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

Anderson, F. M.; McMillan, David; Dawid, Rafael; Cava, David García

doi:10.1002/we.2806

Cited by 3 publications

(8 citation statements)

References 49 publications

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“…The current analysis is based on the same historical database as is used by Anderson et al 12,64 It consists of supervisory control and data acquisition (SCADA), weather and operational data describing the performance, meteorological conditions and maintenance actions performed for a large group of modern, geared WTs with a multi-MW power rating. The farm utilizes a typical condition-based maintenance strategy and is located around an hour-long trip from its operational base by CTV.…”

Section: Dataset Description and Identification Of Failuresmentioning

confidence: 99%

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

Anderson,

Dawid,

McMillan

et al. 2023

Wind Energy

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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.

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Section: Dataset Description and Identification Of Failuresmentioning

confidence: 99%

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

Anderson,

Dawid,

McMillan

et al. 2023

Wind Energy

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“…This analysis is based upon the same dataset as described previously by Anderson et al [18,19]. The dataset contains operational data provided by a large offshore wind farm, consisting of a fleet of geared wind turbines with a multi-MW power rating.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…We provide a detailed metadata table for the dataset in reference [19]. Those data-tables which are relevant for a reliability analysis are summarised in table 1, which presents the information derived and disadvantages of each data-table.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Third, a methodology could be employed to assess uncertainty both inherent in the data itself and in the calculated metrics. The current authors have employed Bayesian techniques to do so in the field of wind industry in previous studies [19,21]. Both of these studies could be improved by retroactively including the work procedure data upon which this study relies so heavily.…”

Section: Future Analysis Based On This Datasetmentioning

confidence: 99%

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

Anderson,

Dawid,

McMillan

et al. 2023

J. Phys.: Conf. Ser.

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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.

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“…This method studied the effect of the shift pattern of technicians, especially the night shift corrective maintenance strategy applied in the field throughout the winter. It was found that the time-based technology availability increased by approximately 0.64% per year in specific locations [11]. Congshan L et al proposed a coordinated control strategy based on frequency division.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Li,

Wang,

Qin

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

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

Cited by 3 publications

References 49 publications

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

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

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

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