Failure Rate Of Wind Turbines Research Articles

Research on the Optimization of Tribological Performance of Wind Power Lubricating Grease Based on Carbon Emission

The manufacturing, installation, operation and dismantling of wind turbines can also have environmental impacts such as excessive carbon emissions and consume large amounts of resources. Optimisation of the tribological properties of wind power greases can reduce the energy consumption and carbon emissions of wind power systems, improve their operational efficiency and reliability, thereby slowing down climate change and improving environmental quality, which is not only important for individual wind power projects, but also has a positive impact on reducing carbon emissions, promoting clean energy development and combating climate change on a global scale. However, the higher the grease lubrication performance, the lower the wind turbine failure rate and the lower the carbon emissions. Good tribological properties can significantly reduce the coefficient of friction of mechanical components, reducing surface wear and heat generation, helping to extend the life of mechanical equipment, improve efficiency and reduce unnecessary repairs and replacements. Therefore, in this paper, Mobil SHC 461WT lithium composite grease was mixed with molybdenum dialkyl dithiocarbamate (RFM3000), nano-boronated rare-earth friction modifiers (SK3115), polysiloxane viscosity modifier (PV611), and zinc dialkyl dithiophosphate (ZDDP), and the comparative analysis of their tribological performances was carried out through the method of cross-experimentation. The results showed that the average coefficient of friction of Mobil added with the four antiwear additives was reduced by 74%, 41%, 54% and 48%, respectively, and the average diameter of wear spots was reduced by 64%, 63%, 69% and 54%.

Reliability analysis of wind turbines considering seasonal weather effects

The failure rate of wind turbines shows obvious fluctuation due to seasonal environmental factors. However, few efforts have been devoted to modeling the seasonal failure rate. This paper develops a novel model that consists of a baseline failure rate function, seasonal indices, and a residual term to describe the monthly failure rate of wind turbines. A two-stage procedure is developed to estimate the 16 unknown parameters in the model. The first stage explores the relationship between the parameters in the baseline function and the monthly coefficients by maximum likelihood estimation and then integrates the properties into the genetic algorithm to estimate the main parameters. In the second stage, the variance of the residual term is estimated based on the analysis of the differences between the observed and predicted failure rates. The failure history of 48 months has been used to illustrate the proposed approach. The results show that the monthly failure rate function can well fit the real failure history of wind turbines, and it outperforms the traditional reliability model.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

Application of the Semi-Markov Processes to Model the Enercon E82-2 Preventive Wind Turbine Maintenance System

The share of wind energy in the energy mix is continuously increasing. However, a very important issue associated with its generation is the high failure rate of wind turbines. This situation particularly concerns large wind turbines, which are expensive and have a lower tolerance for system damage caused by various failures and faults. Vulnerable components include sensors, electronic control units, electrical systems, hydraulic systems, generators, gearboxes, rotor blades, and so on. As a result, significant emphasis is placed on improving the reliability, availability, and productivity of wind turbines. It is extremely important to detect and identify abnormalities as early as possible and predict potential failures and damages and the remaining useful life of components. One way to ensure turbine efficiency is to plan and implement preventive repairs. This work shows a semi-Markov model of a preventive maintenance system based on Enercon E82-2 wind turbines. The system’s performance quality is evaluated based on profit over time and an asymptotic availability coefficient. The developed model establishes formulas describing the efficiency functions and formulates the conditions for the existence of extremes (maxima) of these functions. Computational examples provided at the end of the paper illustrate the obtained research results. A preventive maintenance model is developed that can be applied to wind turbine hazard prevention (determining optimal times for wind turbine preventive maintenance).

On the Sensitivity of Wind Turbine Failure Rate Estimates to Failure Definitions

This study presents a wind turbine reliability analysis at the turbine and assembly level. It is concerned with the uncertainties associated with data-processing for wind turbine failure rate figures. These uncertainties are prominent in discussions of failure data in the literature. In particular, the influence of different failure definitions on failure rate estimates are investigated. The baseline estimate is 9.06 failures per turbine per year. This figure changes significantly when introducing a lower downtime limit, repair limit or limit on time between subsequent downtimes of the same turbine for a downtime event to be considered a failure. It changes significantly depending on which maintenance actions are categorised as corrective and by what data points represent an intervention. From the one dataset analysed here, results show derived failure rates ranging from below 1 failures per turbine per year to over 10 failures per turbine per year using failure definitions which have previously been used in the literature. When restricting failures to those that can be attributed to a particular assembly, the failure rate estimate reduces to 7.47 failures per turbine per year. Assemblies that fail the most are the frequency converter (at around 1 failures per turbine per year) and the cooling system (at around 0.9 failure per turbine per year). The gearbox, blades, yaw system and hydraulic group were the next most frequently failing assemblies.

