A Novel Methodology for Determination of Soiling on PV Panels by Means of Grey Box Modelling

This article presents a novel methodology for the determination of soiling appearance on photovoltaic panels by means of data analysis of their energy production and operating conditions. The proposed methodology is based on the generation of a daily-based grey box model for each supervised panel, fitted through the sequential quadratic programming optimization approach, and the evolution analysis of the fitted coefficients to determine the appearance of soiling through the calculation of its monotonic drift and slope. The presented approach has been developed in the framework of a CORFO R&D project and validated under real operating conditions in a utility-scale photovoltaic power plant of one axis mount, located in Chile.

[1]  Carlos Rodríguez Monroy,et al.  The impact of sustainable construction and knowledge management on sustainability goals. A review of the Venezuelan renewable energy sector , 2013 .

[2]  Walid G. Morsi,et al.  Detection and prediction of faults in photovoltaic arrays: A review , 2018, 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018).

[3]  Li Yan,et al.  Study on the Impact of PV Connection to Grid on Power Flow Based on Time Series Output Characteristics , 2018, 2018 37th Chinese Control Conference (CCC).

[4]  Dezso Sera,et al.  Sensorless PV Array Diagnostic Method for Residential PV Systems , 2011 .

[5]  D. Mahinda Vilathgamuwa,et al.  DC Arc-Fault Detection in PV Systems Using Multistage Morphological Fault Detection Algorithm , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[6]  Andreas Spanias,et al.  Signal processing for photovoltaic applications , 2012, 2012 IEEE International Conference on Emerging Signal Processing Applications.

[7]  Heeyoung Kim,et al.  A new metric of absolute percentage error for intermittent demand forecasts , 2016 .

[8]  Rohit Pillai,et al.  Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations , 2010 .

[9]  Janice Yim Mei Lee,et al.  Electricity consumption from renewable and non-renewable sources and economic growth: Evidence from Latin American countries , 2014 .

[10]  R. Teodorescu,et al.  Detection of increased series losses in PV arrays using Fuzzy Inference Systems , 2012, 2012 38th IEEE Photovoltaic Specialists Conference.

[11]  B. Lehman,et al.  Decision tree-based fault detection and classification in solar photovoltaic arrays , 2012, 2012 Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[12]  Andreas Spanias,et al.  Shading prediction, fault detection, and consensus estimation for solar array control , 2018, 2018 IEEE Industrial Cyber-Physical Systems (ICPS).

[13]  Saeed Mohajeryami,et al.  Effects of photovoltaic systems on power quality , 2016, 2016 North American Power Symposium (NAPS).

[14]  Ashot Mnatsakanyan,et al.  Assessing dust on PV modules using image processing techniques , 2016, 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC).

[15]  Tao Peng,et al.  A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance , 2012, IEEE Transactions on Energy Conversion.

[16]  King Abdullah,et al.  Measured soiling loss and its economic impact for PV plants in central Saudi Arabia , 2016, 2016 Saudi Arabia Smart Grid (SASG).

[17]  Ye Zhao,et al.  Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays , 2015, IEEE Transactions on Power Electronics.

[18]  C. Hsueh,et al.  Photovoltaic module performance and soiling analysis for field environment , 2015, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC).