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RISS 인기검색어
- 검색키워드
- 저자 : E. Tedeschi (검색결과 2 건)
- 무료
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Unbalanced Load Compensation by Power-Based Control in the Synchronous Reference Frame
F. Gothner,D. I. Brandao,E. Tedeschi 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5
Power-based control has proven to be an effective and robust way for having a centralized control scheme in a microgrid. The scheme has a minimal communication requirement, which enables the distributed energy resources (DERs) in the system to share the load proportionally according to their capability. This paper proposes a modification of the conventional power-based control, which is applicable to the synchronous reference frame. The proposed scheme enables unbalanced load compensation, while reducing the necessary communication between the master controller and the local inverter controllers. Finally, the scheme also proposes weighting factors for reducing the voltage unbalance factor of the grid. The proposed scheme is valid for three-phase three-wire systems. The effectiveness of the scheme is shown through numerical simulations.
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Evaluation of a Nutrition Model in Predicting Performance of Vietnamese Cattle
Parsons, David,Van, Nguyen Huu,Malau-Aduli, Aduli E.O.,Ba, Nguyen Xuan,Phung, Le Dinh,Lane, Peter A.,Ngoan, Le Duc,Tedeschi, Luis O. Asian Australasian Association of Animal Productio 2012 Animal Bioscience Vol.25 No.9
The objective of this study was to evaluate the predictions of dry matter intake (DMI) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ${\pm}33.2$ kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination ($r^2$), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision ($r^2$ of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under-or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.
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