Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

Abstract

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling.

Keywords:

• machine learning-based PV prediction
• fault analysis system
• predictive models
• photovoltaic systems
• forecast outcomes

AUTHORSHIP CONTRIBUTIONS

All authors are contributed equally to this work.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

No participation of humans takes place in this implementation process.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

Additional information

Notes on contributors

G. Karthikeyan

G. Karthikeyan graduated with B.Engg from Anna University, Chennai, India and post graduation with M.E in Power electronics and Drives from Anna University, Chennai, India. He is currently working toward his Ph.D degree in the field of Electrical Engineering from Anna university, Chennai, India. He has about ten years of teaching experience which includes five years of experience in research and development at Knowledge Institute of Technology, Salem, India. His research interest include renewable energy sources, DC-DC Converters, power quality improvement.

A. Jagadeeshwaran

A. Jagadeeshwaran graduated with B.Engg from University of Madras India and post graduation with M.Tech in Power electronics from VIT. He holds his doctoral degree in the field of Electrical Engineering from Anna university, Chennai, India. He has about twenty three years of teaching experience which includes fifteen years of experience in research and development at Sona College of Technology, Salem, India. He has over 48 publications in the field of special electrical machines development, control electronics and power quality. His prime interest areas include power quality improvement, analysis of multilevel inverters, design and development of Permanent magnet motor drives for special purpose and control of electric drives. He is a member of IEEE, ISSE, IE and life member of ISTE.