Yaw system restart strategy optimization of wind turbines in mountain wind farms based on operational data mining and multi-objective optimization

The wind resources of mountain wind farms are affected by more complex terrain than are found at flat wind farms. Wind turbine (WT) failures caused by frequent operation of the yaw system occur often in mountain wind farms, resulting in significantly reduced economic benefits over the WT life cycle. Considering the problem of frequent yaw motions of WTs in mountain wind farms, this study introduces multi-objective optimization theory into the yaw system restart strategy optimization of WTs for the first time and proposes an optimized yaw system restart strategy with wind speed segmentation in the wind speed region below the rated wind speed. The yaw behavior of mountain wind farms in southern China is analyzed from multiple perspectives; a segmentation scheme of wind speed intervals for yaw control was determined from the results. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the control parameters for each sub-interval with the goals of minimizing yaw operation time and maximizing energy generation. A set of Pareto solutions is obtained and evaluated with the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) method to produce the optimal solution. The simulation results indicated that after the WT adopts the optimized yaw system restart strategy in the wind speed region below the rated wind speed, the number of yaw motions is decreased by 82.9%, with only a slight decrease in energy generation. The optimization method proposed in this study can significantly reduce yaw operation time with minimal loss of energy generation, which provides a new approach to balancing the relationship between reducing the WT failure rate and ensuring power generation efficiency and is expected to significantly improve the economic benefits to wind farms.

A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines

With the rapid development and increasing energy production capacity of high-power wind turbines, a corresponding increase in maintenance requirements has been observed. Reducing the failure rate of wind turbines is a critical objective, alongside decreasing affiliated operation and maintenance costs. This review focuses on the status monitoring, fault diagnosis, fault prediction, and status evaluation of wind turbines. The early fault diagnosis of wind turbines is explored with regard to existing condition monitoring technology. Moreover, the current mathematics-based fault diagnosis and smart fault diagnosis technologies are further explored. Through comprehensive investigation, this paper summarizes the research status of wind turbine fault prediction and complete machine status evaluation, conclusively presenting relevant research points and trends in the fault diagnosis, fault prediction, and status assessment of high-power wind turbines.

Failure rate and downtime survey of wind turbines located in Spain

Publicly available wind turbine failure rate and downtime studies are scarce in the scientific literature. Recent surveys in the field reveal that only 20 studies of wind farms based in Europe, Asia and the U.S. have been published, but there are no studies analysing wind farms located in Spain. With Spain holding the second place in terms of installed capacity in Europe, and the fifth worldwide, the importance of the Spanish wind power sector is evident. The present study comprehensively analyses the Spanish WT fleet in terms of yearly installed capacity and number of machines installed per technology and capacity ranges. The current fleet age ranges are also analysed. Under this scenario, the present work aims to contribute to the literature with a new failure rate and downtime study, where 75 WTs from six wind farms located in Spain are analysed over a period of 11 years. The most critical assemblies are identified per WT technology type and in general for the whole data set. Finally, the failure rate and downtime results obtained for the Spanish survey are compared against a selection of similar previously published studies.

Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events

The cybersecurity of wind farms is an increasing concern in recent years, and its impacts on the power system reliability have not been fully studied. In this paper, the pressing issues of wind farms, including cybersecurity and wind power ramping events (WPRs) are incorporated into a new reliability evaluation approach. Cyber–physical failures like the instantaneous failure and longtime fatigue of wind turbines are considered in the reliability evaluation. The tripping attack is modeled in a bilevel optimal power flow model which aims to maximize the load shedding on the system’s vulnerable moment. The time-varying failure rate of wind turbine is approximated by Weibull distribution which incorporates the service time and remaining life of wind turbine. Various system defense capacities and penetration rates of wind power are simulated on the typical reliability test system. The comparative and sensitive analyses show that power system reliability is challenged by the cybersecurity of wind farms, especially when the installed capacity of wind power continues to rise. The timely patching of network vulnerabilities and the life management of wind turbines are important measures to ensure the cyber–physical security of wind farms.

