Hamdy Mohamed, Ibrahim Amal, Abozalam Belal, Helmy Salah
Department of Electrical Engineering, Faculty of Engineering, Pharos University in Alexandria, Canal El-Mahmoudia St., Smouha, Alexandria, Egypt; Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.
Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt.
ISA Trans. 2024 Nov;154:488-511. doi: 10.1016/j.isatra.2024.09.006. Epub 2024 Sep 11.
The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.
光伏（PV）太阳能系统的性能会受到部分遮挡条件（PSC）以及环境条件（如太阳辐照度和环境温度）的影响，而这些条件在一天中会不断变化。这导致太阳能光伏输出特性曲线上的最大功率点（MPP）发生变化。因此，人们采用了各种经典的最大功率点跟踪（MPPT）技术来跟踪MPP并从光伏系统中提取最大功率。然而，这些技术存在一些缺点，如稳定性较低、在稳态附近振荡加剧以及向MPP的收敛速度较慢。为了克服这个问题，人们提出了新的区间三型直觉模糊逻辑（T3IFL）控制器。T3IFL MPPT控制器将三型模糊逻辑（T3FL）控制器的不确定性与直觉概念相结合。与经典技术和T3FL控制器相比，T3IFL控制器在不断变化的气候和稳态条件下更精确，并且能更快地收敛到MPP。T3IFL算法通过控制DC-DC降压变换器的占空比，在出色的MPP跟踪方面提供了更好的性能。研究了四种情况：均匀辐射条件、在恒温下太阳辐射的阶跃变化、在恒定辐射和温度下用欧姆负载取代电池负载以及部分遮挡条件。T3IFL的实验验证是在一个使用实时硬件在环（HIL）的DC-DC降压变换器上进行的。最后，通过比较研究的仿真和实验结果验证了所提方法在跟踪期望值以及具有更好响应的干扰/不确定性衰减方面的准确性。
