Enhanced adaptive control techniques for extracting maximum power from photovoltaic system

Photovoltaic (PV) systems experience performance fluctuations due to changes in irradiance and temperature, which makes maximum power point tracking (MPPT) essential for stable grid integration. This paper investigates four adaptive MPPT controllers integrated with the incremental conductance (IC) algorithm: (i) a PID controller optimized by manta ray foraging optimization (MRFO), (ii) an Adaptive PI (API) controller, (iii) a Single Perceptron Adaptive PI (SP-API) controller, and (iv) a Set Membership Affine Projection Algorithm (SMAPA)-based PI controller. Unlike conventional or offline-trained approaches, the proposed controllers adapt in real time to environmental changes without requiring large datasets. The main novelty lies in applying SP-API and SMAPA for MPPT in PV systems for the first time, and in enhancing PID and API controllers with MRFO-based tuning for improved robustness. To evaluate performance, two case studies are conducted on a grid-connected PV system: 1- uniform irradiance and temperature variations to simulate daily operating conditions, and 2- partial shading scenarios to test adaptability under local irradiance mismatches. Results show that the SMAPA-based PI controller achieves near-ideal efficiency (~ 99.8%), minimal ripples, and negligible energy losses (~ 0.2%), followed by the SP-API controller. In contrast, the conventional PID and API controllers demonstrate weaker dynamic responses and higher losses. These findings confirm that SMAPA and SP-API offer superior adaptability and stability, making them promising solutions for reliable MPPT in grid-connected PV systems.