Impact of Correlation Between Wind Speed and Turbine Availability on Wind Farm Reliability

This paper proposes a new method to evaluate the reliability of a wind farm considering the correlation between wind turbine reliability and wind speed. With increasing integration of wind generation into the grid, the reliability and stability of the grid are increasingly impacted. Although there are obvious benefits from wind generation, the stochastic nature of wind speed causes several operational challenges. Recent research has shown that wind speeds also impact the failure rates of the turbines, thereby compounding the effect of wind speed variation. The objective of this paper is to evaluate the impact of wind speed on the reliability of a wind farm considering the correlation between wind speed and wind turbine failure rate. The method proposed in this paper is implemented using discrete convolution. The effectiveness of the proposed method is proved by comparing reliability indexes of the modified IEEE RTS-79 system with and without considering the impacts of the negative correlation between wind turbine reliability and wind speed.

A lifecycle financial analysis model for offshore wind farms

Simulation and modelling allow a range of offshore wind farm stakeholders to test and improve a project's viability in a cost-effective and safe manner. This paper presents a model developed to conduct detailed financial analysis of an offshore wind farm. It extends the current state of the art by employing stochastic time-series simulation modules performing in-depth analysis of the technologies, strategies and procedures applied during the installation, operation and maintenance, and decommissioning phases of a wind farm lifecycle. The model was designed for versatility and can consider both fixed and floating technologies, a wide variety of strategies, and any site specified by the user. Results include energy production, costs and the duration of activities at each stage. These populate financial spreadsheets, which calculate key performance indicators including the Levelised Cost of Energy. The model has been successfully validated against real-life case-studies where possible; published data; and uses sensitivity analysis to ensure the model is working as expected. Through a case-study, the paper demonstrates how 1) the model enables the identification of key cost and time drivers, facilitating scenario optimisation; 2) the stochastic nature of the model considers the impact of uncertain variables on results such as weather conditions and wind turbine failure rates; 3) the model can be used to assess different business models and financing structures. This comprehensive range of abilities means that the model is suited to a variety of end-users and meets the demands of a growing industry, striving to achieve further cost-reductions across a range of site conditions, technologies and markets.

A Sequential MCMC Model for Reliability Evaluation of Offshore Wind Farms Considering Severe Weather Conditions

The offshore wind farms (OWF) are susceptible to the severe weather, which can cause the increase of component failure rate and has significant influence on the maintenance process and the reliability of wind farms. This paper proposes a sequential Markov chain Monte Carlo (MCMC) model for reliability evaluation of the OWF considering the impact of severe offshore weather. First, main factors affecting the wind turbine (WT) failure rate are analyzed. Second, a time-varying analytical model for the WT failure rate affected by wind speed and lightning is established. Three types of WT failure rates are considered: the failure rate under normal weather, that under strong wind, and that affected by lightning. Moreover, a time-varying analytical model for the repair time of main components of OWF is established by considering the influence of severe weather on offshore transportation time and maintenance efficiency after component failure. The MCMC model takes into account the temporal correlation of the weather and the repair process of failed component in the reliability evaluation. The model enables simultaneous simulation of the weather intensity and component state. For each system state generated by the MCMC model, a breadth-first search (BFS) method is applied to analyze the connectivity of the WTs and the sink node. Finally, the output of the wind farm is determined based on the wind speed data at this state. The expected energy not supply (EENS) and the generation ratio availability (GRA) indices of the OWF are evaluated to demonstrate the effectiveness of the proposed models. Further, the effects of other factors such as the enhanced protection for WT, the use of helicopter, and the weather characteristics of the OWF location on the reliability of OWF are discussed.

Analysis of Optimized Wind Turbine Failure Using Computational Fluid Dynamics

The main objective of this work is to analyze the causes of failure in wind turbine and eliminate the highest contributing root cause problem, thereby reducing the failure rate of wind turbine. As we know, Wind Energy is a renewable source of energy, so there will be a constant generation of wind energy throughout the year. In the next 50 to 100 years the fossil fuels may get exhausted. So, we may not get coal continuously for thermal power generation. Hence it is essential to cease the dependence on conventional energy sources and switch over to non-conventional energy sources like wind energy. Wind possesses energy by virtue of its motion. Any device that is capable of slowing down the mass of moving air, like a sail or propeller, can extract a part of the energy and convert it into useful work. The factors which determine the output, getting from a wind energy converter includes; wind speed, cross section of the windswept by the rotor, overall conversion efficiency of the rotor, transmission system and generator or pump. In this paper, complete analysis is performed on the existing wind turbine blade for better performance. A Reference blade of 10m length approximately with a rated output of 100KW was taken for study.

