Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Parameter identification of the fermentative production of fructo-oligosaccharides by Aureobasidium pullulans

In this study, a mathematical model for the production of Fructo-oligosaccharides (FOS) by Aureobasidium pullulans is developed. This model contains a relatively large set of unknown parameters, and the identification problem is analyzed using simulation data, as well as experimental data. Batch experiments were not sufficiently informative to uniquely estimate all the unknown parameters, thus, additional experiments have to be achieved in fed-batch mode to supplement the missing information. © 2015 IEEE.funded by the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office. The authors thank the financial support from the F.R.S.-FNRS, the Belgium National Fund for the Scientific Research (Research Project 24643.08). C. Nobre thanks the Fundação para a Ciência e Tecnologia for the strategic funding of UID/BIO/04469/2013 uni

Distribution of Arsenic Resistance Genes in Prokaryotes

Arsenic is a metalloid that occurs naturally in aquatic and terrestrial environments. The high toxicity of arsenic derivatives converts this element in a serious problem of public health worldwide. There is a global arsenic geocycle in which microbes play a relevant role. Ancient exposure to arsenic derivatives, both inorganic and organic, has represented a selective pressure for microbes to evolve or acquire diverse arsenic resistance genetic systems. In addition, arsenic compounds appear to have been used as a toxin in chemical warfare for a long time selecting for an extended range of arsenic resistance determinants. Arsenic resistance strategies rely mainly on membrane transport pathways that extrude the toxic compounds from the cell cytoplasm. The ars operons, first discovered in bacterial R-factors almost 50 years ago, are the most common microbial arsenic resistance systems. Numerous ars operons, with a variety of genes and different combinations of them, populate the prokaryotic genomes, including their accessory plasmids, transposons, and genomic islands. Besides these canonical, widespread ars gene clusters, which confer resistance to the inorganic forms of arsenic, additional genes have been discovered recently, which broadens the spectrum of arsenic tolerance by detoxifying organic arsenic derivatives often used as toxins. This review summarizes the presence, distribution, organization, and redundance of arsenic resistance genes in prokaryotes

Sensitivity analysis and reduction of a dynamic model of a bioproduction of fructo-oligosaccharides

Starting from a relatively detailed model of a bioprocess producing fructo-oligosaccharides, a set of experimental data collected in batch and fed-batch experiments is exploited to estimate the unknown model parameters. The original model includes the growth of the fungus Aureobasidium pullulans which produces the enzymes responsible for the hydrolysis and transfructosylation reactions, and as such contains 25 kinetic parameters and 16 pseudo-stoichiometric coefficients, which are not uniquely identifiable with the data at hand. The aim of this study is, therefore, to show how sensitivity analysis and quantitative indicators based on the Fisher information matrix can be used to reduce the detailed model to a practically identifiable model. Parametric sensitivity analysis can indeed be used to progressively simplify the model to a representation involving 15 kinetic parameters and 8 pseudo-stoichiometric coefficients. The reduced model provides satisfactory prediction and can be convincingly cross validated.The authors thank the financial support from the F.R.S.-FNRS, the Belgium National Fund for the Scientific Research (Research Project 24643.08). C. Nobre thanks the Fundação para a Ciência e Tecnologia for the strategic funding of UID/BIO/04469 /2013 unit.info:eu-repo/semantics/publishedVersio