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.     

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.     

Monitoring wind turbine gearboxes

Concerns amongst wind turbine (WT) operators about gearbox reliability arise from complex repair procedures, high replacement costs and long downtimes leading to revenue losses. Therefore, reliable monitoring for the detection, diagnosis and prediction of such faults are of great concerns to the wind industry. Monitoring of WT gearboxes has gained importance as WTs become larger and move to more inaccessible locations. This paper summarizes typical WT gearbox failure modes and reviews supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) approaches for monitoring them. It then presents two up‐to‐date monitoring case studies, from different manufacturers and types of WT, using SCADA and CMS signals. The first case study, applied to SCADA data, starts from basic laws of physics applied to the gearbox to derive robust relationships between temperature, efficiency, rotational speed and power output. The case study then applies an analysis, based on these simple principles, to working WTs using SCADA oil temperature rises to predict gearbox failure. The second case study focuses on CMS data and derives diagnostic information from gearbox vibration amplitudes and oil debris particle counts against energy production from working WTs. The results from the two case studies show how detection, diagnosis and prediction of incipient gearbox failures can be carried out using SCADA and CMS signals for monitoring although each technique has its particular strengths. It is proposed that in the future, the wind industry should consider integrating WT SCADA and CMS data to detect, diagnose and predict gearbox failures.Copyright © 2012 John Wiley & Sons, Ltd.     

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

This research investigates the prediction of failure and remaining useful life (RUL) of gearboxes for modern multi‐megawatt wind turbines. Failure and RUL are predicted through the use of machine learning techniques and large amounts of labelled wind turbine supervisory control and data acquisition (SCADA) and vibration data. The novelty of this work stems from unprecedented access to one of the world's largest wind turbine operational and reliability databases, containing thousands of turbine gearbox failure examples and complete SCADA and vibration data in the build up to those failures. Through access to that data, this paper is unique in having enough failure examples and data to draw the conclusions detailed in the remainder of this abstract. This paper shows that artificial neural networks provide the most accurate failure and RUL prediction out of three machine learning techniques trialled. This work also demonstrates that SCADA data can be used to predict failure up to a month before it occurs, and high frequency vibration data can be used to extend that accurate prediction capability to 5 to 6 months before failure. This paper demonstrates that two class neural networks can correctly predict gearbox failures between 72.5% and 75% of the time depending on the failure mode when trained with SCADA data and 100% of the time when trained with vibration data. Data trends in the build up to failure and weighting of the SCADA data inputs are also provided. Lastly, this work shows how multi‐class neural networks demonstrate more potential in predicting gearbox failure when trained with vibration data as opposed to training with SCADA data.     

Effect of preventive maintenance intervals on reliability and maintenance costs of wind turbine gearboxes

As many of the installed wind turbines (WTs) get older or approach their design life, there will be a drive to keep extending the lives of the main components especially the gearbox. The challenge of operations and maintenance will potentially be even more as there will be a need to keep the cost to a minimum. Similarly, as years of experience of operating WTs accumulate, knowledge about the behaviour and failure of subsystems is gained as well. Also with good documentation and repository of historical operational, performance and failure data, future decisions of operations and maintenance can be taken on the basis of insights from past experience. This paper presents an approach for implementing preventive maintenance (PM) by using historical failure data to determine the optimal PM interval required to maintain desired reliability of a typical module or subassembly. This paper builds upon previous research in the area of WT gearbox reliability analysis and prediction, taking it further by examining the relationships between the frequency of a PM task and the reliability, availability and maintenance costs. The approach presented demonstrates how historical in‐service failure data can be used in PM task selection based on the minimum maintenance cost and maximum availability. Available historical field failure data of the high speed module of a Vestas 2MW WT gearbox is used to validate the approach and show its practicality. The results of this study are then presented—indicating that choosing the right PM interval based on the minimum unit maintenance cost and maximum availability also improves WT gearbox reliability. Copyright © 2014 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.     

Modelling the effects of environmental conditions on wind turbine failures

Operation and maintenance is one of the main cost drivers of modern wind farms and has become an emerging field of research over the past years. Understanding the failure behaviour of wind turbines (WTs) can significantly enhance operation and maintenance processes and is essential for developing reliability and strategic maintenance models. Previous research has shown that especially the environmental conditions, to which the turbines are exposed to, affect their reliability drastically. This paper compares several advanced modelling techniques and proposes a novel approach to model WT system and component failures based on the site‐specific weather conditions. Furthermore, to avoid common problems in failure modelling, procedures for variable selection and complexity reduction are discussed and incorporated. This is applied to a big failure database comprised of 11 wind farms and 383 turbines. The results show that the model performs very well in several situations such as modelling general WT failures as well as failures of specific components. The latter is exemplified using gearbox failures.     

