基于SCADA运行数据的风电机组发电性能劣化监测研究

风电机组的发电效率和发电性能对风电场的运行水平和经济效益有重要影响。文章采用风电机组SCADA运行数据对机组发电性能劣化进行监测。首先,采用偏最小二乘方法确定对风电机组发电功率有密切影响的多个变量;然后,采用高斯过程回归方法建立反映机组发电性能的功率曲线模型,有效提高建模精度;在监测阶段,引入指数加权移动平均值控制图(EWMA)分析功率曲线模型的功率预测残差,及时准确地发出风电机组发电性能劣化预警;最后,以某风电机组叶轮转速传感器故障导致的发电性能劣化实例,验证了该方法的有效性。     

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.     

Wind turbine condition monitoring based on three fitted performance curves

Based on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM-KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM-KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM-KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection.     

Online wind turbine fault detection through automated SCADA data analysis

This paper describes a set of anomaly‐detection techniques and their applicability to wind turbine fault identification. It explains how the anomaly‐detection techniques have been adapted to analyse supervisory control and data acquisition data acquired from a wind farm, automating and simplifying the operators' analysis task by interpreting the volume of data available. The techniques are brought together into one system to collate their output and provide a single decision support environment for an operator. The framework used is a novel multi‐agent system architecture that offers the opportunity to corroborate the output of the various interpretation techniques in order to improve the accuracy of fault detection. The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines. Copyright © 2009 John Wiley & Sons, Ltd.     

基于SCADA数据的风力发电机组振动的多元线性回归分析与研究

文章应用多元线性回归函数分析各参量对机组振动影响的大小,找出影响风力发电机组振动的主要因素和次要因素。通过分析发现,不论是机组的轴向振动还是侧向振动,风轮转速所带动的传动链的振动对机组的振动影响最大,桨距角的变化导致的机组载荷的变化对机组振动的影响次之,风速变化所产生的载荷的影响最小。     

An engineering condition indicator for condition monitoring of wind turbine bearings

Condition monitoring (CM) of wind turbine becomes significantly important part of wind farms in order to cut down operation and maintenance costs. The large amount of CM system vibration data collected from wind turbines are posing challenges to operators in signal processing. It is crucial to design sensitive and reliable condition indicator (CI) in wind turbine CM system. Bearing plays an important role in wind turbine because of its high impact on downtime and component replacement. CIs for wind turbine bearing monitoring are reviewed in the paper, and the advantages and disadvantages of these indicators are discussed in detail. A new engineering CI (ECI), which combined the energy and kurtosis representation of the vibration signal, is proposed to meet the requirement of easy applicability and early detection in wind turbine bearing monitoring. The quantitative threshold setting method of the ECI is provided for wind turbine CM practice. The bearing run‐to‐failure experiment data analysis demonstrates that ECI can evaluate the overall condition and is sensitive to incipient fault of bearing. The effectiveness in engineering of ECI is validated though a certain amount of real‐world wind turbine generator and gearbox bearing vibration data.     

基于SCADA及振动监测数据的风电机组变权综合状态评价

数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统是一套广泛应用于风电机组状态监测的系统。文章提出基于SCADA及振动监测数据的多数据源风电机组变权综合状态评价模型,并进行实例分析。研究表明,该模型在风电机组状态评价中是合理有效且可实现的,其评价结果为机组的维修决策及风电场运行优化提供了科学依据。     

Wind turbine generation performance monitoring with Jaya algorithm

Wind turbine (WT) power curves effectively reflect the generation performance of WTs and depict the relationship between the wind speed and the WT power output. This paper aims at developing an effective method for learning the intrinsic representations of WT power curves, which are robust to external environmental changes. Based on the obtained representations, WT generation performance is monitored. In the proposed approach, data of the supervisory control and data acquisition (SCADA) system is employed to derive the representations. Parametric models of WT power curves are developed using the two‐parameter and four‐parameter logic models. The parameters of these model are identified via Jaya algorithm. To detect the changes of WT power curve model parameters over different time, multivariate control charts are employed. The effectiveness of the proposed WT generation performance monitoring approach is validated based on SCADA data collected from real commercial WTs.     

