S
Sanjeevikumar Padmanaban
Researcher at Aarhus University
Publications - 431
Citations - 9689
Sanjeevikumar Padmanaban is an academic researcher from Aarhus University. The author has contributed to research in topics: Photovoltaic system & Computer science. The author has an hindex of 34, co-authored 367 publications receiving 5244 citations. Previous affiliations of Sanjeevikumar Padmanaban include Sathyabama University & National Institute of Technology, Puducherry.
Papers
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Journal ArticleDOI
Concept of net zero energy buildings (NZEB) - A literature review
TL;DR: In this paper , a review of the existing NZEB to make them self-sustaining and net zero in order to improve energy efficiency of the buildings is presented, and a detailed literature review is given.
Proceedings ArticleDOI
Nine-phase hex-tuple inverter for five-level output based on double carrier PWM technique
Sanjeevikumar Padmanaban,Mahajan Sagar Bhaskar,Frede Blaabjerg,Lars Norum,Sridhar Seshagiri,Amin Hajizadeh +5 more
Proceedings ArticleDOI
L-L multilevel boost converter topology for renewable energy applications: A new series voltage multiplier L-L converter of XY family
Mahajan Sagar Bhaskar,Sanjeevikumar Padmanaban,Viliam Fedak,Frede Blaabjerg,Patrick Wheeler,Vigna K. Ramachandaramurthy +5 more
TL;DR: A new L-L Multilevel Boost Converter (L-LMBC) topology for high gain renewable energy applications is proposed and the result validates the functionality, feasibility and design of the converter.
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Energy Conservation Approach for Continuous Power Quality Improvement: A Case Study
Lalith Pankaj Raj Nadimuthu,Kirubakaran Victor,Chakarajamula Hussaian Basha,T. Mariprasath,C. Dhanamjayulu,Sanjeevikumar Padmanaban,Baseem Khan +6 more
TL;DR: In this article, the effect of the harmonic mitigating filter in the textile industry with innovative energy conservation strategies for energy bill reduction, which covers a pathway to climate change mitigation is investigated.
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Insulation condition assessment of high-voltage rotating machines using hybrid techniques
TL;DR: This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN-genetic algorithm.