D
Dipankar Deb
Researcher at Institute of Infrastructure Technology Research and Management
Publications - 159
Citations - 1777
Dipankar Deb is an academic researcher from Institute of Infrastructure Technology Research and Management. The author has contributed to research in topics: Adaptive control & Wind power. The author has an hindex of 18, co-authored 149 publications receiving 1137 citations. Previous affiliations of Dipankar Deb include General Electric & University of Virginia.
Papers
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Review of yield increase of solar panels through soiling prevention, and a proposed water-free automated cleaning solution
Dipankar Deb,Nisarg Brahmbhatt +1 more
TL;DR: In this paper, a detailed review of soiling prevention methods is presented with a view to increase the energy capture from solar panels, and the technical details of an indigenously developed automated water-free cleaning device to remove soiling over the solar panels are discussed.
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Hybrid machine intelligent SVR variants for wind forecasting and ramp events
TL;DR: A hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform is discussed, and two new regression models are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites.
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An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals
Manish Sharma,P.V. Achuth,Dipankar Deb,Subha D. Puthankattil,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +6 more
TL;DR: A computer aided depression diagnosis system using newly designed bandwidth-duration localized (BDL) three-channel orthogonal wavelet filter bank and EEG signal for the detection of depression and attained the perfect value of 1 for area under the curve (AUC) of receiver’s operating characteristics (ROC) using seven features.
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Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms
Harsh S. Dhiman,Dipankar Deb +1 more
TL;DR: Fuzzy-based Multi-criteria decision-making techniques are applied to this cluster of three hybrid wind farms and indicate that A3, that is, paying penalty for power borrowed from a neighboring wind farm is the best alternative for a hybrid wind farm.
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Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines
TL;DR: The proposed method for anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability, and comparison with previous studies shows superior performance.