M
Mehdi Bagheri
Researcher at Nazarbayev University
Publications - 200
Citations - 3297
Mehdi Bagheri is an academic researcher from Nazarbayev University. The author has contributed to research in topics: Transformer & Distribution transformer. The author has an hindex of 25, co-authored 180 publications receiving 2109 citations. Previous affiliations of Mehdi Bagheri include Islamic Azad University & Sharif University of Technology.
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Wireless Charging Techniques for UAVs: A Review, Reconceptualization, and Extension
TL;DR: This study focuses on presenting wireless techniques available for drone mission duration improvement as well as discuss and practically examine the most feasible and reliable technique to charge UAV using power lines.
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Advanced transformer winding deformation diagnosis: moving from off-line to on-line
TL;DR: In this article, the authors have concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques, such as frequency response analysis (FRA), short circuit impedance measurement and transfer function measurement.
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Frequency response analysis and short-circuit impedance measurement in detection of winding deformation within power transformers
TL;DR: In this paper, power transformers are considered to be the heart of the transmission and distribution sectors of electric power systems; monitoring their condition and diagnosing faults are important parts of the maintenance function.
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IN-YOLO: Real-Time Detection of Outdoor High Voltage Insulators Using UAV Imaging
TL;DR: This paper provides a cost-effective solution for detecting insulators under the conditions of an uncluttered background, varied object resolution and illumination conditions using You Only Look Once (YOLO) deep learning neural network model from aerial images.
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Improved EMD-Based Complex Prediction Model for Wind Power Forecasting
Oveis Abedinia,Mohamed Lotfi,Mehdi Bagheri,Behrouz Sobhani,Miadreza Shafie-khah,Joao P. S. Catalao +5 more
TL;DR: A novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements is proposed to demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.