Maximum Power Point Tracking Using Cross-Correlation Algorithm for Pv System

This paper suggests an online, rapid, dynamic, and pragmatic cross-correlation (CC) technique to effectively realize maximum power point tracking (MPPT) of the photovoltaic (PV) array. The PV voltage and current are extracted, averaged, and the rate of change of the power and terminal voltage dynamics are integrally cross-correlated to regulate the duty ratio of the converter. According to this approach, the maximum power point (MPP) is defined in terms of the polarity of the CC coefficient (rcc). If the CC coefficient is positive, the operating point is at the left of the MPP, while a negative CC coefficient indicates that the operating point is located at the right of the MPP, otherwise, the MPP is attained at zero CC coefficient with lower steady-state fluctuations. In this regard, a tracking optimization can be realized with a confined drift either under gradual or step solar irradiance changes. The proposed CC technique quickly converges to the MPP without the identification of PV array parameters. Besides, the proposed CC tracking algorithm tracks MPP with wide-range small step-size iterations since the search area is restricted to the vicinity of the MPP rather than the entire PV curve. Moreover, the proposed technique is featured by robustness against system parameters and load variations with simple hardware implementation as compared to several existing techniques. The effectiveness of the proposed technique is thoroughly explored via time-domain, experimental, and hardware-in-loop investigations and meanwhile compared to the two most popular techniques: perturb and observe (P&O) and incremental conductance (INC) methods, for both gradual and step irradiance changes and also shadow effects. The performed case studies revealed superior behavior, rapid convergence with high tracking efficiency and low oscillations, and overriding capability of the influence of the system parameters and load variations.