Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants.

Sensors (Basel, Switzerland)·2025
Same author

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.

Sensors (Basel, Switzerland)·2024
Same author

Thermal Modeling of Polyamide 12 Powder in the Selective Laser Sintering Process Using the Discrete Element Method.

Materials (Basel, Switzerland)·2023
Same author

Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?

Sensors (Basel, Switzerland)·2023
Same author

Optimization of UHF RFID Five-Slotted Patch Tag Design Using PSO Algorithm for Biomedical Sensing Systems.

International journal of environmental research and public health·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2026

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells
09:19

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells

Published on: October 3, 2018

8.3K

Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation.

Younes Ledmaoui1, Adila El Maghraoui2, Mohamed El Aroussi1

  • 1Laboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, Morocco.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

An AI model using CNN and VGG16 detects solar panel anomalies, improving efficiency and lifespan. This helps extend the operational life of solar photovoltaic (PV) systems and reduces environmental risks.

Keywords:
artificial intelligencefault detectionpredictive maintenancerenewable energysolar energysolar panelsustainability

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

470
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

982

Related Experiment Videos

Last Updated: Jun 30, 2026

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells
09:19

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells

Published on: October 3, 2018

8.3K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

470
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

982

Area of Science:

  • Renewable Energy Engineering
  • Artificial Intelligence in Energy
  • Materials Science for Photovoltaics

Background:

  • Solar photovoltaic (PV) systems are crucial for renewable energy, but module end-of-life management is a growing concern.
  • Regular maintenance and inspection are vital for PV system longevity, energy efficiency, and environmental protection.
  • Detecting anomalies early can prevent significant performance degradation and system failures.

Purpose of the Study:

  • To develop an innovative, explainable AI model for detecting anomalies in solar PV panels.
  • To enhance the lifespan and power generation efficiency of PV systems through early fault detection.
  • To provide a user-friendly tool for informed decision-making in PV system maintenance.

Main Methods:

  • Utilized an enhanced Convolutional Neural Network (CNN) combined with the VGG16 architecture for anomaly detection.
  • Implemented dataset balancing via oversampling and data augmentation to improve model robustness.
  • Developed a user interface using PyQt5 for intuitive interaction and decision support.

Main Results:

  • The AI model achieved high performance metrics: 91.46% accuracy, 98.29% specificity, and an F1 score of 91.67%.
  • Successfully identified physical and electrical anomalies such as dust accumulation and bird droppings.
  • The PyQt5 interface facilitated user-friendly operation and decision-making.

Conclusions:

  • The developed explainable AI model effectively detects anomalies in solar PV panels, enhancing system efficiency.
  • The approach contributes to prolonging the lifespan of photovoltaic systems and minimizing environmental risks.
  • This AI-driven solution offers a promising method for proactive maintenance and management of solar energy infrastructure.