Bi, Ziqiang
(2021)
Intelligent Global Maximum Power Point Tracking Strategies Based on Shading Perception for Photovoltaic Systems.
PhD thesis, University of Liverpool.
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Abstract
When a Photovoltaic (PV) system is partially shaded in the environment, the current-voltage (I-V) and power-voltage (P-V) curves exhibit multiple stairs/peaks and the locus of Maximum Power Point (MPP) varies over a wide range. Such Partial Shading Conditions (PSC) bring challenges to the Maximum Power Point Tracking (MPPT) systems. This thesis presents some novel shading information to characterize the complex PSC and MPPT techniques based on the shading perception. Shading information is the mathematical indicator to express the shading patterns. The existing shading information, such as shading rate and shading strength, has the limitations that they can only characterize the PSC with two irradiation levels. To improve the application range of the shading information, the shading matrix and shading vector are proposed in this thesis. The identification and detection methods for the proposed shading information are also included. Results from simulations and experiments have shown the effectiveness and accuracy of the proposed shading detection methods. Under PSC, the power characteristics of the PV systems are too complicated that there exist multiple MPPs. The traditional MPPT techniques may be trapped in the Local MPPs (LMPPs) instead of the Global MPP (GMPP). In this thesis, some novel methods are proposed to estimate the GMPP location from the detected shading information. The proposed MPPT techniques based on the shading perception are capable of tracking the GMPP fast and accurately. Simulations and experiments are conducted to validate the performance of the proposed MPPT methods with the comparison with some well-known MPPT methods.
| Item Type: | Thesis (PhD) |
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| Uncontrolled Keywords: | Photovoltaic system; partial shading conditions; global maximum power point tracking; shading detection; shading information |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 10 Sep 2021 08:41 |
| Last Modified: | 18 Jan 2023 21:29 |
| DOI: | 10.17638/03136237 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3136237 |
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