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.     

Soil–structure reliability of offshore wind turbine monopile foundations

An overview of offshore wind turbine (OWT) foundations is presented, focusing primarily on the monopile foundation. The uncertainty in offshore soil conditions as well as random wind and wave loading is currently treated with a deterministic design procedure, though some standards allow engineers to use a probability‐based approach. Laterally loaded monopile foundations are typically designed using the American Petroleum Institute p‐y method, which is problematic for large OWT pile diameters. Probabilistic methods are used to examine the reliability of OWT pile foundations under serviceability limit states using Euler–Bernoulli beam elements in a two‐dimensional pile–spring model, non‐linear with respect to the soil springs. The effects of soil property variation, pile design parameters, loading and large diameters on OWT pile reliability are presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Incorporating a stochastic data‐driven inflow model for uncertainty quantification of wind turbine performance

Incorporating uncertainty in wind turbine analysis and design is very necessary based on the fact that inherent variability exists in wind turbine systems. Examples of these uncertainties include fluctuations in material properties across turbine blades, variable structure parameters and stochasticity in the inflow—which is considered to be a critical factor affecting the reliability of wind turbines. However, it has been difficult to construct a low‐dimensional yet accurate representation of the stochastic inflow, which precludes rigorous uncertainty propagation and quantification. Recently, we have developed a comprehensive data‐driven approach [called temporal–spatial decomposition (TSD)] for constructing a stochastic, low‐dimensional model that accurately represents stochastic inflow data. We leverage this approach to construct distributional forecasts of key wind turbine performance indicators. To this end, we integrated the stochastic wind model created by the TSD framework with the wind turbine solver FAST. Uncertainty propagation is performed using an adaptive sparse grid collocation approach. We investigate how the order of approximation of the stochastic model affects the quality of the predicted distribution. We observe that the probability distributions of key indicators are not necessarily Gaussian, which has implications for reliability analysis and for failure prediction. Furthermore, the distributions are sensitive to only the first few eigenmodes of the inflow wind model, which indicates that comprehensive uncertainty quantification can potentially be accomplished with moderate computational effort. The approach suggested in this paper enables seamless integration of uncertainty quantification into current deterministic codes for wind turbine simulation and has implications for the design of the next generation of wind turbines including offshore turbines. Copyright © 2017 John Wiley & Sons, Ltd.     

Failure rate,repair time and unscheduled O&M cost analysis of offshore wind turbines

Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub‐assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub‐assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers. Copyright © 2015 John Wiley & Sons, Ltd.     

Errors and uncertainties associated with missing wind data and short records

A near‐complete 4 year data set of 10 min average 80 m wind speeds is used to examine the impact of missing data on monthly and yearly estimates of mean wind speed and energy production from a generic wind turbine. Missing data is a source of uncertainty in wind energy resource assessment studies. Quantifying that uncertainty can improve the reliability of P90 and related wind farm energy production estimates. An empirical relationship between missing data percentage and relative uncertainty in monthly mean wind speed is derived. Relationships between uncertainties in monthly average wind speed and uncertainties in monthly energy production are also explored. In many cases with monthly data losses of 10% or less the contribution to the overall uncertainty in annual energy production will be small (<1%), but with substantial losses in cold winters, typically caused by icing; the uncertainties can become more significant. The data set is also used to indicate uncertainties associated with short data periods. Annual average wind speed estimates based on less than a complete year's data also add significant uncertainty to wind resource assessments. Copyright © 2013 John Wiley & Sons, Ltd.     

Effect of wind and wave directionality on the structural performance of non‐operational offshore wind turbines supported by jackets during hurricanes

Risk of hurricane damage is an important factor in the development of the offshore wind energy industry in the United States. Hurricane loads on an offshore wind turbine (OWT), namely wind and wave loads, not only exert large structural demands, but also have temporally changing characteristics, especially with respect to their directions. Waves are less susceptible to rapid changes, whereas wind can change its properties over shorter time scales. Misalignment of local winds and ocean waves occurs regularly during a hurricane. The strength capacity of non‐axisymmetric structures such as jackets is sensitive to loading direction and misalignment relative to structural orientation. As an example, this work examines the effect of these issues on the extreme loads and structural response of a non‐operational OWT during hurricane conditions. The considered OWT is a 5 MW turbine, supported by a jacket structure and located off the Massachusetts coast. A set of 1000 synthetic hurricane events, selected from a catalog simulating 100,000 years of hurricane activity, is used to represent hurricane conditions, and the corresponding wind speeds, wave heights and directions are estimated using empirical, parametric models for each hurricane. The impact of wind and wave directions and structural orientation are quantified through a series of nonlinear static analyses under various assumptions for combining the directions of wind and wave and structural orientation for the considered example structure. Copyright © 2016 John Wiley & Sons, Ltd.     

Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads

According to the wind turbine standard IEC 61400-1, structural integrity of wind turbines is determined either by direct reference to wind data or by load calculation. In both cases, deterministic values are applied and uncertainties neglected for the wind climate parameters and the structural resistance.The uncertainty related to the wind climate parameters depends highly on the presence, duration and quality of on-site wind measurements, and the perturbations introduced by flow modelling. For the wind speed distribution, the uncertainty is considered in assessment of the annual energy production. For other wind climate parameters which potentially have a large influence on the wind turbine loads, the uncertainty is often not well investigated.This paper presents a probabilistic framework for assessment of the structural reliability level of wind turbines in fatigue loading. Uncertainty of the site specific wind climate parameters at each turbine position is estimated based on the local wind measurements, speed-up factors and the distance between the wind turbine and the measuring position. The framework is demonstrated for a wind turbine project in flat terrain. The results show that the uncertainty in the site specific wind climate parameters normally accounts for 10–30% of the total uncertainty in the structural reliability analyses.     

Wind turbine downtime and its importance for offshore deployment

While the performance and the efficiency of wind turbines and their energy yields have been improved with time, their reliability still needs improvement, particularly when considering their deployment offshore. IWES has been gathering operational experience from wind turbines since 1989, being involved in different projects dealing with the topic of availability and reliability. This paper draws statistical data from Germany's ‘250 MW Wind’ programme, evaluated by IWES. The prime objective of the survey was to extract information about the reliability characteristics of wind turbines. The main purpose of this paper is to discuss the frequency of failures and duration of downtimes for different wind turbine subassemblies based on existing onshore experience and point out the likely outcomes when turbines are deployed offshore. Copyright © 2010 John Wiley & Sons, Ltd.     

An evaluation of the predictive accuracy of wake effects models for offshore wind farms

Wake losses are perceived as one of the largest uncertainties in energy production estimates (EPEs) for new offshore wind projects. In recent years, significant effort has been invested to improve the accuracy of wake models. However, it is still common for a standard wake loss uncertainty of 50% to be assumed in EPEs for new offshore wind farms. This paper presents a body of evidence to support reducing that assumed uncertainty. It benchmarks the performance of four commonly used wake models against production data from five offshore wind farms. Three levels of evidence are presented to substantiate the performance of the models:

• Case studies, i.e. efficiencies of specific turbines under specific wind conditions;
• Array efficiencies for the wind farm as a whole for relatively large bins of wind speed and direction; and
• Validation wake loss, which corresponds to the overall wake loss within the proportion of the annual energy production where validation is possible.

The most important result for predicting annual energy production is the validation wake loss. The other levels of evidence demonstrate that this result is not unduly reliant on cancellation of errors between wind speed and/or wind direction bins. All of the root‐mean‐squared errors in validation wake loss are substantially lower than the 50% uncertainty commonly assumed in EPEs; indeed, even the maximum errors are below 25%. It is therefore concluded that there is a good body of evidence to support reducing this assumed uncertainty substantially, to a proposed level of 25%. Copyright © 2015 John Wiley & Sons, Ltd.     

A Bayesian hierarchical assessment of night shift working for offshore wind farms

This article presents a Bayesian data-modelling approach to assessing operational efficiency at offshore wind farms. Input data are provided by an operational database provided by a large offshore wind farm which employs an advanced data management system. We explore the combination of datasets making up the database, using them to train a Bayesian hierarchical model which predicts weekly lost production from corrective maintenance and time-based availability. The approach is used to investigate the effect of technician work shift patterns, specifically addressing a strategy involving night shifts for corrective maintenance which was employed at the site throughout the winter. It was found that, for this particular site, there is an approximate annual increase in time-based technical availability of 0.64%. We explore the effect of modelling assumptions on cost savings; specifically, we explore variations in failure rate, price of electricity, number of technicians working night shift, extra staff wages, months of the year employing 24/7 working and extra vessel provision. Results vary quite significantly among the scenarios investigated, exemplifying the need to consider the question on a farm-by-farm basis.     

