A Comparative study of static and fatigue behaviors for various composite orthotropic properties for a wind turbine using a coupled FEM-BEM method

In the wind industry, the current trend is towards building larger and larger turbines. This presents additional structural challenges and requires blade materials that are both lighter and stiffer than the ones presently used. [1] This work is aimed to aid the work of designing new wind turbine blades by providing a comparative study of different composite materials. A coupled Finite-Element-Method (FEM) - Blade Element Momentum (BEM) code was used to simulate the aerodynamic forces subjected on the blade. The developed BEM code was written using LabView allowing an iterative numerical approach solver taking into the consideration the unsteady aerodynamic effects and off –design performance issues such as Tip Loss, Hub Loss and Turbulent Wake State therefore developing a more rational aerodynamic model. For this thesis, the finite element study was conducted on the Static Structural Workbench of ANSYS, as for the geometry of the blade it was imported from a previous study prepared by Cornell University [2]. Confirmation of the performance analysis of the chosen wind turbine blade are presented and discussed blade including the generated power, tip deflection, thrust and tangential force for a steady flow of 8m/s. The elastic and ultimate strength properties were provided by Hallal et al [3]. The Tsai-Hill and Hoffman failure criterions were both conducted to the resulting stresses and shears for each blade composite material structure to determine the presence of static rupture. A progressive fatigue damage model was conducted to simulate the fatigue behavior of laminated composite materials, an algorithm developed by Shokrieh [4]. It is concluded that with respect to a material blade design cycle, the coupling between a finite element package and blade element and momentum code under steady and static conditions can be useful. Especially when an integration between this coupled approach and a dynamic simulation tool could be established, a more advanced flexible blade design can be then analyzed for a novel generation of more flexible wind turbine blades

Optimisation of wind turbine blade structures using a genetic algorithm

The current diminution of fossil-fuel reserves, stricter environmental guidelines and the world’s ever-growing energy needs have directed to the deployment of alternative renewable energy sources. Among the many renewable energies, wind energy is one of the most promising and the fastest growing installed alternative-energy production technology. In order to meet the production goals in the next few decades, both significant increases in wind turbine installations and operability are required, while maintaining a profitable and competitive energy cost. As the size of the wind turbine rotor increases, the structural performance and durability requirements tend to become more challenging. In this sense, solving the wind turbine design problem is an optimization problem where an optimal solution is to be found under a set of design constraints and a specific target. Seen the world evolution towards the renewable energies and the beginning of an implementation of a local wind industry in Quebec, it becomes imperative to follow the international trends in this industry. Therefore, it is necessary to supply the designers a suitable decision tool for the study and design of optimal wind turbine blades. The developed design tool is an open source code named winDesign which is capable to perform structural analysis and design of composite blades for wind turbines under various configurations in order to accelerate the preliminary design phase. The proposed tool is capable to perform a Pareto optimization where optimal decisions need to be taken in the presence of trade-offs between two conflicting objectives: the annual energy production and the weight of the blade. For a given external blade shape, winDesign is able to determine an optimal composite layup, chord and twist distributions which either minimizes blade mass or maximizes the annual energy production while simultaneously satisfying design constraints. The newly proposed graphical tool incorporates two novel VCH and KGA techniques and is validated with numerical simulation on both mono-objective and multi-objective optimization problems

A real time simulation of a photovoltaic system with maximum power point tracking

International audienceThis work presents an experimental stand for the study of a power electronics control system to locate and track the maximum power point of a photovoltaic (PV) array to ensure efficient power transfer from the solar cells to the load under varying environmental conditions. A real-time photovoltaic solar cell measurements and a control system was developed to guarantee that the maximum power output is attained. This stand is built at the Electrical Machinery Laboratory of “Vasile Alecsandri” University of Bacau, Romania

Wind Turbine Design: Multi‐Objective Optimization

Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of manufacturers has been to minimize the cost of energy of wind turbines in order to compete with fossil‐fuel sources. Therefore, it has been argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi‐objective optimization algorithms, the designers are able to identify a trade‐off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. In this chapter, we present the fundamental principles of multi‐objective optimization in wind turbine design and solve a classic multi‐objective wind turbine optimization problem using a genetic algorithm

