Autoencoder-based Fault Diagnosis for Hydropower Plants

Hajimohammadali, Fatemeh; Fontana, Nunzia; Tucci, Mauro; Crisostomi, Emanuele

doi:10.1109/powertech55446.2023.10202753

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The autoencoder is a widely recognized AI technique for detecting anomalies. It has been successfully applied in various domains, such as fault detection in wind turbines [19], monitoring hydroelectric power plants using shallow autoencoders coupled with hotelling control charts [20], detecting anomalies in surveillance videos [21], and identifying anomalies in MRI images [22]. The autoencoder is known for its versatility and reliability, especially when combined with a robust statistical distance.…”

Section: Related Work For Autoencodersmentioning

confidence: 99%

An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations

Fera,

Spandonidis

2024

Smart Cities

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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.

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Section: Related Work For Autoencodersmentioning

confidence: 99%

An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations

Fera,

Spandonidis

2024

Smart Cities

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Diagnostic Method for Hydropower Plant Condition-based Maintenance combining Autoencoder with Clustering Algorithms

Jad,

Desforges,

Villard

et al. 2024

IFAC-PapersOnLine

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Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

Hajimohammadali,

Crisostomi,

Tucci

et al. 2024

Energies

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One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems.

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Autoencoder-based Fault Diagnosis for Hydropower Plants

Cited by 3 publications

References 15 publications

An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations

An Artificial Intelligence and Industrial Internet of Things-Based Framework for Sustainable Hydropower Plant Operations

Diagnostic Method for Hydropower Plant Condition-based Maintenance combining Autoencoder with Clustering Algorithms

Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

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