SolarClique: Detecting Anomalies in Residential Solar Arrays

The proliferation of solar deployments has significantly increased over the years. Analyzing these deployments can lead to the timely detection of anomalies in power generation, which can maximize the benefits from solar energy. In this paper, we propose SolarClique, a data-driven approach that can flag anomalies in power generation with high accuracy. Unlike prior approaches, our work neither depends on expensive instrumentation nor does it require external inputs such as weather data. Rather our approach exploits correlations in solar power generation from geographically nearby sites to predict the expected output of a site and flag anomalies. We evaluate our approach on 88 solar installations located in Austin, Texas. We show that our algorithm can even work with data from few geographically nearby sites (>5 sites) to produce results with high accuracy. Thus, our approach can scale to sparsely populated regions, where there are few solar installations. Further, among the 88 installations, our approach reported 76 sites with anomalies in power generation. Moreover, our approach is robust enough to distinguish between reduction in power output due to anomalies and other factors such as cloudy conditions.

[1]  Patrick Traxler,et al.  Locating Faults in Photovoltaic Systems Data , 2016, DARE@PKDD/ECML.

[2]  Philip T. Krein,et al.  Photovoltaic Hot-Spot Detection for Solar Panel Substrings Using AC Parameter Characterization , 2016, IEEE Transactions on Power Electronics.

[3]  Abdessamad Kobi,et al.  A Novel Method for Investigating Photovoltaic Module Degradation , 2013 .

[4]  Bernhard Schölkopf,et al.  Removing systematic errors for exoplanet search via latent causes , 2015, ICML.

[5]  Na Li,et al.  Solar generation prediction using the ARMA model in a laboratory-level micro-grid , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[6]  Prashant J. Shenoy,et al.  A Cloud-Based Black-Box Solar Predictor for Smart Homes , 2017, ACM Trans. Cyber Phys. Syst..

[7]  T. Funabashi,et al.  Application of Recurrent Neural Network to Short-Term-Ahead Generating Power Forecasting for Photovoltaic System , 2007, 2007 IEEE Power Engineering Society General Meeting.

[8]  Takashi Oozeki,et al.  Monitoring and Evaluation of Photovoltaic System , 2013 .

[9]  Illtyd Trethowan Causality , 1938 .

[10]  David Moser,et al.  Monitoring of Photovoltaic Systems: Good Practices and Systematic Analysis , 2013 .

[11]  Wayne Gibson,et al.  A PROBABILISTIC-SPATIAL APPROACH TO THE QUALITY CONTROL OF CLIMATE OBSERVATIONS , 2003 .

[12]  Maryam A. Hejazi,et al.  The Comprehensive Study of Electrical Faults in PV Arrays , 2016, J. Electr. Comput. Eng..

[13]  Bernhard Schölkopf,et al.  Modeling confounding by half-sibling regression , 2016, Proceedings of the National Academy of Sciences.

[14]  Ying Sun,et al.  Statistical fault detection in photovoltaic systems , 2017 .

[15]  Abdelhamid Rabhi,et al.  Real Time Fault Detection in Photovoltaic Systems , 2017 .

[16]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[17]  Prashant J. Shenoy,et al.  Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[18]  Florence March,et al.  2016 , 2016, Affair of the Heart.

[19]  Lukasz Golab,et al.  What's Wrong with my Solar Panels: a Data-Driven Approach , 2015, EDBT/ICDT Workshops.

[20]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[21]  C. Coimbra,et al.  Intra-hour DNI forecasting based on cloud tracking image analysis , 2013 .

[22]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Chris Deline,et al.  Partially shaded operation of a grid-tied PV system , 2009, 2009 34th IEEE Photovoltaic Specialists Conference (PVSC).

[24]  Patrick Traxler,et al.  Fault Detection of Large Amounts of Photovoltaic Systems , 2013 .

[25]  Joshua M. Pearce,et al.  The Effects of Snowfall on Solar Photovoltaic Performance , 2013 .

[26]  G. Heilscher,et al.  FAILURE DETECTION ROUTINE FOR GRID CONNECTED PV SYSTEMS AS PART OF THE PVSAT-2 PROJECT , 2005 .

[27]  A. Azzouz 2011 , 2020, City.

[28]  Violeta Holmes,et al.  Parallel fault detection algorithm for grid-connected photovoltaic plants , 2017 .

[29]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .