TY - JOUR
T1 - Online modules to introduce students to solar array control using neural nets
AU - Narayanaswamy, Vivek Sivaraman
AU - Shanthamallu, Uday Shankar
AU - Dixit, Abhinav
AU - Rao, Sunil
AU - Ayyanar, Raja
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas S.
AU - Banavar, Mahesh K.
AU - Katoch, Sameeksha
AU - Pedersen, Emma
AU - Spanias, Photini
AU - Turaga, Pavan
AU - Khondoker, Farib
N1 - Funding Information:
This educational research is funded in part by the NSF IUSE program and the
Funding Information:
This educational research is funded in part by the NSF IUSE program and the NSF CPS Grant #1646542.
Publisher Copyright:
© American Society for Engineering Education, 2019
PY - 2019/6/15
Y1 - 2019/6/15
N2 - The growth in the field of machine learning (ML) can be attributed in part to the success of several algorithms such as neural networks as well as the availability of cloud computing resources. Recently, neural networks combined with signal processing analytics have found applications in renewable energy systems. With machine learning tools for solar array systems becoming popular, there is a need to train undergraduate students on these concepts and tools. In our undergraduate signal processing classes, we have developed self-contained modules to train students in this field. We specifically focused on developing modules with built-in software for applying neural nets (NN) to solar array systems where the NNs are used for solar panel fault detection and solar array connection topology optimization which are essentially ML classification tasks. We initially developed software modules in MATLAB and also developed these models on the user-friendly HTML-5 JavaDSP (JDSP) online simulation environment. J-DSP allows us to create and disseminate web-based laboratory exercises to train undergraduate students from different disciplines, in neural network applications. In this paper, we describe our efforts to enable students understand the properties of the main features of the data used, the types of ML algorithms that can be applied on solar energy systems, and the statistics of the overall results. The modules are injected in our undergraduate DSP class. The project outcomes are assessed using pre and post quizzes and student interviews.
AB - The growth in the field of machine learning (ML) can be attributed in part to the success of several algorithms such as neural networks as well as the availability of cloud computing resources. Recently, neural networks combined with signal processing analytics have found applications in renewable energy systems. With machine learning tools for solar array systems becoming popular, there is a need to train undergraduate students on these concepts and tools. In our undergraduate signal processing classes, we have developed self-contained modules to train students in this field. We specifically focused on developing modules with built-in software for applying neural nets (NN) to solar array systems where the NNs are used for solar panel fault detection and solar array connection topology optimization which are essentially ML classification tasks. We initially developed software modules in MATLAB and also developed these models on the user-friendly HTML-5 JavaDSP (JDSP) online simulation environment. J-DSP allows us to create and disseminate web-based laboratory exercises to train undergraduate students from different disciplines, in neural network applications. In this paper, we describe our efforts to enable students understand the properties of the main features of the data used, the types of ML algorithms that can be applied on solar energy systems, and the statistics of the overall results. The modules are injected in our undergraduate DSP class. The project outcomes are assessed using pre and post quizzes and student interviews.
UR - http://www.scopus.com/inward/record.url?scp=85078748521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078748521&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85078748521
SN - 2153-5965
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
T2 - 126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019
Y2 - 15 June 2019 through 19 June 2019
ER -