Parameter-varying modelling and fault reconstruction for wind turbine systems

In this paper, parameter-varying technique is firstly addressed for modelling a 4.8 MW wind turbine system which is nonlinear in essence. It is worthy to point out that the proposed parameter-varying model is capable of describing a nonlinear real-time process by using real-time system parameter updating. Secondly, fault reconstruction approach is proposed to reconstruct system component fault and actuator fault by utilizing augmented adaptive observer technique with parameter-varying. Different from the offline tuning adaptive scheme, the proposed adaptive observer includes adaptive tuning ability to online adjust the observer based on varying parameter. The effectiveness of the proposed parameter-varying modelling and fault reconstruction methods is demonstrated by using a widely-recognized 4.8 MW wind turbine benchmark system

Phenotypic and Genetic Correlations of Feed Efficiency Traits with Growth and Carcass Traits in Nellore Cattle Selected for Postweaning Weight

<div><p>This study evaluated phenotypic (r_ph) and genetic correlations (r_g) between 8 feed efficiency traits and other traits of economic interest including weight at selection (WS), loin-eye area (LEA), backfat thickness (BF), and rump fat thickness (RF) in Nellore cattle. Feed efficiency traits were gain:feed, residual feed intake (RFI), residual feed intake adjusted for backfat thickness (RFI_b) and for backfat and rump fat thickness (RFI_sf), residual body weight gain (RG), residual intake and body weight gain (RIG), and residual intake and body weight gain using RFI_b (RIG_b) and RFI_sf (RIG_sf). The variance components were estimated by the restricted maximum likelihood method using a two-trait animal model. The heritability estimates (h²) were 0.14, 0.24, 0.20, 0.22, 0.19, 0.15, 0.11 and 0.11 for gain:feed, RFI, RFI_b, RFI_sf, RG, RIG, RIG_b and RIG_sf, respectively. All r_ph values between traits were close to zero, except for the correlation of feed efficiency traits with dry matter intake and average daily gain. High r_g values were observed for the correlation of dry matter intake, average daily gain and metabolic weight with WS and hip height (>0.61) and low to medium values (0.15 to 0.48) with the carcass traits (LEA, BF, RF). Among the feed efficiency traits, RG showed the highest r_g with WS and hip height (0.34 and 0.25) and the lowest r_g with subcutaneous fat thickness (-0.17 to 0.18). The r_g values of RFI, RFI_b and RFI_sf with WS (0.17, 0.23 and 0.22), BF (0.37, 0.33 and 0.33) and RF (0.30, 0.31 and 0.32) were unfavorable. The r_g values of gain:feed, RIG, RIG_b and RIG_sf with WS were low and favorable (0.07 to 0.22), while medium and unfavorable (-0.22 to -0.45) correlations were observed with fat thickness. The inclusion of subcutaneous fat thickness in the models used to calculate RFI did not reduce the r_g between these traits. Selecting animals for higher feed efficiency will result in little or no genetic change in growth and will decrease subcutaneous fat thickness in the carcass.</p></div

Phenotypic and genetic correlations of dry matter intake, average daily gain, weight at selection, hip height, chest circumference, loin-eye area, backfat thickness and rump fat thickness with the feed efficiency traits.

<p>Phenotypic and genetic correlations of dry matter intake, average daily gain, weight at selection, hip height, chest circumference, loin-eye area, backfat thickness and rump fat thickness with the feed efficiency traits.</p

Heritability (diagonal), phenotypic correlation (below the diagonal) and genetic correlation (above the diagonal) between dry matter intake, average daily gain, metabolic body weight, growth and carcass traits.

<p>Heritability (diagonal), phenotypic correlation (below the diagonal) and genetic correlation (above the diagonal) between dry matter intake, average daily gain, metabolic body weight, growth and carcass traits.</p

Variance components and heritability estimates for the feed efficiency traits.

<p>Variance components and heritability estimates for the feed efficiency traits.</p

Genomic regions associated with residual feed intake (RFI) in Nellore cattle, percentage of additive genetic variance and candidate genes.

<p>Genomic regions associated with residual feed intake (RFI) in Nellore cattle, percentage of additive genetic variance and candidate genes.</p

Genomics regions associated with dry matter intake (DMI) in Nellore cattle, percentage of additive genetic variance and candidate genes.

<p>Genomics regions associated with dry matter intake (DMI) in Nellore cattle, percentage of additive genetic variance and candidate genes.</p

Genomics regions associated with average daily gain (ADG) in Nellore cattle, percentage of additive genetic variance and candidate genes.

<p>Genomics regions associated with average daily gain (ADG) in Nellore cattle, percentage of additive genetic variance and candidate genes.</p

Descriptive statistics for dry matter intake (DMI), average daily gain (ADG), feed efficiency (G:F) and residual feed intake (RFI).

<p>Descriptive statistics for dry matter intake (DMI), average daily gain (ADG), feed efficiency (G:F) and residual feed intake (RFI).</p

Enriched GO terms and KEGG pathways from DAVID software for ADG.

<p>Enriched GO terms and KEGG pathways from DAVID software for ADG.</p