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1 Ergebnisse
1
Maximum Power Tracking Using Machine Learning Technique in ..:
, In:
2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)
,
Ramakrishna, B.
;
Gopala Krishna Rao, C.V.
;
Harshavardhan, L.
. - p. 451-456 , 2024
Link:
https://doi.org/10.1109/ICPC2T60072.2024.10474801
RT T1 <cTitle>2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)</cTitle>: T1 <Title>Maximum Power Tracking Using Machine Learning Technique in Hybrid Solar-Wind Renewable Energy Power Generation</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10474801&Exemplar=1&LAN=DE A1 Ramakrishna, B. A1 Gopala Krishna Rao, C.V. A1 Harshavardhan, L. A1 Raghu Krishna, V. YR 2024 K1 Maximum power point trackers K1 Renewable energy sources K1 Wind speed K1 Wind power generation K1 Boosting K1 Hybrid power systems K1 Mathematical models K1 Maximum power point tracking (MPPT) K1 Gradient boosting decision tree (GBDT) K1 Duty cycle K1 Walrus optimization algorithm (WOA) K1 and Renewable energy sources (RES) SP 451 OP 456 LK http://dx.doi.org/https://doi.org/10.1109/ICPC2T60072.2024.10474801 DO https://doi.org/10.1109/ICPC2T60072.2024.10474801 SF ELIB - SuUB Bremen
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