Shading prediction, fault detection, and consensus estimation for solar array control

Katoch, Sameeksha; Muniraju, Gowtham; Rao, Sunil; Spanias, Andreas; Turaga, Pavan; Tepedelenlioğlu, Cihan; Banavar, Mahesh K.; Srinivasan, Devarajan

doi:10.1109/icphys.2018.8387662

Cited by 23 publications

(6 citation statements)

References 35 publications

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“…Currently, research is focused on using machine learning methods, including the deep neural network, unsupervised data fusion and hybrid models to fuse wide variety of data sources. Typically, data fusion is implemented on the edge computation platform [ 62 ], fog computation platform [ 63 ], cloud computation platform [ 64 , 65 ] or a hybrid computation platform where the processing is realized at both edge or cloud [ 66 ]. However, by using the method described in Section 4.3 , various data fusion algorithms can be distributed at sinks or gateways, and the results can be sent to the knowledge plane, where new knowledge can be further extracted by using the knowledge in the knowledge plane.…”

Section: Perspectives Of Knowledge-driven Sdn For Iotmentioning

confidence: 99%

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Ding

et al. 2020

Sensors

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The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

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Section: Perspectives Of Knowledge-driven Sdn For Iotmentioning

confidence: 99%

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Ding

et al. 2020

Sensors

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“…Recent work [2,6,7,17] shows how various machine learning and signal processing techniques are used to detect faults, predict shading, and select connection topologies. Certain machine learning methods provide a generalized system that learns a useful mapping function between the input features and the output.…”

Section: Description Of the Solar Monitoring And Control Systemmentioning

confidence: 99%

“…We have used the k-means algorithm as part of our Cyber Physical systems project [6] and have described a method to detect and characterize solar array faults [4,5,17]. In this education project, we form a J-DSP simulation of k-means for fault detection to present to class for the purpose of showing how ML is used in solar energy systems.…”

Section: Module and Exercise On Fault Detection Using Machine Learningmentioning

confidence: 99%

“…Although there are distinct courses in solar energy generation and machine learning, the creation of content at the overlap of these two areas offers several opportunities for education and research. Machine learning [1] promises to solve several problems in solar energy generation including a) fault detection [2,3,4,5], b) shading prediction [6], and c) topology optimization [7,8,9]. Preparing students early in their plans of study to tackle these problems requires: a) training in machine learning, b) exposition to solar energy systems simulation [10], c) skill building in terms of developing or using software to integrate machine learning to obtain solar system analytics and control the overall system [11].…”

Section: Introductionmentioning

confidence: 99%

“…The program consists of online modules developed specifically to: a) provide machine learning training to undergraduate students and b) show how ML can be applied to improve the performance of solar energy systems. The program was motivated in part by our Cyber-physical system research [6] and projects developed in our REU site [14,15] in which we embedded undergraduate students in machine learning tasks for solar energy applications. In our program, we used online modules in undergraduate classes and also adapted these modules for use in high school outreach.…”

Section: Introductionmentioning

confidence: 99%

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

Narayanaswamy

Shanthamallu²,

Dixit

et al.

2019 ASEE Annual Conference &Amp; Exposition Proceedings

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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. Mr. Uday Shankar Shanthamallu "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

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Deep Learning at the Edge for Operation and Maintenance of Large-Scale Solar Farms

Shapsough

Zualkernan

Dhaouadi

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Shading prediction, fault detection, and consensus estimation for solar array control

Cited by 23 publications

References 35 publications

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

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

Deep Learning at the Edge for Operation and Maintenance of Large-Scale Solar Farms

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