KF‐based technique for detection of anomalous condition of the PV panels

Abstract

This study deals with an approach for photovoltaic (PV) panel monitoring based on extended Kalman filter (EKF). For detection of anomalous condition, I–V and P–V characteristics derived by the PV model in normal condition (named reference model) are compared with the corresponding ones extracted from the PV model in the test condition (named test model). Parameters of the reference model are obtained from the standard test condition. Generally, maximum power point (MPP) tracking algorithms deviate operating point of PV systems to find the global MPP. In the proposed approach, the measured operating points of the PV system are passed through the EKF. In fact, the EKF is an adaptive low-pass filter for estimation of the operating MPP dictated by the MPP tracker. During a predefined inspection time interval, parameters of the test model are derived from the estimated MPP. The performance of the proposed method is evaluated using some simulations and experiments in different normal and anomalous conditions. The results confirm desirable accuracy and reliability of the proposed method for detection of the PV panels’ anomalous condition.