Performance analysis of solar PV parameter estimation techniques

Abstract

Firstly, this paper investigates the performances of the three different established parameter estimation techniques, namely multi-objective function, five-point metaheuristic, and hybrid-based approaches. Using these three different techniques, unknown model parameters, such as photovoltaic current (Ipv), dark saturation current (Io), series resistance (Rs), shunt resistance (Rsh), and Ideality factor (A), of mono-and poly-crystalline technology-based solar PV modules, are predicted. Afterwards, using the estimated unknown parameters and P-V &I-V characteristics curves, two different benchmarks, namely absolute relative maximum power error (ARMPE) and overall model error (OME), of these modules, are evaluated to reveal their best performances. In addition, various other parameter estimation techniques, such as iterative, analytical, curve-fitting, and metaheuristics-based methods, have also been considered for performance analysis. It is shown that the hybrid-based approach provides a better characterization than the other various established approaches. The value of %ARMPE is found almost zero with the hybrid approach for both modules. In addition, the smallest values of OME are obtained with the hybrid approach, i.e., 0.002 or 0.003 for poly- and 0.031 for the mono-crystalline technology-based solar PV module. Hence, a hybrid-based parameter estimation technique is suggested to achieve accurate characteristics of solar PV.