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Hybrid Neural Network Incremental Conduct MPPT in MATLAB

Hybrid Neural Network Incremental conductance MPPT in MATLAB

Introduction:

In the quest for efficient solar power utilization, integrating advanced technologies becomes crucial. One such innovation involves the use of a boost converter in conjunction with Maximum Power Point Tracking (MPPT) algorithms. This system aims to optimize energy extraction from solar panels, addressing the non-linear characteristics influenced by factors like temperature and irradiation.


Solar Panel Characteristics:

The system incorporates a 250 Watts solar panel with a maximum power point at 13.7 volts and 8.85 amps. The panel is organized in a series of four strings, providing a total power output of approximately 1000 Watts under standard test conditions (STC). The non-linear nature of solar panels necessitates the implementation of MPPT algorithms.

MPPT Algorithm - Incremental Conductance Method:

To achieve optimal power output, an Incremental Conductance MPPT algorithm is employed. This algorithm continually adjusts the duty cycle of a boost converter based on incremental conductance calculations. The key parameters include voltage, current, duty cycle, and previous instant values, all processed through a control loop.

Neural Network for Irradiation Control:

Considering the impact of irradiation on solar panel performance, a neural network is implemented. This network, trained with varying irradiation levels, provides the necessary adjustments for the boost converter. The neural network output is then combined with the MPPT algorithm for precise control over the boost converter's duty cycle.

Battery and Bidirectional DC-DC Converter:

To ensure a continuous power supply, a bidirectional DC-DC converter is introduced along with an energy storage system, represented by a battery. This converter dynamically shifts between buck and boost modes, optimizing power flow between the solar panel, battery, and load. The battery serves as an energy reservoir, charged during excess solar power and discharged during insufficient power conditions.

Integration with the Grid:

The system is further integrated with the grid through an inverter. Inverter control is facilitated by reference models, generating sine and cosine waveforms. Current control loops ensure that the inverter feeds power into the grid according to the user-defined current reference. The system adapts to changes in solar power and battery conditions, maintaining a stable and efficient grid connection.

Conclusion:

The implemented system showcases a comprehensive approach to harnessing solar power effectively. The combination of MPPT algorithms, neural networks, and bidirectional converters ensures optimal energy extraction and utilization. As the world increasingly turns towards renewable energy sources, such advanced systems play a pivotal role in maximizing the potential of solar panels.

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