الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
As electricity consumption continues to increase and fossil fuel prices rise, it is
crucial to shift towards renewable energy sources to combat extinction risk and
global climate change. PV-based power plants, in particular, are becoming more
prominent due to their affordability, abundance, eco-friendliness, and lack of
moving components. However, the non-linear characteristics of PV energy present
a challenge that necessitates Maximum Power Point Tracking (MPPT) to achieve
optimal power output. This study proposes a new MPPT control method for a gridconnected PV system that utilizes the Arithmetic Optimization Algorithm (AOA).
The adopted MPPT algorithm is variable-step Incremental Conductance (IC) as it
is the most popular and efficient compared with numerous conventional MPPT
algorithms. The step size of IC is obtained by the Proportional Integral (PI)
controller. The PI regulator gains are obtained utilizing the contemporary AOA. To
perform this study, a 100 kW PV system linked to the utility is established and
evaluated employing MATLAB/SIMULINK. The optimization method pursues
minimizing four standard error indicators without being biased towards a specified
index, thereby more precisely conveying the result of the suggested methodology.
To validate the findings of the proposed procedure, the obtained results while
employing AOA-based IC-MPPT controllers are contrasted with the Grey Wolf
Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization
(PSO) and Modified Incremental Conductance (MIC) based controllers.
Considering five different weather conditions patterns: step irradiance with
constant temperature, ramp irradiance and temperature, the later one is various
irradiance with a constant temperature, the fourth one is realistic irradiance and
temperature, and the last one is variable irradiance with variable temperature.The results reveal that AOA has decreased the rise time by 61, 3, 4.5, and 26.9%
compared to the MIC, GWO, GA, and PSO in extracting MPPT of the proposed
system, respectively. Moreover, the settling time has a reduction of 94, 84.7, 86.6,
and 79.3%, respectively, against the MIC, GWO, GA, and PSO in the extraction of
the MPPT in step condition. Moreover, the AOA and GWO assist with enhancing
dynamic response to other scenarios over GA and PSO.
After that, the three PI controllers of MPPT, DC link voltage, and inverter current
are tuned using AOA. The results of AOA-based-PI regulators are compared with
those of GWO and conventional algorithms to prove the effectiveness of the
recommended control strategy. The results clarified that the performance of the
AOA-based-MPPT controller achieved a reduction in the settling time by 14.1%
and 76.9%, respectively, contrasted to the GWO and conventional methodologies
to track the MPP. Besides, the AOA-based MPPT controller slightly decreased the
overshot percentage by 3% and 1.4%, correspondingly compared to the GWO and
conventional approaches. Further, the AOA-based DC link voltage controller
contributes to a reduction in overshot percentages of 11.3% and 2.7%, respectively,
compared to the GWO and conventional algorithms, while the GWO settles
slightly faster than AOA by 1.3%. However, AOA has a decrease in the settling
time of 87.9% compared to the conventional technique. Additionally, the AOAbased inverter current regulator improves the performance of the grid reactive
power and reduces the settling time by 43.1% and 85.5%, correspondingly,
compared to the GWO and conventional techniques.
Finally, this work addresses the major limitation of PV-based power plants that
only provide active power by proposing a control circuit that allows the PV
inverter to inject or absorb reactive power into the grid.