Improved $k$ NN-Based Monitoring Schemes for Detecting Faults in PV Systems
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2019-03-18Online Publication Date
2019-03-18Print Publication Date
2019-05Permanent link to this record
http://hdl.handle.net/10754/653087
Metadata
Show full item recordAbstract
This paper presents a model-based anomaly detection method for supervising the direct current (dc) side of photovotaic (PV) systems. Toward this end, a framework combining the benefits of k-nearest neighbors (kNN) with univariate monitoring approaches has been proposed. Specifically, kNN-based Shewhart and exponentially weighted moving average (EWMA) schemes with parametric and nonparametric thresholds have been introduced to suitably detect faults in PV systems. The choice of kNN method to separate normal and abnormal features is motivated by its capacity to handle nonlinear features and do not make assumptions on the underlying data distribution. In addition, because the EWMA approach is sensitive in detecting small changes. First, a simulation model for the inspected PV array is constructed. Then, residuals generated from this model are employed as the input for kNN-based schemes for anomaly detection. Parametric and nonparametric thresholds using kernel density estimation have been used to detect faults. The effectiveness of the kNN-based procedures is verified using actual measurements from a 9.54-kWp grid-connected system in Algeria. Results proclaim the efficiency of the proposed strategy to supervise the dc side of PV systems.Citation
Harrou F, Taghezouit B, Sun Y (2019) Improved $k$NN-Based Monitoring Schemes for Detecting Faults in PV Systems. IEEE Journal of Photovoltaics 9: 811–821. Available: http://dx.doi.org/10.1109/JPHOTOV.2019.2896652.Sponsors
This work was supported by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award OSR-2015-CRG4-2582.Journal
IEEE Journal of PhotovoltaicsAdditional Links
https://ieeexplore.ieee.org/document/8657711ae974a485f413a2113503eed53cd6c53
10.1109/JPHOTOV.2019.2896652