An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades

Besnard, François; Bertling, Lina

doi:10.1109/tste.2010.2049452

Cited by 199 publications

(116 citation statements)

References 14 publications

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“…Data from operational wind farms are used to populate the model in order to establish an optimal maintenance policy. Similar studies can be found in [6,7,8] which deal with different aspects of the problem such as specific component parts or the effect of seasonal weather patterns.…”

Section: Previous Work and Literature Reviewsupporting

confidence: 58%

Towards Reliability Centred Maintenance of Wind Turbines

McMillan

Ault

2014

Reliability Modeling and Analysis of Smart Power Systems

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Section: Previous Work and Literature Reviewsupporting

confidence: 58%

Towards Reliability Centred Maintenance of Wind Turbines

McMillan

Ault

2014

Reliability Modeling and Analysis of Smart Power Systems

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“…This method has been applied to 20-kV XLPE service aged cables [22], or to wind power plants [23,24]. The failure rate is also an input parameter in riskbased maintenance optimization [25] or to demonstrate the effect of imperfect preventive maintenance [26]. In addition, the failure rate is a crucial parameter in power system reliability assessment to calculate component availabilities, reliability block diagrams, Markov models, and interruption indices [27,28].…”

Section: Failure Rate Modelingmentioning

confidence: 99%

Individual failure rates for transformers within a population based on diagnostic measures

Jürgensen

Nordström

Hilber

2016

Electric Power Systems Research

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This is the published version of a paper published in Electric power systems research. Citation for the original published paper (version of record):Jürgensen, J H., Nordström, L., Hilber, P. (2016) Individual failure rates for transformers within a population based on diagnostic measures. The high monetary value of a transformer has placed the transformer life-time optimization into the focus of asset management. The average failure rate has created reasonable results within reliability modeling, however, it cannot reflect the probability of failure for an individual transformer. In this paper, a method is introduced to calculate individual failure rates for a transformer population based on failure statistics and diagnostic measurements such as dissolved gas, and 2-furfuraldehyde analysis. The method is applicable to all types of components and the comprehensibility makes it effective for practical implementation. The results are evaluated against two health indices based on a weight factor and fuzzy logic. It can be observed that the presented individual failure rates are plausible representatives of the transformer's probability of failure. Therefore, the results can also be utilized for asset management decision-making.

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“…With the higher requirements of the economy and safety of the power grid, online condition-based maintenance (CBM) of power transformers without power outages is an inevitable trend for equipment maintenance mode [1,2]. The transformer CBM analysis relies on two basic groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives).…”

Section: Introductionmentioning

confidence: 99%

Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

et al. 2017

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This paper documents the condition-based maintenance (CBM) of power transformers, the analysis of which relies on two basic data groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives) which accounts for 80% of data required. However, unstructured data comprised of malfunction inspection reports, as recorded by operation and maintenance of the power grid, constitutes an abundant untapped source of power insights. This paper proposes a method for malfunction inspection report processing by deep learning, which combines the text data mining-oriented recurrent neural networks (RNN) with long short-term memory (LSTM). In this paper, the effectiveness of the RNN-LSTM network for modeling inspection data is established with a straightforward training strategy in which we replicate targets at each sequence step. Then, the corresponding fault labels are given in datasets, in order to calculate the accuracy of fault classification by comparison with the original data labels and output samples. Experimental results can reflect how key parameters may be selected in the configuration of the key variables to achieve optimal results. The accuracy of the fault recognition demonstrates that the method we proposed can provide a more effective way for grid inspection personnel to deal with unstructured data.

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An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades

Cited by 199 publications

References 14 publications

Towards Reliability Centred Maintenance of Wind Turbines

Towards Reliability Centred Maintenance of Wind Turbines

Individual failure rates for transformers within a population based on diagnostic measures

Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

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