Ecological and economic cost-benefit analysis of offshore wind energy

Wind energy has experienced dramatic growth over the past decade. A small fraction of this growth has occurred offshore, but as the best wind resources become developed onshore, there is increasing interest in the development of offshore winds. Like any form of power production, offshore wind energy has both positive and negative impacts. The potential negative impacts have stimulated a great deal of opposition to the first offshore wind power proposals in the U.S. and have delayed the development of the first offshore wind farm in the U.S. Here we discuss the costs and benefits of offshore wind relative to onshore wind power and conventional electricity production. We review cost estimates for offshore wind power and compare these to estimates for onshore wind and conventional power. We develop empirical cost functions for offshore wind based on publicly reported projects from 2000 to 2008, and describe the limitations of the analysis. We use this analysis to inform a discussion of the tradeoffs between conventional, onshore and offshore wind energy usage.     

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.     

A framework for data integration of offshore wind farms

Operation and maintenance play an important role in maximizing the yield and minimizing the downtime of wind turbines, especially offshore wind farms where access can be difficult due to harsh weather conditions for long periods. It contributes up to 25–30% to the cost of energy generation. Improved operation and maintenance (O&M) practices are likely to reduce the cost of wind energy and increase safety. In order to optimize the O&M, the importance of data exchange and knowledge sharing within the offshore wind industry must be realized. With more data available, it is possible to make better decisions, and thereby improve the recovery rates and reduce the operational costs. This article describes the development of a framework for data integration to optimize the remote operations of offshore wind farms.     

A macro-level decision analysis of wind power as a solution for sustainable energy in the USA

This study aims to quantify the socio-economic and environmental impacts of producing electricity by wind power plants for the US electricity mix. To accomplish this goal, all direct and supply chain-related impacts of different onshore and offshore wind turbines are quantified using a hybrid economic input-output-based triple bottom line (TBL) life cycle assessment model. Furthermore, considering TBL sustainability implications of each onshore and offshore wind energy technology, a multi-criteria decision-making tool which is coupled with Monte Carlo simulation is utilised to find the optimal choice of onshore and offshore wind energy. The analysis results indicate that V90-3.0 MW wind turbines have lower impacts than V80-3.0 MW for both socio-economic and environmental indicators. The Monte Carlo simulation results reveal that when environmental issues are more important than socio-economic impacts, V90-3.0 MW offshore is selected among the alternatives.     

Environmental and social footprint of offshore wind energy. Comparison with onshore counterpart

Offshore wind power comprises a relatively new challenge for the international wind industry with a demonstration history of around twenty years and a ten-year commercial history for large, utility-scale projects. By comparison to other forms of electric power generation, offshore wind energy is generally considered to have relatively benign effects on the marine environment. However, offshore projects include platforms, turbines, cables, substations, grids, interconnection and shipping, dredging and associated construction activity. The Operation & Maintenance (O&M) activities include the transport of employees by vessel or helicopter and occasional hardware retrofits. Therefore, various impacts are incurred in the construction, operation and decommissioning phases; mainly the underwater noise and the impacts on the fauna. Based on the fact that in many of the aforementioned issues there are still serious environmental uncertainties, contradictive views and emerging research, the present work intents to provide a thorough literature review on the environmental and social impacts of offshore wind energy projects in comparison with the onshore counterparts.     

基于ABAQUS的海上风力机动力分析软件平台开发

基于通用有限元程序ABAQUS和风力机开源设计软件FAST,开发海上风力机动力响应分析软件平台ABA-OWT.选用NREL 5 MW海上单桩式风力机标准模型,首先,在ABAQUS中实现风力机结构的自动化建模,并根据风力机的结构特性初步验证模型的正确性;然后,在时域内通过子程序建立风力机结构与FAST子模块(气动、水动和...     

