ANN Based Mppt Techniques For Solar Power System

Abstract:-In this explores the application of Artificial Neural Networks (ANN) in Maximum Power Point Tracking (MPPT) for solar power systems, aiming to optimize power quality and system efficiency. Traditional MPPT techniques often fall short under rapidly changing environmental conditions, leading to decreased performance and energy yield. This research proposes an ANN-based MPPT approach that leverages the learning and predictive capabilities of neural networks to dynamically and accurately determine the maximum power point (MPP) in real-time. The ANN model is trained using historical data on solar irradiance, temperature, and corresponding output power, enabling it to predict the optimal operating point for varying conditions. The performance of the proposed ANN-based MPPT is evaluated through extensive simulations and experimental setups, comparing its efficiency, tracking speed, and accuracy against conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond). Results indicate that the ANN-based approach significantly enhances the MPPT performance, achieving faster convergence to the MPP and higher energy harvest under diverse and fluctuating environmental scenarios. The study concludes that integrating ANN into MPPT systems not only improves the power quality and efficiency of solar power installations but also demonstrates the potential for artificial intelligence to drive advancements in renewable energy technologies.

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