Lourdes, Jotham Jeremy (2021) Cuckoo Search Algorithm-Based Zeta Converter In A Photovoltaic System Under Partial Shading Condition. Masters thesis, Universiti Sains Malaysia.
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Abstract
The key issue with the conventional maximum power point tracking algorithms is the system efficiency in partial shaded conditions due to the presence of local maxima points on the power-voltage curve. This thesis proposes a new integration of Cuckoo Search maximum power point tracking algorithm and Zeta converter in a photovoltaic system where it highlights an improvement of output power efficiency and maximum power point tracking time, as well as reduction of output power ripple. To validate the effectiveness of the work, a Cuckoo Search maximum power point tracking model was constructed in MATLAB Simulink using various irradiance patterns for Zeta converter. In Zeta converter, the output power ripple has been reduced by 2.92 %, thus improving the output power efficiency by 1.25 %. The maximum power point tracking time has been reduced by 14 % compared to that in buck-boost. In addition, the effect of having more eggs (solution) in the Cuckoo Search algorithm was also investigated for both Zeta and Buck-boost converters to study its impact on the maximum power point tracking accuracy. At Pattern 4, the maximum output power of the photovoltaic system in this work is 523.97 W. The proposed integration is able to gain input power 523.4 W and output power of 493.2 W, which are 99.89 % and 94.13 % of the maximum power respectively. With conventional integration, the system is only able to gain 516.0 W input power and 469.1 W output power, which are 98.47 % and 89.52 % of maximum power.
| Item Type: | Thesis (Masters) |
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| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis |
| Depositing User: | Mr Mohamed Yunus Mat Yusof |
| Date Deposited: | 22 Dec 2022 07:22 |
| Last Modified: | 22 Dec 2022 07:22 |
| URI: | http://eprints.usm.my/id/eprint/56103 |
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