A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

In order to increase the efficiency of the Photovoltaic (PV) system, the PV system should be operated at the Maximum Power Point (MPP). The MPP Tracking (MPPT) is an essential part in achieving this improvement. Some of the existing techniques such as Perturb-and-Observe (P&O) and Incremental Conductance (INC) are relatively simpler to implement, but under rapidly changing irradiance and temperature conditions, they fail to track the MPP. Although methods such as Multilayer Perceptron (MLP) and Fuzzy Logic (FL) are efficient in tracking the MPP, their implementation increases the system complexity. In this paper, we propose a novel artificial intelligence based controller for MPPT, which can efficiently track the MPP, while keeping the computational complexity within the limits. Our technique uses Functional Link Artificial Neural Network (FLANN) to predict the PV output voltage at the MPP. Since there is no hidden layer, FLANN is computationally inexpensive. Simulation results verify that the proposed FLANN controller is computationally less intensive and exhibits higher efficiency under rapidly changing weather conditions.