Reduction of the double-circuit flashovers on a 400 kV overhead line

Double circuit flashovers may cause very severe system disturbances when taking place on some critical double-circuit lines of an electrical network. Line arresters offer an efficient solution to protect these specific lines against double circuit outages due to lightning. This paper will study, on a double-circuit 400-kV line, the protection provided by line arresters against double circuit outages due to lightning. The efficiency of several configurations of line arresters will be compared. For that purpose, the double-circuit lightning flashover rates of the line with and without line arresters will be calculated using a newly developed software which includes a three-dimensional electro-geometric model and is able to take into account the random nature of lightning. This software automatically launches EMTP-RV (restructured version of EMTP) for analyzing fast front overvoltages impressed on line insulation. The energy stressing the line arresters will also be calculated in order to evaluate the risk of failure of the line arresters due to excess energy absorption. Furthermore, the effects of several other parameters such as the tower footing resistances, the lightning withstand voltages of insulator strings as well as the protective levels of line arresters will also be investigated

Probabilistic Program Analysis with Martingales

We present techniques for the analysis of infinite state probabilistic programs to synthesize probabilistic invariants and prove almost-sure termination. Our analysis is based on the notion of (super) martingales from probability theory. First, we define the concept of (super) martingales for loops in probabilistic programs. Next, we present the use of concentration of measure inequalities to bound the values of martingales with high probability. This directly allows us to infer probabilistic bounds on assertions involving the program variables. Next, we present the notion of a super martingale ranking function (SMRF) to prove almost sure termination of probabilistic programs. Finally, we extend constraint-based techniques to synthesize martingales and super-martingale ranking functions for probabilistic programs. We present some applications of our approach to reason about invariance and termination of small but complex probabilistic programs

Gérer collectivement la biodiversité cultivée

National audienceLes semences, avec la terre et l'eau, sont les ressources de l'agriculture. Conserver la maîtrise de ces ressources est un enjeu de souveraineté alimentaire. La biodiversité cultivée, dont les semences font partie, comme les autres ressources, n'échappe pas à la menace d'accaparement par des intérêts privés. Dans ce contexte, sont apparus en France depuis une dizaine d'années des projets de gestion collective de la biodiversité cultivée. Il s'agit de prendre en charge collectivement la gestion in situ des semences à un moment de notre histoire où celle-ci est principalement assurée ex situ par des sélectionneurs et des semenciers professionnels. C'est sur cette gestion collective dans les fermes et les jardins que porte cet ouvrage, une gestion collective qui vise à produire et diffuser des semences, à sélectionner de nouvelles populations de plantes et à conserver les anciennes.L'ouvrage cherche d'abord à susciter l'envie en donnant à voir quatre expériences collectives de gestion de la biodiversité cultivée, autour du maïs et des espèces fourragères, sans pour autant en occulter les difficultés. Ensuite, leur exposé et leur analyse aideront des collectifs qui souhaitent développer leurs propres initiatives à se poser des questions auxquelles ils n'auraient peut-être pas pensé, mais qui semblent déterminantes à prendre en compte pour monter un tel projet. Ce livre souhaite également porter à la connaissance d'un public plus large une thématique insuffisamment connue du grand public, et montrer que d'autres modes de gestion de la biodiversité sont possibles, dont pourraient s'emparer les formations agricoles et environnementales

Multi-robot LTL planning under uncertainty

Robot applications are increasingly based on teams of robots that collaborate to perform a desired mission. Such applications ask for decentralized techniques that allow for tractable automated planning. Another aspect that current robot applications must consider is partial knowledge about the environment in which the robots are operating and the uncertainty associated with the outcome of the robots’ actions. Current planning techniques used for teams of robots that perform complex missions do not systematically address these challenges: (1) they are either based on centralized solutions and hence not scalable, (2) they consider rather simple missions, such as A-to-B travel, (3) they do not work in partially known environments. We present a planning solution that decomposes the team of robots into subclasses, considers missions given in temporal logic, and at the same time works when only partial knowledge of the environment is available. We prove the correctness of the solution and evaluate its effectiveness on a set of realistic examples