Fuzzy finite element model updating of a laboratory wind turbine blade for structural modification detection

Peer reviewedPublisher PD

A simplified analytical approach for optimal planning of distributed generation in electrical distribution networks

DG-integrated distribution system planning is an imperative issue since the installing of distributed generations (DGs) has many effects on the network operation characteristics, which might cause significant impacts on the system performance. One of the most important characteristics that mostly varies because of the installation of DG units is the power losses. The parameters affecting the value of the power losses are number, location, capacity, and power factor of the DG units. In this paper, a new analytical approach is proposed for optimally installing DGs to minimize power loss in distribution networks. Different parameters of DG are considered and evaluated in order to achieve a high loss reduction in the electrical distribution networks. The algorithm of the proposed approach has been implemented using MATLAB software and has been tested and investigated on 12-bus, 33-bus, and 69-bus IEEE distribution test systems. The results show that the proposed approach can provide an accurate solution via simple algorithm without using exhaustive process of power flow computations

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

Optimal Wind Farm Cabling

Wind farm cable length has a direct impact on the project cost, reliability and electrical losses. The optimum cable layout results in a lower unit cost of generating electricity offshore. This paper explores three cabling structures: the string structure, ring structures and multi-loop structure on a 3D seabed. The newly proposed multi-loop structure increases reliability and proves to be most economic when the failure rate and mean time to repair (MTTR) of cables are relatively high. Particle swarm optimization (PSO) is used to find the optimal substation location that minimizes the overall cable distance

Differential Evolution With a New Encoding Mechanism for Optimizing Wind Farm Layout

This paper presents a differential evolution algorithm with a new encoding mechanism for efficiently solving the optimal layout of the wind farm, with the aim of maximizing the power output. In the modeling of the wind farm, the wake effects among different wind turbines are considered and the Weibull distribution is employed to estimate the wind speed distribution. In the process of evolution, a new encoding mechanism for the locations of wind turbines is designed based on the characteristics of the wind farm layout. This encoding mechanism is the first attempt to treat the location of each wind turbine as an individual. As a result, the whole population represents a layout. Compared with the traditional encoding, the advantages of this encoding mechanism are twofold: 1) the dimension of the search space is reduced to two, and 2) a crucial parameter (i.e., the population size) is eliminated. In addition, differential evolution serves as the search engine and the caching technique is adopted to enhance the computational efficiency. The comparative analysis between the proposed method and seven other state-of-the-art methods is conducted based on two wind scenarios. The experimental results indicate that the proposed method is able to obtain the best overall performance, in terms of the power output and execution time

Collection grid optimization of a floating offshore wind farm using particle swarm theory

Floating substructures for offshore wind turbines is a promising solution in order to harness the vast wind potential of deep water sites where bottom-fixed turbines are not feasible. The electrical system of large scale floating offshore wind farms will experience the application of new technologies and installation procedures that likely affect the cost-competitiveness. Thus, in this work, an optimization model based on the particle swarm theory is presented that allows optimizing the collection grid of a floating offshore wind farm. The developed model is applied to a study case consisting of a 500MW floating offshore wind farm located at the Golfe de Fos in the Mediterranean Sea. The resulting layout allows to reduce the total cost of the collection grid by more than 6% and to decrease the energy losses by 8% compared to the actual layout. Besides this, a further study analyzes the effect of a quantity discount with a reduced number of power cable cross sections.Postprint (published version