Review on Unconventional Wind Energy

Gulabani, Gaurav; Karim, Beegum Shahnaz Abdul; Radhakrishnan, Jayakrishnan; Shenoy, Bhaskar; Zuber, Mohammad

doi:10.5614/j.eng.technol.sci.2020.52.4.8

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…In the expectation of public safety concerns, there was a consensus in the reviewed literature that the industry has to prove reliable operation to increase the support for the technology from investors, regulators, and the general public [22,44,56,57]. Proving reliable operation includes establishing safety regulations [58][59][60], having AWE systems with high fault tolerance [56,61], and minimizing the risk of accidents to an acceptable level [62]. Furthermore, the reviewed literature argued that AWE test sites should be far away from populated areas until the systems are proven to be safe to avoid concerns among nearby residents [12,63,64].…”

Section: Safety and Related Aspectsmentioning

confidence: 99%

The Social Acceptance of Airborne Wind Energy: A Literature Review

et al. 2022

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Airborne wind energy (AWE) systems use tethered flying devices to harvest higher-altitude winds to produce electricity. For the success of the technology, it is crucial to understand how people perceive and respond to it. If concerns about the technology are not taken seriously, it could delay or prevent implementation, resulting in increased costs for project developers and a lower contribution to renewable energy targets. This literature review assessed the current state of knowledge on the social acceptance of AWE. A systematic literature search led to the identification of 40 relevant publications that were reviewed. The literature expected that the safety, visibility, acoustic emissions, ecological impacts, and the siting of AWE systems impact to which extent the technology will be accepted. The reviewed literature viewed the social acceptance of AWE optimistically but lacked scientific evidence to back up its claims. It seemed to overlook the fact that the impact of AWE’s characteristics (e.g., visibility) on people’s responses will also depend on a range of situational and psychological factors (e.g., the planning process, the community’s trust in project developers). Therefore, empirical social science research is needed to increase the field’s understanding of the acceptance of AWE and thereby facilitate development and deployment.

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Section: Safety and Related Aspectsmentioning

confidence: 99%

The Social Acceptance of Airborne Wind Energy: A Literature Review

et al. 2022

View full text Add to dashboard Cite

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“…In expectation of public safety concerns, there is a consensus in the field that the industry has to prove safe operation to gain favourable public responses [5,15,51,52]. Proofing reliable operations includes establishing safety regulations [53][54][55], having AWE systems with high fault tolerance [51,56], and minimizing the risk of accidents to an acceptable level [57]. Furthermore, the literature argues that to avoid public concerns, AWE systems should be located away from populated areas until the systems are proven to be safe [24,58,59].…”

Section: Safety and Related Aspectsmentioning

confidence: 99%

Public Responses to Airborne Wind Energy: A Literature Review

Schmidt

Vries

Renes

et al. 2021

Preprint

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Airborne wind energy (AWE) systems use tethered flying devices to harvest higher-altitude winds to produce electricity. For a successful deployment of these systems, it is crucial to understand how the public perceives them. If public concerns about the technology are not taken seriously, implementation could be delayed or, in some cases, prevented, resulting in increased costs for project developers and a lower contribution of the sector to renewable energy targets. This literature review assessed the current state of knowledge on public responses to AWE. An exhaustive literature search led to the identification of 40 relevant publications that were reviewed. The literature assumed that the safety, visibility, acoustic emissions, ecological impacts, and the siting of AWE systems shape public responses to the technology. The reviewed literature views people’s responses to AWE very optimistically but lacks scientific evidence to back up its claims. It seems to overlook that the influence of AWE’s characteristics (e.g., visibility) on public responses will also depend on a range of situational and psychological factors (e.g., people’s general attitude towards AWE, the public’s trust in project developers). Therefore, empirical social scientific research is needed to increase the field’s understanding of public responses to AWE and thereby facilitate deployment.

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Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Abu Jami'in,

Munadhif,

et al. 2024

IEEJ Transactions Elec Engng

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Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Review on Unconventional Wind Energy

Cited by 5 publications

References 30 publications

The Social Acceptance of Airborne Wind Energy: A Literature Review

The Social Acceptance of Airborne Wind Energy: A Literature Review

Public Responses to Airborne Wind Energy: A Literature Review

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

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