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Operating Regions (operating + regions)
Selected AbstractsOutput power leveling of wind turbine generators using pitch angle control for all operating regions in wind farmELECTRICAL ENGINEERING IN JAPAN, Issue 4 2007Tomonobu Senjyu Abstract Effective utilization of renewable energies such as wind energy instead of fossil fuels is desirable. Wind energy is not constant and windmill output is proportional to the cube of the wind speed, which causes the generated power of wind turbine generators (WTGs) to fluctuate. In order to reduce the output power fluctuation of wind farms, this paper presents an output power leveling control strategy for a wind farm based on both the average wind farm output power and the standard deviation of the wind farm output power, a cooperative control strategy for WTGs, and pitch angle control using a generalized predictive controller (GPC) in all WTG operating regions. Simulation results using an actual detailed model for wind farm systems show the effectiveness of the proposed method. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 158(4): 31, 41, 2007; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/eej.20448 [source] Nonstationary fault detection and diagnosis for multimode processesAICHE JOURNAL, Issue 1 2010Jialin Liu Abstract Fault isolation based on data-driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T2 and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Methane steam reforming at microscales: Operation strategies for variable power output at millisecond contact timesAICHE JOURNAL, Issue 1 2009Georgios D. Stefanidis Abstract The potential of methane steam reforming at microscale is theoretically explored. To this end, a multifunctional catalytic plate microreactor, comprising of a propane combustion channel and a methane steam reforming channel, separated by a solid wall, is simulated with a pseudo 2-D (two-dimensional) reactor model. Newly developed lumped kinetic rate expressions for both processes, obtained from a posteriori reduction of detailed microkinetic models, are used. It is shown that the steam reforming at millisecond contact times is feasible at microscale, and in agreement with a recent experimental report. Furthermore, the attainable operating regions delimited from the materials stability limit, the breakthrough limit, and the maximum power output limit are mapped out. A simple operation strategy is presented for obtaining variable power output along the breakthrough line (a nearly iso-flow rate ratio line), while ensuring good overlap of reaction zones, and provide guidelines for reactor sizing. Finally, it is shown that the choice of the wall material depends on the targeted operating regime. Low-conductivity materials increase the methane conversion and power output at the expense of higher wall temperatures and steeper temperature gradients along the wall. For operation close to the breakthrough limit, intermediate conductivity materials, such as stainless steel, offer a good compromise between methane conversion and wall temperature. Even without recuperative heat exchange, the thermal efficiency of the multifunctional device and the reformer approaches ,65% and ,85%, respectively. © 2008 American Institute of Chemical Engineers AIChE J, 2009 [source] Local dynamic partial least squares approaches for the modelling of batch processesTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2008N. M. Fletcher Abstract The application of multivariate statistical projection based techniques has been recognized as one approach to contributing to an increased understanding of process behaviour. The key methodologies have included multi-way principal component analysis (PCA), multi-way partial least squares (PLS) and batch observation level analysis. Batch processes typically exhibit nonlinear, time variant behaviour and these characteristics challenge the aforementioned techniques. To address these challenges, dynamic PLS has been proposed to capture the process dynamics. Likewise approaches to removing the process nonlinearities have included the removal of the mean trajectory and the application of nonlinear PLS. An alternative approach is described whereby the batch trajectories are sub-divided into operating regions with a linear/linear dynamic model being fitted to each region. These individual models are spliced together to provide an overall nonlinear global model. Such a structure provides the potential for an alternative approach to batch process performance monitoring. In the paper a number of techniques are considered for developing the local model, including multi-way PLS and dynamic multi-way PLS. Utilising the most promising set of results from a simulation study of a batch process, the local model comprising individual linear dynamic PLS models was benchmarked against global nonlinear dynamic PLS using data from an industrial batch fermentation process. In conclusion the results for the local operating region techniques were comparable to the global model in terms of the residual sum of squares but for the global model structure was evident in the residuals. Consequently, the local modelling approach is statistically more robust. L'application de techniques basées sur la projection statistique multivariée est reconnue comme étant une approche qui contribue à une meilleure compréhension du comportement des procédés. Les méthodologies clés incluent l'analyse des composantes principales (PCA) à plusieurs critères de classification, les moindres carrés partiels (PLS) à plusieurs critères de classification et l'analyse des niveaux d'observation discontinus. Les procédés discontinus présentent typiquement un comportement non linéaire et variable dans le temps et ces caractéristiques mettent au défi les techniques mentionnées ci-dessus. Devant ces défis, la méthode PLS dynamique est proposée pour saisir la dynamique des procédés. Des approches semblables pour supprimer la non linéarité des procédés incluent le retrait de la trajectoire principale et l'application des PLS non linéaires. On décrit une autre approche où les trajectoires discontinues sont subdivisées en régions opératoires avec un modèle dynamique linéaire/linéaire adapté à chaque région. Ces modèles individuels sont raccordés pour obtenir un modèle non linéaire global. Une telle structure présente un potentiel pour une approche différente du suivi des performances des procédés discontinus. Dans cet article, plusieurs techniques sont considérées pour la mise au point du modèle local, incluant les PLS à plusieurs critères de classification et les PLS à plusieurs critères de classification dynamique. En utilisant la série de résultats les plus prometteurs d'une étude de simulation d'un procédé discontinu, le modèle local comprenant les modèles de PLS dynamiques linéaires individuels a été comparé à la méthode de PLS non linéaires dynamique globale utilisant des données d'un procédé de fermentation discontinu industriel. En conclusion, les résultats pour les techniques des régions opératoires locales sont comparables au modèle global en termes de somme des carrés des résidus mais pour le modèle global, la présence d'une structure dans les résidus est évidente. En conséquence, l'approche de modélisation locale est statistiquement plus robuste. 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