Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis
This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by f...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2025-04-01
|
| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/40520 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849419475668959232 |
|---|---|
| author | Mohammed Bouzidi Abdelfatah Nasri Omar Ouledali Messaoud Hamouda |
| author_facet | Mohammed Bouzidi Abdelfatah Nasri Omar Ouledali Messaoud Hamouda |
| author_sort | Mohammed Bouzidi |
| collection | DOAJ |
| description | This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by forecasting their performance through real-time data analysis and modelling essential operational variables. The research was carried out in a solar field in AOULEF-ADRAR (South of Algeria), which covers six hectares and consists of 20,460 solar panels with an efficiency of 15 % to 20 %. The cumulative installed capacity is 5 MW, and the system is connected to a 30 kV electrical grid. The experimental findings validated the efficacy of the suggested ANN-based fault detection method. Subsequent to a sandstorm, the system exceeded standard operational limits, culminating in a total power overshoot of 200 KW. This procedure facilitated the identification of system faults and the execution of corrective measures, including the cleaning of PV modules to restore efficiency. The research highlights the importance of artificial intelligence (AI)-based monitoring systems to reduce downtime and maintenance expenses and guarantee consistent operation of photovoltaic plants under various environmental conditions. Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. Augmenting the data set by integrating data from various PV stations in different regions may improve the adaptability of the model to different environmental conditions. This method improves the creation of intelligent self-diagnosing solar power systems, promoting increased reliability and efficiency in the integration of global renewable energy. |
| format | Article |
| id | doaj-art-d69f7eb0732147c899966f4e71ac44b6 |
| institution | Kabale University |
| issn | 1392-1215 2029-5731 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj-art-d69f7eb0732147c899966f4e71ac44b62025-08-20T03:32:04ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312025-04-01312323910.5755/j02.eie.4052045774Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and DiagnosisMohammed Bouzidi0Abdelfatah Nasri1Omar Ouledali2Messaoud Hamouda3Department of Sciences and Technology, Faculty of Sciences and Technology, University of Tamanrasset, Tamanghasset, AlgeriaLaboratory Smart Grid and Renewable Energy SGRE, University Tahri Mohamed Bechar, Bechar, AlgeriaSustainable Development and Informatics Laboratory (LDDI), Faculty of Science and Technology, University of Adrar, Algeria Sustainable Development and Informatics Laboratory (LDDI), Faculty of Science and Technology, University of Adrar, Algeria This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by forecasting their performance through real-time data analysis and modelling essential operational variables. The research was carried out in a solar field in AOULEF-ADRAR (South of Algeria), which covers six hectares and consists of 20,460 solar panels with an efficiency of 15 % to 20 %. The cumulative installed capacity is 5 MW, and the system is connected to a 30 kV electrical grid. The experimental findings validated the efficacy of the suggested ANN-based fault detection method. Subsequent to a sandstorm, the system exceeded standard operational limits, culminating in a total power overshoot of 200 KW. This procedure facilitated the identification of system faults and the execution of corrective measures, including the cleaning of PV modules to restore efficiency. The research highlights the importance of artificial intelligence (AI)-based monitoring systems to reduce downtime and maintenance expenses and guarantee consistent operation of photovoltaic plants under various environmental conditions. Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. Augmenting the data set by integrating data from various PV stations in different regions may improve the adaptability of the model to different environmental conditions. This method improves the creation of intelligent self-diagnosing solar power systems, promoting increased reliability and efficiency in the integration of global renewable energy.https://eejournal.ktu.lt/index.php/elt/article/view/40520synthetic neural networkpv systemerror detectiondiagnostic systemresidue analysis |
| spellingShingle | Mohammed Bouzidi Abdelfatah Nasri Omar Ouledali Messaoud Hamouda Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis Elektronika ir Elektrotechnika synthetic neural network pv system error detection diagnostic system residue analysis |
| title | Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis |
| title_full | Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis |
| title_fullStr | Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis |
| title_full_unstemmed | Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis |
| title_short | Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis |
| title_sort | optimising solar power plant reliability using neural networks for fault detection and diagnosis |
| topic | synthetic neural network pv system error detection diagnostic system residue analysis |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/40520 |
| work_keys_str_mv | AT mohammedbouzidi optimisingsolarpowerplantreliabilityusingneuralnetworksforfaultdetectionanddiagnosis AT abdelfatahnasri optimisingsolarpowerplantreliabilityusingneuralnetworksforfaultdetectionanddiagnosis AT omarouledali optimisingsolarpowerplantreliabilityusingneuralnetworksforfaultdetectionanddiagnosis AT messaoudhamouda optimisingsolarpowerplantreliabilityusingneuralnetworksforfaultdetectionanddiagnosis |