Offshore wind turbine fault alarm prediction

Offshore wind operations and maintenance (O&M) costs could reach up to one third of the overall project costs. In order to accelerate the deployment of offshore wind farms, costs need to come down. A key contributor to the O&M costs is the component failures and the downtime caused by them. Thus, an understanding is needed on the root cause of these failures. Previous research has indicated the relationship between wind turbine failures and environmental conditions. These studies are using work‐order data from onshore and offshore assets. A limitation of using work orders is that the time of the failure is not known and consequently, the exact environmental conditions cannot be identified. However, if turbine alarms are used to make this correlation, more accurate results can be derived. This paper quantifies this relationship and proposes a novel tool for predicting wind turbine fault alarms for a range of subassemblies, using wind speed statistics. A large variation of the failures between the different subassemblies against the wind speed are shown. The tool uses 5 years of operational data from an offshore wind farm to create a data‐driven predictive model. It is tested under low and high wind conditions, showing very promising results of more than 86% accuracy on seven different scenarios. This study is of interest to wind farm operators seeking to utilize the operational data of their assets to predict future faults, which will allow them to better plan their maintenance activities and have a more efficient spare part management system.     

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...     

Condition based maintenance optimization for wind power generation systems under continuous monitoring

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.     

基于FMECA和FTA正向综合分析的风电场设备故障管理

将FMECA和FTA综合方法引入到风电场设备故障管理中,在剖析风电场设备结构、功能的基础上,利用FMECA分析风电场设备的故障模式、故障原因、故障影响及危害性,利用故障树分析方法,对高固有风险的故障进行定性、定量分析,指导工作人员制定风电场设备预防性维修决策。     

Physics of Failure approach to wind turbine condition based maintenance

Wind turbine condition monitoring systems provide an early indication of component damage, allowing the operator to plan system repair prior to complete failure. However, the resulting cost savings are limited because of the relatively low number of failures that may be detected and the high cost of installing the required measurement equipment. A new approach is proposed for continuous, online calculation of damage accumulation using standard turbine performance parameters and Physics of Failure methodology. The wind turbine system is assessed in order to identify the root cause of critical failure modes and theoretical damage models are developed to describe the relationship between the turbine operating environment, applied loads and the rate at which damage accumulates. Accurate estimates may then be made in real time concerning the probability of failure for specific failure modes and components. The methodology is illustrated for a specific failure mode using a case study of a large wind farm where a significant number of gearbox failures occurred within a short space of time. Such an approach may be implemented at relatively low cost and offers potential for significant improvements in overall wind turbine maintenance strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

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。     

Modelling wind turbine degradation and maintenance

This paper presents an asset model for offshore wind turbine reliability accounting for the degradation, inspection and maintenance processes. The model was developed based on the Petri net method that effectively captures the stochastic nature of the dynamic processes through the use of appropriate statistical distributions. The versatility of the method allows the details of the degradation and maintenance operations to be incorporated in the model. In particular, there are dependent deterioration processes between wind turbine subsystems, complex maintenance rules and the incorporation of condition monitoring systems for early failure indication to enable replacement prior to failure. The purposes of the model are to predict the future condition of wind turbine components and to investigate the effect of a specified maintenance strategy. The model outputs are statistics indicating the performance of the wind turbine components; these include the probability of being in different condition states, the expected number of maintenance actions and the average number and duration of system downtime under any maintenance strategy. Copyright © 2015 John Wiley & Sons, Ltd.     

基于风电机故障的电力系统运营风险评估及保险转移的研究

风电机故障导致的系统电力缺额会给系统运营带来经济损失,目前由系统运营部门承担。为实现风险转移,建立了风电场风电机故障概率出力模型,并考虑了风电场风电机故障下电力系统运营的风险,通过对风电机故障条件下电力系统的蒙特卡罗抽样仿真,计算了不同负荷条件下电力系统运营调度的经济风险,并制定相应的保险转移机制。为全面评估风电的经济价值和风电场的规划设计提供了参考,并为风电场风电机故障风险转移提供了一种可供选择的方法。     

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.     

基于多状态空间划分的风电机组非完美维修决策

对风电机组的合理维护维修是减少风电场运维成本的重要方式。同一风电场的多台风力发电机构成了一个典型的多部件系统,各风力发电机的运行性能共同决定了系统整体的运行效率和维修需求。同时,对各风力发电机的维修效果也将影响到系统后续的可利用率和维修决策。该文以同一风电场中多台风力发电机的主轴组成的同型多部件系统为对象,在考虑非完美维修的条件下制定基于周期检测的视情机会维修策略;构建考虑非完美维修的多状态退化空间划分模型,以定义系统状态与维修需求的表示及关系,并归纳推导系统维修需求概率的计算模型和非完美维修干预下的系统退化及维修恢复过程中的状态转移概率;在此基础上,建立系统平均费用率解析模型,以确定最优的检测周期和维修阈值。通过某风电场的主轴实际运行数据进行数值实验,验证策略和模型的正确性和有效性,并对参数进行灵敏度分析以说明模型的适用性。结果表明该策略能有效减少风电场的运维成本。     

