Wind turbine reliability: A comprehensive review towards effective condition monitoring development

Artigao, Estefanía; Martín-Martínez, Sergio; Honrubia-Escribano, A.; Gómez‐Lázaro, Emilio

doi:10.1016/j.apenergy.2018.07.037

Cited by 175 publications

(113 citation statements)

References 76 publications

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“…In both cases, the time between failures is assumed to follow an exponential distribution, with the failure process being a homogeneous Poisson process (HPP) with a constant parameter, λ , representing the failure rate. This assumption has been tested and proven to be reasonable for the ReliaWind failure data and is widely accepted for WT reliability data studies …”

Section: Reliability Data Collection Methodsologymentioning

confidence: 99%

“…Based on the median values from Figure , subassemblies with the highest downtimes include Gearbox Generator Shafts and bearings Structure Subassemblies with lower downtimes in all surveys include Nacelle Sensor Hydraulics It is noted that there are similar types of box plots in Artigao et al compared with Figures and . However, the graphs in Artigao et al present normalised data in percentage, while Figures and present absolute failure rates and downtimes values, and thus can provide additional information such as the comparison of reliability data between different subassemblies. In addition, compared with Artigao et al there are more outliers in Figure , which can be explained by the use of absolute reliability data instead of normalised percentage reliability data.…”

Section: Wind Turbine Reliability Data Analysismentioning

confidence: 99%

“…The additional offshore databases allow us to do further analysis and comparison between the reliability of onshore and offshore WTs. The reliability analysis in this paper focuses not only on critical subassemblies identification and intersubassembly reliability comparison as in Pinar Pérez et al, Pfaffel et al, and Artigao et al but also on the failure rate and downtime variations and identification of possible sources of uncertainty. Moreover, the paper presents a new model for LCOE estimation using reliability data, and the surveyed reliability data are employed to evaluate the impacts of WT reliability on LCOE for different populations of WTs.…”

Section: Introductionmentioning

confidence: 98%

“…Reliability data of offshore WTs are available at different levels as a result from the competitive nature of the industry causing manufacturers and operators to be protective of their data and reluctant to share information about their wind farms and failures openly. Nevertheless, offshore WT reliability data are very useful for reliability, O&M analysis, and performance evaluation and published in many papers such as in the literatures . In a review of WT performance and reliability by Pfaffel et al 24 data sources are referenced, but only three of these include data from offshore WTs.…”

Section: Introductionmentioning

confidence: 99%

“…There are some studies in the literature that compare the general operational performance of onshore and offshore wind turbines; however, due to the scarcity of offshore reliability data, few studies are available that compare onshore and offshore reliability data. Recently, Artigao et al reviews 13 reliability data sources including two offshore, six onshore in Pinar Pérez et al and five other onshore published from 2011 to 2016. The subassembly reliability data from different sources are normalised and measured in percentage and thus are used to identify critical WT subassemblies for condition monitoring development.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Wind turbine reliability data review and impacts on levelised cost of energy

2019

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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.

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Section: Reliability Data Collection Methodsologymentioning

confidence: 99%

Section: Wind Turbine Reliability Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind turbine reliability data review and impacts on levelised cost of energy

2019

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An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

Huang

Shao

Xiahou

et al. 2023

Quality & Reliability Eng

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Modern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time-to-failure data. For a hierarchical system, different levels' reliability-related data from monitoring or observations can provide additional information to update the system's reliability. However, with the limitation of sensing and monitoring technologies and vague judgments from experts, the reliability data inevitably contain epistemic uncertainty. Properly fusing the imprecise data can still improve the accuracy of system reliability assessment. In this article, we propose a reliability assessment approach based on the evidential network (EN) model to update the hierarchical systems' reliability by aggregating the system-level imprecise knowledge. First, the evidence theory is applied to quantify the epistemic uncertainty associated with the imprecise system-level reliability data. Then, an EN model is created to realize the uncertainty propagation and fusion of imprecise data. Finally, at any particular time instant, the system reliability can be updated with the aid of imprecise data based on the proposed EN model. The effectiveness of the proposed method is validated by a real engineering case of a wind turbine system. Overall, the results shown from the case study support that the proposed method can update the reliability of hierarchical systems by using system-level imprecise data.

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Simplified automatic fault detection in wind turbine induction generators

et al. 2020

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This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.

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Wind turbine reliability: A comprehensive review towards effective condition monitoring development

Cited by 175 publications

References 76 publications

Wind turbine reliability data review and impacts on levelised cost of energy

Wind turbine reliability data review and impacts on levelised cost of energy

An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

Simplified automatic fault detection in wind turbine induction generators

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