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dc.contributor.authorDorado-Rojas, Sergio A.
dc.contributor.authorXu, Shunyao
dc.contributor.authorVanfretti, Luigi
dc.contributor.authorOlvera, Galilea
dc.contributor.authorAyachi, M. Ilies I.
dc.contributor.authorAhmed, Shehab
dc.date.accessioned2022-05-11T08:24:50Z
dc.date.available2022-05-11T08:24:50Z
dc.date.issued2022-05-09
dc.identifier.citationDorado-Rojas, S. A., Xu, S., Vanfretti, L., Olvera, G., Ayachi, M. I. I., & Ahmed, S. (2022). Low-Cost Hardware Platform for Testing ML-Based Edge Power Grid Oscillation Detectors. 2022 10th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES). https://doi.org/10.1109/mscpes55116.2022.9770146
dc.identifier.doi10.1109/mscpes55116.2022.9770146
dc.identifier.urihttp://hdl.handle.net/10754/676745
dc.description.abstractThis paper introduces a low-cost hardware testing platform designed to investigate the performance of a Machine Learning (ML)-based edge application developed to detect forced oscillations in power grids. The core of the ML application lies in a Convolutional Neural Network (CNN) model deployed on an ML edge device (NVIDIA Jetson TX2). The proposed platform consists of a method for real-time signal emulation using the WaveForms Software Development Kit (SDK) that defines low-voltage signals generated by Digilent’s Analog Discovery Board. The output of the signal generator is read by the Jetson board using an Analog-to-Digital Converter (ADC). Our experiments compare the performance of different ADCs when performing inference with the same CNN model. Additionally, we give an overview of the communication scheme that allows experiment automation, which is particularly useful when experiment design is time-consuming and laborious.
dc.publisherIEEE
dc.relation.urlhttps://ieeexplore.ieee.org/document/9770146/
dc.rightsArchived with thanks to IEEE
dc.titleLow-Cost Hardware Platform for Testing ML-Based Edge Power Grid Oscillation Detectors
dc.typeConference Paper
dc.contributor.departmentElectrical and Computer Engineering Program
dc.contributor.departmentAli I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.conference.date3-3 May 2022
dc.conference.name2022 10th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES)
dc.conference.locationMilan, Italy
dc.eprint.versionPost-print
dc.contributor.institutionRensselaer Polytechnic Institute,Electrical, Computer, and Systems Engineering,Troy,United States
kaust.personAyachi, M. Ilies I.
kaust.personAhmed, Shehab


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