Modal analysis of an operational offshore wind turbine using enhanced Kalman filter-based subspace identification

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches.     

Validation and uncertainty quantification of metocean models for assessing hurricane risk

A reliable metocean model, with its uncertainty quantified and its accuracy validated for conditions appropriate to assessing risk, is essential to understand the risk posed by hurricanes to offshore infrastructure such as offshore wind turbines. In this paper, three metocean models are considered, with the seastate predicted using the commercial software Mike 21, and the meteorological forcing defined by three conditions. The three conditions include (1) reanalysis data within and surrounding the hurricane, (2) predictions from the empirical Holland model within the hurricane and reanalysis data surrounding the hurricane, and (3) predictions from the empirical Holland model within the hurricane and wind‐free conditions surrounding the hurricane. The accuracy of the first metocean model is validated with (1) measurements of wind speed, wave height, wave period, and storm surge during 23 historical hurricanes from 1999 to 2012 and (2) a comparison to hindcast data from WaveWatch III, another numerical metocean model. The prediction performance of the second and third metocean models is then compared with that of the first to evaluate the impact of meteorological conditions on model predictions, as the third metocean model is necessary for risk analysis, where reanalysis data of meteorological conditions is not available. This study shows that the inconsistency between the modeling of meteorological conditions for risk assessment and for validation is influential for hurricanes with low maximum wind speeds, when model predictions are significantly better if the meteorological conditions surrounding the hurricane wind field are included. This study also shows that this inconsistency is effectively diminished when considering only events with high maximum wind speeds. Since high wind speeds are what is relevant to risk assessments, the third metocean model can be reasonably used to assess hurricane risk. Finally, the uncertainties, biases, and correlations of uncertainties in the model predictions for wind speed, wave height, wave period, and storm surge are quantified for the third metocean model, and a numerical example is constructed to illustrate the impact of including uncertainty on the assessment of risk to offshore infrastructure during hurricanes. The example demonstrates how uncertainty and correlation of uncertainty influence the size and shape of a 50‐year environmental contour of wind speed and wave height.     

Survey of Failures in Wind Power Systems With Focus on Swedish Wind Power Plants During 1997–2005

The wind power industry has expanded greatly during the past few years, has served a growing market, and has spawned the development of larger wind turbines. Different designs and technical advances now make it possible to erect wind turbines offshore. The fast expansion of the wind power market faces some problems. The new designs are not always fully tested, and the designed 20-year lifetime is typically never achieved before the next generation of turbines are erected. This paper presents results from an investigation of failure statistics from four sources, i.e., two separate sources from Sweden, one from Finland, and one from Germany. Statistics reveal reliability performance of the different components within the wind turbine. The gearbox is the most critical, because downtime per failure is high compared to the other components. The statistical data for larger turbines also show trends toward higher, ever-increasing failure frequency when compared to small turbines, which have a decreasing failure rate over the operational years     

Offshore wind energy potential in China: Under technical, spatial and economic constraints

This paper investigates available offshore wind energy resources in China’s exclusive economic zone (EEZ) with the aid of a Geographical Information System (GIS), which allows the influence of technical, spatial and economic constraints on offshore wind resources being reflected in a continuous space. Geospatial supply curves and spatial distribution of levelised production cost (LPC) are developed, which provide information on the available potential of offshore wind energy at or below a given cost, and its corresponding geographical locations. The GIS-based models also reflect the impacts of each spatial constraint as well as various scenarios of spatial constraints on marginal production costs of offshore wind energy. Furthermore, the impacts of differing Feed-in-tariff (FIT) standards on the economic potential are calculated. It confirms that economic potential of offshore wind energy could contribute to 56%, 46% and 42% of the coastal region’s total electricity demands in 2010, 2020 and 2030. The shallow waters along the coasts of Fujian, Zhejiang, Shanghai, Jiangsu and northern Guangdong are identified as suitable areas for developing offshore wind energy in terms of wind resources and economic costs. However, the influence of tropical cyclone risks on these regions and detailed assessments at regional or local scale are worth of further discussions. Nevertheless, the models and results provide a foundation for a more comprehensive regional planning framework that would address additional infrastructure, planning and policy issues.