Maximum power point computation using current–voltage data from open and short circuit regions of photovoltaic module: A teaching learning based optimization approach

Abstract

Solar photovoltaic module operating with constant solar irradiance needs entire power (P)-voltage (V) curve tracing to find the maximum power point (MPP), if perturb and observe or incremental conductance algorithms are used. Therefore, there is a need of an algorithm which tracks the MPP without tracing entire P-V curve. This paper focuses on an MPP prediction method which is only based on a few data collected in the open-circuit and short-circuit regions of current (I)–voltage (V) curves of a cell or a module. The work presented here assumes I-V curve of a module to follow I-V power relation, I = aVb + c. The power model coefficients a, b, and c are determined by teaching-learning based optimization. The voltage at the MPP (Vmp) is computed by correlating the incremental conductance algorithm with the I-V power model and thereafter power at the MPP (Pmax) is obtained. The effectiveness of the algorithm is confirmed by applying it to the reported I-V curve of different types of solar cells. The obtained values of Vmp and Pmax are found to be in excellent agreement with their reported values. The algorithm is successfully applied to experimentally measured I-V curves of a silicon cell and a module.