  Parameter extraction of photovoltaic (PV) models is crucial for the planning,
optimization, and control of PV systems. Although some methods using
meta-heuristic algorithms have been proposed to determine these parameters, the
robustness of solutions obtained by these methods faces great challenges when
the complexity of the PV model increases. The unstable results will affect the
reliable operation and maintenance strategies of PV systems. In response to
this challenge, an improved RIME algorithm with enhanced exploration and
exploitation is proposed for robust and accurate parameter identification for
various PV models. Specifically, the differential evolution mutation operator
is integrated in the exploration phase to enhance the population diversity.
Meanwhile, a new exploitation strategy incorporating randomization and
neighborhood strategies simultaneously is developed to maintain the balance of
exploitation width and depth. The improved RIME algorithm is applied to
estimate the optimal parameters of the single-diode model (SDM), double-diode
model (DDM), and triple-diode model (TDM) combined with the Lambert-W function
for three PV cell and module types including RTC France, Photo Watt-PWP 201 and
S75. According to the statistical analysis in 100 runs, the TEIMRE achieves
more accurate and robust parameter estimations than other techniques to various
PV models in varying environmental conditions. All of our source codes are
publicly available at https://github.com/dirge1/TERIME.
