Real‐time implementation of MPPT for renewable energy systems based on Artificial intelligence

It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non-linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real-time using an incremental training mode based on the back-propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real-time. The results have proven their effectiveness and conformity with the required optimal values.