Optimization of Support Structures for Offshore Wind Turbines Using Genetic Algorithm with Domain‐Trimming

AlHamaydeh, Mohammad; Barakat, Samer; Nasif, Omar

doi:10.1155/2017/5978375

“…25 rived gradients. Other optimization approaches using meta-heuristic algorithms were reported by AlHamaydeh et al (2017) and Kaveh and Sabeti (2018), however, without realistic load assumptions. The problem of discrete design variables was addressed by Stolpe and Sandal (2018).…”

Section: Introductionmentioning

confidence: 99%

A comparison study on jacket substructures for offshore wind turbines based on optimization

Häfele¹,

Gebhardt²,

Rolfes³

2018

Preprint

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Abstract. The structural optimization problem of jacket substructures for offshore wind turbines is commonly considered as a pure tube dimensioning problem with given topology, minimizing the entire mass of the structure. However, this approach goes along with the assumption that the given topology is fixed in any case. The present work contributes to the improvement of the state of the art by utilizing more detailed models for geometry, costs, and structural design code checks. They are assembled in an optimization scheme, in order to consider the jacket optimization problem from a different point of view 5 that is closer to practical applications. The objective function is replaced by a sum term comprising several cost terms. To address the issue of high demand of numerical capacity, a machine learning approach based on Gaussian process regression is applied to reduce numerical cost and enhance the number of considered design load cases. The proposed approach is meant to provide decision guidance in the first phase of wind farm planning. A numerical example for a NREL 5 MW turbine under FINO3 environmental conditions is computed by two effective optimization methods (sequential quadratic programming and 10 an interior-point method), allowing for the estimation of characteristic design variables of a jacket substructure. In order to resolve the mixed-integer problem formulation, multiple subproblems with fixed integer design variables are solved. The approach shows reasonable and promising results, useful both for further research and technical applications.

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“…In principle, SQP can be seen as an adaption of Newton's method to nonlinear constrained optimization problems, computing the solution of the Karush-Kuhn-Tucker equations (necessary conditions for constrained problems). Here, a common approach is deployed, based on the works of Biggs (1975), Han (1977), and Powell (1978a, b). In the first step, the Hessian of the so-called Lagrangian (a term incorporating the objective and the sum of all constraints weighted by Lagrange multipliers) is approximated by the Broyden-Fletcher-Goldfarb-Shanno method (Fletcher, 1987).…”

Section: Sequential Quadratic Programming Methodsmentioning

confidence: 99%

Response to referee comment (RC1)

Häfele¹

2018

Preprint

0

View full text Add to dashboard Cite

The structural optimization problem of jacket substructures for offshore wind turbines is commonly regarded as a pure tube dimensioning problem, minimizing the entire mass of the structure. However, this approach goes along with the assumption that the given topology is fixed in any case. The present work contributes to the improvement of the state of the art by utilizing more detailed models for geometry, costs, and structural design code checks. They are assembled in an optimization scheme, in order to consider the jacket optimization problem from a different point of view that is closer to practical applications. The conventional mass objective function is replaced by a sum of various terms related to the cost of the structure. To address the issue of high demand of numerical capacity, a machine learning approach based on Gaussian process regression is applied to reduce numerical expenses and enhance the number of considered design load cases. The proposed approach is meant to provide decision guidance in the first phase of wind farm planning. A numerical example for a National Renewable Energy Laboratory (NREL) 5 MW turbine under FINO3 environmental conditions is computed by two effective optimization methods (sequential quadratic programming and an interior-point method), allowing for the estimation of characteristic design variables of a jacket substructure. In order to resolve the mixed-integer problem formulation, multiple subproblems with fixed-integer design variables are solved. The results show that three-legged jackets may be preferable to four-legged ones under the boundaries of this study. In addition, it is shown that mass-dependent cost functions can be easily improved by just considering the number of jacket legs to yield more reliable results.

show abstract

“…In principle, SQP can be seen as an adaption of Newton's method to nonlinear constrained optimization problems, computing the solution of the Karush-Kuhn-Tucker equations (necessary conditions for constrained problems). Here, a common approach is deployed, based on the works of Biggs (1975), Han (1977), and Powell (1978a, b). In the first step, the Hessian of the so-called Lagrangian (a term incorporating the objective and the sum of all constraints weighted by Lagrange multipliers) is approximated by the Broyden-Fletcher-Goldfarb-Shanno method (Fletcher, 1987).…”

Section: Sequential Quadratic Programming Methodsmentioning

confidence: 99%

Wrong subject of previous reply (AC2)

Häfele¹

2018

Preprint

0

View full text Add to dashboard Cite

The structural optimization problem of jacket substructures for offshore wind turbines is commonly regarded as a pure tube dimensioning problem, minimizing the entire mass of the structure. However, this approach goes along with the assumption that the given topology is fixed in any case. The present work contributes to the improvement of the state of the art by utilizing more detailed models for geometry, costs, and structural design code checks. They are assembled in an optimization scheme, in order to consider the jacket optimization problem from a different point of view that is closer to practical applications. The conventional mass objective function is replaced by a sum of various terms related to the cost of the structure. To address the issue of high demand of numerical capacity, a machine learning approach based on Gaussian process regression is applied to reduce numerical expenses and enhance the number of considered design load cases. The proposed approach is meant to provide decision guidance in the first phase of wind farm planning. A numerical example for a National Renewable Energy Laboratory (NREL) 5 MW turbine under FINO3 environmental conditions is computed by two effective optimization methods (sequential quadratic programming and an interior-point method), allowing for the estimation of characteristic design variables of a jacket substructure. In order to resolve the mixed-integer problem formulation, multiple subproblems with fixed-integer design variables are solved. The results show that three-legged jackets may be preferable to four-legged ones under the boundaries of this study. In addition, it is shown that mass-dependent cost functions can be easily improved by just considering the number of jacket legs to yield more reliable results.

show abstract

Optimization of Support Structures for Offshore Wind Turbines Using Genetic Algorithm with Domain‐Trimming

Cited by 25 publications

References 33 publications

A comparison study on jacket substructures for offshore wind turbines based on optimization

A comparison study on jacket substructures for offshore wind turbines based on optimization

Response to referee comment (RC1)

Wrong subject of previous reply (AC2)

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