Algerian Journal of Renewable Energy and Sustainable Development
Volume 7, Numéro 1, Pages 97-103
2025-06-15

Photovoltaic Panel Faults Diagnosis: The Use Of The Bishop Model For Confirmation Of The Diagnosis Method Based On The Fill Factor Analysis And Use Of Artificial Intelligence Techniques

Authors : Abdelhamid Bouzaher .

Abstract

Solar Energy Has Become a Demanded Renewable Source of Electricity, Pushed by Significant Improvements in Solar Panel Efficiency Due To Advancements in Semiconductor Materials Science Worldwide. The Performance of a Solar Panel Is Not Limited by Design and Materials; It Is Greatly Affected by Faults That Can Disturb or Reduce Its Efficiency. To Face These Malfunctions and Identify Them Promptly, Various Techniques and Methods Have Been Proposed Such as The Use Artificial Intelligence Techniques. On A Previous Work [1] Through the Model with a Single Diode of The Photovoltaic Cell, We Proposed a Diagnostic Method Based on The Artificial Intelligence Techniques and The Use of The Fill Factor (FF) And the Maximum Value of The Short Circuit Current (Isc) As Input Parameters. This Method, in Addition to Its Simplicity, Has Proven to Be Reliable and Efficient. The Choice of These Two Input Criteria Is Justified by Their Importance: The Variation in Short Circuit Current Is a Significant and Variable Value Depending on The State of The Photovoltaic Cell, While the Fill Factor Provides a Better Visualization of Efficiency and The Resultant Current-Voltage Characteristic. The Diagnostic Process, Simulated in The MATLAB Environment as Like: The First Step Involves Threshold Detection, Where Defect Identification Is Based on The Threshold of Each Symptom, While the Second Step Use the Artificial Intelligence Techniques in Defect Identification, Particularly for Cases with Identical Fault Symptoms. In This Work, We Propose the Use of The Bishop Model of the Photovoltaic Cell to Redo the Diagnosis and Confirm the Reliability of The Proposed Method. This Method Enables the Diagnosis of Faults in Photovoltaic Panels.

Keywords

Photovoltaic Panel Faults Diagnosis Bishop Model Artificial Intelligence Fill Factor