Remote supervision and fault detection on OPC monitored PV systems

This paper presents a new approach for automatic supervision and remote fault detection of grid connected photovoltaic (PV) systems by means of OPC technology-based monitoring. The use of standard OPC for monitoring enables data acquisition from a set of devices that use different communication protocols as inverters or other electronic devices present in PV systems enabling universal connectivity and interoperability. Using the OPC standard allows promoting interoperation of software objects in distributed-heterogeneous environments and also allows incorporating in the system remote supervision and diagnosis for the evaluation of grid connected PV facilities. The supervision system analyses the monitored data and evaluates the expected behaviour of main parameters of the PV array: Output voltage, current and power. The monitored data and evaluated parameters are used by the fault detection procedure in order to identify possible faults present in the PV system. The methodology presented has been experimentally validated in the supervision of a grid connected PV system located in Spain. Results obtained show that the combination of OPC monitoring along with the supervision and fault detection procedure is a robust tool that can be very useful in the field of remote supervision and diagnosis of grid connected PV systems. The RMSE between real monitored data and results obtained from the modelling of the PV array were below 3.6% for all parameters even in cloudy days.

[1]  Mariano Sidrach de Cardona,et al.  Binding Machine Learning Models and OPC Technology for Evaluating Solar Energy Systems , 2010, IEA/AIE.

[2]  D. W. Holley.  Understanding and using OPC for maintenance and reliability applications , 2004 .

[3]  Arthur Tay,et al.  Using the OPC standard for real-time process monitoring and control , 2005, IEEE Software.

[4]  Ildefonso Martínez Marchena.  Marco de trabajo para la generación de software para la gestión de sistemas de energía solar , 2015 .

[5]  Giuseppe Acciani,et al.  Descriptive and Inferential Statistics for Supervising and Monitoring the Operation of PV Plants , 2009, IEEE Transactions on Industrial Electronics.

[6]  Detlev Heinemann,et al.  Monitoring and remote failure detection of grid-connected PV systems based on satellite observations , 2007 .

[7]  Aissa Chouder,et al.  Automatic supervision and fault detection of PV systems based on power losses analysis , 2010 .

[8]  Janusz Zalewski,et al.  Intelligent fault detection and diagnostics in solar plants , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.

[9]  S. Firth,et al.  A Simple Model of PV System Performance and its Use in Fault Detection , 2010 .

[10]  Hongxing Yang,et al.  Validation of the Sandia model with indoor and outdoor measurements for semi-transparent amorphous silicon PV modules , 2015 .

[11]  Anil K. Bera,et al.  A test for normality of observations and regression residuals , 1987 .

[12]  Engin Karatepe,et al.  New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators , 2014 .

[13]  Llanos Mora-López,et al.  Framework for Monitoring and Assessing Small and Medium Solar Energy Plants , 2015 .

[14]  Gustavo Nofuentes,et al.  Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions , 2015 .

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

[16]  Yuchuan Wu,et al.  Application of BP neural network fault diagnosis in solar photovoltaic system , 2009, 2009 International Conference on Mechatronics and Automation.

[17]  Adrien Desportes,et al.  Automatic fault detection on BIPV systems without solar irradiation data , 2014 .

[18]  William E. Boyson,et al.  Photovoltaic array performance model. , 2004 .