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.     

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator (DFIG) wind turbines. At present, to the best of the authors’ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabilistic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table (COPT), considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.     

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.     

Study of weather and location effects on wind turbine failure rates

Understanding the availability of wind turbines (WT) is vital to maximize WT energy production and minimize the capital payback period. Previous work on this subject concentrated on reliability and the location of WT failure modes rather than root causes. This paper concentrates on the influence of weather and WT location on failure rate and downtime, to try to understand root causes and the consequences of failure. The paper goes further than a previous study, which used Windstats data from the whole of Denmark, by considering a limited population of identical WTs at three locations on the German Nordzee, Ostzee and in western Germany, using data from WMEP and local weather stations. This new study focuses more precisely than the previous study by using more reliable data. The data were analysed to find the WT failures and weather conditions and then cross‐correlate them. To confirm their representativeness, the reliability characteristics of these smaller WT populations followed the average trends of the overall WMEP survey. However, clear differences were observed in the failure behaviour of the WTs at the three locations. Annual periodicity was seen in the weather data, as expected, but not in individual WT population failure data. However, clear cross‐correlations can be seen between WT failures and weather data, in particular wind speed, maximum temperature and humidity. These cross‐correlations were more convincing than those found in the earlier, larger Danish study, vindicating the more focused approach. It is also clear from the analysis that Operation & Maintenance also has an impact on WT failure rates. These factors will be important for the operation of offshore WTs with the work indicating how weather conditions may affect offshore WT failure rates. Copyright © 2012 John Wiley & Sons, Ltd.     

Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines

As a result of increasing wind farms penetration in power systems, the wind farms begin to influence power system, and thus the modelling of wind farms has become an interesting research topic. Nowadays, doubly fed induction generator based on wind turbine is the most widely used technology for wind farms due to its main advantages such as high-energy efficiency and controllability, and improved power quality. When the impact of a wind farm on power systems is studied, the behavior of the wind farm at the point common coupling to grid can be represented by an equivalent model derived from the aggregation of wind turbines into an equivalent wind turbine, instead of the complete model including the modelling of all the wind turbines. In this paper, a new equivalent model of wind farms with doubly fed induction generator wind turbines is proposed to represent the collective response of the wind farm by one single equivalent wind turbine, even although the aggregated wind turbines operate receiving different incoming winds. The effectiveness of the equivalent model to represent the collective response of the wind farm is demonstrated by comparing the simulation results of equivalent and complete models both during normal operation and grid disturbances.     

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.     

Survey of modelling methods for wind turbine wakes and wind farms

This article provides an overview and analysis of different wake‐modelling methods which may be used as prediction and design tools for both wind turbines and wind farms. We also survey the available data concerning the measurement of wind magnitudes in both single wakes and wind farms, and of loading effects on wind turbines under single‐ and multiple‐wake conditions. The relative merits of existing wake and wind farm models and their ability to reproduce experimental results are discussed. Conclusions are provided concerning the usefulness of the different modelling approaches examined, and difficult issues which have not yet been satisfactorily treated and which require further research are discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

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.     

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

This article presents a Bayesian reliability modelling approach for wind turbines that incorporates the effect of time-dependent variables. Namely, the technique is used to explore the effect of annual services on wind turbine failure intensity through time for turbines within a currently operational wind farm. In the operator's experience, turbines seemed to fail more frequently after scheduled maintenance was performed; however, this is an unexplored effect in the literature. Additionally, the effects of seasonality, year of operation and position in the array on failure intensity are explored. These features were included in a Cox-like model formulation which allows for time-dependent covariates. Inference was performed via Bayes rule. Results show a spike in failure intensity reaching 1.57 times the baseline in the six days directly proceeding annual servicing, after which failure intensity is reduced compared to baseline. Also observed is a significant year-on-year reduction of failure intensity since the introduction of the site's data management system in 2018, a clear preference for modelling time to failure via a Weibull distribution and a dependence on location in the array with respect to the prominent wind direction. Results also show the benefit of employing a Bayesian regime, which provides easily interpretable uncertainty quantification.     

