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Adrian Ilinca

Researcher at Université du Québec à Rimouski

Publications -  155
Citations -  5394

Adrian Ilinca is an academic researcher from Université du Québec à Rimouski. The author has contributed to research in topics: Wind power & Renewable energy. The author has an hindex of 28, co-authored 128 publications receiving 4338 citations. Previous affiliations of Adrian Ilinca include Université du Québec & Lebanese University.

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A review of Industry 4.0 characteristics and challenges, with potential improvements using blockchain technology

TL;DR: In this paper, the authors investigated the potential of blockchain technology as an empowering tool for the 4th industrial era, its challenges, barriers and limitations, and finally explored areas where blockchain technology can bring new features and add value to the deployment of Industry 4.0.
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Hyperspectral imaging applied for the detection of wind turbine blade damage and icing

TL;DR: In this paper, the potential of the use of hyperspectral imaging system in the detection of wind turbine blade damage and icing incident is introduced, and the types of damage, its causes and the techniques used to detect it.
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Assessing the potential for a wind power incentive for remote villages in Canada

TL;DR: In this paper, a wind-diesel production incentive designed specifically for Canadian northern and remote communities is discussed, where the authors demonstrate that such an incentive could result in 14.5MW of wind energy projects in remote villages in Canada over a 10-year period, saving 11.5 $Cdn million dollars in diesel costs.

Behaviour and Modeling of Cup Anemometers under Icing Conditions

TL;DR: In this article, the authors evaluated the performance of two cup anemometers in a wind tunnel under a very severe freezing fog, in order to estimate and model the performance loss in adverse atmospheric conditions.
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Optimal management of compressed air energy storage in a hybrid wind-pneumatic-diesel system for remote area's power generation

TL;DR: In this article, the authors compared several strategies of management of compressed air energy storage (CAES) and showed that using an algorithm based on wind speed forecast, is the most efficient.