Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis

Abstract

This paper deals with a data-driven fault diagnosis method for photovoltaic (PV) system. The proposed method is based on the Principal Component Analysis (PCA) to detect and identify different shading types. The PCA uses the current-voltage (I-V) curves that are experimentally determined for a monocrystalline PV module of 250Wc. The experimental tests were carried out for several shading patterns covering the PV cells. For the diagnosis process, three features (current, voltage and power of PV module) are extracted for each test to build the database which is then analyzed through the PCA algorithm. Simulation results using the experimental data, prove the efficiency of the proposed method in terms of discrimination. The healthy data are clearly separated from the faulty ones despite sudden irradiation variations.

Deep‐learning–based method for faults classification of PV system

“…Voltage, current, and power, which are obtained from the I‐V and P‐V curves, are the three common indicators used in identifying different faults of the PV array, see examples in [14–16, 20, 21]. Although, these indicators could discriminate between the healthy the faulty conditions, implementing these indicators, in our study, has made an overlapping between the different selected faults for the irradiance variation.…”

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

“…The selection of the variables is very important to obtain the best representation and discrimination of the data. In order to detect the shading fault, Fadhel et al (2018) have used the voltage, the current and the power of the PV module as variables. Thanks to PCA, they have successfully distinguished the healthy data from the faulty one.…”

Feature extraction and health status prediction in PV systems

“…Faults in PV systems can be caused by aspects such as the useful life of the components, increases in temperature during their operation, external factors (environmental and non-environmental) or interactions between materials [8]. In addition, depending on their location, these faults can affect the AC or DC side of the PV system.…”