Shading anomaly detection framework for bi-facial photovoltaic modules

ABSTRACT

Solar photovoltaics have been widely used as solar energy harvesting systems for many years throughout the world. Lately, bi-facial modules have been gaining a lot of popularity due to utilizing solar irradiance on both sides of the module. Therefore, the presence of partial static shading on such modules may lead to some ambiguity regarding their output powers and efficiencies. In this work, a shading anomaly detection framework comprised three stages: An autoencoder-convolutional neural networks CNN model, a mean absolute error MAE threshold, and data filters. The framework was developed to detect the occurrence and location of partial shading on bi-facial modules. Several experiments were carried out using two bi-facial modules under different shading settings. The modules were connected to solar chargers and batteries to analyze their performances. The experimental results showed the modules’ generated current and the batteries’ state of charge SOC in all shading settings. The results also showed that anomalies or shading can be detected with an accuracy of more than 99% merely from the second stage of the framework. However, the location of shading can be classified and predicted with an accuracy of 91% by utilizing all three stages of the framework.

KEYWORDS:

• Bi-facial
• PV module
• autoencoder
• CNN
• shading detection

Acknowledgements

The authors would like to thank the Arab Open University and Al-Zaytoonah University of Jordan for providing the necessary scientific research supplies to implement this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors extend their appreciation to the Arab Open University for funding this work through AOU research fund no. (AOURG 2023-006).

Notes on contributors

Ahmad Manasrah

Ahmad Manasrah is currently an associate professor of Mechanical Engineering at Al Zaytoonah University of Jordan. He received the B.S. degree from Al Balqaa University, Amman, Jordan and the M.S. and Ph.D. degrees from The University of South Florida, all in mechanical engineering. He was a research assistant and a member of Rehabilitation Engineering and Electromechanical Design Lab at the USF. He is also a member of ASHRAE, Jordan. His interests include Renewable Energy, Smart Energy Technology Mechanical Control, and Education.

Mohammad Masoud

Mohammad Masoud is currently an Associate Professor of electrical Engineering at Al Zaytoonah University of Jordan, Amman, Jordan. He received his BSc. degree in Computer Engineering from Mu’tah University, Karak, Jordan in 2006. He received his MSc. in Electrical and Computer Engineering from (NYIT), Amman, Jordan. He received his PhD. in Communication Engineering and Information Systems from Huazhong University of Science and Technology (HUST), Wuhan,China in 2012. He is a reviewer in many computer and communication journals. His research interests include computer network measurements, network security, machine learning algorithms andapplications, Software Defined Networking (SDN), embedded systems, control theory and Cyber Physical Systems (CPS).

Yousef Jaradat

Yousef Jaradat received the B.S. degree in electrical and computer engineering from the Jordan University of Science and Technology, Jordan, in 2000, and the Ph.D. degree in electrical and computer engineering from New Mexico State University, Las Cruces, NM, USA, in 2012. He is a Professor of Computer and Communications Engineering at Al-Zaytoonah University of Jordan. His research includes work in the capacity of wireless networks, obstacles analysis in network areas, and simulation of wireless networks.

Mohammad Alia

Mohammad Alia is a professor of computer science at AL-Zaytoonah University of Jordan. He received his PhD from the Universiti Sains Malaysia (USM), Penang, Malaysia, 2008. His research interests include public key cryptosystems, fractals, image processing and steganography, wireless networks and machine learning.

Sally Almanasra

Sally Almanasra is an associate professor at the faculty of computer studies, Arab Open University, Saudi Arabia. She received her PhD in AI and Software Engineering from Universiti Sains Malaysia in 2014. Her main teaching and research interests include AI, information security, and game theory.

Khaled Suwais

Khaled Suwais is an associate professor in computer science. He received his PhD in computer science from University Sains Malaysia, Malaysia in 2009. Dr. Suwais is specialized in the field of information security and cryptography. His research interest includes: cryptography, information security and operational research.