Browsing by Author "Encadreur: YOUNES Mimoun"

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    La gestion optimale des réseaux électriques par l’intelligence collective
    (2017-06-28) KHERFANE Naas; Encadreur: YOUNES Mimoun
    Résumé (Français et/ou Anglais) : To achieve optimal management of electrical energy networks and for demonstrate the performance and effectiveness of collective intelligence algorithms and their capabilities in finding optimal solutions (better global research, convergence, minimal time Compared to conventional optimization algorithms), For this purpose the approach had used the algorithms: Firefly FFA, ants algorithm ACO, modified frog leaping MSFLA, monkey algorithm (Monkey) MA, algorithm of the swarms PSO and Biogeography-based optimization (BBO). Thus the work and in its quest for more reliable and accurate solutions the use of hybridization technique (the combination of methods): 1) -Hybridization FFA-MPSO, 2) -Hybridization ACO-MSFLA , 3) - HybridizationPSO-MA,4) - HybridizationBBO-MA . The problem EPD (economic dispatching) has been solved using the previously mentioned methods by integrating single-objective or multi-objective functions applied on the IEEE-6, IEEE-14, IEEE-30 networks and Networks with 10 and 20 generators.
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    Optimisation Multiobjectif de L’écoulement de Puissance par des Algorithmes Multiagents Probabilistes
    (2017-03-01) KHERFANE Riad Lakhdar; Encadreur: YOUNES Mimoun
    Résumé (Français et/ou Anglais) : The main current concerns of power generators using thermal fossil fuel power plants are producing electricity with a low cost of fuel and minimizing emissions of toxic gases into the atmosphere. Note that for cost minimization and reduction of emissions, it is necessary to consider the transmission losses that depend on the network map, the values of the impedances brought into play and the load distribution and productions. The latter plays the most important role in this area. For this it is necessary to express the total losses as a function of the generated power. The high level of competitiveness today forced power plants to continuously improve reliability, efficiency and profitability, while enhancing their performance and compliance with environmental. The use of probabilistic multi-agent algorithms enables solving problems of minimization of greenhouse gas emissions, improving dust control, optimizing the performance of fuels and yield optimization turbines. Our work has focused on the exploitation of effective cooperation between software agents that require a high degree of coordination. The intelligence gained here is adaptive, agents must use the information received in order to behave properly in a scalable environment. This work is validated through IEEE networks.