This paper proposes modeling and simulation of photovoltaic model. Taking in to account the temperature and sun"s irradiance, the PV array is modeled and its voltage current characteristics and the power and voltage characteristics are simulated. This enables the dynamics of PV system to be easily simulated and optimized. It is noticed that the output characteristics of a PV array are influenced by the environmental factors and the conversion efficiency is low. Therefore a maximum power tracking (MPPT) technique is needed to track the peak power to maximize the produced energy. The maximum power point in the power-voltage graph is identified by an algorithm called perturbation & observation (P&O) method or Hill climbing. This algorithm will identify the suitable duty ratio in which the DC/DC converter should be operated to maximize the power output. The results confirm that the photo voltaic array with proposed MPPT controller can operate in the maximum power point for the whole range of assumed solar data (irradiance and temperature).
Abstract:The power output of the photovoltaic (PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point (MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking (MPPT) algorithms like perturb and observe (P&O) to get stuck with local maxima instead of capturing global maxima. Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution (DE). The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.
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