A Comparative Study of Fault Diagnosis Methods of Photovoltaic Cells

Zhao, J., Zhang, J., Walton, F. , Ghannam, R. , Wang, C. and Heidari, H. (2022) A Comparative Study of Fault Diagnosis Methods of Photovoltaic Cells. In: 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2022), Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9971101)

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Due to their high efficiency, photovoltaic (PV) cells can power the Internet of Things (IoT) devices, including sensors, actuators, and communication devices. Generally, PV cells are connected in series to obtain a greater voltage without losing energy and active area. However, a series connection is unstable, and any fault in an array inevitably leads to a breakdown of the branch or even the system. Therefore, fault detection is essential. This study presents a systematic review of stat-of-the-art fault diagnosis methods (FDMs) of PV cells. We categorise, evaluate and summarise the fault detection methods into three broad areas: physical, threshold and artificial intelligence (AI) techniques. Physical FDMs detect the faults by comparing the inner characteristics of Photovoltaic (PV) cells or their derived parameters with the expected values. Threshold FDMs compare the fault PV characteristics with the ones under normal conditions. AI FDMs detect faults by employing a trained intelligent classifier. Regarding the accuracy, the AI FDMs achieved an accuracy of no less than 90%, and some of AI FDMs even obtained 100% accuracy. Methods belonging to each category are introduced in detail. Finally, the summary is given, and the developing tendency is recommended for future work.

Item Type:Conference Proceedings
Additional Information:This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Doctoral Prize Research Fellowship 'Scalable Controlled Treatment impLAntables for Neurological Disorders' (SCOTLAND) under Grant no. EP/TS17896/1, the EPSRC Impact Acceleration Account Grant ProtoFlex, no. EP/RS11705/1.
Glasgow Author(s) Enlighten ID:Walton, Mr Finlay and Ghannam, Professor Rami and Zhao, Jinwei and Heidari, Professor Hadi
Authors: Zhao, J., Zhang, J., Walton, F., Ghannam, R., Wang, C., and Heidari, H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
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