Reliability of onshore wind turbines based on linking power curves to failure and maintenance records: A case study in central Spain

Wind turbine (WT) reliability has come to the forefront of research due to the rapid growth of wind energy in recent years. Reliability information can help understand failure causes and focus maintenance and prevention efforts on the most critical components, reducing costs and increasing profits. This paper offers new insights into WT reliability after analysing the data provided by the Supervisory Control And Data Acquisition (SCADA) system collected from seven onshore WTs located in central Spain from January 2014 to September 2021. To this end, we propose a method to link SCADA data to failure and maintenance records based on checking whether each 10-min average time sample was collected when any failure or maintenance action had been reported. These records have been manually mapped to the WT taxonomy based on the standard Reference Designation System for Power Plants (RDS-PP®) with minor changes. We present three different results: (i) The capacity factor and time-based availability of each WT; (ii) the subsystem failure rate and downtime to identify the most critical ones; and (iii) each WT power curve with the 10-min time samples labelled as healthy, under maintenance, or failure states, along with a ranking of the subsystems causing the most failures in each part of the power curves. It is the first time that time samples are linked to failure and maintenance records to visualise their distribution on the power curves. These results can help research point in the right direction to improve reliability and increase electricity production worldwide.     

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.     

Reliability assessment for Chinese domestic wind turbines based on data mining techniques

In this work, based on the field operating data of a Chinese domestic wind farm, which came from the supervisory control and data acquisition system, data mining techniques are applied to analyze the reliability characteristics of wind turbines and their components. The reliability indexes including time among failures, failure rate, and downtime are analyzed. On that basis, the key components that influence the wind turbines' reliability most seriously are determined. The internal relation between the failure rate and the environmental temperature is identified with correlation function, and time series approach is used to analyze the seasonal feature of the wind turbines' failure rate. The results show that compared with the wind turbines mentioned in the literatures, the failure rate of the current sample is higher. Among the components, the failure rates of electrical and control systems are the highest, while the corresponding repair time is relatively short; on the contrary, the failure rates of main shaft, gearbox, and generator are relatively low, while the average time for maintenance is comparably long. Furthermore, there is an obvious dependency between the failure rate and environmental temperature, and the failure rate has a clear seasonal feature.     

考虑疲劳均衡的海上风电场主动尾流控制研究

海上风电场运行维护成本高,而其尾流效应影响更加突出,不但会影响风电场的发电效率,还会增大风电场内机组的疲劳载荷,增加运维成本。文章针对基于疲劳均匀的海上风电场主动尾流控制展开研究,通过GH-Bladed软件计算建立了风电机组在典型控制工况下关键零部件的疲劳损伤量数据库。其中的工况包括最大功率追踪、桨距角控制和偏航控制3种,并引用了量子粒子群算法,通过变桨和偏航两种方法进行优化控制,以实现海上风电场发电量提升和风电机组疲劳均匀的多目标主动尾流优化控制策略,降低海上风电场运维成本。仿真结果表明了所提出控制方法的可行性。     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

Active power variation in wind farms by varying the number of wind turbine units

Aggregated representation of wind turbine units in the wind farm has been normally adopted for modelling and analysis of dynamic performance, with power variation obtained by change of wind speed in the literature. This paper presents a different scenario of power variation of wind farms by the addition and removal of turbines in wind farms and its implication in modelling and stability of wind farms. The steady-state and dynamic stability with the aggregated model of the wind farm has been analysed with variation in the number of turbine units and has been corroborated with time domain simulation in DIgSILENT Power Factory software. It has been concluded that the variation in the number of wind turbines generators connected to the same transmission line has a minimal impact on the stability in nonseries-compensated line; however, significant impact on the stability has been observed in series-compensated system.     

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.     

Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups

Several different models provided by researchers to maintain a wind turbine, but most of these models only focused on the case involved a single objective optimization problem. In practice, real cases of wind farms lead to multi-objective approach to optimize maintenance efforts. In this paper, based on an opportunistic approach, a multi-objective based model is proposed to optimize the maintenance of a farm involved several different types of wind turbines. The assumptions of stochastic behavior of wind velocity as well as the existence of a limited number for maintenance groups are also considered in this new approach. The proposed model considering imperfect maintenance, attempts (1) maximizing the expected rate of energy and (2) minimizing the total expected costs related to maintenance efforts. The opportunistic approach is also provided by the component's reliability threshold values. The comparative analysis addresses that the capability of the proposed model is more efficient compared to models addressed in literature.     

A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.     

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.     

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.