A Photovoltaic (PV)-Wind Hybrid Energy System Using an Improved Deep Neural Network (IDNN)-Based Voltage Source Controller for a Microgrid Environment
Presently, there has been a huge rise in the demand for power owing to increases in population and commercial organizations. Traditional power plants are not able to keep up with the increasing needs of customers. Finding a different way to meet consumers’ needs is the main problem in the current si...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/30 |
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| Summary: | Presently, there has been a huge rise in the demand for power owing to increases in population and commercial organizations. Traditional power plants are not able to keep up with the increasing needs of customers. Finding a different way to meet consumers’ needs is the main problem in the current situation. Most RESs (renewable energy sources) like wind, solar, hydro/water sources and fuel cells are environmentally beneficial. The number of available resources has no bearing on how much electricity can be produced using RESs. Due to differences in natural resources, there are constant fluctuations in the availability of RESs. In this technical study, two significant RE (Renewable Energy) power sources—PV (photovoltaic) cells and WES (wind energy systems)—are studied in various weather scenarios. First, a cutting-edge intelligent controller system was created, which aids in tracking the peak power point. Due to the unpredictable nature of weather, a MPPT (maximum power point tracking) controller is required for RES. This work aims to present IDNN- (improved deep neural network) and MPPT-based unique methods for power generation using solar and winds. When a hybrid PV/WES system is integrated into MG s(microgrids), power quality may be improved and THD values can be reduced. It was confirmed from the results of the simulation that the proposed IDNN system yields better performance in different operating situations by means of lower MSE (mean square error) rates, lower THD (total harmonic distortion) and lower computational complexity than the existing method. |
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| ISSN: | 2673-4591 |