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dc.contributor.authorSenapati, Manoj Kumar
dc.contributor.authorPradhan, Chittaranjan
dc.contributor.authorCalay, Rajnish Kaur
dc.date.accessioned2022-02-24T09:23:49Z
dc.date.available2022-02-24T09:23:49Z
dc.date.issued2021-10-01
dc.description.abstractThere are multiple peak functions in its output power characteristic curve of a photovoltaic (PV) array under partial shading conditions (PSCs), the perturb and observe (P&O) may fail to track the global maximum power point (GMPP). Therefore, a reliable maximum power point tracking (MPPT) technique is essential to track the GMPP within an appropriate time. This article proposes a hybrid technique by combining an evolutionary optimization technique, namely the modified invasive weed optimization (MIWO) with the conventional P&O algorithm to enhance the search performance for the maximum power output of the PV system. MIWO executes in the initial stages of the tracking followed by the P&O at the final stages in the MPPT search process. The combined approach ensures faster convergence and better search to the GMPP under rapid climate change and PSCs. The search performance of the hybrid MIWO+P&O technique is examined on a standalone PV system through both MATLAB/Simulink environment and experimentally using dSPACE (DS1103)-based real-time microcontroller hardware setup. The performance of the proposed hybrid MPPT scheme is compared with the recent state-of-the-art MPPPT techniques. In addition, the small-signal analysis of the PV system is carried out to evaluate the loop robustness of the controller design. For a given set of system parameters, simulations for the small-signal model and robustness studies are analyzed to verify the results. The overall results justify the efficacy of the proposed hybrid MPPT algorithm.en_US
dc.descriptionThis is the peer reviewed version of the following article: Senapati, M.K., Pradhan, C., Calay, R.K. (2021). A computational intelligence-based maximum power point tracking for photovoltaic power generation system with small‐signal analysis. Optimal control applications & methods, which has been published in final form at <a href=https://doi.org/10.1002/oca.2798>https://doi.org/10.1002/oca.2798</a>. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.en_US
dc.identifier.citationSenapati, M.K., Pradhan, C., Calay, R.K. (2021). A computational intelligence-based maximum power point tracking for photovoltaic power generation system with small‐signal analysis. <i>Optimal control applications & methods</i>.en_US
dc.identifier.cristinIDFRIDAID 1948375
dc.identifier.doi10.1002/oca.2798
dc.identifier.issn0143-2087
dc.identifier.issn1099-1514
dc.identifier.urihttps://hdl.handle.net/10037/24125
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalOptimal control applications & methods
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleA computational intelligence-based maximum power point tracking for photovoltaic power generation system with small‐signal analysisen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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