Extremum seeking has been applied to MPPT design for photovoltaic (PV) microconverter systems, where each PV module is coupled with its own DC/DC converter. Most existing MPPT designs are distributed (decentralized), i.e., they employ one MPPT loop around each converter, and all designs, whether distributed or multivariable, are gradient-based2. 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 conditions, and/or PV module degradation, results in non-uniform transients in the convergence to the MPP. Using a multivariable gradient- based ES algorithm for the entire system instead of a scalar one for each PV module, decreases sensitivity to the Hessian, but does not eliminate this dependence. The Newton-based ES algorithm is used, as it 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. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison to existing scalar designs, as well as multivariable, gradient-based ES.