Availability analysis of heat recovery steam generators used in thermal power plants

The combined-cycle gas and steam turbine power plant presents three main pieces of equipment: gas turbines, steam turbines and heat recovery steam generator (HRSG). In case of HRSG failure the steam cycle is shut down, reducing the power plant output. Considering that the technology for design, construction and operation of high capacity HRSGs is quite recent its availability should be carefully evaluated in order to foresee the performance of the power plant.This study presents a method for reliability and availability evaluation of HRSGs installed in combined-cycle power plant. The method’s first step consists in the elaboration of the steam generator functional tree and development of failure mode and effects analysis. The next step involves a reliability and availability analysis based on the time to failure and time to repair data recorded during the steam generator operation. The third step, aiming at availability improvement, recommends the fault-tree analysis development to identify components the failure (or combination of failures) of which can cause the HRSG shutdown. Those components maintenance policy can be improved through the use of reliability centered maintenance (RCM) concepts. The method is applied on the analysis of two HRSGs installed in a 500 MW combined-cycle power plant.     

Offshore wind turbine reliability and operational simulation under uncertainties

The fast‐growing offshore wind energy sector brings opportunities to provide a sustainable energy resource but also challenges in offshore wind turbine (OWT) operation and maintenance management. Existing operational simulation models assume deterministic input reliability and failure cost data, whereas OWT reliability and failure costs vary depending on several factors, and it is often not possible to specify them with certainty. This paper focuses on modelling reliability and failure cost uncertainties and their impacts on OWT operational and economic performance. First, we present a probabilistic method for modelling reliability data uncertainty with a quantitative parameter estimation from available reliability data resources. Then, failure cost uncertainty is modelled using fuzzy logic that relates a component's failure cost to its capital cost and downtime. A time‐sequential Monte Carlo simulation is presented to simulate operational sequences of OWT components. This operation profile is later fed into a fuzzy cost assessment and coupled with a wind power curve model to evaluate OWT availability, energy production, operational expenditures and levelised cost of energy. A case study with different sets of reliability data is presented, and the results show that impacts of uncertainty on OWT performance are magnified in databases with low components' reliability. In addition, both reliability and cost uncertainties can contribute to more than 10% of the cost of energy variation. This research can provide practitioners with methods to handle data uncertainties in reliability and operational simulation of OWTs and help them to quantify the variability and dependence of wind power performance on data uncertainties.     

Review on wind turbines with focus on drive train system dynamics

The strong growth within the wind technology market, underpinned by policy goals around the world, has highlighted the demand for advanced engineering analysis to improve wind turbine (WT) design, both in terms of reliability and design of larger turbines. This paper presents a review of the latest research that has been carried out in modeling and analysis of load transmission in WT drive train systems and their components. Common failure roots are elaborated, and probable hypotheses are presented. A modeling approach is derived by classification into engineering, mathematical and computational models with a focus on gearbox modeling efforts. Precise understanding of drive train system dynamics and load transmission is necessary for a cost efficient and robust system design to enhance reliability and reduce the maintenance costs. Design optimization of WTs and their subsystems will make future WTs more attractive compared with fossil and nuclear power plants, and it is therefore an important issue for a more sustainable environment. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Risk-based decision making method for maintenance policy selection of thermal power plant equipment

This study presents a decision-making method for maintenance policy selection of power plants equipment. The method is based on risk analysis concepts. The method first step consists in identifying critical equipment both for power plant operational performance and availability based on risk concepts. The second step involves the proposal of a potential maintenance policy that could be applied to critical equipment in order to increase its availability. The costs associated with each potential maintenance policy must be estimated, including the maintenance costs and the cost of failure that measures the critical equipment failure consequences for the power plant operation. Once the failure probabilities and the costs of failures are estimated, a decision-making procedure is applied to select the best maintenance policy. The decision criterion is to minimize the equipment cost of failure, considering the costs and likelihood of occurrence of failure scenarios. The method is applied to the analysis of a lubrication oil system used in gas turbines journal bearings. The turbine has more than 150 MW nominal output, installed in an open cycle thermoelectric power plant. A design modification with the installation of a redundant oil pump is proposed for lubricating oil system availability improvement.     

Copula‐based model for wind turbine power curve outlier rejection

Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contain significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. Using such raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates, which would make the actual fault detection unreliable and would potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic method for excluding outliers, developed around a copula‐based joint probability model. This approach has the capability of capturing the complex non‐linear multivariate relationship between parameters, based on their univariate marginal distributions; through the use of a copula, data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After filtering the data in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults. Copyright © 2013 John Wiley & Sons, Ltd.     

发电设备可靠性与设备检修管理策略的研究

介绍了发电设备是可靠性管理和检修管理的策略,包括主动性检修,预知性检修,故障原因分析方法,以可靠性分析为中心的检修等设备管理策略,旨在探索电力企业改进和提高发电设备可靠性与检修管理水平的途径。图1表参1。     

某电厂风机轴承缺陷的诊断与失效原因分析

风机是电厂生产过程中的关键性设备之一,能否及时发现滚动轴承的缺陷是风机可靠性运行的关键。本文介绍了通过SKF加速度包络测试,在轴承失效前及时发现风机滚动轴承的缺陷并进行更换,避免了由于轴承失效而引起的非计划停机,最后分析了轴承失效的根本原因,以消除轴承重复性的故障。     

AP1000核电厂设备可靠性数据库开发及应用

  目的  设备可靠性数据在核电厂PSA、RCM、维修规则、可靠性保障大纲、风险指引管理等领域的重要价值,提出了开发AP1000设备可靠性数据库的必要性。  方法  对AP1000核电厂特定数据库开发过程中的一些重要问题,如独立失效的分析流程,设备和设备类的选取,可靠性参数估计方法,共因失效的分析方法和流程,核电厂特定数据以及通用数据收集和分析以及可靠性数据的应用等方面进行了探讨。  结果  提出了开发AP1000电站特定的设备可靠性数据库的技术路线。  结论  AP1000核电厂设备可靠性数据库的建立和应用,可以积累宝贵的核电厂特定运行、维修、失效、试验等数据,为核电厂运行PSA提供可靠性参数,为运行维修优化、安全管理等多个领域提供重要参考。     

Improving wind turbine drivetrain bearing reliability through pre‐misalignment

Improving the reliability of wind turbines (WT) is an essential component in the bid to minimize the cost of energy, especially for offshore wind because of the difficulties associated with access for maintenance. Numerous studies have shown that WT gearbox and generator failure rates are unacceptably high, particularly given the long downtime incurred per failure. There is evidence that bearing failures of the gearbox high‐speed stage (HSS) and generator account for a significant proportion of these failures. However, the root causes of these failure data are not known, and there is therefore a need for fundamental computational studies to support the valuable ‘top down’ reliability analyses. In this paper, a real (proprietary) 2 MW geared WT was modelled to compute the gearbox–generator misalignment and predict the impact of this misalignment upon the gearbox HSS and generator bearings. At rated torque, misalignment between the gearbox and generator of 8500 µm was seen. For the 2 MW WT analysed, the computational data show that the L₁₀ fatigue lives of the gearbox HSS bearings were not significantly affected by this misalignment but that the L₁₀ fatigue lives of the generator bearings, particularly the drive‐end bearing, could be significantly reduced. It is proposed to apply a nominal offset to the generator to reduce the misalignment under operation, thereby reducing the loading on the gearbox HSS and generator bearings. The value of performing integrated system analyses has been demonstrated, and a robust methodology has been outlined. Copyright © 2013 John Wiley & Sons, Ltd.     

电站锅炉引风机故障模式与效应分析

随着电力企业的改革 ,发电企业面临越来越大的竞争压力。为了提高市场的竞争力 ,在保证安全的前提下 ,增强发电设备的可靠性和经济性 ,降低发电成本 ,特别是降低庞大的维修费用 ,变得日益迫切。简要介绍了某火电站锅炉引风机的故障模式与效应分析程序 (FMEA) ,供有关单位在开展以可靠性为中心的引风机状态检修 (RCM )工作时参考     

Reliability and availability analysis of low power portable direct methanol fuel cells

This paper presents a methodology for modeling and calculating the reliability and availability of low power portable direct methanol fuel cells (DMFCs). System reliability and availability are critical factors for improving market acceptance and for determining the competitiveness of the low power DMFC. Two techniques have been used for analyzing the system reliability and availability requirements for various system components. Reliability block diagram (RBD) is formed based on the failure rates of irreparable system components. A state-space method is developed to calculate system availability using the Markov model (MM). The state-space method incorporates three different states—operational, derated, and fully faulted states. Since most system components spend their lifetime in performing normal functional task, this research is focused mainly on this operational period. The failure and repair rates for repairable DMFC systems are estimated on the basis of a homogeneous Poisson process (HPP) and exponential distribution. Extensive analytical modeling and simulation study has been performed to verify the effectiveness of the proposed technique.     

On the operation strategy of steam power plants working at variable load: Technical and economic issues

The context of the deregulated energy market leads to high competitiveness among producers and requires suitable strategies in plants and systems management: strongly irregular and discontinuous operation is required in order to meet the user demand and produce energy mainly during peak hours, when the electricity price is higher. This operation strategy is generally asked of all power plants, not only those traditionally devoted to load regulation and peak request, but also those originally designed to cover the base load (steam power plants, for example). As a consequence, greater income is ensured in the short term, but a reduction in the lifetime of the most critical components is likely to occur, due to creep and thermo-mechanical fatigue loadings. This will cause additional costs associated with unplanned maintenance and unavailability of the plant if a failure occurs.This paper presents a procedure aimed at evaluating this extra cost related to flexible operation, and at assisting the management decision about power plants’ operation and maintenance scheduling. The procedure, on the basis of the historical data, predicts the residual life of the most critical components, considering the effects of creep, thermo-mechanical fatigue, welding, corrosion and oxidation. It also permits one to choose different future strategies for plant management and evaluate the residual life and the economic effects for each of them. An example of application to a real steam power plant will also be presented.