Comparison of adaptive tuning fuzzy PID and Ziegler-Nichols PID for photovoltaic cooling system

Abstract

Renewable energy, particularly solar power, is widely recognized as a clean and sustainable resource, with rooftop photovoltaic (PV) systems playing a vital role in electricity generation. However, high temperatures can significantly reduce their efficiency, making effective cooling systems essential. This study proposes a proportional-integral-derivative (PID) based cooling control system for rooftop PV panels, integrating an adaptive Mamdani fuzzy logic controller to optimize PID parameters dynamically. The methodology includes system modeling, hardware and software implementation, and comparative testing between the Mamdani fuzzy-PID controller and the Ziegler-Nichols PID method. Experimental results show that both controllers effectively regulate PV panel temperature at 36 °C. The Ziegler-Nichols PID achieves faster settling time of 6.45 minutes with a steady-state error of 1.345%, whereas the Mamdani fuzzy-PID reduces the steady-state error to 0.93% but with a longer settling time of 9.15 minutes. These results indicate that the fuzzy-PID controller offers better accuracy and system stability, making it a promising solution for maintaining PV performance under varying environmental conditions. The key novelty of this study lies in its adaptive approach, where the Mamdany fuzzy-PID controller continuously adjust control parameters (Kp,Ki,Kd) in real time, resulting in more consistent and precise temperature regulation than conventional PID tuning methods.