Abstract
The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.
Highlights
In the last few decades, increased demand for electricity has made the search for the use of renewable energy sources increasingly necessary, attracting strong interest in the diversification of power generation plants
This is due to the fact that, in most cases, the maximum power point tracking (MPPT) techniques mentioned above only reach the local maximum power point (LMPP) instead of the global maximum power point (GMPP) [18]
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Summary
In the last few decades, increased demand for electricity has made the search for the use of renewable energy sources increasingly necessary, attracting strong interest in the diversification of power generation plants. Karami et al [6] have shown that the energy conversion efficiency of the best solar cells, which are laboratory manufactured and made of monocrystalline silicon, is of the order of 25%, which represents a low value when a comparison is made with other currently used forms of energy generation. Another important point to be considered is that the voltage and output current of the PV modules vary according to environmental conditions such as temperature and the incidence of solar radiation. MPPT techniques can be classified into four categories [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]: 1. Model-based methods: Constant voltage Constant current Pilot cell Curve fitting Look up table Bisect search theorem Best fixed voltage Temperature parametric Linear reoriented coordinates method Analytical solution (AS) based method Gradient descent
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