Power Optimization for Photovoltaic Microconverters 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) microconverter systems, where each PV module is coupled with its own dc/dc converter. Most of the existing MPPT designs are scalar, i.e., employ one MPPT loop around each converter, and all 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. Therefore, when applied to large PV arrays, the variability in environmental conditions and/or PV module degradation results in nonuniform 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 as well as the experimental results that show the effectiveness of the proposed algorithm in comparison with the existing scalar designs, and also to multivariable gradient-based ES.