The research of photovoltaic power generation model is of great significance for predicting and analyzing
the influence of photovoltaic power generation by the outside factors. This paper studies and analyzes the applicability
of two types of photovoltaic power generation models proposed at home and abroad in recent years. One is
called as the simplified model based on parallel resistance infinite ( referred to as the simplified model) ,the other
is called exponential model based on power law function ( referred to as exponential model) . The results show firstly
that both models can reproduce the experimental data generally accurately. However,the theoretical and experimental
errors of the two models increase obviously with the increase of bias voltage. The reason is that the cell's characteristics
under high bias have been completely changed,can not use the ideal constant current source and diode to
describe. Secondly,it was found that the errors of both models are increased when the cell's work condition is not in
the standard environments. The reasons are in two aspects,the first is that the external environment changes,the
cell has a significant nonlinear effect and the model can not adapt to the actual change of the cell. In addition,both
models predict the power generation characteristics based on the technical reference values in the case of standard
tests. As a result,the models will have obvious errors in the analysis of the case in the non standard conditions. Finally,
noting that the exponential model is better than the simplified model in any situation. The reason lies in two
aspects,on the one hand,the exponential model considers the influence of light intensity on current and voltage,
while the simplified model only considers the influence of light intensity on current. On the other hand,the exponential
model considers temperature coefficients of short - circuit current,open circuit voltage,maximum power
point current and voltage,while the simplified model only considers two temperature compensation systems.