A comparative study of an exponential adaptive perturb and observe algorithm and ripple correlation control for real-time optimization

This paper proposes an exponential adaptive perturb and observe algorithm (EAPO) for real-time optimization of dynamic systems. Other adaptive methods are reviewed, and the mathematical formulation of the proposed EAPO is presented. Convergence and stability are discussed. Design requirements for applying EAPO are also addressed in an example for impedance matching. The proposed EAPO is compared to two prior realtime optimization techniques: conventional perturb and observe algorithm (P&O), and ripple correlation control (RCC). The method is shown to track the optimum with lower amplitude oscillations than P&O in the context of maximum power point tracking (MPPT) of a photovoltaic array. It is also compared to RCC in several applications including MPPT, impedance matching, and loss minimization of a separately-excited dc machine. Simulations and experiments show that the proposed EAPO achieves maximum power transfer with less than 4% error. Over all, the steady-state error and tracking response of the proposed EAPO are shown to be similar to RCC.