Comparative analysis between recursive least squares state space (RLSS)
and nonlinear least squares (NLS) methods for parameter identification
in buck converters applied to solar energy systems
Abstract
The lifespan of a converter depends on component reliability. In
non-redundant designs like buck converters, a single component failure
can shut down the circuit. Capacitors are more prone to failure than
inductors or semiconductors. Monitoring parameters such as equivalent
series resistance, rather than manual data, better assesses a
converter’s useful life. This article compares two parameter estimation
methods: recursive state space and non-linear least squares, with R²
values and residuals analysis ensuring reliable results that closely
correlate with real converter values.