Deep Neural Network with Hilbert–Huang Transform for Smart Fault Detection in Microgrid
نویسندگان
چکیده
The fault detection method (FDM) plays a crucial role in controlling and operating microgrids (MGs), because it allows for systems to rapidly isolate restore faults. Due the fact that MGs use inverter-interfaced distributed production, conventional FDMs are no longer appropriate they dependent on substantial currents. This study presents smart FDM based Hilbert–Huang transform (HHT) deep neural networks (DNNs). suggested layout aims prepare fast of kind, phase, place data protect services. HHT pre-processes branch current measurements obtained from protective relays extract characteristics, singular value decomposition (SVD) is used some features intrinsic mode functions (IMFs) as input DNNs. As part development, all information eventually enters Compared with prior studies, this provides considerably superior fault-type identification accuracy. It also possible determine new locations. A detailed assessment analysis was conducted IEEE 34-bus MG demonstrate its effectiveness. simulations indicated proposed effective detecting precision, computing time, robustness measurement uncertainties.
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12030499