Open AccessAn Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach
Author(s) -
Imad A. Elzein,
Ю. Н. Петренко
Publication year - 2020
Publication title -
izvestiâ vysših učebnyh zavedenij i ènergetičeskih obʺedinennij sng. ènergetika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.333
H-Index - 6
eISSN - 2414-0341
pISSN - 1029-7448
DOI - 10.21122/1029-7448-2020-63-5-441-449
Subject(s) - control theory (sociology) , maximum power point tracking , maximum power principle , pid controller , photovoltaic system , controller (irrigation) , power (physics) , model predictive control , computer science , converters , control engineering , engineering , voltage , temperature control , control (management) , physics , electrical engineering , artificial intelligence , agronomy , quantum mechanics , inverter , biology
Photovoltaic power plants have non-linear voltage-current characteristic, with specific maximum power point, which depends on operating conditions, viz. irradiation and temperature. In targeting the maximum power, it is by far known that the photovoltaic arrays have to operate at the maximum power point despite unpredicted weather changes. For this reason the controllers of all photovoltaic power electronic converters employ some method for maximum power point tracking. This paper makes an emphasis on model predictive controller as a control method for controlling the maximum power point tracking through the utilization of the well-known algorithm namely the Perturb and Observe technique. Further, during the advanced stages of this research study, the model will compare the results obtained for tracking the maximum power point from model predictive controller and a PID-controller as they are integrated Perturb and Observe algorithm. The obtained results will verify that the adaptive PID-controller Perturb and Observe algorithm has limitation for tracking the precise MPP during the transient insulation conditions. As compared to the proposed adaptive/modified model predictive controller for Perturb and Observe algorithm we illustrate that by adopting this method we will get to establish more accurate and efficient results of the obtained power in any dynamic environmental conditions: advantages as on regulation time (six times under the accepted experimental conditions) and by the number of fluctuations.