Performance Benchmarking of Conventional and AI-Based MPPT Controllers for Photovoltaic Systems

Abstract

The operating performance of photovoltaic systems depends on environmental conditions that vary over time. Changes in irradiance and temperature alter the position of the maximum power point, requiring advanced control strategies to maintain efficient energy conversion. The fluctuations in environmental conditions, aside from the nonlinear behavior of PV generators, make the tracking of MPP the main challenging assignment. In this paper, a comparison is made between the intelligent and conventional approaches. All controllers are implemented in the same 100 KW photovoltaic system and evaluated under identical operating conditions. The intelligent controllers are trained using real-time data collected in Amiens, France. At last, performance evaluation evaluates the behavior of each MPPT method in terms of tracking efficiency, dynamic response, voltage regulation, and oscillatory behavior. The results offer valuable insights into the strengths and limitations of the considered techniques and demonstrate that AI-based controllers provide superior tracking accuracy and smoother operation compared with conventional approaches, providing useful guidance for researchers working on MPPT strategies for photovoltaic systems.