基于运行数据的风电机组偏航优化控制方法研究

针对风电机组偏航控制系统在实际应用中自适应水平差、控制精度低等问题,首先利用某风电场的实际运行数据,分析了当前该风场风电机组偏航系统的控制性能;其次根据对风误差随风速的变化特点,提出了分风速段的偏航控制策略优化方案;最后选择相关性最好的两台机组进行了实验验证。实验结果表明,优化后的偏航控制策略能够在不增加机组偏航次数的前提下有效降低机组的对风误差,提升机组的出力性能。     

S-Transform and its contribution to wind turbine condition monitoring

Condition monitoring (CM) has long been recognised as one of the best methods of reducing the operation and maintenance (O&M) costs of wind turbines (WTs). However, its potential in the wind industry has not been fully exploited. One of the major reasons is due to the lack of an efficient tool to properly process the WT CM signals, which are usually non-stationary in both time and frequency domains owing to the constantly varying operational and loading conditions experienced by WTs. For this reason, S-transform and its potential contribution to WT CM are researched in this paper. Following the discussion of the superiorities of S-transform to the Short-Time Fourier Transform (STFT) and Wavelet Transform, two S-transform based CM techniques are developed, dedicated for use on WTs. One is for tracking the energy variations of those fault-related characteristic frequencies under varying operational conditions (the energy rise of these frequencies usually indicates the presence of a fault); another is for assessing the health condition of WT gears and bearings, which have shown significant reliability issues in both onshore and offshore wind projects. In the paper, both proposed techniques have been verified experimentally, showing that they are valid for detecting both the mechanical and electrical faults occurring in the WT despite its varying operational and loading conditions.     

Wind turbine angular frequency analysis by means of computer vision techniques

The turbine synchronization phenomenon is of great interest in order to estimate the flicker produced by a wind farm. This paper proposes an initial approach to analyze the appearance of this phenomenon by the use of various image processing techniques: a method to automatically calculate the angular frequency of an unknown number of wind turbines from a video. The recorded video images were obtained at the Manzanal wind farm, province of León (Spain).     

Wind turbine sensor array for monitoring avian and bat collisions

Assessment of avian and bat collisions with wind turbines is necessary to ensure that the benefits of renewable wind power generation are not outweighed by mortality of protected species. An onboard, integrated multisensor system capable of providing detection of turbine collision events, including taxonomic information, was developed. The conceptual design of a multisensor system including a vibration sensing node, an optics node, and an bioacoustic node with an event‐driven trigger architecture was field‐tested on utility‐scale wind turbines. A pixel density computational model was built to estimate the spatial coverage and target resolution to the optimized configuration for camera placement. Field test results of the vibration node showed that nearly half of the recorded impact events were successfully identified by visual inspection and running short‐time Fourier transform on recorded vibration signals. The remaining undetected impact events were masked under background noise due to low impact energy and high background noise of the operating turbine, which result in subsequent low signal‐to‐noise ratio. Our results demonstrate the feasibility of triggering the system through single impact event sensed by vibration sensors.     

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.     

基于状态监测的风电机组变桨系统故障诊断

以风电机组中故障率较高的变桨系统作为研究对象,从数据采集与监视控制系统的数据库中选择与变桨系统运行相关的特征参数,基于相似性原理,利用非线性状态评估方法,建立能够涵盖变桨系统全部正常运行状态的健康模型。当变桨系统发生故障时,会出现模型预测值与正常状态的偏差,根据每一个特征参数对偏差的影响来确定故障的原因。应用实例验证表明,该模型能够准确地识别故障类型,可以解决在排除故障及设备维修时,因缺少相关信息而造成停机时间过长、维修难度大等问题。     

