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。     

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.     

风电场的组合维修策略研究

为降低风电场维修成本,提出针对风电场的风电机组部件组合维修策略。在各部件故障率服从威布尔分布的基础上,确定各部件的最优预防性维修周期,进而确定各部件后续预防性维修的实施时刻。将未来一段时间内的全部维修任务按分组方案组合为多个维修组,单一维修组内包含的全部维修任务采用分层优化方法安排给多支维修队一起执行,以使在风电机组停机时间最短情况下维修队工作时间最短。对比组合维修策略与预防性维修策略的维系成本,以分组方案节约维修成本为适应度,使用遗传算法求解最优分组方案。仿真结果验证了该策略可有效减少风电机组的停机时间,降低风电场维修成本。     

基于改进粒子群算法的风力机选型应用研究

风力机的选型是风电场建设的重要内容,它对风电场建设造价、投产后的发电量以及运行维护成本等有直接影响。文章在给定风资源的情况下,综合考虑风电场的容量系数和实际发电量,以风力机性能指数作为选型的依据,针对采用常规方法进行风力机参数线性化求解的缺陷,采用智能化的改进粒子群算法对风力机参数进行寻优。与常规计算方法相比,该方法寻得的风力机性能指数更优。结合具体实例计算候选机型的风速加权标准差,选出最优风力机。该研究结果为风电场的风力机选型提供了一种有效可行的方法,具有一定的应用参考价值。     

Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications

Wind energy is an important source of renewable energy with significant untapped potential around the world. However, the cost of wind energy production is high, and efforts to lower the cost of energy generation will help enable more widespread use of wind energy. Yaw error reduces the efficiency of turbines as well as lowers the reliability of key components in turbines. Light detection and ranging (LIDAR) devices can correct the yaw error; however, they are expensive, and there is a trade‐off between their costs and benefits. In this study, a stochastic discrete‐event simulation was developed that models the operation of a wind farm. We maximize the net present value (NPV) changes associated with using LIDAR devices in a wind farm and determine the optimum number of LIDAR devices and their associated turbine stay time as a function of number of turbines in the wind farm for specific turbine sizes. The outcome of this work will help wind farm owners and operators make informed decisions about purchasing LIDAR devices for their wind farms.     

风力机状态监测与故障诊断技术研究

介绍了风力发电机组的基本构成,对风力机常用状态监测技术及主要测量参数进行了分析研究,并分析了风力机部件的常见故障,研究了部件的故障机理,最后,分析研究了适合于风力机的多种故障诊断方法,对国内外风力机状态监测、诊断技术和系统应用现状进行了概述。研究结果对保证风力发电机组安全运行,减少故障发生率,提高风力发电机组的运行可靠性．实现基于状态维修起到了指导作用。     

基于网格-坐标化遗传算法的风电场布局优化

采用Jensen尾流模型来描述风电机组间尾流的干扰效应。首先基于网格化的改进遗传算法获得风电机组的数量和布局初始位置,再通过坐标化遗传算法对风电机组位置进行进一步调整优化,从而提高单位成本的发电量。根据所提出的方法,在3种不同的风场（定风向定风速、定风速变风向和变风速变风向）下,针对2 km×2 km的标准风场区域进行风电机组布局优化,再将其应用到不规则的实际案例,对比分析表明所提出的方法能有效提高发电量。     

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.     

基于运行数据的风电机组转矩控制性能评估

风电机组的性能评估方法具有多样性及复杂性的特点,基于风电场SCADA系统中采集的大量风电机组运行数据,对风电机组转矩控制的性能评估方法进行了研究。在深入分析风电机组中发电机转速与发电机转矩关系的基础上,提出了风电机组在最佳风能利用系数Cp(max)跟踪区内的转矩优化控制的性能评估方法。通过筛选有效数据,拟合计算出风电机组的实际运行转矩增益系数;再通过与理论最优转矩增益系数进行对比,找出风能捕获能力较弱的风电机组,进而采取措施提高其发电量。通过软件仿真及案例分析表明,该方法在不增加设备及成本的情况下,可有效识别因转矩控制的性能差而影响发电量的风电机组,以便及时进行控制策略调校,维护风电场的利益。     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

A new method for spatial allocation of turbines in a wind farm based on lightning protection efficiency

Existing studies of the spatial allocation of wind farms are typically based on turbine power generation efficiency and rarely consider the damage caused by lightning strikes. However, lightning damage seriously affects the economic performance of wind farms because of the high cost of repairing or replacing damaged blades. This paper proposes a method for the spatial optimization of multiple turbines based on lightning protection dependability. Firstly, the lightning protection efficiency of turbine blade protection systems is analyzed by combining the physical mechanisms of lightning leader progression with a conventional electro‐geometric model to develop an electro‐geometric model of turbine blades (EGMTB). Then, the optimized spatial allocation of multiple turbines in a wind farm is investigated using the EGMTB. The results are illustrated from an example wind farm with 1.5 MW turbines, which shows that the optimal spacing between two turbines perpendicular to the prevailing wind direction L_⊥ is 4R‐6R, where R is the length of a turbine blade. This spacing is shown to effectively shield turbine blades from lightning damage over a wide range of lightning currents (>26‐60 kA). Note that, the suggested L_⊥ will be smaller considering the influence of lightning polarity as it takes more difficulty developing upward leader (UL) in the condition of positive lightning striking. Experiments verify the effectiveness and correctness of this method.     

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。     

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.     

基于深度卷积自编码器的风电机组故障预警方法研究

通过风电机组状态监测进行故障预警,可防止故障进一步发展,降低风场运维成本。为充分挖掘风电机组监控与数据采集（SCADA）各状态参数时序信息,以及不同参数之间的非线性关系,该文将深度学习中自动编码器（AE）与卷积神经网络（CNN）相结合,提出基于深度卷积自编码（DCAE）的风电机组状态监测故障预警方法。首先基于历史SCADA数据离线建立基于DCAE的机组正常运行状态模型,然后分析重构误差确定告警阈值,使用EMWA控制图对实时对机组状态监测并进行故障预警。以北方某风电场2 MW双馈型风电机组叶片故障为实例进行实验分析,结果表明该文提出DCAE状态监测故障预警方法,可有效对机组故障提前预警,且优于现有基于深度学习的风电机组故障预警方法,可显著提升重构精度、减少模型参数和训练时间。     

An artificial neural network‐based condition monitoring method for wind turbines,with application to the monitoring of the gearbox

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition‐based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA‐based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post‐processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.     

Wind turbine condition monitoring by the approach of SCADA data analysis

Wind turbines are being increasingly deployed in remote onshore and offshore areas due to the richer wind resource there and the advantages of mitigating the land use and visual impact issues. However, site accessing difficulties and the shortage of proper transportation and installation vehicles/vessels are challenging the operation and maintenance of the giants erected at these remote sites. In addition to the continual pressure on lowering the cost of energy of wind, condition monitoring is being regarded as one of the best solutions for the maintenance issues and therefore is attracting significant interest today. Much effort has been made in developing wind turbine condition monitoring systems and inventing dedicated condition monitoring technologies. However, the high cost and the various capability limitations of available achievements have delayed their extensive use. A cost-effective and reliable wind turbine condition monitoring technique is still sought for today. The purpose of this paper is to develop such a technique through interpreting the SCADA data collected from wind turbines, which have already been collected but have long been ignored due to lack of appropriate data interpretation tools. The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational conditions. Both laboratory and site verification tests have been conducted. It has been shown that the proposed technique not only has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration.     

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.     

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.     

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.