Ocean data classification using unsupervised machine learning: Planning for hybrid wave-wind offshore energy devices

Masoumi, Masoud

doi:10.1016/j.oceaneng.2020.108387

Cited by 23 publications

(6 citation statements)

References 24 publications

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“…The model's performance was comparable to physical modeling hindcasts and aligned with existing sea-state models in the North Sea and the Irish Sea. Furthermore, Masoumi used K-means clustering to group U.S. coastal regions based on wave height, wave period, and wind speed data from the National Data Buoy Center [17]. Three models were created using data from different time periods (2019, 2015-2019, and 2010-2019).…”

Section: Climatic Data Prediction and Environmental Effectsmentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

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Section: Climatic Data Prediction and Environmental Effectsmentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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“…Based on data given by the National Data Buoy Center, a data mining and machine learning approach was employed to identify the areas in the United States in this study [92]. The goal was to construct an early evaluation tool analysis of the data obtained to facilitate decision-making in the design process for wave-Wind hybrid systems with great flexibility within each location.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

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“…𝑟 𝑟 = 1 − 𝑝 𝐹 (2) In this equation, 𝑡 𝑠 is the time interval of recorded data, and 𝑇 𝑟 is return period. So considering 50 years return period, the failure probability is calculated.…”

Section: Joint Probability Distribution Of Wave Period and Amplitudementioning

confidence: 99%

“…Surveying and resource assessments are the primary steps in the market stationing of marine renewable energy [1]. The wave characteristics of the installation site affect the devices' design and efficiency [2].…”

Section: Introductionmentioning

confidence: 99%

Environmental Extreme Conditions for a Wave Energy Converter: An Integrated Wave-Structure Approach

Saeidtehrani

Lavidas

Metrikine

2022

Volume 8: Ocean Renewable Energy

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The process of finding values of extreme waves and their impacts on wave energy converter (WEC) responses, depends on both wave resources and WEC dynamic characteristics. A wide range of waves can influence the reliability of operation and survivability of a WEC. In this paper, a methodology is described to find specific values of wave heights and periods that can be critical for a flap-type WEC, through the use of an integrated wave-structure approach. The goodness of the fit is studied and the corresponding return periods for ranges that a selected WEC is mostly operating are evaluated. Further discussions for engineering applications and making a clear understanding of the extreme and operational conditions of the WEC are also provided. The critical wave characteristics are defined based on power production and the significant change in the nonlinear dynamic behavior of the device in various ranges. The WEC behavior is represented by an experimentally validated numerical tool considering the nonlinear behaviour of a flap as single and in an array. The study aims to provide insights in the expected behavior and loading on the WEC, affecting its reliability, considering potential catastrophic wave return values that correspond to their power production phase. The finding can be used for determining both efficient operations and limiting environmental conditions or weather windows.

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Ocean data classification using unsupervised machine learning: Planning for hybrid wave-wind offshore energy devices

Cited by 23 publications

References 24 publications

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Environmental Extreme Conditions for a Wave Energy Converter: An Integrated Wave-Structure Approach

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