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Devarajan Srinivasan
Arizona State University
8Publications
2H-index
23Citations
Publications 8
Newest
#1Sameeksha Katoch (ASU: Arizona State University)H-Index: 1
#2Gowtham Muniraju (ASU: Arizona State University)H-Index: 1
Last.Devarajan SrinivasanH-Index: 2
view all 8 authors...
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...
#1Sunil Rao (ASU: Arizona State University)H-Index: 2
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 1
Last.Mahesh K. Banavar (Clarkson University)H-Index: 12
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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...
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 1
#2Sunil Rao (ASU: Arizona State University)H-Index: 2
Last.Devarajan SrinivasanH-Index: 2
view all 8 authors...
#1Henry Braun (ASU: Arizona State University)H-Index: 4
#2Santoshi T. Buddha (ASU: Arizona State University)H-Index: 3
Last.Devarajan Srinivasan (ASU: Arizona State University)H-Index: 2
view all 7 authors...
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,...
Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
#1Sunil Rao (ASU: Arizona State University)H-Index: 2
#2David Ramirez Dominguez (ASU: Arizona State University)H-Index: 3
Last.Yoshitaka MorimotoH-Index: 1
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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...
#1Henry Braun (ASU: Arizona State University)H-Index: 4
#2Shwetang Peshin (ASU: Arizona State University)H-Index: 1
Last.Devarajan SrinivasanH-Index: 2
view all 7 authors...
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...
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