Identification and assessment of risk factors in offshore wind-integrated hydrogen production system

El-Kady, Ahmed Hamdy; Amin, Md Tanjin; Khan, Faisal; El-Halwagi, Mahmoud M.

doi:10.1016/j.ijhydene.2023.10.184

Cited by 11 publications

(1 citation statement)

References 88 publications

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“…In this paper, El-Kady et al 8 employed various data science techniques to optimize offshore wind-integrated hydrogen production systems, enhance efficiency, and mitigate risks. This paper includes some key techniques such as geospatial modeling, which utilizes geospatial methods to estimate the levelized cost of hydrogen production; ML techniques to predict the wind speed, which is essential for optimizing energy production; data analysis methods to analyze vast amounts of data in real-time, providing actionable insights for decision-making; and asset management techniques using unsupervised ML for life-extension classification of offshore wind assets, aiding in asset management and maintenance planning.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Simiyon,

Sachidanand,

Halmakki Krishnamurthy

et al. 2024

ACS Omega

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine

Simiyon,

Sachidanand,

Halmakki Krishnamurthy

et al. 2024

ACS Omega

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Offshore Wind‐Hydrogen Systems Fault Detection Based on CNN‐BiLSTM‐AM Algorithm

Zhao,

Sun,

Zhou

et al. 2024

Quality & Reliability Eng

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This study presents a novel deep learning‐based approach for fault detection in offshore wind‐hydrogen systems. A fault detection model is developed using convolutional neural networks (CNNs), bidirectional long short‐term memory networks (BiLSTMs), and an attention mechanism (AM). The model is trained on a dataset generated through fault injection techniques, which simulate real‐world faults in the system. Key operational parameters, such as wind speed and hydrogen production rate, are used to detect faults effectively. This paper reduces reliance on actual experiments, and introducing artificial faults allows system performance assessment under different fault scenarios, lowering project risks and costs. This work facilitates automatic feature extraction and high‐precision classification of time‐series fault data, which covers a fully automated learning process from data to fault detection. The outstanding performance of the method is validated through computation and result comparison.

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