3 * 𝑉 (13), where P is the power rating of WT 𝑁 = 𝐼 𝐶 𝐼 𝑊𝑇 (14), where the N is the maximum number of WTs inter-connection the current carrying capacity of 630 mm2 cross-sectional cable is 1200A as show in table 1, from (14) 1200/88.3 ≈13 indicated the maximum allowable current is the sum of current from 13 WTs with eight GW capacity. In the HV cables, the process of calculation the maximum allowable WTs inter connection number is similar with the MV cable; when the total WTs current flow large than the HV cable rated current capacity, the collection design may require more OSSs to distribute the power, therefore, the magnitude of cross-section cable selection could determine the number of OSSs.…”

Section: 𝐼 =mentioning

confidence: 99%

“…Hence, heuristic algorithms have been widely applied in solving WTs collection system. Well acknowledged methods are Genetic Algorithm (GA) [9][10], Particle Swarm Optimization (PSO) [11], Simulated Annealing (SA) [12], Ant Colony Optimization (ACO) [13][14], The authors in [14] first determined the location of WT with minimize the wake loss, then proposed an ACO multiple traveling salesman model to minimize the length of cabling.…”

Section: Introductionmentioning

confidence: 99%

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GA based algorithms for offshore wind farm collector cable optimization

2022

J. Phys.: Conf. Ser.

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In the capital-intensive offshore wind farm industry, the collector cable optimization problem arises primarily due to electric infrastructure expense contributing high rate of initial investment cost. In this study, a multistage solution was developed for optimal locations, the number of offshore substations (OSSs), and inter-array cable routing determination. In the first, a novel GA method picks up the initial OSSs number and location from the candidate site list, then capacities minimum span tree (CMST) is applied to identify valid cable routings. Finally, optimal OSSs location, quantity, and cable paths are obtained through an iterative process. The results of a library real wind farms case are introduced in paper proves the feasibility of the proposed method and showed this study can apply on real practice for optimal decision in OWF cable design.

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Section: 𝐼 =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GA based algorithms for offshore wind farm collector cable optimization

2022

J. Phys.: Conf. Ser.

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“…The design of wind turbines has been approached by many researchers using classical methods [2][3][4][5][6][7][8] and meta-heuristics [9][10][11][12][13]. However, works considering more than one station are scarce [14][15][16][17][18][19][20][21][22]. Moreover, they use meta-heuristics or optimization in two phases, which do not guarantee obtaining optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Wu and Wang [21] use an ant colony algorithm to reduce wind farms' construction costs and to improve the reliability of the collector system. They use the K-means clustering algorithm to partition the wind turbines into several groups to find the shorter collection lines in a radial configuration for the wind farm.…”

Section: Related Workmentioning

confidence: 99%

Wind Farm Cable Connection Layout Optimization with Several Substations

2021

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Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

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“…Wu and Wang [23] use the k-means clustering and ant colony algorithm to reduce wind farms' construction costs and the collector system's reliability. They present a problem with four substations, using the clustering algorithm to assign the turbines to a substation.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

Pires,

Cerveira,

Baptista

2023

Computation

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This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to 0.17% of economic savings when compared to the clustering with ILP approach (an exact approach).

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Collection line optimisation in wind farms using improved ant colony optimisation

Cited by 5 publications

References 15 publications

GA based algorithms for offshore wind farm collector cable optimization

GA based algorithms for offshore wind farm collector cable optimization

Wind Farm Cable Connection Layout Optimization with Several Substations

Wind Farm Cable Connection Layout Optimization Using a Genetic Algorithm and Integer Linear Programming

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