Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/4424
DC Field | Value | Language |
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dc.contributor.author | Moyo, Ranganai T. | en_US |
dc.contributor.author | Tabakov, Pavel Y. | en_US |
dc.contributor.author | Moyo, Sibusiso | en_US |
dc.date.accessioned | 2022-10-20T07:16:56Z | - |
dc.date.available | 2022-10-20T07:16:56Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | Moyo, R.T., Tabakov, P.Y. and Moyo, S. 2021. Design and modeling of the ANFIS-based MPPT controller for a solar photovoltaic system. Journal of Solar Energy Engineering-transactions of the ASME. 143(4). doi:10.1115/1.4048882 | en_US |
dc.identifier.issn | 0199-6231 | - |
dc.identifier.issn | 1528-8986 (Online) | - |
dc.identifier.other | isidoc: WG2SM | - |
dc.identifier.uri | https://hdl.handle.net/10321/4424 | - |
dc.description.abstract | Abstract Maximum power point tracking (MPPT) controllers play an important role in improving the efficiency of solar photovoltaic (SPV) modules. These controllers achieve maximum power transfer from PV modules through impedance matching between the PV modules and the load connected. Several MPPT techniques have been proposed for searching the optimal matching between the PV module and load resistance. These techniques vary in complexity, tracking speed, cost, accuracy, sensor, and hardware requirements. This paper presents the design and modeling of the adaptive neuro-fuzzy inference system (ANFIS)-based MPPT controller. The design consists of a PV module, ANFIS reference model, DC–DC boost converter, and the fuzzy logic (FL) power controller for generating the control signal for the converter. The performance of the proposed ANFIS-based MPPT controller is evaluated through simulations in the matlab/simulink environment. The simulation results demonstrated the effectiveness of the proposed technique since the controller can extract the maximum available power for both steady-state and varying weather conditions. Moreover, a comparative study between the proposed ANFIS-based MPPT controller and the commonly used, perturbation and observation (P&O) MPPT technique is presented. The simulation results reveal that the proposed ANFIS-based MPPT controller is more efficient than the P&O method since it shows a better dynamic response with few oscillations about the maximum power point (MPP). In addition, the proposed FL power controller for generating the duty cycle of the DC–DC boost converter also gave satisfying results for MPPT. | en_US |
dc.format.extent | 12 p | en_US |
dc.language.iso | en | en_US |
dc.publisher | ASME International | en_US |
dc.relation.ispartof | Journal of Solar Energy Engineering-transactions of the ASME; Vol. 143, Issue 4 | en_US |
dc.subject | Maximum power point tracking (MPPT) | en_US |
dc.subject | Adaptive neuro-fuzzy inference system (ANFIS) | en_US |
dc.subject | DC-DC boost converter | en_US |
dc.subject | Solar photovoltaic (SPV) system | en_US |
dc.subject | Perturbation and observation (P&O) method | en_US |
dc.subject | Efficiency | en_US |
dc.subject | Energy | en_US |
dc.subject | Photovoltaics | en_US |
dc.subject | Simulation | en_US |
dc.subject | Solar | en_US |
dc.subject | 0913 Mechanical Engineering | en_US |
dc.subject | 0915 Interdisciplinary Engineering | en_US |
dc.title | Design and modeling of the ANFIS-based MPPT controller for a solar photovoltaic system | en_US |
dc.type | Article | en_US |
dc.date.updated | 2022-10-11T13:35:07Z | - |
dc.identifier.doi | 10.1115/1.4048882 | - |
local.sdg | SDG07 | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Research Publications (Engineering and Built Environment) |
Files in This Item:
File | Description | Size | Format | |
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JSEE Copyright Clearance.docx | Copyright Clearance | 216.13 kB | Microsoft Word XML | View/Open |
MoyoTabakovMoyo_2021.pdf | Article | 1.35 MB | Adobe PDF | View/Open |
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