PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

Abstract

Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required.

Photovoltaic Fault Detection and Classification Using Common Vector Approach Based on I-V Curve

“…Due to the activation of bypass diodes the faulty modes are represented differently in the I-V curve. In the following, we simulated a PV panel with the same specifications as the one considered in [12]. standard test conditions (STC) in the simulation circuit.…”

“…In the literature using PCA also the same results were obtained. Authors however stated that additional data processing or/and additional information should be included or done in their study in order to separate the healthy case from the degraded series resistance [12].…”

“…To our best knowledge, CVA method has not yet been used for PV systems diagnosis in such operating conditions in the literature. In recent years, the use of the principal component analysis (PCA) method for detecting PV system faults has been frequently seen in the literature [12,13]. The PCA, an optimal linear dimension reduction technique, in which data is projected into a lower dimensional feature subspace, is a feature extraction method like CVA method.…”

“…In this study, the current, voltage, and power data are generated by using PSIM. PV panel configurations used in these simulations are generated as given in [12] in which fault detection is performed using PCA. According to the results of the analysis, it is seen that the CVA method, which is proposed for the first time in the literature by us in the PV system fault detection, gives considerably better results than the PCA method.…”

Partial Shading Condition on PV Array: Causes, Effects and Shading Mitigation using DSMPPT

“…1(b). If the solar module functions at current Ia, the shaded solar module operate in the reverse biased region and serves as a load somewhat than power source [2]. Due to this kind of high-power diffusion, heat is produced which often causes irreversible damage in order to the shaded solar modules as demonstrated in [3][4].…”