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

Abstract

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.

A Review on Favourable Maximum Power Point Tracking Systems in Solar Energy Application

“…The performance and comparison between these two methods had been carried out by [15], who concluded that the transient response of P&O is better than HC. The comparison between P&O with other methods also had been carried out by [31][32][33][34][35]. Principally, the operation of P&O or hill climbing technique is fully based on the perturbation or shifting of solar PV operation point according to the sign of the last increment of PV power.…”