Detecting Permanently Activated Bypass Diodes in Utility-Scale PV Plant Monitoring Data

Aarseth, Bjørn Lupton; Skomedal, Åsmund; Øgaard, Mari Benedikte; Marstein, Erik Stensrud

doi:10.1109/jphotov.2022.3187381

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…The reason behind this, electrically speaking, is that the bypass diodes are only activated during shading conditions. For example, recent studies [17][18][19] have shown that partial shading can drop the PV output power; however, it can be effectively alleviated when bypass diodes are equipped with the modules. In addition, the faulty conditions of PV modules, such as faults in bypass diodes or DC arcing, can result in current leakage.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Dhimish

Tyrrell

2023

IEEE Trans. Instrum. Meas.

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Due to the importance of determining faulty bypass diodes in photovoltaic systems, faulty bypass diodes have been of widespread interest in recent years due to their importance to improving PV system durability, operation, and overall safety. This paper presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANN) to detect short and open PV bypass diodes fault conditions. With only three inputs from the PV system, namely the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

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Section: Introductionmentioning

confidence: 99%