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.     

Aeroelastic design of large wind turbine blades considering damage tolerance

Modern offshore turbine blades can be designed for high fatigue life and damage tolerance to avoid excessive maintenance and therefore significantly reduce the overall cost of offshore wind power. An aeroelastic design strategy for large wind turbine blades is presented and demonstrated for a 100 m blade. High fidelity analysis techniques like 3D finite element modeling are used alongside beam models of wind turbine blades to characterize the resulting designs in terms of their aeroelastic performance as well as their ability to resist damage growth. This study considers a common damage type for wind turbine blades, the bond line failure, and explores the damage tolerance of the designs to gain insight into how to improve bond line failure through aeroelastic design. Flat‐back airfoils are also explored to improve the damage tolerance performance of trailing‐edge bond line failures. Copyright © 2016 John Wiley & Sons, Ltd.     

Condition based maintenance optimization for wind power generation systems under continuous monitoring

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.     

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.     

Vibration response of a 2.3 MW wind turbine to yaw motion and shut down events

Structural health monitoring (SHM) is a process of implementing a damage detection strategy for a mechanical system. Wind turbine machinery stands to benefit from SHM significantly as the ability to detect early stages of damage before significant malfunction or structural failure occurs would reduce costs of wind power projects by reducing maintenance costs. Vibration analysis of dynamic structural response is an approach to SHM that has been successfully applied to mechanical and civil systems and shows some promise for wind turbine application. Traditionally, a setback to turbine vibration‐based SHM techniques has been the unavailability of turbine vibration response data. This study begins to address this issue by presenting vibration response for a commercial 2.3 MW turbine to a limited number of operating conditions. A database of acquired vibration response signals detailing turbine response to yaw motion, start‐up, operation and shutdown has been assembled. A Daubechies sixth‐order wavelet was used to perform an eight‐level discrete wavelet decomposition such that general trends and patterns within the signals could be identified. With further development, the presented analysis of vibration response may be integrated into routines to reduce downtime and failure frequency of utility scale wind turbines. Copyright © 2011 John Wiley & Sons, Ltd.     

风电机组高强度螺栓的失效形式分析

随着我国能源结构转型升级,风电装机容量不断增加.但近年来,风电机组倒塔事故频发,且该类事故是因风电机组高强度螺栓失效造成的.通过整理、总结风电机组不同部位高强度螺栓的受力情况,以及其常见失效形式的产生机理后发现,风电机组高强度螺栓的失效形式主要表现为疲劳断裂、脆性断裂、变形脱扣等;并从金属监督的角度提出了预防风电机组高...     

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.     

Political and institutional analysis of the successes and failures of China’s wind power policy

This paper identifies and explains how political and institutional factors have determined the relative successes and failures of China’s wind power policy over the period 2005–2011. It finds that China has made significant progress in pursuing its wind power policy in terms of cumulative installed capacity, wind turbine manufacturing industry development and wind turbine cost, and argues that these achievements can be attributed to the political motives and institutional arrangements of the Chinese government as well as to institutional changes. On the other hand, the paper finds that there are two prominent policy failures, namely the low proportion of grid-connected capacity and the rising trend of wind turbine incidents. These have undermined the efficiency and effectiveness of China’s wind power program. The paper holds that the institutional sources for the first policy failure lies in the preference for setting wind power development targets in terms of installed capacity rather than generation and in coordination problems while the second policy failure lies in the lack of state technical codes for wind power integration and the unfair competition from the large state-owned power companies. The paper contributes to the academic literature on the political and institutional roles in China’s wind power policy.     

Predictive repair scheduling of wind turbine drive‐train components based on machine learning

We devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.     

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.     

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.     

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.     

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.     

Model simplification and optimization of a passive wind turbine generator

In this paper, the design of a “low cost full passive structure” of wind turbine system without active electronic part (power and control) is investigated. The efficiency of such device can be obtained only if the design parameters are mutually adapted through an optimization design approach. For this purpose, sizing and simulating models are developed to characterize the behavior and the efficiency of the wind turbine system. A model simplification approach is presented, allowing the reduction of computational times and the investigation of multiple Pareto-optimal solutions with a multiobjective genetic algorithm. Results show that the optimized wind turbine configurations are capable of matching very closely the behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control device.     

Wind turbine generator trends for site‐specific tailoring

Turbine optimization for specific wind regimes and climate conditions is becoming more common as the market expands into new territories (offshore, low‐wind regimes) and as technology matures. Tailoring turbines for specific sites by varying rotor diameter, tower height and power electronics may be a viable technique to make wind energy more economic and less intermittent. By better understanding the wind resource trends and evaluating important wind turbine performance parameters such as specific power (ratio of rated power and rotor swept area), developers and operators can optimize plant output and better anticipate operational impacts. This article presents a methodology to evaluate site‐specific wind data for turbine tailoring. Wind characteristics for the Tehachapi wind resource area in California were utilized for this study. These data were used to evaluate the performance of a range of wind turbine configurations. The goal was to analyse the variations in wind power output for the area, assess the changes in these levels with the time of day and season and determine how turbine configuration affects the output. Wind turbine output was compared with California statewide system electrical demand to evaluate the correlation of the wind resource site with local peak demand loads. A comparison of the commercial value of electricity and corresponding wind generation is also presented using a time‐dependent valuation methodology. Copyright © 2005 John Wiley & Sons, Ltd.     

