Deep Learning Based Solar Maximum Power Point Tracking System with Grid Connected System

The proposed research develops a unique solar power Maximum Power Point Tracking (MPPT) system which implements Deep Learning approaches within a connected grid power system. Artificial neural networks (ANNs) inside the system search for operational conditions that achieve maximum energy generation in solar panel power outputs. Solar panels use installed sensor arrays to obtain data points including voltage measurements and current readings together with temperature levels and irradiance values and environmental parameters. To use neural networks data must pass through multiple procedures starting from preprocessing to normalization for achieving reliability and practicality. Both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) operate in the core system to conduct analysis of current sensor readings and historical data using deep learning topologies. The models identify Maximum Power Point (MPP) to achieve optimal energy extraction from different environmental conditions. The MPPT algorithm determines optimal operating conditions after which the inverter changes solar panel settings for maximum efficiency delivery. A stable grid-connected solar power generation demands that the system first synchronize with the power grid while fulfilling necessary standards before obtaining operational stability. The deep learning model processes repeated training cycles which enable it to adjust and enhance performance level while environmental conditions change. The proposed system provides benefits such as improved operational efficiency alongside environmental adaptability as well as perfect grid compliance. The execution of these advanced systems demands current engineering solutions to develop quick powerful calculating abilities. The investigation gives scientific backing to deploying deep learning systems for solar-grid integration thus advancing renewable power technologies. Research findings demonstrate that ANN-based MPPT system controls reached a tracking efficiency of 93.8% surpassing conventional approaches which yielded 85.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> tracking efficiency. The dynamic response time equaled 0.6 seconds and the system kept a minimal tracking error at 0.23V.