Abstract
This paper emphasizes on a multidimensional search-driven MPPT aided by particle swarm optimization (PSO) and differential evolutionary algorithm (DEA). Maximum power point trackers are crucial components in PV panels to make operate the PV panel with its maximum efficiency. Partially shaded conditions are quite common in PV panels which in turn hinder the performance of PV system. Further, the partially shaded PV panels exhibit multipower peaks which cannot be catered wisely by the conventional MPPT techniques. The entrenched evolutionary algorithms such as PSO and DEA on the hand grasp the global power peak. In this work, a multidimensional search technique embedded with DC–DC converter is proposed. By this proposed technique, during shaded conditions each panel peak is grasped rather than the cumulative GMPP. Therefore, there is an appreciable increase in extracted power. A comparison in simulation results shows the performance of PSO and DEA-aided Multidimensional MPPT.
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Sridhar, R., Jeevananthan, S., Sai Pranahita, B. (2016). Investigations on Multidimensional Maximum Power Point Tracking in Partially Shaded Photovoltaic Arrays with PSO and DE Algorithms. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_104
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DOI: https://doi.org/10.1007/978-81-322-2656-7_104
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