An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-12-27
Print Publication Date2019-02
Permanent link to this recordhttp://hdl.handle.net/10754/631218
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AbstractOne of the greatest challenges in a photovoltaic solar power generation is to keep the designed photovoltaic systems working with the desired operating efficiency. Towards this goal, fault detection in photovoltaic plants is essential to guarantee their reliability, safety, and to maximize operating profitability and avoid expensive maintenance. In this context, a model-based anomaly detection approach is proposed for monitoring the DC side of photovoltaic systems and temporary shading. First, a model based on the one-diode model is constructed to mimic the characteristics of the monitored photovoltaic array. Then, a one-class Support Vector Machine (1SVM) procedure is applied to residuals from the simulation model for fault detection. The choice of 1SVM approach to quantify the dissimilarity between normal and abnormal features is motivated by its good capability to handle nonlinear features and do not make assumptions on the underlying data distribution. Experimental results over real data from a 9.54 kWp grid-connected plant in Algiers, show the superior detection efficiency of the proposed approach compared with other binary clustering schemes (i.e., K-means, Birch, mean-shift, expectation–maximization, and agglomerative clustering).
CitationHarrou F, Dairi A, Taghezouit B, Sun Y (2019) An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine. Solar Energy 179: 48–58. Available: http://dx.doi.org/10.1016/j.solener.2018.12.045.
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.