A framework for data integration of offshore wind farms

Operation and maintenance play an important role in maximizing the yield and minimizing the downtime of wind turbines, especially offshore wind farms where access can be difficult due to harsh weather conditions for long periods. It contributes up to 25–30% to the cost of energy generation. Improved operation and maintenance (O&M) practices are likely to reduce the cost of wind energy and increase safety. In order to optimize the O&M, the importance of data exchange and knowledge sharing within the offshore wind industry must be realized. With more data available, it is possible to make better decisions, and thereby improve the recovery rates and reduce the operational costs. This article describes the development of a framework for data integration to optimize the remote operations of offshore wind farms.     

海上风电机组智能故障预警系统研究

  [目的]  随着海上风电机组装机容量的飞速发展,业主对海上风电机组的安全运行越来越重视,对风机设备可靠性的要求越来越高。传统的设备故障事后处理模式不仅不能保证发电设备运行的可靠性,而且海上风电运行维护的可达性差,被动的故障后维修无形中增加了巨大的电量损失,已完全不能满足海上风电的要求。设备故障早期智能预警系统可以提前预知设备存在的问题,把设备隐患消除在萌芽状态之内,真正做到“防患于未然”。  [方法]  通过对海上风电机组关键部件的数据采集,结合历史数据提取故障特征,利用神经网络等大数据算法,实现发电机温度异常、发电机轴承异常、齿轮箱散热异常、齿形带断裂警告等设备故障的提前预判。  [结果]  根据对设备早期故障的提前预判,可以综合考虑海上风电的气象、台风、海况、海事等维护特点,有计划地执行积极的预防性维护策略,能够有效地避免大部件故障的发生或风机整机失效情况的发生。  [结论]  研究成果可提高海上风电机组的可靠性和风电场整体发电效益。     

考虑时间窗约束的海上风电机组运维方案优化

降低运维成本是保障海上风电经济效益的关键,运维方案优化对降低海上风电机组运维成本和提高发电量起着双重作用。根据风电机组零部件的可靠度模型,计算出每台风电机组最佳维修时机对应的时间窗,考虑提前维修和故障后维修的经济损失,建立包含时间窗约束的海上风电机组运维方案优化模型,然后设计基于参数优化的改进遗传算法计算出最优运维方案。最后采用某海上风电场内风电机组运维案例验证模型和算法,结果表明考虑时间窗约束的运维方案可大幅度提高海上风电的经济效益,改进遗传算法比传统遗传算法具有更强的寻优能力。     

Risk analysis of maintenance ship collisions with offshore wind turbines

A large number of offshore wind farms are planned to be built in remote deep-sea areas over the next five years. Though offshore wind sites are often located away from commercial ship traffic, the increased demand for repair or replacement services leads to high traffic densities of “maintenance ships”. To date, the risk analysis of collision between maintenance ship vessels and offshore wind turbines has received very little attention. In this paper, we propose a methodology to evaluate and prioritise the collision risks associated with various kinds of ships used for carrying out maintenance tasks on different subassemblies of wind turbines in an offshore wind farm. It is also studied how the risks of ship collision with wind turbines are distributed between two main types of maintenance tasks, namely corrective and preventative. The proposed model is tested on an offshore wind turbine with seventeen components requiring five kinds of ships to perform the maintenance tasks. Our results indicate that collision risks are mostly associated with maintenance of few components of the wind turbine and in particular, those undergoing a corrective maintenance (replacement). Finally, several mitigation strategies are introduced to minimise the risk of maintenance ship collisions with offshore wind turbines.     

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.     

Wind turbine downtime and its importance for offshore deployment

While the performance and the efficiency of wind turbines and their energy yields have been improved with time, their reliability still needs improvement, particularly when considering their deployment offshore. IWES has been gathering operational experience from wind turbines since 1989, being involved in different projects dealing with the topic of availability and reliability. This paper draws statistical data from Germany's ‘250 MW Wind’ programme, evaluated by IWES. The prime objective of the survey was to extract information about the reliability characteristics of wind turbines. The main purpose of this paper is to discuss the frequency of failures and duration of downtimes for different wind turbine subassemblies based on existing onshore experience and point out the likely outcomes when turbines are deployed offshore. Copyright © 2010 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.     

海上风电筒型基础结构体系阻尼特性研究

针对某筒型基础海上风电结构,基于实测振动响应数据,采用随机子空间法对所测海上风电结构的阻尼进行识别,研究海上风电筒型基础结构在停机及运行状态下阻尼的变化规律。结果表明：在停机状态下,海上风电筒型基础结构的顺风向阻尼和横风向阻尼均随外界风速的增大呈增长趋势,两者的平均值分别为0.952%和0.973%;当风电机组处于运行状态时,顺风向运行阻尼随外界风速的增大呈增长趋势,阻尼平均值在3.87%~5.28%之间变化,而对于横风向运行阻尼,其变化趋势受外界因素的影响较小,阻尼平均值在整个风速范围内在1.74%~3.37%之间变化,横风向运行阻尼小于顺风向运行阻尼。     

Using airborne and satellite SAR for wake mapping offshore

Offshore wind energy is progressing rapidly around Europe. One of the latest initiatives is the installation of multiple wind farms in clusters to share cables and maintenance costs and to fully exploit premium wind resource sites. For siting of multiple nearby wind farms, the wind turbine wake effect must be considered. Synthetic aperture radar (SAR) is an imaging remote sensing technique which offers a unique opportunity to describe spatial variations of wind speed offshore. For the first time an airborne SAR instrument was used for data acquisition over a large offshore wind farm. The aim was to identify the turbine wake effect from SAR‐derived wind speed maps as a downstream region of reduced wind speed. The aircraft SAR campaign was conducted on 12 October 2003 over the wind farm at Horns Rev in the North Sea. Nearly simultaneous measurements were acquired over the area by the SAR on board the ERS‐2 satellite. In addition, meteorological data were collected. Both aircraft and satellite SAR‐derived wind speed maps showed significant velocity deficits downstream of the wind farm. Wind speed maps retrieved from aircraft SAR suggested deficits of up to 20% downstream of the last turbine, whereas satellite SAR‐derived maps showed deficits of the order of 10%. The difference originated partly from the two different reference methods used for normalization of measured wind speeds. The detected region of reduced wind speed had the same width as the wind turbine array, indicating a low degree of horizontal wake dispersion. The downstream wake extent was approximately 10 km, which corresponds well with results from previous studies and with wake model predictions. Copyright © 2006 John Wiley & Sons, Ltd.     

Nonstationarity in correlation matrices for wind turbine SCADA-data

Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for analysis to improve operation and maintenance of turbines. We analyze high-frequency SCADA-data from the Thanet offshore wind farm in the United Kindom and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible a quantitative assessment of nonstationarity in mutual dependencies of different types of data. We show that a clustering algorithm applied to the correlation matrices reveals distinct correlation structures for different states. Looking first at only one and then at multiple turbines, the main dependence of these states is shown to be on wind speed. This is in accordance with known turbine control systems, which change the behavior of the turbine depending on the available wind speed. We model the boundary wind speeds separating the states based on the clustering solution. Our analysis shows that for high-frequency data, the control mechanisms of a turbine lead to detectable nonstationarity in the correlation matrix. The presented methodology allows accounting for this with an automated preprocessing by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the nonstationarity into account for an analysis.     

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time.In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions.The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved.Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures.Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities.     

智慧型海上风电场一体化监控系统方案设计

  [目的]  针对海上风电场的运行管理情况,整合了海上风电机组、海上升压站、陆上集控中心的监控系统,提出了全面的智慧型海上风电场一体化监控系统方案。  [方法]  方案分析了其体系架构、功能要求、数据处理以及安全分区,以实现风电场海上升压站无人值守,陆上集控中心少人运行的运营模式。方案有效地整合了风机监控、升压站监控、视频及环境监控、风电功率预测、海缆故障监测、设备状态监测等多个子系统。  [结果]  对海上风电场内各主要电气设备提供了完善的监控功能,可实现运行监视、操作与控制、信息综合分析与智能告警、运行管理、辅助功能这五大应用。  [结论]  方案可有效降低风电场运营成本,达到技术先进、功能完备、性能可靠、经济合理的智能化运营要求。     

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...     

