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Board 162: Online Modules to Introduce Students to Solar Array Control using Neural Nets

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

18

Permanent URL

https://peer.asee.org/31906

Download Count

10

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Paper Authors

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Vivek Sivaraman Narayanaswamy Arizona State University

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Vivek Sivaraman Narayanaswamy received his B.E degree in electronics and communication engineering at S.S.N College of Engineering, Anna University, Tamil Nadu, India, in 2017. He is currently a graduate student in the school of electrical, computer and energy engineering at ASU, Tempe, AZ. He completed an internship with Qualcomm R&D this summer. His current research interests include developing automated techniques for topology reconfiguration in PV panels to maximize power and to investigate other possible metrics and deep learning frameworks that can be used for speaker diarization.

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Uday Shankar Shanthamallu

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“I received my B.S degree in Electronics and Communications from the National Institute of Engineering, India in 2011. I am currently pursuing my Master’s and PhD program in Electrical Engineering at Arizona State University(ASU). I am advised by Dr. Andreas Spanias. I joined Sensor, Signal and Information Processing Center (SenSIP) at ASU in Jan 2016.
My research interests lie at the overlap of sensors and Machine learning and Big Data including, but not limited to Pattern recognition and Anomaly detection. In summer 2016, I did a summer internship at NXP Semiconductors where I worked on sensor data analytics for anomaly detection. I worked on integrating machine learning algorithms on an embedded sensor systems for Internet of Things applications, which can identify anomalies in real time. Before joining ASU, I worked as Systems engineer for 4 years at Hewlett Packard Research and Development, Bangalore, India.”

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Abhinav Dixit ARIZONA STATE UNIVERSITY

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I am a PhD student at School of Electrical, Computer and Energy Engineering at Arizona State University. My research interest includes early detection of neurological diseases through irregularities in speech.
I also work as a Research Assistant at SenSIP Center, ECEE at ASU. I am currently involved in developed of JDSP HTML5, interactive DSP software developed in HTML5.

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Sunil Rao

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Raja Ayyanar Arizona State University

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Raja Ayyanar received the M.S. degree from the Indian Institute of Science, Bangalore, India, and the Ph.D. degree from the University of Minnesota, Minneapolis. He is presently an Associate Professor at the Arizona State University, Tempe. His current research activities are in the area of power electronics for renewable energy integration, dc-dc converters, power management, fully modular power system architecture and new control and pulse—width modulation techniques. He received an ONR Young Investigator Award in 2005.

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Cihan Tepedelenlioglu Arizona State University

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Andreas S Spanias Arizona State University

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Andreas Spanias is a professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. He is also the founder and director of the SenSIP industry consortium. His research interests are in the areas of adaptive signal processing, speech processing, and audio sensing. He and his student team developed the computer simulation software Java-DSP (J-DSP - ISBN 0-9724984-0-0). He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach. He served as associate editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing vice-president for conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as distinguished lecturer for the IEEE Signal processing society in 2004.

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Mahesh K Banavar Clarkson University Orcid 16x16 orcid.org/https://0000-0002-3916-7137

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Mahesh K. Banavar is an assistant professor in the Department of Electrical and Computer Engineering at Clarkson University. He received the BE degree in Telecommunications Engineering from Visvesvaraya Technological University, Karnataka, India in 2005, the MS degree and the Ph.D. degree, both in Electrical Engineering from Arizona State University in 2007 and 2010, respectively. His research areas are Signal Processing and Communications, Sensor Networks, and Engineering Education.

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Sameeksha Katoch

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Emma Pedersen

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Photini Spanias

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Pavan Turaga

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Farib Khondoker SenSIP Center - Arizona State University

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Abstract

The rapid growth in the field of artificial intelligence (AI) and pattern recognition can be attributed in part to the success of neural network architectures and machine learning (ML) algorithms as well as the availability of cloud computing resources. The applications of neural networks range from simple classification or prediction tasks to complex tasks such as feature learning for complex data. Recently, neural networks combined with signal processing analytics have found applications in renewable energy systems. With artificial intelligence tools for solar array systems and other applications becoming popular in both academia and industry, there is a need to train undergraduate students on AI tools and their applications. In our case, we will focus training activities on applying neural nets (NN) to renewable energy systems. We planned an initial immersive learning experience with hands-on experiments to help students understand basic ML and NN concepts and applications. We will follow then with a learning activity that will cover neural-network online modules and software for solar energy applications in the HTML5 J-DSP environment. We choose J-DSP HTML 5 as the software environment to allow students to learn concepts using a user-friendly block diagram programming approach. We have previously used this approach in our signals and systems and DSP classes for online labs in filter design and spectral analysis. We note that the utility of J-DSP has also been extended through the years in several multi-disciplinary areas. This paper describes online teaching modules with HTML5 J-DSP software exercises with the focus on the application of neural networks for optimizing the efficiency of utility scale solar arrays. The module exposes the students to solar array analytics, monitoring and control by simulating a system in J-DSP. The module contains software functions that apply neural networks to optimize the performance of a solar array. The module also introduces students to the basics of ML and neural networks. The simulation enables students to experiment with a neural net and attempt to reconfigure the connection topology of the solar panels in order to optimize the overall output power. The solar array simulation considered is based on an existing 104 panels / 18kW testbed at our university which is equipped with programmable relays that enable dynamic and real-time series to parallel or parallel to series connection changes. The modules and exercises are assigned as a project in our undergraduate DSP class. The project outcomes are assessed using pre and post quizzes and interviews. The final paper will provide a comprehensive description of the modules, software, exercises and assessments. This work is supported by an NSF IUSE award.

Narayanaswamy, V. S., & Shanthamallu, U. S., & Dixit, A., & Rao, S., & Ayyanar, R., & Tepedelenlioglu, C., & Spanias, A. S., & Banavar, M. K., & Katoch, S., & Pedersen, E., & Spanias, P., & Turaga, P., & Khondoker, F. (2019, June), Board 162: Online Modules to Introduce Students to Solar Array Control using Neural Nets Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/31906

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