Predicting the Single Diode Model Parameters using Machine Learning Model

Abstract

The increasing energy demands, requires the usage of solar photovoltaic (SPV) more largely. The work focuses on the Single Diode Model (SDM) of solar panels with the help of five parameters namely series resistance (R_se), shunt resistance (R_sh), diode ideality factor (A), light generated current (I_LG), and diode reverse saturation current (I_sat). To identify unknown parameters, modeling is done by deriving the equations and initial values are assumed under Standard Test Conditions (STC). The deviations of Rse and Rsh values are very less when the experimented and analytical values of the AlokP module are compared. The predicted values hold good for the panel and show that the NR (Newton Raphson) method is simple, robust, and computationally less intensive. With the help of NR methodology, the dataset is created for various solar panels. The proposed methodology is checked with various other PV (Photovoltaic) models and the best-fit machine learning model is predicted. The unknown values are predicted under cross-out validation and hold-out validation methods and the best-fit machine learning model is identified by checking the least RMSE (Root Mean Squared Error) value. Nineteen machine learning models are compared and the model with the minimum RMSE value is chosen to the best-fit model.

Keywords:

• SDM (single diode model)
• MPP (maximum power point)
• machine learning models
• parameter estimation

Additional information

Funding

No funding is involved in this work.

Notes on contributors

Abinaya Inbamani

Abinaya Inbamani is a full time research scholar in the Department of Electrical & Electronics Engineering at Dr. N.G.P Institute of Technology, Coimbatore. She is having teaching experience of 6 + years in various institutions in Tamil Nadu. She is currently pursuing her Ph.D in the Stream of Electrical & Electronics Engineering at Anna University Chennai. Her main research interests are renewable energy resources, IoT and machine learning models.

S. U. Prabha

S. U. Prabha received her Ph.D degree in 2010 from Faculty of Engineering and Technology, Multimedia University, Malaysia. She received her M.E degree in Electrical Machines from PSG College of Technology, Bharathiyar University, Coimbatore in the year 1997, and her B.E degree in Electrical & Electronics Engineering from Coimbatore Institute of Technology, Bharathiyar University, Coimbatore in the year 1993. She has also pursued her MBA in Education Management. She possesses 28 years of teaching experience and around two decades of research experience. She is also having 10 years of International experience in teaching and research. Currently she is the Head of the Institution at Dr. N.G.P. Institute of Technology, Coimbatore. She has published her research work in many international journals and international conferences. She has received funding to the tune of 50 Lakhs from various funding agencies like UGC, AICTE, MNRE, ISTE, IEEE, etc. She is having 2 Copyrights and 4 patents. She has visited countries like Malaysia, Singapore, Thailand and Indonesia for academic and research purposes. She is a recognized research supervisor of Anna University, Chennai. Under her guidance she has produced 7 Ph.Ds and presently she is supervising 7 Ph.D scholars. She has published around 120 International Journals and International Conferences to her credit. She is constantly acting as a Keynote speaker and reviewer in various International Conferences. She is also a reviewer in renowned international journals. She is passionate in organizing various events like Faculty Development Programs, Seminars, workshops, Conferences, etc for the benefit of the Faculty and Student community. She is a certified Resource Person of National Board of Accreditation, New Delhi and has conducted many workshops and guest lectures on “Outcome Based Education and Outcome Based Accreditation”. She is also a certified Auditor of National Board of Accreditation and helps many institutions to face the NBA Committee by conducting Mock Audits and providing suggestions for improvements. She is well versed in the accreditation process of NAAC and has grabbed funding from UGC under the scheme PARAMARSH for handholding and mentoring the institutions aspiring for NAAC Accreditation. She is a Senior Member of IEEE and Life member of ISTE. She is the recipient of the following awards: i) An International Award, Instituted by Venus Foundation Distinguished Women in Engineering in the field of Electrical and Electronics Engineering. ii) Best Teacher Award, Instituted by Institute for Exploring Advances in Engineering (IEAE). Her main research interests are power system analysis, power system optimization, power system stability, power quality issues and renewable energy resources.