Wind turbine SCADA alarm analysis for improving reliability

Previous research for detecting incipient wind turbine failures, using condition monitoring algorithms, concentrated on wind turbine Supervisory Control and Data Acquisition (SCADA) signals, such as power output, wind speed and bearing temperatures, using power‐curve and temperature relationships. However, very little research effort has been made on wind turbine SCADA alarms. When wind turbines are operating in significantly sized wind farms, these alarm triggers are overwhelming for operators or maintainers alike because of large number occurring in a 10 min SCADA period. This paper considers these alarms originating in two large populations of modern onshore wind turbines over a period of 1–2 years. First, an analysis is made on where the alarms originate. Second, a methodology for prioritizing the alarms is adopted from an oil and gas industry standard to show the seriousness of the alarm data volume. Third, two methods of alarm analysis, time‐sequence and probability‐based, are proposed and demonstrated on the data from one of the wind turbine populations, considering pitch and converter systems with known faults. The results of this work show that alarm data require relatively little storage yet provide rich condition monitoring information. Both the time‐sequence and probability‐based analysis methods have the potential to rationalize and reduce alarm data, providing valuable fault detection, diagnosis and prognosis from the conditions under which the alarms are generated. These methods should be developed and integrated into an intelligent alarm handling system for wind farms, aimed at improving wind turbine reliability to reduce downtime, increase availability and leading to a well‐organized maintenance schedule. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

Reliability of wind turbine subassemblies

We have investigated the reliability of more than 6000 modern onshore wind turbines and their subassemblies in Denmark and Germany over 11 years and particularly changes in reliability of generators, gearboxes and converters in a subset of 650 turbines in Schleswig Holstein, Germany. We first start by considering the average failure rate of turbine populations and then the average failure rates of wind turbine subassemblies. This analysis yields some surprising results about which subassemblies are the most unreliable. Then we proceed to consider the failure intensity function variation with time for wind turbines in one of these populations, using the Power Law Process, of three subassemblies; generator, gearbox and converter. This analysis shows that wind turbine gearboxes seem to be achieving reliabilities similar to gearboxes outside the wind industry. However, wind turbine generators and converters are both achieving reliabilities considerably below that of other industries but the reliability of these subassemblies improves with time. The paper also considers different wind turbine concepts. Then we conclude by proposing that offshore wind turbines should be subject to more rigorous reliability improvement measures, such as more thorough subassembly testing, to eliminate early failures. The early focus should be on converters and generators.     

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。     

Power generation efficiency analysis of offshore wind farms connected to a SLPC (single large power converter) operated with variable frequencies considering wake effects

This paper deals with the power generation efficiency analysis of a proposed offshore wind farm topology, consisting of a SLPC (single large power converter) that simultaneously controls a group of generators. This common converter can operate at a VF (variable frequency) or at a CF (constant frequency). The results are compared with the conventional onshore wind farm scheme, where individual power converters are connected to each turbine, guaranteeing maximum power generation for the entire wind farm. A methodology to analyze different wind speed and direction scenarios, and to compute the optimal electrical frequency for each one, is presented and applied to different case studies depending on the wind farm size. In order to obtain more realistic values of wind speeds, the wake effect amongst wind turbines is considered. A wake model considering single, partial and multiple wakes inside a wind farm and taking into account different wind directions, is presented. Both wind farm topologies are analyzed by means of simulations, taking into account both wind speed variability in wind farms and the number of wind turbines. The possible resulting benefits of simplifying the MPCs (multiple power converters) of each turbine, namely saving costs, reducing losses and maintenance and increasing the reliability of the system, are analyzed, focusing on the total power extraction. The SLPC-VF scheme is also compared with a CF scheme SLPC-CF, and it is shown that a significant power increase of more than 33% can be obtained with SLPC-VF.     

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.     

Availability,operation and maintenance costs of offshore wind turbines with different drive train configurations

Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with two‐stage and three‐stage gearboxes. Copyright © 2016 John Wiley & Sons, Ltd.