• Suraj Patidar
• Deepak Kumar Patidar
• Hemendra Khedekar

The major challenge with tapping solar energy using solar cells is the variable and intermittent nature of solar energy. The maximum power point tracking (MPPT) is the technique to maximize the output of the solar panels. While conventional technique have been used for attaining the condition of MPPT, off late neural networks have been used as the mathematical model to optimize the values of voltage and currents of the panel in order to attain the condition of MPPT. The Maximum power point tracking (MPPT) is critical in the design and use of solar PV cells, and harnessing the maximum power from them. Neural networks have the advantage of being able to process complex data patterns in real time with high accuracy. In the proposed work, the Levenberg Margaret (LM) algorithm has been used to train a neural network with training features. Subsequently, the neural network is tested and an accuracy of 98.84% has been achieved. The high accuracy can be attributed to the structuring of the training data and the effectiveness of the Levenberg Margaret back-propagation algorithm which is both fast and stable. The performance of the system has been evaluated in terms of the number of epochs for training, the mean absolute percentage error, accuracy and regression.

Maximum Power Point Tracking (MPPT), Solar panel, Solar radiation, Artificial Neural Networks, MATLAB, simulation

"“Implementation of MPPT technique in Solar PV System using Artificial Neural Networks”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.e359-e363, September-2025, Available :http://www.jetir.org/papers/JETIR2509439.pdf

"“Implementation of MPPT technique in Solar PV System using Artificial Neural Networks”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppe359-e363, September-2025, Available at : http://www.jetir.org/papers/JETIR2509439.pdf