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|Title:||Identifying microgrid disturbances using independent component analysis||Authors:||Ray, Prakash K.
Md Shafquat Ullah Khan
Foo, Eddy Yi Shyh
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Ray, P. K., Krishnan, A., Chaudhari, K., Md Shafquat Ullah Khan, & Foo, E. Y. S. (2018). Identifying microgrid disturbances using independent component analysis. Proceedings of IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 3627-3633. doi:10.1109/iecon.2018.8591353||Abstract:||This paper focuses on the identification of islanding and power quality (PQ) disturbances in microgrids using signal processing techniques such as wavelet transform (WT) and independent component analysis (ICA). WT is known to be a suitable approach for detecting various disturbances such as PQ events, islanding, transients and harmonics due to better multiresolution analysis. However, it is observed that WT is susceptible to increased noise levels or transients in the voltage signal extracted at the point of common coupling (PCC). Therefore, ICA is proposed to find the significant components of the voltage signal which will help in identifying the disturbances more accurately. A comparative analysis, both qualitative and quantitative, is presented in this paper to demonstrate the superior detection capabilities of the proposed ICA approach when compared with the WT approach under noisy scenarios. The proposed technique is validated in real time using OPAL-RT's OP5600 to demonstrate its feasibility for practical applications.||URI:||https://hdl.handle.net/10356/143286||ISBN:||9781509066841||DOI:||10.1109/IECON.2018.8591353||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/IECON.2018.8591353.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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Updated on Jan 29, 2023
Updated on Jan 29, 2023
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