Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants
This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/206 |
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| author | Younes Ledmaoui Adila El Maghraoui Mohamed El Aroussi Rachid Saadane |
| author_facet | Younes Ledmaoui Adila El Maghraoui Mohamed El Aroussi Rachid Saadane |
| author_sort | Younes Ledmaoui |
| collection | DOAJ |
| description | This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures. |
| format | Article |
| id | doaj-art-49554bb739ef4232b756d736a9914179 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-49554bb739ef4232b756d736a99141792025-01-10T13:21:13ZengMDPI AGSensors1424-82202025-01-0125120610.3390/s25010206Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar PlantsYounes Ledmaoui0Adila El Maghraoui1Mohamed El Aroussi2Rachid Saadane3Laboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, MoroccoGreen Tech Institute, Mohammed VI Polytechnic University, Benguerir BP 43150, MoroccoLaboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, MoroccoLaboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, MoroccoThis paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures.https://www.mdpi.com/1424-8220/25/1/206artificial intelligencecybersecuritypredictive maintenancerenewable energysolar plantreview |
| spellingShingle | Younes Ledmaoui Adila El Maghraoui Mohamed El Aroussi Rachid Saadane Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants Sensors artificial intelligence cybersecurity predictive maintenance renewable energy solar plant review |
| title | Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants |
| title_full | Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants |
| title_fullStr | Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants |
| title_full_unstemmed | Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants |
| title_short | Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants |
| title_sort | review of recent advances in predictive maintenance and cybersecurity for solar plants |
| topic | artificial intelligence cybersecurity predictive maintenance renewable energy solar plant review |
| url | https://www.mdpi.com/1424-8220/25/1/206 |
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