Field-data-based reliability analysis of power converters in wind turbines: Assessing the effect of explanatory variables

Frequent failures of power converters affect the availability of wind turbines and cause considerable maintenance costs. To enhance the reliability of power converters in wind turbines, the prevailing causes and modes of failures have to be identified. This publication contributes to root-cause analysis of the power-converter failures in wind turbines from a statistical point of view. For this purpose, the failure behavior of power-converters is modeled via lifetime models as well as repairable-system models. By means of regression models, covariates are incorporated, including both design-related and site-specific covariates. The analysis is based on a worldwide extensive field-data collection covering more than 9000 turbines, including different turbine designs, sites, and ages. The results obtained by means of the applied regression models indicate that the location of the power converter within the turbine, the cooling system, the converter rated power, the DC-link voltage, the IGBT-module manufacturer, and the commissioning date of the turbine as design-related covariates have a significant effect on the phase-module failure behavior and with that on converter reliability. Among the site-specific covariates, the analysis results confirm humidity as a likely significant driver of failures.     

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.     

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.     

Comparative study of the behavior of wind-turbines in a wind farm

The Sotavento wind farm is an experimental wind farm which has different types of wind turbines. It is located in an area whose topography is moderately complex, and where wake effects can be significant. One of the objectives of Sotavento wind farm is to compare the performances of the different machines; particularly regarding power production, maintenance and failures. However, because of wakes and topography, the different machines are not working under identical conditions. Two linearized codes have been used to estimate topography effects: UPMORO and WAsP. For wind directions in which topography is abrupt, the non-linear flow equations have been solved with the commercial code FLUENT, although the results are only qualitatively used. For wake effects, the UPMPARK code has been applied. As a result, the incident velocity over each wind turbine is obtained, and the power production is estimated by means of the power curve of each machine. Experimental measurements give simultaneously the wind characteristics at the measuring stations, the wind velocity, at the nacelle anemometer, and the power production of each wind turbine. These experimental results are employed to validate the numerical predictions. The main objective of this work is to deduce and validate a relationship between the wind characteristics measured in the anemometers and the wind velocity and the power output in each machine.     

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.     

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.     

Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models

Wind farm control (WFC) algorithms rely on an estimate of the ambient wind speed, wind direction, and turbulence intensity in the determination of the optimal control setpoints. However, the measurements available in a commercial wind farm do not always carry sufficient information to estimate these atmospheric quantities. In this paper, a novel measure (“observability”) is introduced that quantifies how well the ambient conditions can be estimated with the measurements at hand through a model inversion approach. The usefulness of this measure is shown through several case studies. While the turbine power signals and the inter‐turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for WFC to sufficiently cover observability for any ambient condition. Further, generally, more wake interaction leads to a higher observability. Also, the mathematical framework presented in this article supports the straightforward notion that turbine power measurements provide no additional information compared with local wind speed measurements, implying that power measurements are superfluous. Irregular farm layouts result in a higher observability due to the increase in unique wake interaction. The findings in this paper may be used in WFC to predict which ambient quantities can (theoretically) be estimated. The authors envision that this will assist in the estimation of the ambient conditions in WFC algorithms and can lead to an improvement in the performance of WFC algorithms over the complete envelope of wind farm operation.     

风力发电机组状态监测系统设计与应用

风力发电机组状态监测系统的设计可以有效降低机组的检修维护费用,保障机组的安全稳定运行。对风力发电机组状态监测和故障诊断技术进行了深入的研究,设计了风力发电机组状态监测系统,并详细介绍了系统的结构与功能。通过系统在大型风力发电场的成功应用,验证了其对风力发电机组状态监测与诊断的有效性。     

Framework design of a hybrid energy system by combining wind farm with small gas turbine power plants

Owing to the stochastic characteristic of natural wind speed, the output fluctuation of wind farm has a negative impact on power grid when a large-scale wind farm is connected to a power grid. It is very difficult to overcome this impact only by wind farm itself. A novel power system called wind-gas turbine hybrid energy system was discussed, and the framework design of this hybrid energy system was presented in detail in this paper. The hybrid energy system combines wind farm with several small gas turbine power plants to form an integrated power station to provide a relatively firm output power. The small gas turbine power plant has such special advantages as fast start-up, shutdown, and quick load regulation to fit the requirement of the hybrid energy system. Therefore, the hybrid energy system uses the output from the small gas turbine power plants to compensate for the output fluctuation from the wind farm for the firm output from the whole power system. To put this hybrid energy system into practice, the framework must be designed first. The capacity of the wind farm is chosen according to the capacity and units of small gas turbine power plants, load requirement from power grid, and local wind energy resource distribution. Finally, a framework design case of hybrid energy system was suggested according to typical wind energy resource in Xinjiang Autonomous Region in China.