Genetic Algorithms for Optimal Reactive Power Compensation of a Power System with Wind Generators based on Artificial Neural Networks

In this paper, we develop a method to maintain an acceptable voltages profile and minimization of active losses of a power system including wind generators in real time. These tasks are ensured by acting on capacitor and inductance benches implemented in the consuming nodes. To solve this problem, we minimize an objective function associated to active losses under constraints imposed on the voltages and the reactive productions of the various benches. The minimization procedure was realised by the use of genetic algorithms (GA). The major disadvantage of this technique is that it requires a significant computing time thus not making it possible to deal with the problem in real time. After a training phase, a neural model has the capacity to provide a good estimation of the voltages, the reactive productions and the losses for forecast curves of the load and the wind speed, in real time

Grafting versus seed propagated apricot populations: two main gene pools in Tunisia evidenced by SSR markers and model-based Bayesian clustering

Apricot was introduced into the Mediterranean Basin from China and Asian mountains through the Middle-East and the Central Europe. Traditionally present in Tunisia, we were interested in accessing the origin of apricot species in the country, and in particular in the number and the location of its introductions. A set of 82 representative apricot accessions including 49 grafted cultivars and 33 seed propagated ‘Bargougs’ were genotyped using 24 microsatellite loci revealing a total of 135 alleles. The model-based Bayesian clustering analysis using both Structure and InStruct programs as well as the multivariate method revealed five distinct genetic clusters. The genetic differentiation among clusters showed that cluster 1, with only four cultivars, was the most differentiated from the four remaining genetic clusters, which constituted the largest part of the studied germplasm. According to their geographic origin, the five identified groups (north, centre, south, Gafsa oasis and other oases groups) enclosed a similar variation within group, with a low level of differentiation. Overall results highlighted the distinction of two apricot gene pools in Tunisia related to the different mode of propagation of the cultivars: grafted and seed propagated apricot, which enclosed a narrow genetic basis. Our findings support the assumption that grafting and seed propagated apricots shared the same origin

Allorecognition in the Tasmanian Devil (Sarcophilus harrisii), an Endangered Marsupial Species with Limited Genetic Diversity

Tasmanian devils (Sarcophilus harrisii) are on the verge of extinction due to a transmissible cancer, devil facial tumour disease (DFTD). This tumour is an allograft that is transmitted between individuals without immune recognition of the tumour cells. The mechanism to explain this lack of immune recognition and acceptance is not well understood. It has been hypothesized that lack of genetic diversity at the Major Histocompatibility Complex (MHC) allowed the tumour cells to grow in genetically similar hosts without evoking an immune response to alloantigens. We conducted mixed lymphocyte reactions and skin grafts to measure functional MHC diversity in the Tasmanian devil population. The limited MHC diversity was sufficient to produce measurable mixed lymphocyte reactions. There was a wide range of responses, from low or no reaction to relatively strong responses. The highest responses occurred when lymphocytes from devils from the east of Tasmania were mixed with lymphocytes from devils from the west of Tasmania. All of the five successful skin allografts were rejected within 14 days after surgery, even though little or no MHC I and II mismatches were found. Extensive T-cell infiltration characterised the immune rejection. We conclude that Tasmanian devils are capable of allogeneic rejection. Consequently, a lack of functional allorecognition mechanisms in the devil population does not explain the transmission of a contagious cancer

Consequences Of On-Track Competition In Railways By Use Of Simulations

Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Artificial Neural Network for Real Time Load Flow Calculation: Application to a Micro Grid with Wind Generators

This work presents a method for solving the problem of load flow in electric power systems including a wind power station with asynchronous generators. For this type of power station, the generated active power is only known and consequently the absorbed reactive power must be determined. So we have used the circular diagram at each iteration and by considering this node as a consuming node in the load flow program. Since the wind speed is not constant, the generated power is neither constant. To predict the state of the network in real time, we have used the artificial neural networks after a stage of training using a rich base of data