Fault detection algorithm for grid-connected photovoltaic plants

This paper presents detailed procedure for automatic fault detection and diagnosis of possible faults occurring in a grid-connected photovoltaic (GCPV) plant using statistical methods. The approach has been validated using an experimental data of climate and electrical parameters based on a 1.98 kWp plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this paper is, therefore, to create a system capable of simulating the theoretical performances of PV systems and to enable statistical analysis of PV measured data. The fault detection algorithm compares the measured and theoretical output power using statistical t-test. In order to determine the location of the fault, the ratio between the measured and theoretical DC power and voltage is monitored. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults. Some of the typical faults are fault in a photovoltaic module, photovoltaic string and faulty maximum power point tracker (MPPT) unit. A virtual instrumentation (VI) LabVIEW software was used in the system development and implementation. This system was used successfully for fault detection on the GCPV plant.

[1]  Mohammad Hassan Moradi,et al.  Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review , 2013 .

[2]  Bill Marion,et al.  Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations , 2013 .

[3]  J. Miller,et al.  Statistics and chemometrics for analytical chemistry , 2005 .

[4]  Giorgio Sulligoi,et al.  A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks , 2016 .

[5]  A. Fahrenbruch,et al.  Fundamentals Of Solar Cells: Photovoltaic Solar Energy Conversion , 2012 .

[6]  Miguel Ángel Egido,et al.  Automatic fault diagnosis in PV systems with distributed MPPT , 2013 .

[7]  Kashif Ishaque,et al.  A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition , 2013 .

[8]  Luis Castañer,et al.  Solar Cells: Materials, Manufacture and Operation , 2004 .

[9]  Radu Platon,et al.  Online Fault Detection in PV Systems , 2015, IEEE Transactions on Sustainable Energy.

[10]  Aissa Chouder,et al.  Monitoring, modelling and simulation of PV systems using LabVIEW , 2013 .

[11]  Saravana Ilango Ganesan,et al.  Positioning of PV panels for reduction in line losses and mismatch losses in PV array , 2015 .

[12]  J. Marcos,et al.  Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale , 2013 .

[13]  Santiago Silvestre,et al.  Improving the performance of PV systems by faults detection using GISTEL approach , 2014 .

[14]  Yi-Hua Liu,et al.  Neural-network-based maximum power point tracking methods for photovoltaic systems operating under fast changing environments , 2013 .

[15]  K. Otani,et al.  Experimental studies of fault location in PV module strings , 2009 .

[16]  Kok Soon Tey,et al.  Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level , 2014 .

[17]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[18]  Soteris A. Kalogirou,et al.  Fault detection method for grid-connected photovoltaic plants , 2014 .

[19]  Aissa Chouder,et al.  Automatic fault detection in grid connected PV systems , 2013 .