Photovoltaic Bypass Diode Fault Detection using Artificial Neural Networks

Mahmoud Dhimish; Andy Tyrrell

Photovoltaic Bypass Diode Fault Detection using Artificial Neural Networks

Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	72
Pages (from-to)	1-10
Journal	IEEE Transactions on Instrumentation and Measurement
Publication status	Published - 13 Feb 2023

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title = "Photovoltaic Bypass Diode Fault Detection using Artificial Neural Networks",

abstract = "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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AU - Dhimish, Mahmoud

AU - Tyrrell, Andy

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N2 - 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.

AB - 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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JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

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ER -

Photovoltaic Bypass Diode Fault Detection using Artificial Neural Networks

Abstract

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