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Gunapriya Devarajan

Researcher at Akshaya College of Engineering and Technology

Publications -  10
Citations -  49

Gunapriya Devarajan is an academic researcher from Akshaya College of Engineering and Technology. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

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A Review on Hydrogen-Based Hybrid Microgrid System: Topologies for Hydrogen Energy Storage, Integration, and Energy Management with Solar and Wind Energy

TL;DR: In this article , a case study of a HMG system that uses hydrogen as one of the main energy sources together with a solar panel and wind turbine is presented, where the bidirectional AC-DC converter (BAC) is designed for HMGs to maintain power and voltage balance between the DC and AC grids.
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Integration of Photovoltaic-Based Transformerless High Step-Up Dual-Output–Dual-Input Converter with Low Power Losses for Energy Storage Applications

TL;DR: In this paper , the authors proposed a non-isolated DC-DC converter, which has a high step-up capability and multiple ports for outputs and inputs for ESS applications.
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Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system

TL;DR: This method provides accurate diagnosis indicator that will be used as a bench mark value for preparing the maintenance schedule under non-destructive mode and adaptive neuro-fuzzy inference system classifier is used to classify the faults based on the diagnosis indicator data base.
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Design and Implementation of Hybrid PV/Battery-Based Improved Single-Ended Primary-Inductor Converter-Fed Hybrid Electric Vehicle

TL;DR: In this paper, an improved three-port two-step-up single-ended primary-induction converters (SEPIC) converter fed (Photovoltaic )PV- Hybrid Electric Vehicle was proposed.
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Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

TL;DR: In this article , the authors presented a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM.