Online Monitoring of Solar Panel Using I–V Curve and Internet of Things

Malik, Praveen Kumar; Singh, Rajesh; Gehlot, Anita

doi:10.1007/978-981-15-3369-3_17

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…As shown in Figure 2 (e), we can finally detect and identify the heavy deviation item (item 3). For more details about the identification algorithm, readers are referred to our previous work [9].…”

Section: Traffic Deviation Indicatormentioning

confidence: 99%

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On detecting and identifying faulty internet of things devices and outages

2021

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As internet of things (IoT) devices play an integral role in our everyday life, it is critical to monitor the health of the IoT devices. However, fault detection in IoT is much more challenging compared with that in traditional wired networks. Traditional observing and polling are not appropriate for detecting faults in resource-constrained IoT devices. Because of the dynamic feature of IoT devices, these detection methods are inadequate for IoT fault detection. In this paper, we propose two methods that can monitor the health status of IoT devices through monitoring the network traffic of these devices. Based on the collected traffic or flow entropy, these methods can determine the health status of IoT devices by comparing captured traffic behavior with normal traffic patterns. Our measurements show that the two methods can effectively detect and identify malfunctioned or defective IoT devices.

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Section: Traffic Deviation Indicatormentioning

confidence: 99%

“…A Hidden Markov random field based method is used to detect faults [8]. Work [9] uses the I-V curve of a solar panel to monitor solar panels online at different time intervals. However, those  ISSN: 2302-9285 Bulletin of Electr Eng & Inf, Vol.…”

Section: Introductionmentioning

confidence: 99%