ANN Based Mppt Techniques For Solar Power System
Volume: 14 - Issue: 05 - Date: 01-05-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU451 | Page No.: 101-105
Author: Navneet Kumar
Co- Author: Ashish Singal
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.
Key Words:-Artificial Neural Networks (ANN), Maximum Power Point Tracking (MPPT), Solar Power, System, Power Quality Optimization, Renewable Energy, Real-time Prediction. Energy Efficiency
Area:-Engineering
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