Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking
Photovoltaic energy is one of the most renowned sources of renewable energy. Its major drawback, however, is the low efficiency of ultra-violet to electrical energy conversion. Irradiance and temperature are the major factors that determine its ability to achieve maximum power output. Maximum...
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf |
| _version_ | 1796979856996564992 |
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| author | Dira, Yasir Sabah |
| author_facet | Dira, Yasir Sabah |
| author_sort | Dira, Yasir Sabah |
| collection | UPM |
| description | Photovoltaic energy is one of the most renowned sources of renewable energy. Its
major drawback, however, is the low efficiency of ultra-violet to electrical energy
conversion. Irradiance and temperature are the major factors that determine its
ability to achieve maximum power output. Maximum power point tracking (MPPT)
is developed in photovoltaic systems to maintain the maximum power output
produced by its source. A boost DC-DC converter with maximum power point
tracking algorithm aids in operating at the desired voltage level. From different
MPPT techniques previously proposed, the online sequential extreme learning
machine algorithm and conventional perturb and observe are combined together as a
proposed MPPT algorithm. This combination is capable of extracting energy at the
maximum operating level of a photovoltaic module.
The simulation work covers modelling of the photovoltaic module, and the boost
DC-DC converter and power LED light as a load, with maximum power point
tracking algorithm to form a photovoltaic system. This system was evaluated under
the actual environmental data based on location and dynamic MPPT efficiency tests.
For comparison purpose, the conventional extreme learning machine and modified
P&O were modelled as well.
Several factors will be triggered on the solar module performance, and the PV
module will be degraded over time. In this thesis, the proposed method will be
emulated under the degradation of maximum output PV current. The diode ideality
factor was chosen to evaluate the PV output current degradation.System elements are individually modelled in MATLAB/M-File and then connected
to assess performance under different environmental conditions. The simulated
results of the complete PV system show that the performances of the PV module
using the proposed MPPT technique provide better output power when compared
with the conventional ELM and modified P&O. It yields not only a reduction in
convergence time to track the maximum power point but also significant output
power when subjected to slow and rapid solar irradiance changes. |
| first_indexed | 2024-03-06T10:15:03Z |
| format | Thesis |
| id | upm.eprints-75444 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:15:03Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | upm.eprints-754442019-11-13T07:28:43Z http://psasir.upm.edu.my/id/eprint/75444/ Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking Dira, Yasir Sabah Photovoltaic energy is one of the most renowned sources of renewable energy. Its major drawback, however, is the low efficiency of ultra-violet to electrical energy conversion. Irradiance and temperature are the major factors that determine its ability to achieve maximum power output. Maximum power point tracking (MPPT) is developed in photovoltaic systems to maintain the maximum power output produced by its source. A boost DC-DC converter with maximum power point tracking algorithm aids in operating at the desired voltage level. From different MPPT techniques previously proposed, the online sequential extreme learning machine algorithm and conventional perturb and observe are combined together as a proposed MPPT algorithm. This combination is capable of extracting energy at the maximum operating level of a photovoltaic module. The simulation work covers modelling of the photovoltaic module, and the boost DC-DC converter and power LED light as a load, with maximum power point tracking algorithm to form a photovoltaic system. This system was evaluated under the actual environmental data based on location and dynamic MPPT efficiency tests. For comparison purpose, the conventional extreme learning machine and modified P&O were modelled as well. Several factors will be triggered on the solar module performance, and the PV module will be degraded over time. In this thesis, the proposed method will be emulated under the degradation of maximum output PV current. The diode ideality factor was chosen to evaluate the PV output current degradation.System elements are individually modelled in MATLAB/M-File and then connected to assess performance under different environmental conditions. The simulated results of the complete PV system show that the performances of the PV module using the proposed MPPT technique provide better output power when compared with the conventional ELM and modified P&O. It yields not only a reduction in convergence time to track the maximum power point but also significant output power when subjected to slow and rapid solar irradiance changes. 2018-03 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf Dira, Yasir Sabah (2018) Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking. Masters thesis, Universiti Putra Malaysia. Photovoltaic power systems |
| spellingShingle | Photovoltaic power systems Dira, Yasir Sabah Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title | Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title_full | Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title_fullStr | Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title_full_unstemmed | Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title_short | Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| title_sort | combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking |
| topic | Photovoltaic power systems |
| url | http://psasir.upm.edu.my/id/eprint/75444/1/FK%202018%20123%20-%20IR.pdf |
| work_keys_str_mv | AT dirayasirsabah combinationofperturbandobservewithonlinesequentialextremelearningmachineforphotovoltaicsystemmaximumpowerpointtracking |