Author
Sami Kahla
Bio: Sami Kahla is an academic researcher. The author has contributed to research in topics: Maximum power point tracking & Control theory. The author has an hindex of 9, co-authored 35 publications receiving 343 citations.
Papers
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TL;DR: In this paper, the Particle Swarm Optimization (PSO) approach is used to select and generate an optimal duty cycle which varies with photovoltaic parameters in order to extract the maximum power.
84 citations
TL;DR: In this article, a feedback linearization controller based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid, the proposed method which aims at maximizing the power captured by WECS.
48 citations
TL;DR: In this article, particle swarm optimization (PSO) is used to generate an On-Off Controller for a wind turbine equipped with doubly fed induction generator connected to the grid with battery storage.
43 citations
01 Oct 2014
TL;DR: In this paper, a fuzzy logic control (FLC) based Mamdani algorithm is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system.
Abstract: In this paper, a fuzzy logic control (FLC) based “Mamdani” is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.
40 citations
TL;DR: The simulation results prove the viability of the proposed control structure and the key feature of sliding mode control is a wisely chosen sliding surface which allows the turbine to operate more or less close to the optimal regimes characteristic.
30 citations
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01 Jan 2015
3,828 citations
TL;DR: A novel multi-objective algorithm called Multi-Objective Grey Wolf Optimizer (MOGWO) is proposed in order to optimize problems with multiple objectives for the first time.
Abstract: Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html. A novel multi-objective algorithm called Multi-objective Grey Wolf Optimizer is proposed.MOGWO is benchmarked on 10 challenging multi-objective test problems.The quantitative results show the superior convergence and coverage of MOGWO.The coverage ability of MOGWO is confirmed by the qualitative results as well.
967 citations
TL;DR: In this article, a robust sliding-mode control using nonlinear perturbation observers for wind energy conversion systems (WECS), in which a doubly-fed induction generator (DFIG) is employed to achieve an optimal power extraction with an improved fault ride-through (FRT) capability.
310 citations
TL;DR: PMSG scheme of permanent magnetic synchronous generator for maximum power point tracking can simultaneously own the promising merits of improved system damping and significant robustness, together with a globally consistent control performance under various operation conditions.
234 citations