Vol. 1 No. 3 (2019): Volume 1, Issue 3, Year 2019
Articles

Estimation of Pv Module Parameters using Generalized Hopfield Neural Network

Ranjith Dharmarajan
PG Scholar, Department of Electrical and Electronics Engineering, Government College of Technology, Coimbatore, TN, India
Rajeswari Ramachandran
Associate Professor, Department of Electrical and Electronics Engineering, Government College of Technology, Coimbatore, TN, India
Published May 25, 2019
Keywords
  • Generalized Hopfield Neural Network (GHNN),
  • Photovoltaic (PV)
How to Cite
Dharmarajan, R., & Ramachandran, R. (2019). Estimation of Pv Module Parameters using Generalized Hopfield Neural Network. International Research Journal of Multidisciplinary Technovation, 1(3), 16-27. Retrieved from https://mapletreejournals.com/index.php/irjmt/article/view/231

Abstract

The estimation of solar photovoltaic (PV) system with help of electrical model parameters, such as photon generated current, the diode saturation current, series resistance, shunt resistance, and diode ideality factor, are desirable to predict the real performance characteristics of solar PV under varying environmental conditions. Finally, performance indices, such as PV characteristics curve are estimated for the various solar PV panels using GHNN optimization technique.

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