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Devarajan Srinivasan
Arizona State University
8Publications
2H-index
23Citations
Publications 8
Newest
Published on Jun 1, 2018
Sai Tatapudi7
Estimated H-index: 7
(ASU: Arizona State University),
Jack David Flicker9
Estimated H-index: 9
(SNL: Sandia National Laboratories)
+ 5 AuthorsGovindasamy Tamizhmani18
Estimated H-index: 18
(ASU: Arizona State University)
Module level power electronics (MLPE), such as microinverters and DC power optimizers, are power electronic devices integrated or attached with PV modules so that there is one power-conditioning unit per module. This distributed architecture offers significant system benefits, including reduced component electrical stress, partial shading gains, and reduced effect of module failure on array performance. While the majority of MLPE studies have focused on performance, there is a distinct lack of l...
Published on May 1, 2018
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated f...
Published on Aug 1, 2017
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 8 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled senso...
Published on Jul 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
Published on Jun 1, 2016in Sustainable Energy, Grids and Networks
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Santoshi T. Buddha3
Estimated H-index: 3
(ASU: Arizona State University)
+ 4 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
(ASU: Arizona State University)
As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve monitoring and management of PV arrays. A procedure is presented here for optimizing the electrical configuration of a PV array under a variety of operating conditions. Computer simulations and analysis with synthetic and real data are presented in this paper. The performance of the optimization system is evaluated for a variety of partial shading conditions using a SPICE circuit simulator. In general,...
Published on Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
David Ramirez Dominguez3
Estimated H-index: 3
(ASU: Arizona State University)
+ 8 AuthorsYoshitaka Morimoto1
Estimated H-index: 1
Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchan...
Published on Sep 1, 2015
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Shwetang Peshin1
Estimated H-index: 1
(ASU: Arizona State University)
+ 4 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module. This occurs because unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. Here we present an irradiance estimation algorithm for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an array's e...
Published on Jun 1, 2015 in PVSC (Photovoltaic Specialists Conference)
Arkanatha Sastry1
Estimated H-index: 1
(ASU: Arizona State University),
Siddharth Kulasekaran2
Estimated H-index: 2
(ASU: Arizona State University)
+ 5 AuthorsIan Tilford1
Estimated H-index: 1
(Bosch)
The scope for module level power electronics (MLPE) is immense in modern day PV industry but it lacks in assessing the reliability. This paper presents the work performed by PREDICTS (Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar) team lead by Sandia National Laboratories to develop a standard reliability assessment protocol for MLPE including microinverters and microconverters. This paper discusses the ground work performed to develop the reliability ass...
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