A novel hybrid GMPPT method for PV systems to mitigate power oscillations and partial shading detection

Abdulbari Talib Naser, Nur Fadilah Ab Aziz, Karam Khairullah Mohammed, Karmila Binti Kamil and Saad Mekhilef

Renewable Energy, 2024, vol. 237, issue PA

Abstract: The Maximum Power Point Tracking (MPPT) algorithm plays a crucial role in maximizing the power output of a PV system. While uniform shading conditions (USCs) present challenges related to dynamic changes in irradiance, partial shading conditions (PSCs) introduce the complexity of multiple local maxima and rapidly changing shading patterns. Conventional algorithms often struggle to effectively track the global maximum power point (GMPP) during PSCs leading to power losses and oscillations around the MPP. In contrast, advanced algorithms are more complex and require additional computational time causing the algorithm to unnecessarily scan the entire PV curve during USCs, thereby extending the tracking time. This paper proposes a hybrid approach that combines the Modified Perturb and Observe (MP&O) and Modified Coot Optimization Algorithm (MCOA) methods termed as MP&O-MCOA as a robust real time MPPT algorithm. The MP&O method with trapezoidal rule and adaptive step size is employed under USCs, while the MCOA with only one tuning parameter and fewer random numbers is utilized during PSCs to rapidly identify varying conditions. Experimental validation using a boost converter has demonstrated the effectiveness of the proposed method, achieving the fastest average tracking times of 0.24 s under USCs and 0.73 s under PSCs and highest average tracking accuracy above 99 % for all operating conditions tested. The proposed method has proven to outperform other existing MPPT methods under various weather conditions due to its ability to distinguish between USCs and PSCs at exceptional speed and accuracy.

Keywords: Modified Coot Optimization Algorithm (MCOA); Modified Perturb and Observe (MP&O); Uniform Shading Conditions (USCs); Photovoltaic system (PV; ) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:237:y:2024:i:pa:s096014812401588x

DOI: 10.1016/j.renene.2024.121520

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