An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations
Hydropower plays a crucial role in supplying electricity to developed nations and is projected to expand its capacity in various developing countries such as Sub-Saharan Africa, Argentina, Colombia, and Turkey. With the increasing demand for sustainable energy and the emphasis on reducing carbon emi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/7/1/20 |
| _version_ | 1797296984794595328 |
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| author | Fation T. Fera Christos Spandonidis |
| author_facet | Fation T. Fera Christos Spandonidis |
| author_sort | Fation T. Fera |
| collection | DOAJ |
| description | Hydropower plays a crucial role in supplying electricity to developed nations and is projected to expand its capacity in various developing countries such as Sub-Saharan Africa, Argentina, Colombia, and Turkey. With the increasing demand for sustainable energy and the emphasis on reducing carbon emissions, the significance of hydropower plants is growing. Nevertheless, numerous challenges arise for these plants due to their aging infrastructure, impacting both their efficiency and structural stability. In order to tackle these issues, the present study has formulated a specialized real-time framework for identifying damage, with a particular focus on detecting corrosion in the conductors of generators within hydropower plants. It should be noted that corrosion processes can be highly complex and nonlinear, making it challenging to develop accurate physics-based models that capture all the nuances. Therefore, the proposed framework leverages autoencoder, an unsupervised, data-driven AI technology with the Mahalanobis distance, to capture the intricacies of corrosion and automate its detection. Rigorous testing shows that it can identify slight variations indicating conductor corrosion with over 80% sensitivity and a 5% false alarm rate for ‘medium’ to ‘high’ severity damage. By detecting and resolving corrosion early, the system reduces disruptions, streamlines maintenance, and mitigates unscheduled repairs’ negative effects on the environment. This enhances energy generation effectiveness, promotes hydroelectric facilities’ long-term viability, and fosters community prosperity. |
| first_indexed | 2024-03-07T22:13:42Z |
| format | Article |
| id | doaj.art-1bf5d2a831b04a8c85e5ed7502f1fdd7 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-07T22:13:42Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-1bf5d2a831b04a8c85e5ed7502f1fdd72024-02-23T15:34:26ZengMDPI AGSmart Cities2624-65112024-02-017149651710.3390/smartcities7010020An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant OperationsFation T. Fera0Christos Spandonidis1Prisma Electronics SA, Agias Kiriakis 45, 17564 Paleo Faliro, GreecePrisma Electronics SA, Agias Kiriakis 45, 17564 Paleo Faliro, GreeceHydropower plays a crucial role in supplying electricity to developed nations and is projected to expand its capacity in various developing countries such as Sub-Saharan Africa, Argentina, Colombia, and Turkey. With the increasing demand for sustainable energy and the emphasis on reducing carbon emissions, the significance of hydropower plants is growing. Nevertheless, numerous challenges arise for these plants due to their aging infrastructure, impacting both their efficiency and structural stability. In order to tackle these issues, the present study has formulated a specialized real-time framework for identifying damage, with a particular focus on detecting corrosion in the conductors of generators within hydropower plants. It should be noted that corrosion processes can be highly complex and nonlinear, making it challenging to develop accurate physics-based models that capture all the nuances. Therefore, the proposed framework leverages autoencoder, an unsupervised, data-driven AI technology with the Mahalanobis distance, to capture the intricacies of corrosion and automate its detection. Rigorous testing shows that it can identify slight variations indicating conductor corrosion with over 80% sensitivity and a 5% false alarm rate for ‘medium’ to ‘high’ severity damage. By detecting and resolving corrosion early, the system reduces disruptions, streamlines maintenance, and mitigates unscheduled repairs’ negative effects on the environment. This enhances energy generation effectiveness, promotes hydroelectric facilities’ long-term viability, and fosters community prosperity.https://www.mdpi.com/2624-6511/7/1/20corrosion detectionautoencoderrobust damage detectionassessment on real-world dataartificial intelligenceInternet of Things (IoT) |
| spellingShingle | Fation T. Fera Christos Spandonidis An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations Smart Cities corrosion detection autoencoder robust damage detection assessment on real-world data artificial intelligence Internet of Things (IoT) |
| title | An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations |
| title_full | An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations |
| title_fullStr | An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations |
| title_full_unstemmed | An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations |
| title_short | An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations |
| title_sort | artificial intelligence and industrial internet of things based framework for sustainable hydropower plant operations |
| topic | corrosion detection autoencoder robust damage detection assessment on real-world data artificial intelligence Internet of Things (IoT) |
| url | https://www.mdpi.com/2624-6511/7/1/20 |
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