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dc.contributor.authorKumar, M. Pavan-
dc.contributor.authorDurgam, Rajababu-
dc.contributor.authorKrishan, Ram-
dc.date.accessioned2025-12-10T10:25:41Z-
dc.date.available2025-12-10T10:25:41Z-
dc.date.issued2024-
dc.identifier.citation10.1109/PIICON63519.2024.10995194en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/3530-
dc.descriptionNITWen_US
dc.description.abstractIn order to maximise the performance of solar PV systems combined with a boost converter, this Paper provides a comparative examination of several Maximum Power Point Tracking (MPPT) approaches, namely Incremental Conductance, Perturb and Observe, and Artificial Neural Network. Extensive MATLAB simulations were conducted to evaluate the output voltage, settling time, and duty cycle of each method under varying input conditions. The results demonstrate that while all methods successfully achieve the desired output voltage of 390V, the ANN method outperforms the others by providing precise tracking with minimal oscillation and a faster settling time of 0.03 seconds. In contrast, the IC and P&O methods, despite achieving similar output voltages, exhibit oscillations and longer settling times. These results demonstrate how well using a boost converter in combination with ANN-based MPPT may improve the stability and efficiency of solar energy systems. The report offers insightful information about the development and application of renewable energy technology.en_US
dc.language.isoenen_US
dc.publisherProceedings of the IEEE Power India International Conference, PIICONen_US
dc.subjectBoost Convertersen_US
dc.subjectFuzzy Logicen_US
dc.titleComparative Analysis of MPPT Controllers for Boost Converter in Solar PV Systems: PO, Incremental Conductance, and ANNen_US
dc.typeOtheren_US
Appears in Collections:Electrical Engineering

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