Hydrogen generation from small-scale wind-powered electrolysis system in different power matching modes

This study presents a techno-economic evaluation on hydrogen generation from a small-scale wind-powered electrolysis system in different power matching modes. For the analysis, wind speed data, which measured as hourly time series in Kirklareli, Turkey, were used to predict the electrical energy and hydrogen produced by the wind–hydrogen energy system and their variation according to the height of the wind turbine. The system considered in this study is primarily consisted of a 6 kW wind-energy conversion system and a 2 kW PEM electrolyzer. The calculation of energy production was made by means of the levelized cost method by considering two different systems that are the grid-independent system and the grid-integrated system. Annual production of electrical energy and hydrogen was calculated as 15,148.26 kWh/year and 102.37 kg/year, respectively. The highest hydrogen production is obtained in January. The analyses showed that both electrical energy and hydrogen production depend strongly on the hub height of wind turbine in addition to the economic indicators. In the grid-integrated system, the calculated levelized cost of hydrogen changes in the range of 0.3485–4.4849 US$/kg for 36 m hub height related to the specific turbine cost. The grid-integrated system can be considered as profitable when the excess electrical energy delivered by system sold to the grid.     

Reliability of wind turbine subassemblies

We have investigated the reliability of more than 6000 modern onshore wind turbines and their subassemblies in Denmark and Germany over 11 years and particularly changes in reliability of generators, gearboxes and converters in a subset of 650 turbines in Schleswig Holstein, Germany. We first start by considering the average failure rate of turbine populations and then the average failure rates of wind turbine subassemblies. This analysis yields some surprising results about which subassemblies are the most unreliable. Then we proceed to consider the failure intensity function variation with time for wind turbines in one of these populations, using the Power Law Process, of three subassemblies; generator, gearbox and converter. This analysis shows that wind turbine gearboxes seem to be achieving reliabilities similar to gearboxes outside the wind industry. However, wind turbine generators and converters are both achieving reliabilities considerably below that of other industries but the reliability of these subassemblies improves with time. The paper also considers different wind turbine concepts. Then we conclude by proposing that offshore wind turbines should be subject to more rigorous reliability improvement measures, such as more thorough subassembly testing, to eliminate early failures. The early focus should be on converters and generators.     

Simulating Feedback Linearization control of wind turbines using high‐order models

As wind turbines are becoming larger, wind turbine control must now encompass load control objectives as well as power and speed control to achieve a low cost of energy. Due to the inherent non‐linearities in a wind turbine system, the use of non‐linear model‐based controllers has the potential to increase control performance. A non‐linear feedback linearization controller with an Extended Kalman Filter is successfully used to control a FAST model of the controls advanced research turbine with active blade, tower and drive‐train dynamics in above rated wind conditions. The controller exhibits reductions in low speed shaft fatigue damage equivalent loads, power regulation and speed regulation when compared to a Gain Scheduled Proportional Integral controller, designed for speed regulation alone. The feedback linearization controller shows better rotor speed regulation than a Linear Quadratic Regulator (LQR) at close to rated wind speeds, but poorer rotor speed regulation at higher wind speeds. This is due to modeling inaccuracies and the addition of unmodeled dynamics during simulation. Similar performance between the feedback linearization controller and the LQR in reducing drive‐train fatigue damage and power regulation is observed. Improvements in control performance may be achieved through increasing the accuracy of the non‐linear model used for controller design. Copyright © 2009 John Wiley & Sons, Ltd.     

An investigation on wind turbine resonant vibrations

Wind turbine resonant vibrations are investigated based on aeroelastic simulations both in frequency and time domain. The investigation focuses on three different aspects: the need of a precise modeling when a wind turbine is operating close to resonant conditions; the importance of estimating wind turbine loads also at low turbulence intensity wind conditions to identify the presence of resonances; and the wind turbine response because of external excitations. In the first analysis, three different wind turbine models are analysed with respect to the frequency and damping of the aeroelastic modes. Fatigue loads on the same models are then investigated with two different turbulence intensities to analyse the wind turbine response. In the second analysis, a wind turbine model is excited with an external force. This analysis helps in identifying the modes that might be excited, and therefore, the frequencies at which minimal excitation should be present during operations. The study shows that significant edgewise blade vibrations can occur on modern wind turbines even if the aeroelastic damping of the edgewise modes is positive. When operating close to resonant conditions, small differences in the modeling can have a large influence on the vibration level. The edgewise vibrations are less visible in high turbulent conditions. Using simulations with low‐level turbulence intensity will ease this identification and could avoid a redesign. Furthermore, depending on the external excitation, different aeroelastic modes can be excited. The investigation is performed using aeroelastic models corresponding to a 1.5 MW class wind turbine with slight variations in blade properties. Copyright © 2015 John Wiley & Sons, Ltd.