<p>Maximum power point tracking (MPPT) algorithms are employed in photovoltaic (PV) systems to make full utilization of PV array output power, which have a complex relationship between ambient temperature and solar irradiation. The power-voltage characteristic of PV array operating under partial shading conditions (PSC) exhibits multiple local maximum power points (LMPP). In this paper, an advanced algorithm has been presented to track the global maximum power point (GMPP) of PV. Compared with the Perturb and Observe (P&O) techniques, the algorithm proposed the advantages of determining the location of GMPP whether partial shading is present.</p>
The interest in electric traction has reached a very high level in recent decades; there is no doubt that electric vehicles have become among the main means of transport and will be the first choice in the future, but to dominate the market, a lot of research efforts are still devoted to this purpose. Electric machines are crucial components of electric vehicle powertrains. The bulk of traction drive systems have converged in recent years toward having some sort of permanent magnet machines because there is a growing trend toward enhancing the power density and efficiency of traction machines, resulting in unique designs and refinements to fundamental machine topologies, as well as the introduction of new machine classes. This paper presents the technological aspect of the different components of the electric powertrain and highlights the important information on the electric vehicle’s architecture. It focuses on a multi-criteria comparison of different electric motors utilized in the electric traction system to give a clear vision to allow choosing the adequate electrical motor for the desired application. The proposed comparative analysis shows that the induction motor better meets the major necessities of the electric powertrain, whereas the permanent magnet synchronous motor is nonetheless the most used by electric vehicle manufacturers.
The paper demonstrates the feasibility of an optimal backstepping controller for doubly fed induction generator based wind turbine (DFIG). The main purpose is the extract of maximum energy and the control of active and reactive power exchanged between the generator and electrical grid in presence of uncertainty. The maximum energy is obtained by applying an algorithm based on artificial bee colony approach. Particle swarm optimization is used to select optimal value of backstepping's parameters. The simulation is carried out on 2.4 MW DFIG based wind turbine system. The optimized performance of the proposed control technique under uncertainty parameters is established by simulation results.
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