DEVELOPMENT AND IMPLEMENTATION OF MAXIMUM POWER POINT TRACKING CONTROLLER FOR PV SYSTEM USING NEURAL NETWORKS
ESSAM TAWFIK MOHAMED EL SHENAWY;
Abstract
The photovoltaic systems, as a renewable energy power sources, are rapidly expanding and have increasing roles in electric power technologies, providing more secure power sources and pollution free electric supplies. Since the PV electricity is an expensive compared to the electricity from the utility grid, the user wants to use all of the available output power. Therefore, the PV systems should be designed to operate at their maximum output power for any temperature and solar radiation level.
The PV modules are characterized by their nonlinear current-voltage (1-V) curves, each of them is related to a certain solar radiation and surface temperature. For each 1-V curve there is a unique point that gives a maximum power output, called the maximum power point, and this is the desired operating point of the PV module. As the nonlinear 1-V curves of the PV modules vary with the solar radiation levels and the module surface temperature, then an accurate tracking control system must be designed to follow the PV module NIPP at all times.
This thesis presents a development and implementation of the PC-based maximum power point tracker for PV system using neural networks. The system consists of a PV module via a maximum power point tracker supplying a de motor that drives an a1r fan. The control algorithm is developed to use the artificial neural networks for detecting the optimal operating point under different operating conditions, then the control action gives the driving signals to the maximum power
• point tracker. A PC is used for data acquisition, running the control algorithm, storage data, as well as data display and analysis. The system has been implemented and tested under various operating conditions.
The experimental results showed that the PV system with MPPT always tracks the peak power point of the PV module under various operating conditions. The MPPT draws about 97% of the actual maximum power generated by the PV module.
The PV modules are characterized by their nonlinear current-voltage (1-V) curves, each of them is related to a certain solar radiation and surface temperature. For each 1-V curve there is a unique point that gives a maximum power output, called the maximum power point, and this is the desired operating point of the PV module. As the nonlinear 1-V curves of the PV modules vary with the solar radiation levels and the module surface temperature, then an accurate tracking control system must be designed to follow the PV module NIPP at all times.
This thesis presents a development and implementation of the PC-based maximum power point tracker for PV system using neural networks. The system consists of a PV module via a maximum power point tracker supplying a de motor that drives an a1r fan. The control algorithm is developed to use the artificial neural networks for detecting the optimal operating point under different operating conditions, then the control action gives the driving signals to the maximum power
• point tracker. A PC is used for data acquisition, running the control algorithm, storage data, as well as data display and analysis. The system has been implemented and tested under various operating conditions.
The experimental results showed that the PV system with MPPT always tracks the peak power point of the PV module under various operating conditions. The MPPT draws about 97% of the actual maximum power generated by the PV module.
Other data
| Title | DEVELOPMENT AND IMPLEMENTATION OF MAXIMUM POWER POINT TRACKING CONTROLLER FOR PV SYSTEM USING NEURAL NETWORKS | Other Titles | تطوير وتطبيق متحكم لتتبع أقصى قدرة لنظام فوتوفولطى باستخدام الشبكات العصبية | Authors | ESSAM TAWFIK MOHAMED EL SHENAWY | Issue Date | 2002 |
Attached Files
| File | Size | Format | |
|---|---|---|---|
| B11222.pdf | 1.54 MB | Adobe PDF | View/Open |
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