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Sunil Rao
Arizona State University
12Publications
4H-index
39Citations
Publications 12
Newest
Last.Farib KhondokerH-Index: 1
view all 13 authors...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 6 authors...
Machine Learning (ML) and Artificial Intelligence (AI) algorithms are enabling several modern smart products and devices. Furthermore, several initiatives such as smart cities and autonomous vehicles utilize AI and ML computational engines. The current and emerging applications and the growing industrial interest in AI necessitate introducing ML algorithms at the undergraduate level. In this paper, we describe a series of activities to introduce ML in undergraduate digital signal processing (DSP...
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#1Sunil Rao (ASU: Arizona State University)H-Index: 4
#2Andreas Spanias (ASU: Arizona State University)H-Index: 28
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 3 authors...
In this paper, we describe a Cyber-Physical system approach to fault detection in Photovoltaic (PV) arrays. More specifically, we explore customized neural network algorithms for fault detection from monitoring devices that sense data and actuate at each individual panel. We develop a framework for the use of feedforward neural networks for fault detection and identification. Our approach promises to improve efficiency by detecting and identifying eight different faults and commonly occurring co...
1 CitationsSource
#1Farib Khondoker (ASU: Arizona State University)H-Index: 1
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-Dsp(j-Dsp)simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate gra...
2 CitationsSource
#1Abhinav Dixit (ASU: Arizona State University)H-Index: 1
#2Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
Last.Huan SongH-Index: 4
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1 Citations
#1Abhinav DixitH-Index: 1
Last.Huan SongH-Index: 4
view all 12 authors...
#1Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
#2Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
Last.Devarajan SrinivasanH-Index: 5
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...
7 CitationsSource
#1Sunil Rao (ASU: Arizona State University)H-Index: 4
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
Last.Devarajan SrinivasanH-Index: 5
view all 11 authors...
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...
10 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last.Devarajan SrinivasanH-Index: 5
view all 8 authors...
5 CitationsSource
Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
#1Sunil Rao (ASU: Arizona State University)H-Index: 4
#2David Ramirez Dominguez (ASU: Arizona State University)H-Index: 5
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 11 authors...
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...
11 CitationsSource
12