2017
DOI: 10.21533/pen.v5i1.67
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A Comparison of Perturb & Observe and Fuzzy-Logic Based MPPT Methods for Uniform Environment Conditions

Abstract: The power generation from photovoltaic (PV) system is not constant and it varies based on solar irradiance and temperature. For any environmental condition, to convert maximum available solar energy, PV systems must be operated at maximum power point. To accomplish that two different maximum power point tracking (MPPT) methods have been presented in this study. The first method can determine MPP point by measuring the derivative of PV cell power (dP) and PV cell voltage (dV) which is called Perturb & Observe (… Show more

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Cited by 7 publications
(9 citation statements)
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References 8 publications
(11 reference statements)
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“…Among the much used algorithms, we have the P&O, used by many authors. Its few code lines and simplicity make it easy to deploy [9]. The disadvantage of this algorithm is that in some cases, especially under partial shading, the algorithm cannot find the maximum power point.…”
Section: Pando Algorithm For Mppt Controlmentioning
confidence: 99%
“…Among the much used algorithms, we have the P&O, used by many authors. Its few code lines and simplicity make it easy to deploy [9]. The disadvantage of this algorithm is that in some cases, especially under partial shading, the algorithm cannot find the maximum power point.…”
Section: Pando Algorithm For Mppt Controlmentioning
confidence: 99%
“…Figure 1 displays a PV cell equivalent circuit model and a PV panel with 9 PV cells. [17]. Variation of solar irradiance and/or temperature affects PV panel power value.…”
Section: Pv Modelling and Equivalent Circuit Modelmentioning
confidence: 99%
“…The main purpose of this study is to investigate and test whether the accuracy of probabilistic credit risk assessment of corporates, evaluated with logistic regression, can be improved using soft and hard data modeling, followed by soft-hard data fusion, in particular using Uncertainty Balance Principle. In literature one can find various methods on how to integrate fuzzy and random data in meaningful ways [34][35][36]53], as well as about the area of "random fuzzy sets" and "fuzzy random variables", as well as various "fuzzy" applications [e.g., 1-2, 5, 11-20, 22-24, 33, 37, 39-45, 47-52]. However, this study does not deal with aforementioned.…”
Section: Introductionmentioning
confidence: 99%