Analysis of pitch system data for condition monitoring

Measurements of pitch motor torque and current give indirect information about the condition of the pitch system and can therefore potentially be used for condition‐based maintenance. This paper presents an analysis of these measurements for a wind turbine, and the measurements are compared with a theoretical model based on aeroelastic simulations. The blade moment is found to have only minor influence on the friction in the blade bearing. The main factors affecting the static friction are the temperature and time after the latest pitch movement. Pitch motor current and torque are proportional at a constant pitch velocity, but the 10 min maximum values are only approximately proportional, because the maximum values occur during acceleration and not simultaneously. These findings are important to consider, if using the pitch motor current or torque as an indicator for the pitch system health is considered. Copyright © 2013 John Wiley & Sons, Ltd.     

Transformation algorithm of wind turbine blade moment signals for blade condition monitoring

To simplify signal analysis on wind turbine blades and enable their efficient monitoring, this paper presents a novel method of transforming blade moment signals on a horizontal axis 3-blade wind turbine. Instead of processing 3-blade moment signals directly, the proposed algorithm transforms the three sinusoidal signals into two static signals relative to the center of blade rotation through vector synthesis and coordinate transformation, and eliminates frequency components due to blade rotation from the obtained signals. Moreover, as an alternative to a rotational sensor, a blade rotation angle estimator is introduced. Its effectiveness was confirmed through simulations and field tests on an actual wind turbine.     

基于流固耦合的风力机叶片模拟分析

文章选取风轮直径为2 m的水平轴风力机为研究对象,采用数值模拟计算的方法研究叶片在不同工况下的位移及应力/应变。研究结果表明:气动载荷使叶片产生挥舞方向的变形,且在展向上有较大幅度的位移;随着风轮转速的增加,叶片的位移有明显的增加,沿弦向前缘压力大于后缘的压力,沿展向叶根处变形要小于叶尖。     

Wind turbine reliability data review and impacts on levelised cost of energy

Reliability is critical to the design, operation, maintenance, and performance assessment and improvement of wind turbines (WTs). This paper systematically reviews publicly available reliability data for both onshore and offshore WTs and investigates the impacts of reliability on the cost of energy. WT failure rates and downtimes, broken down by subassembly, are collated from 18 publicly available databases including over 18 000 WTs, corresponding to over 90 000 turbine‐years. The data are classified based on the types of data collected (failure rate and stop rate) and by onshore and offshore populations. A comprehensive analysis is performed to investigate WT subassembly reliability data variations, identify critical subassemblies, compare onshore and offshore WT reliability, and understand possible sources of uncertainty. Large variations in both failure rates and downtimes are observed, and the skew in failure rate distribution implies that large databases with low failure rates, despite their diverse populations, are less uncertain than more targeted surveys, which are easily skewed by WT type failures. A model is presented to evaluate the levelised cost of energy as a function of WT failure rates and downtimes. A numerical study proves a strong and nonlinear relationship between WT reliability and operation and maintenance expenditure as well as annual energy production. Together with the cost analysis model, the findings can help WT operators identify the optimal degree of reliability improvement to minimise the levelised cost of energy.     

基于数据驱动的风电机组状态评估方法研究

为了延长风电机组平稳运行时长和减少故障停机次数,文章基于有监督主成分分析(SPCA)的Hotelling-T~2和Q统计量控制图,提出了一种风电机组状态监测与评估方法。首先,根据风电机组SCADA历史数据提取正常状态数据。然后,训练集成学习模型拟合主要状态变量,采用贝叶斯优化算法优化其中的超参数。最后,在移动时间窗内利用SPCA方法将监测数据分解到主成分空间与残差空间,计算真实数据与参考状态数据的Hotelling-T~2和Q统计量,并同时求取两种统计量的斯皮尔曼系数,通过划定阈值对机组进行状态评估。将该方法用于某风电场1.5 MW级风电机组,结果表明,该方法能够有效地对机组当前状态进行监测并识别出功率输出故障。