Optimal design for a composite wind turbine blade with fatigue and failure constraints

The search for more efficient and sustainable renewable energies is rapidly growing. Throughout the years, wind turbines matured towards a lowered cost-of-energy and have grown in rotor size therefore stretched the role of composite materials that offered the solution to more flexible, lighter and stronger blades. The objective of this paper is to present an improved version of the preliminary optimization tool called Co-Blade, which will offer designers and engineers an accelerated design phase by providing the capabilities to rapidly evaluate alternative composite layups and study their effects on static failure and fatigue of Wind turbine blades. In this study, the optimization formulations include non-linear failure constraints. In addition a comparison between 3 formulations is made to show the importance of choosing the blade mass as the main objective function and the inclusion of failure constraints in the wind turbine blade design. La recherche pour des énergies renouvelables plus efficaces et durables est en forte croissance. Au fil des années, les éoliennes ont acquis de la maturité avec un coût plus réduit et des tailles de rotor plus grandes élargissant ainsi l’utilisation des matériaux composites qui offrent plus de flexibilité, plus de légèreté et plus de solidité. L’objectif de cet article est de proposer une version améliorée du logiciel d’optimisation préliminaire Co-Blade, qui permettra aux concepteurs d’accélérer la phase de conception des pales d’éolienne en matériaux composites grâce à des outils d’études de diverses configuration des laminés composites et de leur comportements en rupture et en fatigue. Dans cette étude, les formules d’optimisation tiennent compte des contraintes de ruptures non linéaires. Additionnellement, une comparaison de 3 formules d’optimisation a été effectuée afin de mettre en évidence l’importance du choix de la masse tel que fonction objective principale et de la considération des contraintes de rupture dans la conception des pales d’éoliennes

Domain-specific risk assessment using integrated simulation: A case study of an onshore wind project

Although many quantitative risk assessment models have been proposed in literature, their use in construction practice remain limited due to a lack of domain-specific models, tools, and application examples. This is especially true in wind farm construction, where the state-of-the-art integrated Monte Carlo simulation and critical path method (MCS-CPM) risk assessment approach has yet to be demonstrated. The present case study is the first reported application of the MCS-CPM method for risk assessment in wind farm construction and is the first case study to consider correlations between cost and schedule impacts of risk factors using copulas. MCS-CPM provided reasonable risk assessment results for a wind farm project, and its use in practice is recommended. Aimed at facilitating the practical application of quantitative risk assessment methods, this case study provides a much-needed analytical generalization of MCS-CPM, offering application examples, discussion of expected results, and recommendations to wind farm construction practitioners

A constraint-handling technique for genetic algorithms using a violation factor

Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA’s) shine as popular evolutionary algorithms (EA’s) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques

Review of performance optimization techniques applied to wind turbines

This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature.The current energy demand combined with depletion of fossil-fuel reserves and stricter environmental regulations have led to the development of alternative renewable energy solutions like wind energy. The current 2030 United States target is to have at least 20% of the US energy supply by onshore and offshore wind farms. To meet these demands, wind energy costs have to be able to compete with traditional fossil fuel sources. Hence, it is essential and vital that wind turbine designers and manufactures search the optimal solution that fits the objectives under a set of design constraints. Throughout the last 30. years, the objective function has evolved from the earlier maximized metric of the power coefficient to the maximization of the annual energy production. Common alternatives such as blade mass minimization and maximization of the rotor thrust and torque have been examined. However, the main objective has been focused on the minimization of the cost of energy in order for wind energy to become more competitive and economically attractive.The purpose of this paper is to review previous work that undertakes the performance optimization of horizontal wind turbines by highlighting the main aspects when tackling the wind turbine optimization problem such as: objective functions, design constraints, tools and models and optimization algorithms. In addition, in a conclusion of the review, a discussion and argument about the challenges, issues and future developments are identified

A Selection Process for Genetic Algorithm Using Clustering Analysis

International audienceThis article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGA f) and via an optimal partitioning K opt (KGA o) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems