Post‐installation adaptation of offshore wind turbine controls

The cost of offshore wind energy can be reduced by incorporating control strategies to reduce the support structures' load effects into the structural design process. While effective in reducing the cost of support structures, load‐reducing controls produce potentially costly side effects in other wind turbine components and subsystems. This paper proposes a methodology to mitigate these side effects at the wind farm level. The interaction between the foundation and the surrounding soil is a major source of uncertainty in estimating the safety margins of support structures. The safety margins are generally closely correlated with the modal properties (natural frequencies, damping ratios). This admits the possibility of using modal identification techniques to reassess the structural safety after installing and commissioning the wind farm. Since design standards require conservative design margins, the post‐installation safety assessment is likely to reveal better than expected structural safety performance. Thus, if load‐reducing controls have been adopted in the structural design process, it is likely permissible to reduce the use of these during actual operation. Here, the probabilistic outcome of such a two‐stage controls adaptation is analyzed. The analysis considers the structural design of a 10 MW monopile offshore wind turbine under uncertainty in the site‐specific soil conditions. Two control strategies are considered in separate analyses: (a) tower feedback control to increase the support structure's fatigue life and (b) peak shaving to increase the support structure's serviceability capacity. The results show that a post‐installation adaptation can reduce the farm‐level side‐effects of load‐reducing controls by up to an order of magnitude.     

Site‐specific Design Optimization of Wind Turbines

This article reports results from a European project, where site characteristics were incorporated into the design process of wind turbines, to enable site‐specific design. Two wind turbines of different concept were investigated at six different sites comprising normal flat terrain, offshore and complex terrain wind farms. Design tools based on numerical optimization and aeroelastic calculations were combined with a cost model to allow optimization for minimum cost of energy. Different scenarios were optimized ranging from modifications of selected individual components to the complete design of a new wind turbine. Both annual energy yield and design‐determining loads depended on site characteristics, and this represented a potential for site‐specific design. The maximum variation in annual energy yield was 37% and the maximum variation in blade root fatigue loads was 62%. Optimized site‐specific designs showed reductions in cost of energy by up to 15% achieved from an increase in annual energy yield and a reduction in manufacturing costs. The greatest benefits were found at sites with low mean wind speed and low turbulence. Site‐specific design was not able to offset the intrinsic economic advantage of high‐wind‐speed sites. It was not possible to design a single wind turbine for all wind climates investigated, since the differences in the design loads were too large. Multiple‐site wind turbines should be designed for generic wind conditions, which cover wind parameters encountered at flat terrain sites with a high mean wind speed. Site‐specific wind turbines should be designed for low‐mean‐wind‐speed sites and complex terrain. Copyright © 2002 John Wiley & Sons, Ltd.     

Optimisation of electrical system for offshore wind farms via genetic algorithm

An optimisation platform based on genetic algorithm (GA) is presented, where the main components of a wind farm and key technical specifications are used as input parameters and the electrical system design of the wind farm is optimised in terms of both production cost and system reliability. The power losses, wind power production, initial investment and maintenance costs are considered in the production cost. The availability of components and network redundancy are included in the reliability evaluation. The method of coding an electrical system to a binary string, which is processed by GA, is developed. Different GA techniques are investigated based on a real example offshore wind farm. This optimisation platform has been demonstrated as a powerful tool for offshore wind farm design and evaluation.     

海上风电嵌岩区风机基础比选原则及依据

  [目的]  随着海上风能的开发中心从江苏扩大至广东、福建等省份,海上风机基础的嵌岩问题已逐步成为海上风能开发中的核心问题,嵌岩区的风机基础造价严重制约着海上风电场的成本,因此海上风电嵌岩区的风机基础比选就显得尤为关键。  [方法]  讨论了几种风机基础的嵌岩施工工艺,探讨了嵌岩区海上风机基础的比选依据及原则,并对海上风电嵌岩问题的发展进行展望。  [结果]  研究表明:海上风电场嵌岩区域风机基础方案的比选原则应主要从结构安全性、施工可行性和经济性三个方面进行综合比选。在上述三个比选因素中,应首先满足结构安全性上的要求,在此基础上再满足施工可行性的要求,最后考虑经济性的影响。  [结论]  研究成果可为嵌岩区海上风电风机基础设计提供参考。     

Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy

This paper presents a method for multidisciplinary design optimization of offshore wind turbines at system level. The formulation and implementation that enable the integrated aerodynamic and structural design of the rotor and tower simultaneously are detailed. The objective function to be minimized is the levelized cost of energy. The model includes various design constraints: stresses, deflections, modal frequencies and fatigue limits along different stations of the blade and tower. The rotor design variables are: chord and twist distribution, blade length, rated rotational speed and structural thicknesses along the span. The tower design variables are: tower thickness and diameter distribution, as well as the tower height. For the other wind turbine components, a representative mass model is used to include their dynamic interactions in the system. To calculate the system costs, representative cost models of a wind turbine located in an offshore wind farm are used. To show the potential of the method and to verify its usefulness, the 5 MW NREL wind turbine is used as a case study. The result of the design optimization process shows 2.3% decrease in the levelized cost of energy for a representative Dutch site, while satisfying all the design constraints.     

The impact of wind resource spatial variability on floating offshore wind farms finance

Wind resource availability determines the financial performance of wind farms as it is directly related to production. Offshore wind developers require great investments to design, build, operate and dismantle offshore wind farms. Furthermore, the investments in the offshore floating wind sector are expected to increase in the future. Because of that, the assessment of the variability of the investments, mainly because of the wind resource variability, seems to be a crucial step in the design methodology. Consequently, a flexible methodology for supporting offshore floating wind farm optimal location assessment is presented in this paper. The proposed methodology is focused on including the offshore wind resource variability and its influence on the power production of floating wind farms, as well as on the main financial indicators (internal rate of return, net present value, pay‐back period and cost of energy). The methodology is applied to the north coast of Spain, and it allows to identify the most promising sites for offshore wind farms deployment. Differences on the cost of energy up to 100% can be found in the area under study. The methodology proposed has been conceived to be site‐independent and applied at any spatial and time horizon. Copyright © 2017 John Wiley & Sons, Ltd.     

智慧海上风电场的定义、架构体系和建设路径

  目的  海上风电场智慧化,是提高海上风电场运行水平和效益的现实需要。  方法  首次提出了智慧海上风电场的定义,规划了智慧海上风电场的架构体系,从智能设备、业务控制、场级管控、集团监管四个层级,阐明了智慧海上风电场的建设路径,提出了具体的工程实施建议。  结果  实现了海上风电场设备、资产的智慧化监控与管理,为覆盖项目设计、基建、运营的全生命周期过程、实现全场设备、资产的数字化、智慧化监控与管理的智慧海上风电场指明了发展路径。  结论  海上风电场智慧化方案,对提高海上风电场自动化水平和运维效率、降低运维成本、提高海上风电的经济和社会效益、提高抵御风险的能力具有重要意义。     

TOPFARM: Multi‐fidelity optimization of wind farms

A wind farm layout optimization framework based on a multi‐fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt – Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi‐fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU‐FP6 TOPFARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake‐induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero‐elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi‐fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective. Copyright © 2013 John Wiley & Sons, Ltd.     

Scenario analysis for techno‐economic model development of U.S. offshore wind support structures

Challenging bathymetry and soil conditions of future US offshore wind power plants might promote the use of multimember, fixed‐bottom structures (or ‘jackets’) in place of monopiles. Support structures affect costs associated with the balance of system and operation and maintenance. Understanding the link between these costs and the main environmental design drivers is crucial in the quest for a lower levelized cost of energy, and it is the main rationale for this work. Actual cost and engineering data are still scarce; hence, we evaluated a simplified engineering approach to tie key site and turbine parameters (e.g. water depth, wave height, tower‐head mass, hub height and generator rating) to the overall support weight. A jacket‐and‐tower sizing tool, part of the National Renewable Energy Laboratory's system engineering software suite, was utilized to achieve mass‐optimized support structures for 81 different configurations. This tool set provides preliminary sizing of all jacket components. Results showed reasonable agreement with the available industry data, and that the jacket mass is mainly driven by water depth, but hub height and tower‐head mass become more influential at greater turbine ratings. A larger sensitivity of the structural mass to wave height and target eigenfrequency was observed for the deepest water conditions (>40 m). Thus, techno‐economic analyses using this model should be based on accurate estimates of actual metocean conditions and turbine parameters especially for deep waters. The relationships derived from this study will inform National Renewable Energy Laboratory's offshore balance of system cost model, and they will be used to evaluate the impact of changes in technology on offshore wind lower levelized cost of energy. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

Feasibility study of offshore wind turbine substructures for southwest offshore wind farm project in Korea

Korea has huge potential for offshore wind energy and the first Korean offshore wind farm has been initiated off the southwest coast. With increasing water depth, different substructures of the offshore wind turbine, such as the jacket and multipile, are the increasing focus of attention because they appear to be cost-effective. However, these substructures are still in the early stages of development in the offshore wind industry. The aim of the present study was to design a suitable substructure, such as a jacket or multipile, to support a 5 MW wind turbine in 33 m deep water for the Korean Southwest Offshore Wind Farm. This study also aimed to compare the dynamic responses of different substructures including the monopile, jacket and multipile and evaluate their feasibility. We therefore performed an eigenanalysis and a coupled aero-hydro-servo-elastic simulation under deterministic and stochastic conditions in the environmental conditions in Korea. The results showed that the designed jacket and multipile substructures, together with the modified monopile, were well located at soft–stiff intervals, where most modern utility-scale wind turbine support structures are designed. The dynamic responses of the different substructures showed that of the three substructures, the performance of the jacket was very good. In addition, considering the simple configuration of the multipile, which results in lower manufacturing cost, this substructure can provide another possible solution for Korean’s first offshore wind farm. This study provides knowledge that can be applied for the deployment of large-scale offshore wind turbines in intermediate water depths in Korea.     

海上风电场设施技术规范综述

  [目的]  文章旨在推动我国海上风电场设施技术规范的建立健全。  [方法]  指出了我国海上风电场建设中存在的问题,总结了国内外海上风电场设施规范标准,介绍了中国船级社对海上风电场设施技术规范的探索及成效。  [结果]  尽管国内海上风电的发展迅猛,但配套的海上风电场设施技术规范并没有及时跟进,更没有形成从设计、建造、安装、运维直至弃置的全生命周期技术规范体系;尽管欧洲拥有较为成熟的海上风电规范体系,但并不能完全照搬使用,如结构设计的LRFD方法,其分项系数与海域的环境条件和可靠性指标相关,适用于欧洲海域的分项系数并不适用于中国海域。  [结论]  因此,国内海上风电场技术规范体系的建立,需要业界共同努力、多方协作。传统的海洋工程专业应在该领域发挥更大的作用。中国船级社多年在海上风电领域的研究和积累,可为构建国内海上风电场设施技术规范体系提供借鉴和参考。     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

Development of a modified stochastic subspace identification method for rapid structural assessment of in‐service utility‐scale wind turbine towers

The strong drive to harness wind energy has recently led to rapid growth of wind farm construction. Wind turbine towers with increased sizes and flexibility experience large vibrations. Structural health monitoring of wind turbines is proposed in the wind energy industry to ensure their proper performance and save maintenance costs. This study proposes a system identification method for vibration‐based structural assessment of wind turbine towers. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitations. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped‐mass system model. The method was then applied to an in‐service utility‐scale wind turbine tower. The field testing campaign and modal parameter identification as well as structural assessment results were presented. Copyright © 2017 John Wiley & Sons, Ltd.     

Structural health and prognostics management for the enhancement of offshore wind turbine operations and maintenance strategies

Offshore wind turbines are an attractive source for clean and renewable energy for reasons including their proximity to population centers and higher capacity factors. One obstacle to the more widespread installation of offshore wind turbines in the USA, however, is that recent projections of offshore operations and maintenance costs vary from two to five times the land‐based costs. One way in which these costs could be reduced is through use of a structural health and prognostics management (SHPM) system as part of a condition‐based maintenance paradigm with smart loads management. This paper contributes to the development of such strategies by developing an initial roadmap for SHPM, with application to the blades. One of the key elements of the approach is a multiscale simulation approach developed to identify how the underlying physics of the system are affected by the presence of damage and how these changes manifest themselves in the operational response of a full turbine. A case study of a trailing edge disbond is analysed to demonstrate the multiscale sensitivity of damage approach and to show the potential life extension and increased energy capture that can be achieved using simple changes in the overall turbine control and loads management strategy. The integration of health monitoring information, economic considerations such as repair costs versus state of health, and a smart loads management methodology provides an initial roadmap for reducing operations and maintenance costs for offshore wind farms while increasing turbine availability and overall profit. Copyright © 2013 John Wiley & Sons, Ltd.     

Aeroelastic design of large wind turbine blades considering damage tolerance

Modern offshore turbine blades can be designed for high fatigue life and damage tolerance to avoid excessive maintenance and therefore significantly reduce the overall cost of offshore wind power. An aeroelastic design strategy for large wind turbine blades is presented and demonstrated for a 100 m blade. High fidelity analysis techniques like 3D finite element modeling are used alongside beam models of wind turbine blades to characterize the resulting designs in terms of their aeroelastic performance as well as their ability to resist damage growth. This study considers a common damage type for wind turbine blades, the bond line failure, and explores the damage tolerance of the designs to gain insight into how to improve bond line failure through aeroelastic design. Flat‐back airfoils are also explored to improve the damage tolerance performance of trailing‐edge bond line failures. Copyright © 2016 John Wiley & Sons, Ltd.     

海上风电机组塔架基础一体化设计

  [目的]  随着国家对于海上风电竞价上网指导意见的出台,降低开发成本的需求越来越迫切,急需通过技术创新降低成本。而海上塔架和基础的成本,显著影响着海上风电的平准化度电成本LCoE(Levelized Cost of Energy),直接决定着海上风电项目的竞争力。  [方法]  为了有效降低塔架基础的成本,文章提出了基于数字化云平台iDO(integrated Design Offshore)的一体化设计方法,对极端极限状态ULS工况下结构的静强度、疲劳极限状态FLS工况下结构的疲劳损伤进行了数值计算分析。为验证一体化设计方法在降低海上风电塔架基础成本的效果,文章针对两个实际工程项目,基于iDO云平台和传统分步迭代法SIA(Sequentially Iterated Approach)进行设计分析,对比ULS工况和FLS工况下的结构安全衡准指标。  [结果]  计算结果表明:ULS和FLS工况下,基于iDO云平台的一体化设计方法比SIA在结构强度、变形、疲劳损伤等指标有较大幅度下降,可显著优化塔架基础结构,降低结构重量,减小整个支撑结构成本,降低海上风电的LCoE。  [结论]  在实际海上风电工程项目应用中,基于iDO云平台的一体化设计方法可有效降低塔架基础结构成本,从而提高海上风电项目的竞争力,同时可对未来海上风电支撑结构优化设计提供借鉴。     

Overall Optimization for Offshore Wind Farm Electrical System

Based on particle swarm optimization (PSO), an optimization platform for offshore wind farm electrical system (OWFES) is proposed in this paper, where the main components of an offshore wind farm and key technical constraints are considered as input parameters. The offshore wind farm electrical system is optimized in accordance with initial investment by considering three aspects: the number and siting of offshore substations (OS), the cable connection layout of both collection system (CS) and transmission system (TS) as well as the selection of electrical components in terms of voltage level and capacity. Because hundreds of optimization variables, continuous or discrete, are involved in the problem, a mix integer PSO (MIPSO) is required to obtain the solution. The fuzzy C‐means clustering (FCM) algorithm is used to partition the wind farm into several sub regions. The collection system layout in each sub region as well as the connection scheme between offshore substations are optimized by an adaptive PSO‐minimum spanning tree algorithm (APSO‐MST) which has been proposed in a previous work. The simulation results show that the proposed optimization platform can find an optimized layout that save 3.01% total cost compared with the industrial layout, and can be a useful tool for OWFES design and evaluation. Copyright © 2016 John Wiley & Sons, Ltd.     

Intelligent integrated maintenance for wind power generation

A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom‐up analysis of the data required to support RCM within this specific industry, combined with a top‐down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost‐effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state‐of‐the‐art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines. © 2015 The Authors. Wind Energy published by John Wiley & Sons Ltd     

海上风电场智慧运维管理系统

[目的]针对海上风电场运维安全管理,提出了海上风电场智慧运维管理系统.[方法]通过海上风电智慧调度系统、海上风电雷达多源跟踪及边界警示系统、海上风电场风机平台作业监管系统,搭建出海上风电场智慧运维管理系统.[结果]通过陆上集控中心的海上风电智慧调度系统,实现人员的安全管理以及船舶调度.通过海上风电雷达多源跟踪及边界警示...