A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network Grid-Connected MPPT-Based Techniques for Hybrid PV-WIND with Battery Energy Storage System
https://orcid.org/http://orcid.org/0000-0003-4696-238X
https://orcid.org/http://orcid.org/0000-0003-4137-6234
https://orcid.org/http://orcid.org/0000-0002-6520-1749
A hybrid photovoltaic–wind–battery–microgrid system is designed and implemented based on an artificial neural network with maximum power point tracking. The proposed method uses the Levenberg–Marquardt approach to train data for the ANN to extract the maximum power under different environmental and load conditions. The control strategies adjust the duty cycle of a DC boost converter to achieve maximum power output for photovoltaic and wind energy systems. DC bus voltage implements voltage control using a bidirectional converter with a battery, and the current flow is controlled by an inverter control for the grid-side converter based on the state of charge of the battery and the photovoltaic current received. A phase-locked loop for frequency and phase synchronization of the grid and an LCL filter to eliminate harmonics in the single-phase grid are also integrated into the microgrid system.
Artificial Neural Network Grid-Connected MPPT-Based Techniques for Hybrid PV-WIND with Battery Energy Storage System
https://orcid.org/http://orcid.org/0000-0003-4696-238X
https://orcid.org/http://orcid.org/0000-0003-4137-6234
https://orcid.org/http://orcid.org/0000-0002-6520-1749
A hybrid photovoltaic–wind–battery–microgrid system is designed and implemented based on an artificial neural network with maximum power point tracking. The proposed method uses the Levenberg–Marquardt approach to train data for the ANN to extract the maximum power under different environmental and load conditions. The control strategies adjust the duty cycle of a DC boost converter to achieve maximum power output for photovoltaic and wind energy systems. DC bus voltage implements voltage control using a bidirectional converter with a battery, and the current flow is controlled by an inverter control for the grid-side converter based on the state of charge of the battery and the photovoltaic current received. A phase-locked loop for frequency and phase synchronization of the grid and an LCL filter to eliminate harmonics in the single-phase grid are also integrated into the microgrid system.
Artificial Neural Network Grid-Connected MPPT-Based Techniques for Hybrid PV-WIND with Battery Energy Storage System
https://orcid.org/http://orcid.org/0000-0003-4696-238X
https://orcid.org/http://orcid.org/0000-0003-4137-6234
https://orcid.org/http://orcid.org/0000-0002-6520-1749
A hybrid photovoltaic–wind–battery–microgrid system is designed and implemented based on an artificial neural network with maximum power point tracking. The proposed method uses the Levenberg–Marquardt approach to train data for the ANN to extract the maximum power under different environmental and load conditions. The control strategies adjust the duty cycle of a DC boost converter to achieve maximum power output for photovoltaic and wind energy systems. DC bus voltage implements voltage control using a bidirectional converter with a battery, and the current flow is controlled by an inverter control for the grid-side converter based on the state of charge of the battery and the photovoltaic current received. A phase-locked loop for frequency and phase synchronization of the grid and an LCL filter to eliminate harmonics in the single-phase grid are also integrated into the microgrid system.
Artificial Neural Network Grid-Connected MPPT-Based Techniques for Hybrid PV-WIND with Battery Energy Storage System
J. Inst. Eng. India Ser. B
Sharma, Samraat (author) / Chauhan, Bhavesh Kumar (author) / Saxena, Nitin Kumar (author)
Journal of The Institution of Engineers (India): Series B ; 104 ; 1217-1226
2023-12-01
10 pages
Article (Journal)
Electronic Resource
English
A hybrid grid connected PV battery energy storage system with power quality improvement
| British Library Online Contents | 2016