Reliability Analysis Of Wind Turbines Research Articles

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.

Fault Tree Analysis for Reliability Analysis of Wind Turbines Considering the Imperfect Repair Effect

Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability.

Probabilistic modeling for long-term fatigue reliability of wind turbines based on Markov model and subset simulation

The success of wind energy, superior to the traditional energy source in sustainability and affordability, is dependent on the reliability of wind turbines. However, to assess the fatigue reliability of wind turbines for long-term operation, massive computational costs are required due to the dynamic response simulations under different environmental scenarios. To reduce the computational cost and to increase the feasibility of long-term fatigue evaluation, a novel probabilistic modeling framework is proposed for long-term fatigue reliability analysis of wind turbines. The proposed framework considers each short-term numerical simulation as a single fatigue event. Thereafter, the long-term sequence of fatigue loading events is determined by the emissions from Markov model which is constrained by environmental conditions. Finally, the statistical information for the sequence of fatigue loading events is obtained so that the counting cycles at each fatigue loading level can be considered a random variable. Therefore, fatigue reliability can be evaluated based on the subset simulation incorporated with the uncertainty of SN curve. The results show that the failure probability can significantly increase as the service lifetime increases without inspections and maintenance. The proposed model, innovatively leveraging the Markov model and subset simulation, is an effective and promising tool for long-term fatigue reliability analysis of wind turbines, as well as any other structures.

Failure modes analysis and reliability enhancement of wind turbines

AbstractThe transition of the global energy system to affordable, reliable and clean sources is an important target of several international climate change accords and conferences such as the United Nations Framework Convention on Climate Change (UNFCCC) and its Conferences of parties (COP), Kyoto protocol and the UN Sustainable Development Goals (SDGs). The wind power is at the heart of these energy systems transformation and climate change mitigation. Indeed, the global capacity of wind turbines has seen tremendous growth, especially, during the past two decades which has made wind energy the fastest growing renewable source in the world. Nevertheless, the problem of premature failures and low‐reliability level of wind turbines have been increasingly noticed. This paper presents a new systematic analytical approach for failure modes and reliability analysis of wind turbines. The approach is a combination of three aspects which are qualitative and quantitative analysis using the fault tree method, reliability assessment as well as lifetime estimation and enhancement. Firstly, the various failure modes are presented. Then, mathematical modeling and reliability evaluation have been developed and simulated. Afterward, the actions that can be considered to improve the reliability and, consequently, the lifetime of wind turbines have been presented and analyzed. The results of the developed approach revealed that major reliability improvements, failure rate reduction and lifetime extension can be achieved, leading to a better return on investment, reduced maintenance as well as a safer and more reliable clean energy production.

Time-dependent reliability analysis of wind turbines considering load-sharing using fault tree analysis and Markov chains

Due to the high failure rates and the high cost of operation and maintenance of wind turbines, not only manufacturers but also service providers try many ways to improve the reliability of some critical components and subsystems. In reality, redundancy design is commonly used to improve the reliability of critical components and subsystems. The load dependencies and failure dependencies among redundancy components and subsystems are crucial to the reliability assessment of wind turbines. However, the redundancy components are treated as a parallel system, and the load correlations among them are ignored in much literature, which may lead to the wrong system’s reliability and much higher costs. For this reason, this article explores the influences of load-sharing on system reliability. The whole system’s reliability is quantitatively evaluated using fault tree analysis and the Markov-chain method. Following this, the optimisation of the redundancy allocation problem considering the load-sharing is conducted to maximise the system reliability and reduce the total cost of the system subjecting to the available system cost and space. The results produced by this methodology can show a realistic reliability assessment of the entire wind turbine from a quantitative point of view. The realistic reliability assessment can help to design a cost-effective and more reliable system and significantly reduce the cost of wind turbines.

Structural Reliability Analysis of Wind Turbines: A Review

The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first- and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.

Reliability Analysis of Wind Turbines Considering Constraints and Legislation

Reliability Analysis of Wind Turbines Considering Constraints and Legislation

Reliability analysis of wind turbines under non‐Gaussian wind load

SummaryBased on translation models, both Gaussian and non‐Gaussian wind fields are generated using the harmony superposition method for examining the reliability of a typical wind turbine at operational and parked conditions. Using the blade aerodynamic model and multibody dynamics, wind turbine responses are calculated and then probability characteristics are analyzed in details. The short‐term extreme response distribution is estimated by the average conditional exceedance rate method at each mean wind speed bin, and the long‐term extreme response distribution is then determined by further integrating the short‐term extreme response distribution conditional on wind speed with the distribution of mean wind speed. Additionally, crack initiation life and crack propagation life are evaluated using the linear cumulative damage theory and linear crack propagation theory, respectively. The results indicate that non‐Gaussian characteristics of wind inflows have a noticeably greater influence on both extreme response and fatigue damage, and the Gaussian assumption cannot suit wind turbine in complex terrain.

Risk-based planning of O&M for wind turbines using physics of failure models

Wind turbines are in some countries contributing significantly the production of electricity. For offshore wind turbines reliability is a key issue since costs to operation and maintenance may be significant contributors to the Levelized Cost Of Energy and OM costs are highly dependent on the reliability of the components implying that it is important to focus on increasing the reliability as much as is economically reasonable. This paper describes aspects for reliability analysis of wind turbines with special focus on structural components, especially the wind turbine blades. In many wind turbine components deterioration processes such as fatigue, wear and corrosion may result in failures, for example in welded details, blades, bearings and gearboxes. In many cases it may be possible to detect the damages before actual failure, and thereby perform preventive maintenance instead of corrective, expensive repair/ maintenance. This requires some type of condition monitoring to give information on the condition of the components. It can either be online monitoring or manual inspections. The use of preventive maintenance can possibly reduce the costs, as repairs can be cheaper to perform before actual failure, and because the downtime will be shorter compared to corrective maintenance. On the other hand, preventive maintenance leads to more repairs in total, and optimally the maintenance effort should be adjusted to minimize the total expected costs. In this paper it is described how risk-based methods can be used to optimally plan operation & maintenance using Bayesian decision theory adapted to offshore wind. An illustrative example is presented considering wind turbine blades and using the reference wind farm in the NORCOWE research project.

Fatigue Reliability of Wind Turbine Fleets: The Effect of Uncertainty on Projected Costs

The cost of repairing or replacing failed components depends on the number and timing of failures. Although the total probability of individual component failure is sometimes interpreted as the percentage of components likely to fail, this perception is often far from correct. Different amounts of common versus independent uncertainty can cause different numbers of components to be at risk of failure. The FAROW tool for fatigue and reliability analysis of wind turbines makes it possible for the first time to conduct a detailed economic analysis of the effects of uncertainty on fleet costs. By dividing the uncertainty into common and independent parts, the percentage of components expected to fail in each year of operation is estimated. Costs are assigned to the failures and the yearly costs and present values are computed. If replacement cost is simply a constant multiple of the number of failures, the average, or expected cost is the same as would be calculated by multiplying by the probability of individual component failure. However, more complicated cost models require a breakdown of how many components are likely to fail. This breakdown enables the calculation of costs associated with various probability of occurrence levels, illustrating the variability in projected costs. Estimating how the numbers of components expected to fail evolves over time is also useful in calculating the present value of projected costs and in understanding the nature of the financial risk.