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Sliding Mode Based P&O and Neural Network MPPT in MATLAB

Sliding Mode Based P&O and Neural Network MPPT in MATLAB

In the realm of Photovoltaic (PV) systems, MATLAB offers a comprehensive environment to simulate and control power generation. Utilizing a 250 Watts PV panel, the behavior of PV currents, voltages, and power capacities under various irradiation levels are analyzed using a sliding mode controller.

Boost Converter and Load Connection Boost Converter interlinks with the load, enabling regulation and power flow between the PV system and the load. Measurements are conducted for PV voltage and current to ensure controlled power output and efficient energy transfer to the load. Sliding Mode Control Principle The sliding mode controller operates on an error-based principle, comparing the reference voltage with the combined voltages of the PV and DC circuits. The controller generates control signals, adjusting the operating points to achieve maximum power extraction from the PV panel. Sliding Mode Control and Variation in Irradiation Irradiation levels are varied at 1.8, 6.4, and 4 units, respectively, every 0.2 seconds. As the irradiation changes, the behavior of the PV system is analyzed, focusing on PV power, load power, voltage, and current under different irradiation conditions. Neural Network Implementation An extension of the PV system includes the application of a Neural Network in place of the PV MPPT algorithm. This network is trained using data acquired from the PV model, varying temperature and radiation as inputs and the maximum power output as the output. MATLAB Tools for Neural Network Training MATLAB provides tools for neural network training. The training is executed by generating and simulating input data and associating them with the desired output. Through the fitting app in MATLAB, the developed neural network is fine-tuned to ensure optimized performance. Comparative Analysis between PV Algorithms Comparative analysis between the traditional MPPT algorithm and the Neural Network MPPT algorithm showcases notable variations in PV voltage based on changes in irradiation and temperature. Conclusion: Enhancing PV Systems with Advanced Control Techniques This analysis demonstrates how MATLAB facilitates the simulation and optimization of PV systems using sliding mode control and advanced techniques like Neural Networks for MPPT. It underscores the significance of control algorithms in enhancing PV system performance under varying environmental conditions. This blog post elucidates the application of MATLAB in simulating, analyzing, and optimizing Photovoltaic systems using sliding mode control and neural network techniques. It highlights the importance of control mechanisms in improving PV system performance and efficiency in diverse operating conditions.

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