7 research outputs found

    LOSS ALLOCATION OF TRANSMISSION LINE AND MINIMIZATION OF LOSS FOR 5 BUS,14 BUS &30 BUS SYSTEMS

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    The paper focuses on the issue of transmission loss allocation and transmission loss minimization by incorporating UPFC injection model using load flow analysis. To investigate the effect of the UPFC on the steady state condition of the system and load flow, different models can be used. These models are usually based on modification of traditional load flow methods. In this project, a mathematical model for UPFC referred as UPFC injection model is used. Since accurate power tracing is very difficult, allocation of losses for a particular transaction (in power business it is buying and selling system) may not be effectively realized. However loss allocation is an important aspect in determining the cost of transmission. Thus a methodology to find the losses accurately is vital. It is imperative to make sure that all users of the transmission network are charged proportionate to their usage and this aspect is all the more important because of the common infrastructure they use. The Z-bus loss allocation method is used to achieve the required objective. This method will promote more efficient network operations when implemented in deregulated electric industries. The Unified Power Flow Controller (UPFC) injection model is incorporated in Load Flow Model by the method of Newton Raphson Algorithm to study its effects for power flow control and losses minimization in the power system. In this project optimal placement of UPFC is conducted based on active power loss Sensitivity factors. Based on these sensitivity factors the UPFC is optimally placed in the required transmission line to investigate the impact of UPFC in the system. The changes in the system are studied to see the impact of the UPFC. The impact of UPFC are analyzed by using 5-Bus, IEE

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    LOSS ALLOCATION OF TRANSMISSION LINE AND MINIMIZATION OF LOSS FOR 5 BUS,14 BUS &30 BUS SYSTEMS

    No full text
    The paper focuses on the issue of transmission loss allocation and transmission loss minimization by incorporating UPFC injection model using load flow analysis. To investigate the effect of the UPFC on the steady state condition of the system and load flow, different models can be used. These models are usually based on modification of traditional load flow methods. In this project, a mathematical model for UPFC referred as UPFC injection model is used. Since accurate power tracing is very difficult, allocation of losses for a particular transaction (in power business it is buying and selling system) may not be effectively realized. However loss allocation is an important aspect in determining the cost of transmission. Thus a methodology to find the losses accurately is vital. It is imperative to make sure that all users of the transmission network are charged proportionate to their usage and this aspect is all the more important because of the common infrastructure they use. The Z-bus loss allocation method is used to achieve the required objective. This method will promote more efficient network operations when implemented in deregulated electric industries. The Unified Power Flow Controller (UPFC) injection model is incorporated in Load Flow Model by the method of Newton Raphson Algorithm to study its effects for power flow control and losses minimization in the power system. In this project optimal placement of UPFC is conducted based on active power loss Sensitivity factors. Based on these sensitivity factors the UPFC is optimally placed in the required transmission line to investigate the impact of UPFC in the system. The changes in the system are studied to see the impact of the UPFC. The impact of UPFC are analyzed by using 5-Bus, IEEE.</jats:p

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease
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