Enhanced Maximum Power Point Tracking Using Embedding Deep Networks and Self-Adaptive Genetic Algorithm

In this paper, we present a new approach for the power point tracking problem in photovoltaic systems by integrating an adaptive genetic algorithm (GA) into neural networks to solve it.The classic MPPT strategies might often fail to adequately accommodate dynamic environmental conditions and system variations, resulting in poor power extraction.To combat this, we present a new framework combining deep learning approaches with the power of evolutionary optimization.The basic idea of our method was to utilize two different representations for continuous and categorical features through an embedding network, which would result in better prediction accuracy using the MPPT algorithm to augment tracking by combining operational and environmental settings within dense continuous embeddings that could then interact with the multidimensional features.We propose a deep neural network architecture to predict optimal power points and train end-to-end over these embedding networks.A novel technique that uses neural networks and a self-adaptive evolutionary algorithm to track the greatest power point in solar systems.A selfadaptive mechanism in our approach dynamically modifies the GA's settings in response to real-time performance feedback.The complex connections between environmental and operational parameters have been captured using embedding networks to improve tracking accuracy.The test results indicate a 20% increase in system efficiency overall, a 25% improvement in voltage stability, a 30% reduction in total harmonic distortion (THD), and a 15% decrease in processing time, confirming the strategy's efficacy compared to traditional MPPT techniques.In addition, our suggested strategy improved the tracking accuracy and time to around 99.98 tracking Acc and 0.12s tracking Time.