INTELLIGENT MODEL FOR MAXIMIZING THE GENERATED POWER OF A RECONFIGURABLE SOLAR POWER PLANT
https://doi.org/10.35266/1999-7604-2023-1-52-58
Abstract
The global maximum power point tracking of a solar power plant in partial shading demands a global optimization. Standard algorithms for tracking of maximum power point do not provide for a maximum global power of a solar power plant during real time mode due to low convergence. A model of maximizing the generated power of a reconfigurable solar power plant was developed as a modified fuzzy deep neural network based on the modified quantum-behaved particle swarm optimizer. This neural network consists of the following: convolutional units, recurrent neural networks, and fuzzy units. By processing the sensor signals and images of the solar array, the set modified fuzzy deep neural network generates a reference voltage and an electrical interconnection matrix of the parallel-serial solar array, maximizing its power under non-uniform insolation. The neural network demonstrates such advantages as robustness, better efficiency, and tracking speed in comparison
with the model of a reconfigurable solar power plant based on the particle swarm optimization.
About the Authors
E. A. Engel
Katanov State University of Khakassia, Abakan
Russian Federation
Russian Federation
Candidate of Sciences (Engineering), Associate Professor
E-mail: ekaterina.en@gmail.com
N. E. Engel
Katanov State University of Khakassia, Abakan
Russian Federation
Russian Federation
Bachelor’s Degree Student
E-mail: nikita.en@gmail.com
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Review
For citations:
Engel E.A., Engel N.E. INTELLIGENT MODEL FOR MAXIMIZING THE GENERATED POWER OF A RECONFIGURABLE SOLAR POWER PLANT. Proceedings in Cybernetics. 2023;22(1):52-58. (In Russ.) https://doi.org/10.35266/1999-7604-2023-1-52-58
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