Faults in photovoltaic (PV) modules may occur due to various environmental and physical
factors. To prevent faults and minimize investment losses, fault diagnosis is crucial to ensure …

This study proposes a novel approach utilizing a voting-based ensemble technique to
diagnose visible faults in photovoltaic (PV) modules from aerial images captured by …

Fault occurrences in photovoltaic (PV) modules can hinder the performance of the system,
resulting in reduced lifetime and performance of the modules. PV module (PVM) faults if …

The present study proposes an ensemble-based deep neural network (DNN) model for
autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and …

Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and
reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination …

Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the
efficient and reliable operation of large-scale photovoltaic plants. This article presents an …

Photovoltaic (PV) power systems have a significant potential to reduce greenhouse gases
and diversify the electricity generation mix. Faults and damages that cause energy losses …

Visual faults in photovoltaic (PV) modules persist as a problem that can create
consequences such as reduced life span, increased output power loss and raising safety …

In this study, a novel technique for identifying and categorizing flaws in small-scale
photovoltaic systems is presented. First, a supervised machine learning (neural network) …

Photovoltaic systems provide an eco-friendly key to meet our increasing energy demand
while mitigating the adverse impacts of conventional fossil fuel-based energy generation …