Voltage-based prediction technique for efficient MPPT method for SPV systems under non-uniform insolation and partial shading conditions
The combination of different modules of photovoltaic (PV) cells produces many peaks under partial shading conditions. Therefore, extracting the global maximum power is crucial to attain better efficiency. However, conventional methods, such as Perturb and Observe (P&O), usually struggle to track...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025020353 |
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| Summary: | The combination of different modules of photovoltaic (PV) cells produces many peaks under partial shading conditions. Therefore, extracting the global maximum power is crucial to attain better efficiency. However, conventional methods, such as Perturb and Observe (P&O), usually struggle to track the global maximum power point under such circumstances, as they tend to converge to local maxima. Due to this attribute, this paper proposes a robust voltage-based adaptation technique for predicting k-future samples of the photovoltaic power. The motivation of this prediction is to accurately extract the maximum power of the PV arrays under different partial shading conditions by adjusting the stepsize of the well-known Zero-Attracting LMS (ZALMS) algorithm in its normalized form using the priorly estimated PV voltages. The PV system has been developed in MATLAB/Simulink, and various patterns of non-uniform insolation and partial shading conditions were used to investigate the performance of the proposed technique. In comparison with the conventional approach, our technique improves dynamic response to environmental variations and the ability to avoid local maximum power point while still maintaining lower power oscillations. Furthermore, in adverse conditions, the proposed method improves power generation and efficiency more than the conventional P&O method, by 97.812 % and 98.923 %, respectively, as opposed to 80.236 % and 88.686 %. The computational complexity of the proposed work is relatively comparable to the traditional P&O algorithm, making it more suitable for real-time implementation in smart energy systems. |
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| ISSN: | 2590-1230 |