Power optimization for photovoltaic micro-converters using multivariable Newton-based extremum-seeking

Abstract

Extremum-seeking (ES) is a real-time optimization technique that has been applied to maximum power point tracking (MPPT) design for photovoltaic (PV) micro-converter systems, where each PV module is coupled with its own DC-DC converter. However, most existing designs are scalar, i.e., employ one ES MPPT loop around each converter, and all current designs, whether scalar or mutivariable, are gradient-based. The convergence rate of gradient-based designs depends on the Hessian, which in turn is dependent on environmental conditions such as irradiance and temperature. Consequently, when applied to large PV arrays, the variability in environmental conditions and/or PV module degradation result in non-uniform transients in the convergence to the maximum power point (MPP). Using a multivariable gradient-based ES algorithm for the entire system instead of a scalar one for each PV module, while decreasing the sensitivity to the Hessian, does not eliminate this dependence. We present a recently developed Newton-based ES algorithm that simultaneously employs estimates of the gradient and Hessian in the peak power tracking. The convergence rate of such a design to the MPP is independent of the Hessian, with tunable transient performance that is independent of environmental conditions. We present simulation results that show the effectiveness of the proposed algorithm in comparison to multivariable gradient-based ES.