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

AbstractDetermining 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.

Wind Turbine Failure Rate Calculation Method Considering Multi-Factor Influences

In the traditional outage model, the failure rate of the wind turbine is often given as a long-term statistics value on average, that is only one value for a wind turbine, this calculation method is not reflect the real reliability level of wind turbines. According to historical failure data, the factors that affect the wind turbine outage are divided into several categories. Based on Markov Process, the calculation methods for wind turbine failure rate under single factor and multi-factor are given respectively. On this basis, the calculation methods for wind turbine failure rate under different levels of single factor and multi-factor are given respectively based on statistical analysis method. Because different factors are taken into account in the failure rate calculation, the failure rate will change according to the actual factors and then it could reflect the real risk level of power systems when uses this failure rate. Simulation calculation is carried out on a real wind farm; the results show the effectiveness of this proposed method.

Reliability Assessment of Power Generation System With Wind Farm

The reliability of power generation system with wind farm is evaluated based on Monte-Carlosimulation, and in the reliability assessment the randomness of wind speed and power load, failure rate ofwind turbine generator(WTG) and conventional generator are take into account. Monte-Carlo simulationused in power generation system with wind power need large sample size and has low efficiency. So thispaper proposes an improved Monte Carlo method (IMC) based on the combination of Latin HypercubeSampling and Cholesky decomposition, this IMC method is effective to improve sample values coverage ofrandom variables input spaces and the sampling efficiency. The calculation and analysis of improvedIEEE-RTS 79 reliability test systems show that the proposed Assessment algorithm is effective.

Study of weather and location effects on wind turbine failure rates

ABSTRACTUnderstanding 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.

Correlation analysis for wind speed and failure rate of wind turbines using time series approach

The correlation between wind speed and failure rate (FR) of wind turbines is analyzed with time series approach. The time series of power index (PI) and FR of wind turbines are established based on historical data, which are pretreated by singularity processing, stationarity processing, and wavelet de-noising. The trend variations of the time series are analyzed from both time domain and frequency domain by extracting the indicator functions, including auto-correlation function, cross-correlation function, and spectral density function. A case study is given out to verify the validity of the model and the method, which is based on the wind speed and failure data from January 1995 to December of 2002 in Nordjylland, Denmark. Auto-correlation function and spectral density function show that time series of PI and FR have strong seasonal characteristics and quite similar periodicity, while the cross-correlation function shows they keep high consistency and strong correlation. The results indicate that by calculating and monitoring PI, the failure rule of wind turbines can be forecast, which provides theoretical basis for preventive maintenance of wind turbines.

Simulation of wind farm operations and maintenance using discrete event system specification

Wind farms provide a source of clean and renewable energy. However, unlike many industries where machines are operated under more or less static conditions, wind turbines suffer from stochastic loading due to the hourly or seasonal variation of wind speed and direction. The stochastic loading of wind turbines makes their degradation or failure prediction rather complex. This in turn makes the decision-making process of when and what type of maintenance action to undertake very challenging. This paper uses the discrete event system specification (DEVS) to develop a simulation model for wind farm operations and maintenance. The DEVS methodology1 provides a formal modeling and simulation framework based on dynamical systems theory and allows for hierarchical and modular model construction. We report on implementation results based on historical data that provide useful insights into wind farm operations under two different maintenance strategies, scheduled maintenance and condition-based maintenance. The results show that condition-based maintenance enables more wind power generation by reducing wind turbine failure rates and thus increasing wind turbine available.

Simulation of wind farm operations and maintenance using discrete event system specification

Wind farms provide a source of clean and renewable energy. However, unlike many industries where machines are operated under more or less static conditions, wind turbines suffer from stochastic loading due to the hourly or seasonal variation of wind speed and direction. The stochastic loading of wind turbines makes their degradation or failure prediction rather complex. This in turn makes the decision-making process of when and what type of maintenance action to undertake very challenging. This paper uses the discrete event system specification (DEVS) to develop a simulation model for wind farm operations and maintenance. The DEVS methodology1 provides a formal modeling and simulation framework based on dynamical systems theory and allows for hierarchical and modular model construction. We report on implementation results based on historical data that provide useful insights into wind farm operations under two different maintenance strategies, scheduled maintenance and condition-based maintenance. The results show that condition-based maintenance enables more wind power generation by reducing wind turbine failure rates and thus increasing wind turbine available.